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arviz-devs/arviz

arviz-devs__arviz-679

2b3852706da382be19322256c30498bd509b1918

diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -345,7 +345,7 @@ def xarray_to_ndarray(data, *, var_names=None, combined=True): def get_coords(data, coords): - """Subselects xarray dataset object to provided coords. Raises exception if fails. + """Subselects xarray DataSet or DataArray object to provided coords. Raises exception if fails. Raises ------ @@ -358,7 +358,7 @@ def get_coords(data, coords): Returns ------- data: xarray - xarray.Dataset object + xarray.DataSet or xarray.DataArray object, same type as input """ try: return data.sel(**coords) diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -1,5 +1,8 @@ """Plot kde or histograms and values from MCMC samples.""" +import warnings +from itertools import cycle import matplotlib.pyplot as plt +from matplotlib.lines import Line2D import numpy as np from ..data import convert_to_dataset @@ -16,12 +19,15 @@ def plot_trace( figsize=None, textsize=None, lines=None, + compact=False, combined=False, + legend=False, plot_kwargs=None, fill_kwargs=None, rug_kwargs=None, hist_kwargs=None, trace_kwargs=None, + max_plots=40, ): """Plot distribution (histogram or kernel density estimates) and sampled values. @@ -47,9 +53,13 @@ def plot_trace( lines : tuple Tuple of (var_name, {'coord': selection}, [line, positions]) to be overplotted as vertical lines on the density and horizontal lines on the trace. + compact : bool + Plot multidimensional variables in a single plot. combined : bool Flag for combining multiple chains into a single line. If False (default), chains will be plotted separately. + legend : bool + Add a legend to the figure with the chain color code. plot_kwargs : dict Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. fill_kwargs : dict @@ -77,7 +87,14 @@ def plot_trace( >>> coords = {'theta_t_dim_0': [0, 1], 'school':['Lawrenceville']} >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords) - Combine all chains into one distribution and trace + Show all dimensions of multidimensional variables in the same plot + + .. plot:: + :context: close-figs + + >>> az.plot_trace(data, compact=True) + + Combine all chains into one distribution .. plot:: :context: close-figs @@ -94,6 +111,7 @@ def plot_trace( >>> lines = (('theta_t',{'theta_t_dim_0':0}, [-1]),) >>> coords = {'theta_t_dim_0': [0, 1], 'school':['Lawrenceville']} >>> az.plot_trace(data, var_names=('theta_t', 'theta'), coords=coords, lines=lines) + """ if divergences: try: @@ -101,16 +119,42 @@ def plot_trace( except (ValueError, AttributeError): # No sample_stats, or no `.diverging` divergences = False - data = convert_to_dataset(data, group="posterior") - var_names = _var_names(var_names, data) - if coords is None: coords = {} + data = get_coords(convert_to_dataset(data, group="posterior"), coords) + var_names = _var_names(var_names, data) + + if divergences: + divergence_data = get_coords( + divergence_data, {k: v for k, v in coords.items() if k in ("chain", "draw")} + ) + if lines is None: lines = () - plotters = list(xarray_var_iter(get_coords(data, coords), var_names=var_names, combined=True)) + num_colors = len(data.chain) + 1 if combined else len(data.chain) + colors = [ + prop + for _, prop in zip( + range(num_colors), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) + ) + ] + + if compact: + skip_dims = set(data.dims) - {"chain", "draw"} + else: + skip_dims = set() + + plotters = list(xarray_var_iter(data, var_names=var_names, combined=True, skip_dims=skip_dims)) + max_plots = len(plotters) if max_plots is None else max_plots + if len(plotters) > max_plots: + plotters = plotters[:max_plots] + warnings.warn( + "max_plots is smaller than the number of variables to plot " + "generating only max_plots traceplots", + SyntaxWarning, + ) if figsize is None: figsize = (12, len(plotters) * 2) @@ -141,27 +185,41 @@ def plot_trace( len(plotters), 2, squeeze=False, figsize=figsize, constrained_layout=True ) - colors = {} for idx, (var_name, selection, value) in enumerate(plotters): - colors[idx] = [] - if combined: - value = value.flatten() value = np.atleast_2d(value) - for row in value: - axes[idx, 1].plot(np.arange(len(row)), row, **trace_kwargs) - - colors[idx].append(axes[idx, 1].get_lines()[-1].get_color()) - plot_kwargs["color"] = colors[idx][-1] - plot_dist( - row, - textsize=xt_labelsize, - ax=axes[idx, 0], - hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, - fill_kwargs=fill_kwargs, - rug_kwargs=rug_kwargs, + if len(value.shape) == 2: + _plot_chains( + axes, + idx, + value, + data, + colors, + combined, + xt_labelsize, + trace_kwargs, + hist_kwargs, + plot_kwargs, + fill_kwargs, + rug_kwargs, ) + else: + value = value.reshape((value.shape[0], value.shape[1], -1)) + for sub_idx in range(value.shape[2]): + _plot_chains( + axes, + idx, + value[..., sub_idx], + data, + colors, + combined, + xt_labelsize, + trace_kwargs, + hist_kwargs, + plot_kwargs, + fill_kwargs, + rug_kwargs, + ) if value[0].dtype.kind == "i": xticks = get_bins(value) @@ -177,11 +235,12 @@ def plot_trace( if divergences: div_selection = {k: v for k, v in selection.items() if k in divergence_data.dims} divs = divergence_data.sel(**div_selection).values - if combined: - divs = divs.flatten() + # if combined: + # divs = divs.flatten() divs = np.atleast_2d(divs) for chain, chain_divs in enumerate(divs): + div_draws = data.draw.values[chain_divs] div_idxs = np.arange(len(chain_divs))[chain_divs] if div_idxs.size > 0: if divergences == "top": @@ -190,7 +249,7 @@ def plot_trace( ylocs = [ylim[0] for ylim in ylims] values = value[chain, div_idxs] axes[idx, 1].plot( - div_idxs, + div_draws, np.zeros_like(div_idxs) + ylocs[1], marker="|", color="black", @@ -219,24 +278,61 @@ def plot_trace( line_values = [vlines] else: line_values = np.atleast_1d(vlines).ravel() - axes[idx, 0].vlines( - line_values, *ylims[0], colors=colors[idx][0], linewidth=1.5, alpha=0.75 - ) + axes[idx, 0].vlines(line_values, *ylims[0], colors="black", linewidth=1.5, alpha=0.75) axes[idx, 1].hlines( - line_values, - *xlims[1], - colors=colors[idx][0], - linewidth=1.5, - alpha=trace_kwargs["alpha"] + line_values, *xlims[1], colors="black", linewidth=1.5, alpha=trace_kwargs["alpha"] ) axes[idx, 0].set_ylim(bottom=0, top=ylims[0][1]) - axes[idx, 1].set_xlim(left=0, right=data.draw.max()) + axes[idx, 1].set_xlim(left=data.draw.min(), right=data.draw.max()) axes[idx, 1].set_ylim(*ylims[1]) + if legend: + handles = [ + Line2D([], [], color=color, label=chain_id) + for chain_id, color in zip(data.chain.values, colors) + ] + if combined: + handles.insert(0, Line2D([], [], color=colors[-1], label="combined")) + axes[0, 1].legend(handles=handles, title="chain") return axes -def _histplot_op(ax, data, **kwargs): - """Add a histogram for the data to the axes.""" - bins = get_bins(data) - ax.hist(data, bins=bins, align="left", density=True, **kwargs) - return ax +def _plot_chains( + axes, + idx, + value, + data, + colors, + combined, + xt_labelsize, + trace_kwargs, + hist_kwargs, + plot_kwargs, + fill_kwargs, + rug_kwargs, +): + for chain_idx, row in enumerate(value): + axes[idx, 1].plot(data.draw.values, row, color=colors[chain_idx], **trace_kwargs) + + if not combined: + plot_kwargs["color"] = colors[chain_idx] + plot_dist( + row, + textsize=xt_labelsize, + ax=axes[idx, 0], + hist_kwargs=hist_kwargs, + plot_kwargs=plot_kwargs, + fill_kwargs=fill_kwargs, + rug_kwargs=rug_kwargs, + ) + + if combined: + plot_kwargs["color"] = colors[-1] + plot_dist( + value.flatten(), + textsize=xt_labelsize, + ax=axes[idx, 0], + hist_kwargs=hist_kwargs, + plot_kwargs=plot_kwargs, + fill_kwargs=fill_kwargs, + rug_kwargs=rug_kwargs, + )

diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -139,6 +139,8 @@ def test_plot_density_bad_kwargs(models): {"var_names": "mu"}, {"var_names": ["mu", "tau"]}, {"combined": True}, + {"compact": True}, + {"combined": True, "compact": True, "legend": True}, {"divergences": "top"}, {"divergences": False}, {"lines": [("mu", {}, [1, 2])]}, @@ -161,6 +163,12 @@ def test_plot_trace_discrete(discrete_model): assert axes.shape +def test_plot_trace_max_plots_warning(models): + with pytest.warns(SyntaxWarning): + axes = plot_trace(models.pymc3_fit, max_plots=1) + assert axes.shape + + @pytest.mark.parametrize( "model_fits", [["pyro_fit"], ["pymc3_fit"], ["stan_fit"], ["pymc3_fit", "stan_fit"]] )

Is there some way to plot individual chains (from PyMC3 data)? ## Short Description I have a data set with four chains where one of them is going badly wrong: <img width="408" alt="image" src="https://user-images.githubusercontent.com/3274/56816333-f327a700-6808-11e9-9810-682587256fb7.png"> It would be nice if I could plot the individual chains separately (i.e., in subplots), and if the plots would display the index of the chain in the plot legend (i.e., add a key to the plot which would show the mapping between chain index and color). Thanks! ## Library versions: Arviz 0.3.3 and PyMC3 3.6

Gosh, the code is _very_ close to letting you do this. I should be able to make a quick p.r. in the next few days to add a `label={chain_idx}` to each line, so that ```python import arviz as az schools = az.load_arviz_data('centered_eight') axes = az.plot_trace(schools, 'mu') axes[0, 0].legend(); ``` will put a legend in the top left subplot (note there's no labels yet, so this gives an empty legend right now). This also _almost_ works, but throws an `IndexError` when it tries to get the divergences from chains 3 and 4: ```python import arviz as az schools = az.load_arviz_data('centered_eight') axes = az.plot_trace(schools, 'mu', coords={"chain": [1, 2]}); ```

2019-05-24T23:21:54Z

arviz-devs/arviz

arviz-devs__arviz-696

acdba3fbb14cbfba52ea6551d807b0b53fd0db78

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -1,4 +1,5 @@ """Data structure for using netcdf groups with xarray.""" +import os from collections import OrderedDict from collections.abc import Sequence from copy import copy as ccopy, deepcopy @@ -46,6 +47,9 @@ def from_netcdf(filename): """Initialize object from a netcdf file. Expects that the file will have groups, each of which can be loaded by xarray. + By default, the datasets of the InferenceData object will be lazily loaded. To + modify this behaviour, the environment variable ``ARVIZ_LOAD`` must be set to + ``EAGER`` (case insensitive) in order to load objects in memory instead. Parameters ---------- @@ -60,9 +64,13 @@ def from_netcdf(filename): with nc.Dataset(filename, mode="r") as data: data_groups = list(data.groups) + arviz_load_mode = os.environ.get("ARVIZ_LOAD", "lazy").lower() for group in data_groups: with xr.open_dataset(filename, group=group) as data: - groups[group] = data + if arviz_load_mode == "eager": + groups[group] = data.load() + else: + groups[group] = data return InferenceData(**groups) def to_netcdf(self, filename, compress=True): diff --git a/arviz/data/io_netcdf.py b/arviz/data/io_netcdf.py --- a/arviz/data/io_netcdf.py +++ b/arviz/data/io_netcdf.py @@ -11,6 +11,16 @@ def from_netcdf(filename): ---------- filename : str name or path of the file to load trace + + Returns + ------- + InferenceData object + + Notes + ----- + By default, the datasets of the InferenceData object will be lazily loaded. To + modify this behaviour, the environment variable ``ARVIZ_LOAD`` must be set to + ``EAGER`` (case insensitive) in order to load objects in memory instead. """ return InferenceData.from_netcdf(filename) diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -11,6 +11,7 @@ from .jointplot import plot_joint from .kdeplot import plot_kde, _fast_kde, _fast_kde_2d from .khatplot import plot_khat +from .loopitplot import plot_loo_pit from .pairplot import plot_pair from .parallelplot import plot_parallel from .posteriorplot import plot_posterior @@ -35,6 +36,7 @@ "_fast_kde", "_fast_kde_2d", "plot_khat", + "plot_loo_pit", "plot_pair", "plot_parallel", "plot_posterior", diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/loopitplot.py @@ -0,0 +1,210 @@ +"""Plot LOO-PIT predictive checks of inference data.""" +import numpy as np +import scipy.stats as stats +import matplotlib.pyplot as plt +from matplotlib.colors import to_rgb, rgb_to_hsv, hsv_to_rgb + +from ..stats import loo_pit as _loo_pit +from .plot_utils import _scale_fig_size +from .kdeplot import _fast_kde +from .hpdplot import plot_hpd + + +def plot_loo_pit( + idata=None, + y=None, + y_hat=None, + log_weights=None, + ecdf=False, + ecdf_fill=True, + n_unif=100, + use_hpd=False, + credible_interval=0.94, + figsize=None, + textsize=None, + color="C0", + legend=True, + ax=None, + plot_kwargs=None, + plot_unif_kwargs=None, + hpd_kwargs=None, + fill_kwargs=None, +): + """Plot Leave-One-Out (LOO) probability integral transformation (PIT) predictive checks. + + Parameters + ---------- + idata : InferenceData + InferenceData object. + y : array, DataArray or str + Observed data. If str, idata must be present and contain the observed data group + y_hat : array, DataArray or str + Posterior predictive samples for ``y``. It must have the same shape as y plus an + extra dimension at the end of size n_samples (chains and draws stacked). If str or + None, idata must contain the posterior predictive group. If None, y_hat is taken + equal to y, thus, y must be str too. + log_weights : array or DataArray + Smoothed log_weights. It must have the same shape as ``y_hat`` + ecdf : bool, optional + Plot the difference between the LOO-PIT Empirical Cumulative Distribution Function + (ECDF) and the uniform CDF instead of LOO-PIT kde. + In this case, instead of overlaying uniform distributions, the beta ``credible_interval`` + interval around the theoretical uniform CDF is shown. This approximation only holds + for large S and ECDF values not vary close to 0 nor 1. For more information, see + `Vehtari et al. (2019)`, `Appendix G <https://avehtari.github.io/rhat_ess/rhat_ess.html>`_. + ecdf_fill : bool, optional + Use fill_between to mark the area inside the credible interval. Otherwise, plot the + border lines. + n_unif : int, optional + Number of datasets to simulate and overlay from the uniform distribution. + use_hpd : bool, optional + Use plot_hpd to fill between hpd values instead of overlaying the uniform distributions. + credible_interval : float, optional + Credible interval of the hpd or of the ECDF theoretical credible interval + figsize : figure size tuple, optional + If None, size is (8 + numvars, 8 + numvars) + textsize: int, optional + Text size for labels. If None it will be autoscaled based on figsize. + color : str or array_like, optional + Color of the LOO-PIT estimated pdf plot. If ``plot_unif_kwargs`` has no "color" key, + an slightly lighter color than this argument will be used for the uniform kde lines. + This will ensure that LOO-PIT kde and uniform kde have different default colors. + legend : bool, optional + Show the legend of the figure. + ax : axes, optional + Matplotlib axes + plot_kwargs : dict, optional + Additional keywords passed to ax.plot for LOO-PIT line (kde or ECDF) + plot_unif_kwargs : dict, optional + Additional keywords passed to ax.plot for overlaid uniform distributions or + for beta credible interval lines if ``ecdf=True`` + hpd_kwargs : dict, optional + Additional keywords passed to az.plot_hpd + fill_kwargs : dict, optional + Additional kwargs passed to ax.fill_between + + Returns + ------- + axes : axes + Matplotlib axes + + References + ---------- + * Gabry et al. (2017) see https://arxiv.org/abs/1709.01449 + * https://mc-stan.org/bayesplot/reference/PPC-loo.html + * Gelman et al. BDA (2014) Section 6.3 + + Examples + -------- + Plot LOO-PIT predictive checks overlaying the KDE of the LOO-PIT values to several + realizations of uniform variable sampling with the same number of observations. + + .. plot:: + :context: close-figs + + >>> import arviz as az + >>> idata = az.load_arviz_data("centered_eight") + >>> az.plot_loo_pit(idata=idata, y="obs") + + Fill the area containing the 94% credible interval of the difference between uniform + variables empirical CDF and the real uniform CDF. A LOO-PIT ECDF clearly outside of these + theoretical boundaries indicates that the observations and the posterior predictive + samples do not follow the same distribution. + + .. plot:: + :context: close-figs + + >>> az.plot_loo_pit(idata=idata, y="obs", ecdf=True) + + """ + if ecdf and use_hpd: + raise ValueError("use_hpd is incompatible with ecdf plot") + + (figsize, _, _, xt_labelsize, linewidth, _) = _scale_fig_size(figsize, textsize, 1, 1) + if ax is None: + _, ax = plt.subplots(1, 1, figsize=figsize, constrained_layout=True) + + if plot_kwargs is None: + plot_kwargs = {} + plot_kwargs["color"] = color + plot_kwargs.setdefault("linewidth", linewidth * 1.4) + plot_kwargs.setdefault("label", "LOO-PIT ECDF" if ecdf else "LOO-PIT") + plot_kwargs.setdefault("zorder", 5) + + if plot_unif_kwargs is None: + plot_unif_kwargs = {} + light_color = rgb_to_hsv(to_rgb(plot_kwargs.get("color"))) + light_color[1] /= 2 + light_color[2] += (1 - light_color[2]) / 2 + plot_unif_kwargs.setdefault("color", hsv_to_rgb(light_color)) + plot_unif_kwargs.setdefault("alpha", 0.5) + plot_unif_kwargs.setdefault("linewidth", 0.6 * linewidth) + + loo_pit = _loo_pit(idata=idata, y=y, y_hat=y_hat, log_weights=log_weights) + loo_pit = loo_pit.flatten() if isinstance(loo_pit, np.ndarray) else loo_pit.values.flatten() + + if ecdf: + loo_pit.sort() + n_data_points = loo_pit.size + loo_pit_ecdf = np.arange(n_data_points) / n_data_points + # ideal unnormalized ECDF of uniform distribution with n_data_points points + # it is used indistinctively as x or p(u<x) because for u~U(0,1) they are equal + unif_ecdf = np.arange(n_data_points + 1) + p975 = stats.beta.ppf( + 0.5 + credible_interval / 2, unif_ecdf + 1, n_data_points - unif_ecdf + 1 + ) + p025 = stats.beta.ppf( + 0.5 - credible_interval / 2, unif_ecdf + 1, n_data_points - unif_ecdf + 1 + ) + unif_ecdf = unif_ecdf / n_data_points + + plot_kwargs.setdefault("drawstyle", "steps-mid" if n_data_points < 100 else "default") + plot_unif_kwargs.setdefault("drawstyle", "steps-mid" if n_data_points < 100 else "default") + + ax.plot( + np.hstack((0, loo_pit, 1)), np.hstack((0, loo_pit - loo_pit_ecdf, 0)), **plot_kwargs + ) + if ecdf_fill: + if fill_kwargs is None: + fill_kwargs = {} + fill_kwargs.setdefault("color", hsv_to_rgb(light_color)) + fill_kwargs.setdefault("alpha", 0.5) + fill_kwargs.setdefault( + "step", "mid" if plot_kwargs["drawstyle"] == "steps-mid" else None + ) + fill_kwargs.setdefault("label", "{:.3g}% credible interval".format(credible_interval)) + + ax.fill_between(unif_ecdf, p975 - unif_ecdf, p025 - unif_ecdf, **fill_kwargs) + else: + ax.plot(unif_ecdf, p975 - unif_ecdf, unif_ecdf, p025 - unif_ecdf, **plot_unif_kwargs) + else: + loo_pit_kde, _, _ = _fast_kde(loo_pit, xmin=0, xmax=1) + + unif = np.random.uniform(size=(n_unif, loo_pit.size)) + x_vals = np.linspace(0, 1, len(loo_pit_kde)) + if use_hpd: + if hpd_kwargs is None: + hpd_kwargs = {} + hpd_kwargs.setdefault("color", hsv_to_rgb(light_color)) + hpd_fill_kwargs = hpd_kwargs.pop("fill_kwargs", {}) + hpd_fill_kwargs.setdefault("label", "Uniform HPD") + hpd_kwargs["fill_kwargs"] = hpd_fill_kwargs + hpd_kwargs["credible_interval"] = credible_interval + + unif_densities = np.empty((n_unif, len(loo_pit_kde))) + for idx in range(n_unif): + unif_densities[idx, :], _, _ = _fast_kde(unif[idx, :], xmin=0, xmax=1) + plot_hpd(x_vals, unif_densities, **hpd_kwargs) + else: + for idx in range(n_unif): + unif_density, _, _ = _fast_kde(unif[idx, :], xmin=0, xmax=1) + ax.plot(x_vals, unif_density, **plot_unif_kwargs) + ax.plot(x_vals, loo_pit_kde, **plot_kwargs) + + ax.tick_params(labelsize=xt_labelsize) + if legend: + if not (use_hpd or (ecdf and ecdf_fill)): + label = "{:.3g}% credible interval".format(credible_interval) if ecdf else "Uniform" + ax.plot([], label=label, **plot_unif_kwargs) + ax.legend() + return ax diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -27,6 +27,7 @@ def plot_posterior( credible_interval=0.94, round_to: Optional[int] = None, point_estimate="mean", + group="posterior", rope=None, ref_val=None, kind="kde", @@ -57,6 +58,8 @@ def plot_posterior( Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger. point_estimate: str Must be in ('mode', 'mean', 'median') + group : str, optional + Specifies which InferenceData group should be plotted. Defaults to ‘posterior’. rope: tuple or dictionary of tuples Lower and upper values of the Region Of Practical Equivalence. If a list is provided, its length should match the number of variables. @@ -145,7 +148,7 @@ def plot_posterior( >>> az.plot_posterior(data, var_names=['mu'], credible_interval=.75) """ - data = convert_to_dataset(data, group="posterior") + data = convert_to_dataset(data, group=group) var_names = _var_names(var_names, data) if coords is None: diff --git a/arviz/stats/__init__.py b/arviz/stats/__init__.py --- a/arviz/stats/__init__.py +++ b/arviz/stats/__init__.py @@ -6,10 +6,12 @@ __all__ = [ + "apply_test_function", "bfmi", "compare", "hpd", "loo", + "loo_pit", "psislw", "r2_score", "summary", @@ -22,4 +24,6 @@ "geweke", "autocorr", "autocov", + "make_ufunc", + "wrap_xarray_ufunc", ] diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -3,6 +3,7 @@ import warnings import logging from collections import OrderedDict +from copy import deepcopy import numpy as np import pandas as pd @@ -10,7 +11,7 @@ from scipy.optimize import minimize import xarray as xr -from ..data import convert_to_inference_data, convert_to_dataset +from ..data import convert_to_inference_data, convert_to_dataset, InferenceData from .diagnostics import _multichain_statistics, _mc_error, ess, _circular_standard_deviation from .stats_utils import ( make_ufunc as _make_ufunc, @@ -23,7 +24,17 @@ _log = logging.getLogger(__name__) -__all__ = ["compare", "hpd", "loo", "psislw", "r2_score", "summary", "waic"] +__all__ = [ + "apply_test_function", + "compare", + "hpd", + "loo", + "loo_pit", + "psislw", + "r2_score", + "summary", + "waic", +] def compare( @@ -462,7 +473,7 @@ def loo(data, pointwise=False, reff=None, scale="deviance"): log_likelihood, func_kwargs={"b_inv": n_samples}, ufunc_kwargs=ufunc_kwargs, - **kwargs + **kwargs, ).values ) p_loo = lppd - loo_lppd / scale_value @@ -1071,7 +1082,7 @@ def waic(data, pointwise=False, scale="deviance"): log_likelihood, func_kwargs={"b_inv": n_samples}, ufunc_kwargs=ufunc_kwargs, - **kwargs + **kwargs, ) vars_lpd = log_likelihood.var(dim="samples") @@ -1132,3 +1143,325 @@ def waic(data, pointwise=False, scale="deviance"): "waic_scale", ], ) + + +def loo_pit(idata=None, *, y=None, y_hat=None, log_weights=None): + """Compute leave one out (LOO) probability integral transform (PIT) values. + + Parameters + ---------- + idata : InferenceData + InferenceData object. + y : array, DataArray or str + Observed data. If str, idata must be present and contain the observed data group + y_hat : array, DataArray or str + Posterior predictive samples for ``y``. It must have the same shape as y plus an + extra dimension at the end of size n_samples (chains and draws stacked). If str or + None, idata must contain the posterior predictive group. If None, y_hat is taken + equal to y, thus, y must be str too. + log_weights : array or DataArray + Smoothed log_weights. It must have the same shape as ``y_hat`` + + Returns + ------- + loo_pit : array or DataArray + Value of the LOO-PIT at each observed data point. + + Examples + -------- + Calculate LOO-PIT values using as test quantity the observed values themselves. + + .. ipython:: + + In [1]: import arviz as az + ...: data = az.load_arviz_data("centered_eight") + ...: az.loo_pit(idata=data, y="obs") + + Calculate LOO-PIT values using as test quantity the square of the difference between + each observation and `mu`. Both ``y`` and ``y_hat`` inputs will be array-like, + but ``idata`` will still be passed in order to calculate the ``log_weights`` from + there. + + .. ipython:: + + In [1]: T = data.observed_data.obs - data.posterior.mu.median(dim=("chain", "draw")) + ...: T_hat = data.posterior_predictive.obs - data.posterior.mu + ...: T_hat = T_hat.stack(samples=("chain", "draw")) + ...: az.loo_pit(idata=data, y=T**2, y_hat=T_hat**2) + + """ + if idata is not None and not isinstance(idata, InferenceData): + raise ValueError("idata must be of type InferenceData or None") + + if idata is None: + if not all(isinstance(arg, (np.ndarray, xr.DataArray)) for arg in (y, y_hat, log_weights)): + raise ValueError( + "all 3 y, y_hat and log_weights must be array or DataArray when idata is None " + "but they are of types {}".format([type(arg) for arg in (y, y_hat, log_weights)]) + ) + + else: + if y_hat is None and isinstance(y, str): + y_hat = y + elif y_hat is None: + raise ValueError("y_hat cannot be None if y is not a str") + if isinstance(y, str): + y = idata.observed_data[y].values + elif not isinstance(y, (np.ndarray, xr.DataArray)): + raise ValueError("y must be of types array, DataArray or str, not {}".format(type(y))) + if isinstance(y_hat, str): + y_hat = idata.posterior_predictive[y_hat].stack(samples=("chain", "draw")).values + elif not isinstance(y_hat, (np.ndarray, xr.DataArray)): + raise ValueError( + "y_hat must be of types array, DataArray or str, not {}".format(type(y_hat)) + ) + if log_weights is None: + log_likelihood = idata.sample_stats.log_likelihood.stack(samples=("chain", "draw")) + posterior = convert_to_dataset(idata, group="posterior") + n_chains = len(posterior.chain) + n_samples = len(log_likelihood.samples) + ess_p = ess(posterior, method="mean") + # this mean is over all data variables + reff = ( + (np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples) + if n_chains > 1 + else 1 + ) + log_weights = psislw(-log_likelihood, reff=reff)[0].values + elif not isinstance(log_weights, (np.ndarray, xr.DataArray)): + raise ValueError( + "log_weights must be None or of types array or DataArray, not {}".format( + type(log_weights) + ) + ) + + if len(y.shape) + 1 != len(y_hat.shape): + raise ValueError( + "y_hat must have 1 more dimension than y, but y_hat has {} dims and y has " + "{} dims".format(len(y.shape), len(y_hat.shape)) + ) + + if y.shape != y_hat.shape[:-1]: + raise ValueError( + "y has shape: {} which should be equal to y_hat shape (omitting the last " + "dimension): {}".format(y.shape, y_hat.shape) + ) + + if y_hat.shape != log_weights.shape: + raise ValueError( + "y_hat and log_weights must have the same shape but have shapes {} and {}".format( + y_hat.shape, log_weights.shape + ) + ) + + kwargs = { + "input_core_dims": [[], ["samples"], ["samples"]], + "output_core_dims": [[]], + "join": "left", + } + ufunc_kwargs = {"n_dims": 1} + + return _wrap_xarray_ufunc(_loo_pit, y, y_hat, log_weights, ufunc_kwargs=ufunc_kwargs, **kwargs) + + +def _loo_pit(y, y_hat, log_weights): + """Compute LOO-PIT values.""" + sel = y_hat <= y + if np.sum(sel) > 0: + value = np.exp(_logsumexp(log_weights[sel])) + return min(1, value) + else: + return 0 + + +def apply_test_function( + idata, + func, + group="both", + var_names=None, + pointwise=False, + out_data_shape=None, + out_pp_shape=None, + out_name_data="T", + out_name_pp=None, + func_args=None, + func_kwargs=None, + ufunc_kwargs=None, + wrap_data_kwargs=None, + wrap_pp_kwargs=None, + inplace=True, + overwrite=None, +): + """Apply a Bayesian test function to an InferenceData object. + + Parameters + ---------- + idata : InferenceData + InferenceData object on which to apply the test function. This function will add + new variables to the InferenceData object to store the result without modifying the + existing ones. + func : callable + Callable that calculates the test function. It must have the following call signature + ``func(y, theta, *args, **kwargs)`` (where ``y`` is the observed data or posterior + predictive and ``theta`` the model parameters) even if not all the arguments are + used. + group : str, optional + Group on which to apply the test function. Can be observed_data, posterior_predictive + or both. + var_names : dict group -> var_names, optional + Mapping from group name to the variables to be passed to func. It can be a dict of + strings or lists of strings. There is also the option of using ``both`` as key, + in which case, the same variables are used in observed data and posterior predictive + groups + pointwise : bool, optional + If True, apply the test function to each observation and sample, otherwise, apply + test function to each sample. + out_data_shape, out_pp_shape : tuple, optional + Output shape of the test function applied to the observed/posterior predictive data. + If None, the default depends on the value of pointwise. + out_name_data, out_name_pp : str, optional + Name of the variables to add to the observed_data and posterior_predictive datasets + respectively. ``out_name_pp`` can be ``None``, in which case will be taken equal to + ``out_name_data``. + func_args : sequence, optional + Passed as is to ``func`` + func_kwargs : mapping, optional + Passed as is to ``func`` + wrap_data_kwargs, wrap_pp_kwargs : mapping, optional + kwargs passed to ``az.stats.wrap_xarray_ufunc``. By default, some suitable input_core_dims + are used. + inplace : bool, optional + If True, add the variables inplace, othewise, return a copy of idata with the variables + added. + overwrite : bool, optional + Overwrite data in case ``out_name_data`` or ``out_name_pp`` are already variables in + dataset. If ``None`` it will be the opposite of inplace. + + Returns + ------- + idata : InferenceData + Output InferenceData object. If ``inplace=True``, it is the same input object modified + inplace. + + Notes + ----- + This function is provided for convenience to wrap scalar or functions working on low + dims to inference data object. It is not optimized to be faster nor as fast as vectorized + computations. + + Examples + -------- + Use ``apply_test_function`` to wrap ``np.min`` for illustration purposes. And plot the + results. + + .. plot:: + :context: close-figs + + >>> import arviz as az + >>> idata = az.load_arviz_data("centered_eight") + >>> az.apply_test_function(idata, lambda y, theta: np.min(y)) + >>> T = np.asscalar(idata.observed_data.T) + >>> az.plot_posterior(idata, var_names=["T"], group="posterior_predictive", ref_val=T) + + """ + out = idata if inplace else deepcopy(idata) + + valid_groups = ("observed_data", "posterior_predictive", "both") + if group not in valid_groups: + raise ValueError( + "Invalid group argument. Must be one of {} not {}.".format(valid_groups, group) + ) + if overwrite is None: + overwrite = not inplace + + if out_name_pp is None: + out_name_pp = out_name_data + + if func_args is None: + func_args = tuple() + + if func_kwargs is None: + func_kwargs = {} + + if ufunc_kwargs is None: + ufunc_kwargs = {} + ufunc_kwargs.setdefault("check_shape", False) + ufunc_kwargs.setdefault("ravel", False) + + if wrap_data_kwargs is None: + wrap_data_kwargs = {} + if wrap_pp_kwargs is None: + wrap_pp_kwargs = {} + if var_names is None: + var_names = {} + + both_var_names = var_names.pop("both", None) + var_names.setdefault("posterior", list(out.posterior.data_vars)) + + in_posterior = out.posterior[var_names["posterior"]] + if isinstance(in_posterior, xr.Dataset): + in_posterior = in_posterior.to_array().squeeze() + + groups = ("posterior_predictive", "observed_data") if group == "both" else [group] + for grp in groups: + out_group_shape = out_data_shape if grp == "observed_data" else out_pp_shape + out_name_group = out_name_data if grp == "observed_data" else out_name_pp + wrap_group_kwargs = wrap_data_kwargs if grp == "observed_data" else wrap_pp_kwargs + if not hasattr(out, grp): + raise ValueError("InferenceData object must have {} group".format(grp)) + if not overwrite and out_name_group in getattr(out, grp).data_vars: + raise ValueError( + "Should overwrite: {} variable present in group {}, but overwrite is False".format( + out_name_group, grp + ) + ) + var_names.setdefault( + grp, list(getattr(out, grp).data_vars) if both_var_names is None else both_var_names + ) + in_group = getattr(out, grp)[var_names[grp]] + if isinstance(in_group, xr.Dataset): + in_group = in_group.to_array(dim="{}_var".format(grp)).squeeze() + + if pointwise: + out_group_shape = in_group.shape if out_group_shape is None else out_group_shape + elif grp == "observed_data": + out_group_shape = () if out_group_shape is None else out_group_shape + elif grp == "posterior_predictive": + out_group_shape = in_group.shape[:2] if out_group_shape is None else out_group_shape + loop_dims = in_group.dims[: len(out_group_shape)] + + wrap_group_kwargs.setdefault( + "input_core_dims", + [ + [dim for dim in dataset.dims if dim not in loop_dims] + for dataset in [in_group, in_posterior] + ], + ) + func_kwargs["out"] = np.empty(out_group_shape) + + out_group = getattr(out, grp) + try: + out_group[out_name_group] = _wrap_xarray_ufunc( + func, + in_group.values, + in_posterior.values, + func_args=func_args, + func_kwargs=func_kwargs, + ufunc_kwargs=ufunc_kwargs, + **wrap_group_kwargs, + ) + except IndexError: + excluded_dims = set( + wrap_group_kwargs["input_core_dims"][0] + wrap_group_kwargs["input_core_dims"][1] + ) + out_group[out_name_group] = _wrap_xarray_ufunc( + func, + *xr.broadcast(in_group, in_posterior, exclude=excluded_dims), + func_args=func_args, + func_kwargs=func_kwargs, + ufunc_kwargs=ufunc_kwargs, + **wrap_group_kwargs, + ) + setattr(out, grp, out_group) + + return out diff --git a/arviz/stats/stats_utils.py b/arviz/stats/stats_utils.py --- a/arviz/stats/stats_utils.py +++ b/arviz/stats/stats_utils.py @@ -74,7 +74,7 @@ def autocorr(ary, axis=-1): def make_ufunc( - func, n_dims=2, n_output=1, index=Ellipsis, ravel=True, check_shape=True + func, n_dims=2, n_output=1, n_input=1, index=Ellipsis, ravel=True, check_shape=None ): # noqa: D202 """Make ufunc from a function taking 1D array input. @@ -87,13 +87,18 @@ def make_ufunc( n_output : int, optional Select number of results returned by `func`. If n_output > 1, ufunc returns a tuple of objects else returns an object. + n_input : int, optional + Number of **array** inputs to func, i.e. ``n_input=2`` means that func is called + with ``func(ary1, ary2, *args, **kwargs)`` index : int, optional Slice ndarray with `index`. Defaults to `Ellipsis`. ravel : bool, optional If true, ravel the ndarray before calling `func`. check_shape: bool, optional If false, do not check if the shape of the output is compatible with n_dims and - n_output. + n_output. By default, True only for n_input=1. If n_input is larger than 1, the last + input array is used to check the shape, however, shape checking with multiple inputs + may not be correct. Returns ------- @@ -103,23 +108,30 @@ def make_ufunc( if n_dims < 1: raise TypeError("n_dims must be one or higher.") - def _ufunc(ary, *args, out=None, **kwargs): + if n_input == 1 and check_shape is None: + check_shape = True + elif check_shape is None: + check_shape = False + + def _ufunc(*args, out=None, **kwargs): """General ufunc for single-output function.""" + arys = args[:n_input] if out is None: - out = np.empty(ary.shape[:-n_dims]) + out = np.empty(arys[-1].shape[:-n_dims]) elif check_shape: - if out.shape != ary.shape[:-n_dims]: + if out.shape != arys[-1].shape[:-n_dims]: msg = "Shape incorrect for `out`: {}.".format(out.shape) - msg += " Correct shape is {}".format(ary.shape[:-n_dims]) + msg += " Correct shape is {}".format(arys[-1].shape[:-n_dims]) raise TypeError(msg) for idx in np.ndindex(out.shape): - ary_idx = ary[idx].ravel() if ravel else ary[idx] - out[idx] = np.asarray(func(ary_idx, *args, **kwargs))[index] + arys_idx = [ary[idx].ravel() if ravel else ary[idx] for ary in arys] + out[idx] = np.asarray(func(*arys_idx, *args[n_input:], **kwargs))[index] return out - def _multi_ufunc(ary, *args, out=None, **kwargs): + def _multi_ufunc(*args, out=None, **kwargs): """General ufunc for multi-output function.""" - element_shape = ary.shape[:-n_dims] + arys = args[:n_input] + element_shape = arys[-1].shape[:-n_dims] if out is None: out = tuple(np.empty(element_shape) for _ in range(n_output)) elif check_shape: @@ -137,8 +149,8 @@ def _multi_ufunc(ary, *args, out=None, **kwargs): msg += " Correct shapes are {}".format(correct_shape) raise TypeError(msg) for idx in np.ndindex(element_shape): - ary_idx = ary[idx].ravel() if ravel else ary[idx] - results = func(ary_idx, *args, **kwargs) + arys_idx = [ary[idx].ravel() if ravel else ary[idx] for ary in arys] + results = func(*arys_idx, *args[n_input:], **kwargs) for i, res in enumerate(results): out[i][idx] = np.asarray(res)[index] return out @@ -153,18 +165,19 @@ def _multi_ufunc(ary, *args, out=None, **kwargs): def wrap_xarray_ufunc( - ufunc, dataset, *, ufunc_kwargs=None, func_args=None, func_kwargs=None, **kwargs + ufunc, *datasets, ufunc_kwargs=None, func_args=None, func_kwargs=None, **kwargs ): """Wrap make_ufunc with xarray.apply_ufunc. Parameters ---------- ufunc : callable - dataset : xarray.dataset + datasets : xarray.dataset ufunc_kwargs : dict Keyword arguments passed to `make_ufunc`. - 'n_dims', int, by default 2 - 'n_output', int, by default 1 + - 'n_input', int, by default len(datasets) - 'index', slice, by default Ellipsis - 'ravel', bool, by default True func_args : tuple @@ -180,6 +193,7 @@ def wrap_xarray_ufunc( """ if ufunc_kwargs is None: ufunc_kwargs = {} + ufunc_kwargs.setdefault("n_input", len(datasets)) if func_args is None: func_args = tuple() if func_kwargs is None: @@ -192,7 +206,7 @@ def wrap_xarray_ufunc( ) kwargs.setdefault("output_core_dims", tuple([] for _ in range(ufunc_kwargs.get("n_output", 1)))) - return apply_ufunc(callable_ufunc, dataset, *func_args, kwargs=func_kwargs, **kwargs) + return apply_ufunc(callable_ufunc, *datasets, *func_args, kwargs=func_kwargs, **kwargs) def update_docstring(ufunc, func, n_output=1): @@ -200,7 +214,7 @@ def update_docstring(ufunc, func, n_output=1): module = "" name = "" docstring = "" - if hasattr(func, "__module__"): + if hasattr(func, "__module__") and isinstance(func.__module__, str): module += func.__module__ if hasattr(func, "__name__"): name += func.__name__ diff --git a/doc/conf.py b/doc/conf.py --- a/doc/conf.py +++ b/doc/conf.py @@ -20,6 +20,7 @@ import os import sys +os.environ["ARVIZ_LOAD"] = "EAGER" sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import sphinx_bootstrap_theme import arviz diff --git a/examples/plot_loo_pit_ecdf.py b/examples/plot_loo_pit_ecdf.py new file mode 100644 --- /dev/null +++ b/examples/plot_loo_pit_ecdf.py @@ -0,0 +1,15 @@ +""" +LOO-PIT ECDF Plot +========= + +_thumb: .5, .7 +""" +import arviz as az + +az.style.use('arviz-darkgrid') + +idata = az.load_arviz_data('radon') +log_like = idata.sample_stats.log_likelihood.sel(chain=0).values.T +log_weights = az.psislw(-log_like)[0] + +az.plot_loo_pit(idata, y="y_like", log_weights=log_weights, ecdf=True, color="maroon") diff --git a/examples/plot_loo_pit_overlay.py b/examples/plot_loo_pit_overlay.py new file mode 100644 --- /dev/null +++ b/examples/plot_loo_pit_overlay.py @@ -0,0 +1,13 @@ +""" +LOO-PIT Overlay Plot +========= + +_thumb: .5, .7 +""" +import arviz as az + +az.style.use('arviz-darkgrid') + +idata = az.load_arviz_data('non_centered_eight') + +az.plot_loo_pit(idata=idata, y="obs", color="indigo")

diff --git a/arviz/tests/test_diagnostics.py b/arviz/tests/test_diagnostics.py --- a/arviz/tests/test_diagnostics.py +++ b/arviz/tests/test_diagnostics.py @@ -32,6 +32,8 @@ # See discussion in https://github.com/stan-dev/rstan/pull/618 GOOD_RHAT = 1.1 +os.environ["ARVIZ_LOAD"] = "EAGER" + @pytest.fixture(scope="session") def data(): diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -1,4 +1,4 @@ -# pylint: disable=redefined-outer-name +# pylint: disable=redefined-outer-name,too-many-lines import os import matplotlib.pyplot as plt from pandas import DataFrame @@ -30,9 +30,11 @@ plot_dist, plot_rank, plot_elpd, + plot_loo_pit, ) np.random.seed(0) +os.environ["ARVIZ_LOAD"] = "EAGER" def create_model(seed=10): @@ -981,3 +983,27 @@ def test_plot_ess_no_divergences(models): idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) with pytest.raises(ValueError): plot_ess(idata, rug=True) + + [email protected]( + "kwargs", + [ + {}, + {"n_unif": 50, "legend": False}, + {"use_hpd": True, "color": "gray"}, + {"use_hpd": True, "credible_interval": 0.68, "plot_kwargs": {"ls": "--"}}, + {"use_hpd": True, "hpd_kwargs": {"smooth": False}}, + {"ecdf": True}, + {"ecdf": True, "ecdf_fill": False, "plot_unif_kwargs": {"ls": "--"}}, + {"ecdf": True, "credible_interval": 0.97, "fill_kwargs": {"hatch": "/"}}, + ], +) +def test_plot_loo_pit(models, kwargs): + axes = plot_loo_pit(idata=models.model_1, y="y", **kwargs) + assert axes + + +def test_plot_loo_pit_incompatible_args(models): + """Test error when both ecdf and use_hpd are True.""" + with pytest.raises(ValueError, match="incompatible"): + plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hpd=True) diff --git a/arviz/tests/test_stats.py b/arviz/tests/test_stats.py --- a/arviz/tests/test_stats.py +++ b/arviz/tests/test_stats.py @@ -1,4 +1,5 @@ # pylint: disable=redefined-outer-name +import os from copy import deepcopy import numpy as np from numpy.testing import assert_allclose, assert_array_almost_equal @@ -8,9 +9,23 @@ from ..data import load_arviz_data, from_dict, convert_to_inference_data, concat -from ..stats import compare, hpd, loo, r2_score, waic, psislw, summary +from ..stats import ( + compare, + hpd, + loo, + r2_score, + waic, + psislw, + summary, + loo_pit, + ess, + apply_test_function, +) from ..stats.stats import _gpinv from ..utils import Numba +from .helpers import check_multiple_attrs + +os.environ["ARVIZ_LOAD"] = "EAGER" @pytest.fixture(scope="session") @@ -334,6 +349,158 @@ def test_multidimensional_log_likelihood(func): assert_array_almost_equal(frm[:4], fr1[:4]) [email protected]( + "args", + [ + {"y": "obs"}, + {"y": "obs", "y_hat": "obs"}, + {"y": "arr", "y_hat": "obs"}, + {"y": "obs", "y_hat": "arr"}, + {"y": "arr", "y_hat": "arr"}, + {"y": "obs", "y_hat": "obs", "log_weights": "arr"}, + {"y": "arr", "y_hat": "obs", "log_weights": "arr"}, + {"y": "obs", "y_hat": "arr", "log_weights": "arr"}, + {"idata": False}, + ], +) +def test_loo_pit(centered_eight, args): + y = args.get("y", None) + y_hat = args.get("y_hat", None) + log_weights = args.get("log_weights", None) + y_arr = centered_eight.observed_data.obs + y_hat_arr = centered_eight.posterior_predictive.obs.stack(samples=("chain", "draw")) + log_like = centered_eight.sample_stats.log_likelihood.stack(samples=("chain", "draw")) + n_samples = len(log_like.samples) + ess_p = ess(centered_eight.posterior, method="mean") + reff = np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples + log_weights_arr = psislw(-log_like, reff=reff)[0] + + if args.get("idata", True): + if y == "arr": + y = y_arr + if y_hat == "arr": + y_hat = y_hat_arr + if log_weights == "arr": + log_weights = log_weights_arr + loo_pit_data = loo_pit(idata=centered_eight, y=y, y_hat=y_hat, log_weights=log_weights) + else: + loo_pit_data = loo_pit(idata=None, y=y_arr, y_hat=y_hat_arr, log_weights=log_weights_arr) + assert np.all((loo_pit_data >= 0) & (loo_pit_data <= 1)) + + [email protected]("input_type", ["idataarray", "idatanone_ystr", "yarr_yhatnone"]) +def test_loo_pit_bad_input(centered_eight, input_type): + """Test incompatible input combinations.""" + arr = np.random.random((8, 200)) + if input_type == "idataarray": + with pytest.raises(ValueError, match=r"type InferenceData or None"): + loo_pit(idata=arr, y="obs") + elif input_type == "idatanone_ystr": + with pytest.raises(ValueError, match=r"all 3.+must be array or DataArray"): + loo_pit(idata=None, y="obs") + elif input_type == "yarr_yhatnone": + with pytest.raises(ValueError, match=r"y_hat.+None.+y.+str"): + loo_pit(idata=centered_eight, y=arr, y_hat=None) + + [email protected]("arg", ["y", "y_hat", "log_weights"]) +def test_loo_pit_bad_input_type(centered_eight, arg): + """Test wrong input type (not None, str not DataArray.""" + kwargs = {"y": "obs", "y_hat": "obs", "log_weights": None} + kwargs[arg] = 2 # use int instead of array-like + with pytest.raises(ValueError, match="not {}".format(type(2))): + loo_pit(idata=centered_eight, **kwargs) + + [email protected]("incompatibility", ["y-y_hat1", "y-y_hat2", "y_hat-log_weights"]) +def test_loo_pit_bad_input_shape(incompatibility): + """Test shape incompatiblities.""" + y = np.random.random(8) + y_hat = np.random.random((8, 200)) + log_weights = np.random.random((8, 200)) + if incompatibility == "y-y_hat1": + with pytest.raises(ValueError, match="1 more dimension"): + loo_pit(y=y, y_hat=y_hat[None, :], log_weights=log_weights) + elif incompatibility == "y-y_hat2": + with pytest.raises(ValueError, match="y has shape"): + loo_pit(y=y, y_hat=y_hat[1:3, :], log_weights=log_weights) + elif incompatibility == "y_hat-log_weights": + with pytest.raises(ValueError, match="must have the same shape"): + loo_pit(y=y, y_hat=y_hat[:, :100], log_weights=log_weights) + + [email protected]("pointwise", [True, False]) [email protected]("inplace", [True, False]) [email protected]( + "kwargs", + [ + {}, + {"group": "posterior_predictive", "var_names": {"posterior_predictive": "obs"}}, + {"group": "observed_data", "var_names": {"both": "obs"}, "out_data_shape": "shape"}, + {"var_names": {"both": "obs", "posterior": ["theta", "mu"]}}, + {"group": "observed_data", "out_name_data": "T_name"}, + ], +) +def test_apply_test_function(centered_eight, pointwise, inplace, kwargs): + """Test some usual call cases of apply_test_function""" + centered_eight = deepcopy(centered_eight) + group = kwargs.get("group", "both") + var_names = kwargs.get("var_names", None) + out_data_shape = kwargs.get("out_data_shape", None) + out_pp_shape = kwargs.get("out_pp_shape", None) + out_name_data = kwargs.get("out_name_data", "T") + if out_data_shape == "shape": + out_data_shape = (8,) if pointwise else () + if out_pp_shape == "shape": + out_pp_shape = (4, 500, 8) if pointwise else (4, 500) + idata = deepcopy(centered_eight) + idata_out = apply_test_function( + idata, + lambda y, theta: np.mean(y), + group=group, + var_names=var_names, + pointwise=pointwise, + out_name_data=out_name_data, + out_data_shape=out_data_shape, + out_pp_shape=out_pp_shape, + ) + if inplace: + assert idata is idata_out + + if group == "both": + test_dict = {"observed_data": ["T"], "posterior_predictive": ["T"]} + else: + test_dict = {group: [kwargs.get("out_name_data", "T")]} + + fails = check_multiple_attrs(test_dict, idata_out) + assert not fails + + +def test_apply_test_function_bad_group(centered_eight): + """Test error when group is an invalid name.""" + with pytest.raises(ValueError, match="Invalid group argument"): + apply_test_function(centered_eight, lambda y, theta: y, group="bad_group") + + +def test_apply_test_function_missing_group(): + """Test error when InferenceData object is missing a required group. + + The function cannot work if group="both" but InferenceData object has no + posterior_predictive group. + """ + idata = from_dict( + posterior={"a": np.random.random((4, 500, 30))}, observed_data={"y": np.random.random(30)} + ) + with pytest.raises(ValueError, match="must have posterior_predictive"): + apply_test_function(idata, lambda y, theta: np.mean, group="both") + + +def test_apply_test_function_should_overwrite_error(centered_eight): + """Test error when overwrite=False but out_name is already a present variable.""" + with pytest.raises(ValueError, match="Should overwrite"): + apply_test_function(centered_eight, lambda y, theta: y, out_name_data="obs") + + def test_numba_stats(): """Numba test for r2_score""" state = Numba.numba_flag # Store the current state of Numba

[Feature request]Graphical check of leave-one-out cross-validated probability integral transform (LOO-PIT) Example see figure 9 in https://arxiv.org/pdf/1709.01449.pdf Reference usage in bayesplot: http://mc-stan.org/bayesplot/reference/PPC-loo.html Related code in bayesplot: https://github.com/stan-dev/bayesplot/blob/master/R/ppc-loo.R Computation of loo_pit: https://github.com/stan-dev/rstantools/blob/fd84944454f8f0fe1ad9d456211f73a24feaa0ef/R/loo-functions.R#L64-L74

2019-06-07T02:23:36Z

arviz-devs/arviz

arviz-devs__arviz-710

6dd6f94fe0f268322e4bfde0c9dd54c955b17b64

diff --git a/arviz/__init__.py b/arviz/__init__.py --- a/arviz/__init__.py +++ b/arviz/__init__.py @@ -6,6 +6,7 @@ import logging from matplotlib.pyplot import style + # add ArviZ's styles to matplotlib's styles arviz_style_path = os.path.join(os.path.dirname(__file__), "plots", "styles") style.core.USER_LIBRARY_PATHS.append(arviz_style_path) @@ -14,6 +15,7 @@ # Configure logging before importing arviz internals _log = logging.getLogger("arviz") + if not logging.root.handlers: handler = logging.StreamHandler() formatter = logging.Formatter("%(name)s - %(levelname)s - %(message)s") @@ -24,3 +26,4 @@ from .data import * from .plots import * from .stats import * +from .utils import Numba diff --git a/arviz/plots/energyplot.py b/arviz/plots/energyplot.py --- a/arviz/plots/energyplot.py +++ b/arviz/plots/energyplot.py @@ -1,6 +1,7 @@ """Plot energy transition distribution in HMC inference.""" import numpy as np import matplotlib.pyplot as plt + from ..data import convert_to_dataset from ..stats import bfmi as e_bfmi from .kdeplot import plot_kde diff --git a/arviz/plots/kdeplot.py b/arviz/plots/kdeplot.py --- a/arviz/plots/kdeplot.py +++ b/arviz/plots/kdeplot.py @@ -5,7 +5,7 @@ from scipy.signal import gaussian, convolve, convolve2d # pylint: disable=no-name-in-module from scipy.sparse import coo_matrix import xarray as xr -from ..data.inference_data import InferenceData +from ..data import InferenceData from ..utils import conditional_jit from .plot_utils import _scale_fig_size diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -4,7 +4,6 @@ import numpy as np from scipy.stats import mode - from ..data import convert_to_dataset from ..stats import hpd from .kdeplot import plot_kde, _fast_kde diff --git a/arviz/stats/diagnostics.py b/arviz/stats/diagnostics.py --- a/arviz/stats/diagnostics.py +++ b/arviz/stats/diagnostics.py @@ -2,7 +2,6 @@ """Diagnostic functions for ArviZ.""" from collections.abc import Sequence import warnings - import numpy as np import pandas as pd from scipy import stats @@ -13,10 +12,11 @@ autocov as _autocov, not_valid as _not_valid, wrap_xarray_ufunc as _wrap_xarray_ufunc, + stats_variance_2d as svar, + histogram, ) from ..data import convert_to_dataset -from ..utils import _var_names - +from ..utils import _var_names, conditional_jit, conditional_vect, Numba, _numba_var __all__ = ["bfmi", "effective_sample_size", "ess", "rhat", "mcse", "geweke"] @@ -46,7 +46,6 @@ def bfmi(data): Examples -------- Compute the BFMI of an InferenceData object - .. ipython:: In [1]: import arviz as az @@ -169,7 +168,6 @@ def ess(data, *, var_names=None, method="bulk", relative=False, prob=None): Notes ----- The basic ess diagnostic is computed by: - .. math:: \hat{N}_{eff} = \frac{MN}{\hat{\tau}} .. math:: \hat{\tau} = -1 + 2 \sum_{t'=0}^K \hat{P}_{t'} @@ -283,7 +281,6 @@ def rhat(data, *, var_names=None, method="rank"): Names of variables to include in the rhat report method : str Select R-hat method. Valid methods are: - - "rank" # recommended by Vehtari et al. (2019) - "split" - "folded" @@ -385,7 +382,6 @@ def mcse(data, *, var_names=None, method="mean", prob=None): Names of variables to include in the rhat report method : str Select mcse method. Valid methods are: - - "mean" - "sd" - "quantile" @@ -453,6 +449,12 @@ def mcse(data, *, var_names=None, method="mean", prob=None): ) +@conditional_vect +def _sqrt(a_a, b_b): + return (a_a + b_b) ** 0.5 + + +@conditional_jit def geweke(ary, first=0.1, last=0.5, intervals=20): r"""Compute z-scores for convergence diagnostics. @@ -493,6 +495,11 @@ def geweke(ary, first=0.1, last=0.5, intervals=20): * Geweke (1992) """ # Filter out invalid intervals + return _geweke(ary, first, last, intervals) + + +def _geweke(ary, first, last, intervals): + _numba_flag = Numba.numba_flag for interval in (first, last): if interval <= 0 or interval >= 1: raise ValueError("Invalid intervals for Geweke convergence analysis", (first, last)) @@ -518,7 +525,10 @@ def geweke(ary, first=0.1, last=0.5, intervals=20): last_slice = ary[int(end - last * (end - start)) :] z_score = first_slice.mean() - last_slice.mean() - z_score /= np.sqrt(first_slice.var() + last_slice.var()) + if _numba_flag: + z_score /= _sqrt(svar(first_slice), svar(last_slice)) + else: + z_score /= np.sqrt(first_slice.var() + last_slice.var()) zscores.append([start, z_score]) @@ -538,7 +548,11 @@ def ks_summary(pareto_tail_indices): df_k : dataframe Dataframe containing k diagnostic values. """ - kcounts, _ = np.histogram(pareto_tail_indices, bins=[-np.Inf, 0.5, 0.7, 1, np.Inf]) + _numba_flag = Numba.numba_flag + if _numba_flag: + kcounts = histogram(pareto_tail_indices) + else: + kcounts, _ = np.histogram(pareto_tail_indices, bins=[-np.Inf, 0.5, 0.7, 1, np.Inf]) kprop = kcounts / len(pareto_tail_indices) * 100 df_k = pd.DataFrame( dict(_=["(good)", "(ok)", "(bad)", "(very bad)"], Count=kcounts, Pct=kprop) @@ -576,7 +590,10 @@ def _bfmi(energy): """ energy_mat = np.atleast_2d(energy) num = np.square(np.diff(energy_mat, axis=1)).mean(axis=1) # pylint: disable=no-member - den = np.var(energy_mat, axis=1) + if energy_mat.ndim == 2: + den = _numba_var(svar, np.var, energy_mat, axis=1, ddof=0) + else: + den = np.var(energy, axis=1) return num / den @@ -621,6 +638,7 @@ def _z_fold(ary): def _rhat(ary): """Compute the rhat for a 2d array.""" + _numba_flag = Numba.numba_flag ary = np.asarray(ary, dtype=float) if _not_valid(ary, check_shape=False): return np.nan @@ -629,9 +647,9 @@ def _rhat(ary): # Calculate chain mean chain_mean = np.mean(ary, axis=1) # Calculate chain variance - chain_var = np.var(ary, axis=1, ddof=1) + chain_var = _numba_var(svar, np.var, ary, axis=1, ddof=1) # Calculate between-chain variance - between_chain_variance = num_samples * np.var(chain_mean, ddof=1) + between_chain_variance = num_samples * _numba_var(svar, np.var, chain_mean, axis=None, ddof=1) # Calculate within-chain variance within_chain_variance = np.mean(chain_var) # Estimate of marginal posterior variance @@ -691,6 +709,7 @@ def _rhat_identity(ary): def _ess(ary, relative=False): """Compute the effective sample size for a 2D array.""" + _numba_flag = Numba.numba_flag ary = np.asarray(ary, dtype=float) if _not_valid(ary, check_shape=False): return np.nan @@ -704,7 +723,7 @@ def _ess(ary, relative=False): mean_var = np.mean(acov[:, 0]) * n_draw / (n_draw - 1.0) var_plus = mean_var * (n_draw - 1.0) / n_draw if n_chain > 1: - var_plus += np.var(chain_mean, ddof=1) + var_plus += _numba_var(svar, np.var, chain_mean, axis=None, ddof=1) rho_hat_t = np.zeros(n_draw) rho_hat_even = 1.0 @@ -881,22 +900,30 @@ def _conv_quantile(ary, prob): def _mcse_mean(ary): """Compute the Markov Chain mean error.""" + _numba_flag = Numba.numba_flag ary = np.asarray(ary) if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): return np.nan ess = _ess_mean(ary) - sd = np.std(ary, ddof=1) + if _numba_flag: + sd = _sqrt(svar(np.ravel(ary), ddof=1), np.zeros(1)) + else: + sd = np.std(ary, ddof=1) mcse_mean_value = sd / np.sqrt(ess) return mcse_mean_value def _mcse_sd(ary): """Compute the Markov Chain sd error.""" + _numba_flag = Numba.numba_flag ary = np.asarray(ary) if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)): return np.nan ess = _ess_sd(ary) - sd = np.std(ary, ddof=1) + if _numba_flag: + sd = np.float(_sqrt(svar(np.ravel(ary), ddof=1), np.zeros(1))) + else: + sd = np.std(ary, ddof=1) fac_mcse_sd = np.sqrt(np.exp(1) * (1 - 1 / ess) ** (ess - 1) - 1) mcse_sd_value = sd * fac_mcse_sd return mcse_sd_value @@ -911,6 +938,28 @@ def _mcse_quantile(ary, prob): return mcse_q +def _circfunc(samples, high, low): + samples = np.asarray(samples) + if samples.size == 0: + return np.nan, np.nan + return samples, _angle(samples, low, high, np.pi) + + +@conditional_vect +def _angle(samples, low, high, p_i=np.pi): + ang = (samples - low) * 2.0 * p_i / (high - low) + return ang + + +def _circular_standard_deviation(samples, high=2 * np.pi, low=0, axis=None): + p_i = np.pi + samples, ang = _circfunc(samples, high, low) + s_s = np.sin(ang).mean(axis=axis) + c_c = np.cos(ang).mean(axis=axis) + r_r = np.hypot(s_s, c_c) + return ((high - low) / 2.0 / p_i) * np.sqrt(-2 * np.log(r_r)) + + def _mc_error(ary, batches=5, circular=False): """Calculate the simulation standard error, accounting for non-independent samples. @@ -932,6 +981,7 @@ def _mc_error(ary, batches=5, circular=False): mc_error : float Simulation standard error """ + _numba_flag = Numba.numba_flag if ary.ndim > 1: dims = np.shape(ary) @@ -944,19 +994,31 @@ def _mc_error(ary, batches=5, circular=False): return np.nan if batches == 1: if circular: - std = stats.circstd(ary, high=np.pi, low=-np.pi) + if _numba_flag: + std = _circular_standard_deviation(ary, high=np.pi, low=-np.pi) + else: + std = stats.circstd(ary, high=np.pi, low=-np.pi) else: - std = np.std(ary) + if _numba_flag: + std = np.float(_sqrt(svar(ary), np.zeros(1))) + else: + std = np.std(ary) return std / np.sqrt(len(ary)) batched_traces = np.resize(ary, (batches, int(len(ary) / batches))) if circular: means = stats.circmean(batched_traces, high=np.pi, low=-np.pi, axis=1) - std = stats.circstd(means, high=np.pi, low=-np.pi) + if _numba_flag: + std = _circular_standard_deviation(means, high=np.pi, low=-np.pi) + else: + std = stats.circstd(means, high=np.pi, low=-np.pi) else: means = np.mean(batched_traces, 1) - std = np.std(means) + if _numba_flag: + std = _sqrt(svar(means), np.zeros(1)) + else: + std = np.std(means) return std / np.sqrt(batches) diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -11,14 +11,15 @@ import xarray as xr from ..data import convert_to_inference_data, convert_to_dataset -from .diagnostics import _multichain_statistics, _mc_error, ess +from .diagnostics import _multichain_statistics, _mc_error, ess, _circular_standard_deviation from .stats_utils import ( make_ufunc as _make_ufunc, wrap_xarray_ufunc as _wrap_xarray_ufunc, logsumexp as _logsumexp, ELPDData, + stats_variance_2d as svar, ) -from ..utils import _var_names +from ..utils import _var_names, Numba, _numba_var _log = logging.getLogger(__name__) @@ -703,13 +704,13 @@ def r2_score(y_true, y_pred): r2: Bayesian R² r2_std: standard deviation of the Bayesian R². """ + _numba_flag = Numba.numba_flag if y_pred.ndim == 1: - var_y_est = np.var(y_pred) - var_e = np.var(y_true - y_pred) + var_y_est = _numba_var(svar, np.var, y_pred) + var_e = _numba_var(svar, np.var, (y_true - y_pred)) else: - var_y_est = np.var(y_pred.mean(0)) - var_e = np.var(y_true - y_pred, 0) - + var_y_est = _numba_var(svar, np.var, y_pred.mean(0)) + var_e = _numba_var(svar, np.var, (y_true - y_pred), axis=0) r_squared = var_y_est / (var_y_est + var_e) return pd.Series([np.mean(r_squared), np.std(r_squared)], index=["r2", "r2_std"]) @@ -859,9 +860,14 @@ def summary( kwargs=dict(high=np.pi, low=-np.pi), input_core_dims=(("chain", "draw"),), ) - + _numba_flag = Numba.numba_flag + func = None + if _numba_flag: + func = _circular_standard_deviation + else: + func = st.circstd circ_sd = xr.apply_ufunc( - _make_ufunc(st.circstd), + _make_ufunc(func), posterior, kwargs=dict(high=np.pi, low=-np.pi), input_core_dims=(("chain", "draw"),), diff --git a/arviz/stats/stats_utils.py b/arviz/stats/stats_utils.py --- a/arviz/stats/stats_utils.py +++ b/arviz/stats/stats_utils.py @@ -8,6 +8,7 @@ from scipy.fftpack import next_fast_len from scipy.stats.mstats import mquantiles from xarray import apply_ufunc +from ..utils import conditional_jit _log = logging.getLogger(__name__) @@ -414,7 +415,7 @@ def __str__(self): base += "\n\nThere has been a warning during the calculation. Please check the results." if kind == "loo" and "pareto_k" in self: - counts, _ = np.histogram(self.pareto_k, bins=[-np.inf, 0.5, 0.7, 1, np.inf]) + counts = histogram(self.pareto_k) extended = POINTWISE_LOO_FMT.format(max(4, len(str(np.max(counts))))) extended = extended.format( "Count", "Pct.", *[*counts, *(counts / np.sum(counts) * 100)] @@ -425,3 +426,36 @@ def __str__(self): def __repr__(self): """Alias to ``__str__``.""" return self.__str__() + + +@conditional_jit +def stats_variance_1d(data, ddof=0): + a_a, b_b = 0, 0 + for i in data: + a_a = a_a + i + b_b = b_b + i * i + var = b_b / (len(data)) - ((a_a / (len(data))) ** 2) + var = var * (len(data) / (len(data) - ddof)) + return var + + +def stats_variance_2d(data, ddof=0, axis=1): + if data.ndim == 1: + return stats_variance_1d(data, ddof=ddof) + a_a, b_b = data.shape + if axis == 1: + var = np.zeros(a_a) + for i in range(a_a): + var[i] = stats_variance_1d(data[i], ddof=ddof) + return var + else: + var = np.zeros(b_b) + for i in range(b_b): + var[i] = stats_variance_1d(data[:, i], ddof=ddof) + return var + + +@conditional_jit +def histogram(data): + kcounts, _ = np.histogram(data, bins=[-np.Inf, 0.5, 0.7, 1, np.Inf]) + return kcounts diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -100,3 +100,86 @@ def format_sig_figs(value, default=None): if value == 0: return 1 return max(int(np.log10(np.abs(value))) + 1, default) + + +def conditional_vect(function=None, **kwargs): # noqa: D202 + """Use numba's vectorize decorator if numba is installed. + + Notes + ----- + If called without arguments then return wrapped function. + @conditional_vect + def my_func(): + return + else called with arguments + @conditional_vect(nopython=True) + def my_func(): + return + + """ + + def wrapper(function): + try: + numba = importlib.import_module("numba") + return numba.vectorize(**kwargs)(function) + + except ImportError: + return function + + if function: + return wrapper(function) + else: + return wrapper + + +def numba_check(): + """Check if numba is installed.""" + numba = importlib.util.find_spec("numba") + return numba is not None + + +class Numba: + """A class to toggle numba states.""" + + numba_flag = numba_check() + + @classmethod + def disable_numba(cls): + """To disable numba.""" + cls.numba_flag = False + + @classmethod + def enable_numba(cls): + """To enable numba.""" + if numba_check(): + cls.numba_flag = True + else: + raise ValueError("Numba is not installed") + + +def _numba_var(numba_function, standard_numpy_func, data, axis=None, ddof=0): + """Replace the numpy methods used to calculate variance. + + Parameters + ---------- + numba_function : function() + Custom numba function included in stats/stats_utils.py. + + standard_numpy_func: function() + Standard function included in the numpy library. + + data : array. + axis : axis along which the variance is calculated. + ddof : degrees of freedom allowed while calculating variance. + + Returns + ------- + array: + variance values calculate by appropriate function for numba speedup + if Numba is installed or enabled. + + """ + if Numba.numba_flag: + return numba_function(data, axis=axis, ddof=ddof) + else: + return standard_numpy_func(data, axis=axis, ddof=ddof) diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py new file mode 100644 --- /dev/null +++ b/benchmarks/__init__.py @@ -0,0 +1 @@ + diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py new file mode 100644 --- /dev/null +++ b/benchmarks/benchmarks.py @@ -0,0 +1,85 @@ +# Write the benchmarking functions here. +# See "Writing benchmarks" in the asv docs for more information. +import numpy as np +from scipy.stats import circstd + + +class Hist: + def time_histogram(self): + try: + data = np.random.rand(10000, 1000) + import numba + + @numba.njit(cache=True) + def _hist(data): + return np.histogram(data, bins=100) + + return _hist(data) + except ImportError: + data = np.random.rand(10000, 1000) + return np.histogram(data, bins=100) + + +class Variance: + def time_variance(self): + try: + data = np.random.randn(10000, 10000) + import numba + + @numba.njit(cache=True) + def stats_variance_1d(data, ddof=0): + a, b = 0, 0 + for i in data: + a = a + i + b = b + i * i + var = b / (len(data)) - ((a / (len(data))) ** 2) + var = var * (len(data) / (len(data) - ddof)) + return var + + def stats_variance_2d(data, ddof=0, axis=1): + a, b = data.shape + if axis == 1: + var = np.zeros(a) + for i in range(a): + var[i] = stats_variance_1d(data[i], ddof=ddof) + else: + var = np.zeros(b) + for i in range(b): + var[i] = stats_variance_1d(data[:, i], ddof=ddof) + return var + + return stats_variance_2d(data) + except ImportError: + data = np.random.randn(10000, 10000) + return np.var(data, axis=1) + + +class CircStd: + def time_circ_std(self): + try: + data = np.random.randn(10000, 1000) + import numba + + def _circfunc(samples, high, low): + samples = np.asarray(samples) + if samples.size == 0: + return np.nan, np.nan + return samples, _angle(samples, low, high, np.pi) + + @numba.vectorize(nopython=True) + def _angle(samples, low, high, pi=np.pi): + ang = (samples - low) * 2.0 * pi / (high - low) + return ang + + def _circular_standard_deviation(samples, high=2 * np.pi, low=0, axis=None): + pi = np.pi + samples, ang = _circfunc(samples, high, low) + S = np.sin(ang).mean(axis=axis) + C = np.cos(ang).mean(axis=axis) + R = np.hypot(S, C) + return ((high - low) / 2.0 / pi) * np.sqrt(-2 * np.log(R)) + + return _circular_standard_deviation(data) + except ImportError: + data = np.random.randn(10000, 1000) + return circstd(data) diff --git a/doc/conf.py b/doc/conf.py --- a/doc/conf.py +++ b/doc/conf.py @@ -135,6 +135,7 @@ ("Quickstart", "notebooks/Introduction"), ("Cookbook", "notebooks/InferenceDataCookbook"), ("InferenceData", "notebooks/XarrayforArviZ"), + ("Numba", "notebooks/Numba"), ("API", "api"), ("Usage", "usage"), ("About", "about"),

diff --git a/arviz/tests/test_data_pyro.py b/arviz/tests/test_data_pyro.py --- a/arviz/tests/test_data_pyro.py +++ b/arviz/tests/test_data_pyro.py @@ -1,6 +1,7 @@ # pylint: disable=no-member, invalid-name, redefined-outer-name import pytest -from arviz import from_pyro + +from ..data.io_pyro import from_pyro from .helpers import ( # pylint: disable=unused-import chains, draws, diff --git a/arviz/tests/test_diagnostics.py b/arviz/tests/test_diagnostics.py --- a/arviz/tests/test_diagnostics.py +++ b/arviz/tests/test_diagnostics.py @@ -4,6 +4,7 @@ import numpy as np from numpy.testing import assert_almost_equal, assert_array_almost_equal import pandas as pd +from scipy.stats import circstd import pytest from ..data import load_arviz_data, from_cmdstan @@ -20,7 +21,12 @@ _z_scale, _conv_quantile, _split_chains, + _sqrt, + _angle, + _circfunc, + _circular_standard_deviation, ) +from ..utils import Numba # For tests only, recommended value should be closer to 1.01-1.05 # See discussion in https://github.com/stan-dev/rstan/pull/618 @@ -56,12 +62,9 @@ def test_deterministic(self): R code: ``` source('~/monitor.R') - data2 <- read.csv("blocker.2.csv", comment.char = "#") data1 <- read.csv("blocker.1.csv", comment.char = "#") - output <- matrix(ncol=15, nrow=length(names(data1))-4) - j = 0 for (i in 1:length(names(data1))) { name = names(data1)[i] @@ -85,7 +88,6 @@ def test_deterministic(self): mcse_quantile(ary, prob=0.1), mcse_quantile(ary, prob=0.3)) } - df = data.frame(output, row.names = names(data1)[5:ncol(data1)]) colnames(df) <- c("rhat_rank", "rhat_raw", @@ -102,7 +104,6 @@ def test_deterministic(self): "mcse_quantile01", "mcse_quantile10", "mcse_quantile30") - write.csv(df, "reference_values.csv") ``` """ @@ -150,7 +151,7 @@ def test_deterministic(self): assert (abs(reference["rhat_rank"] - arviz_data["rhat_rank"]) < 6e-5).all(None) assert abs(np.median(reference["rhat_rank"] - arviz_data["rhat_rank"]) < 1e-14).all(None) not_rhat = [col for col in reference.columns if col != "rhat_rank"] - assert (abs(reference[not_rhat] - arviz_data[not_rhat]) < 1e-11).all(None) + assert (abs((reference[not_rhat] - arviz_data[not_rhat])).values < 1e-8).all(None) assert abs(np.median(reference[not_rhat] - arviz_data[not_rhat]) < 1e-14).all(None) @pytest.mark.parametrize("method", ("rank", "split", "folded", "z_scale", "identity")) @@ -206,6 +207,30 @@ def test_rhat_ndarray(self): with pytest.raises(TypeError): rhat(np.random.randn(2, 300, 10)) + def test_angle(self): + x = np.random.randn(100) + high = 8 + low = 4 + res = (x - low) * 2 * np.pi / (high - low) + assert np.allclose(_angle(x, low, high, np.pi), res) + + def test_circfunc(self): + school = load_arviz_data("centered_eight").posterior["mu"].values + a_a, b_b = _circfunc(school, 8, 4) + assert np.allclose(a_a, school) + assert np.allclose(b_b, _angle(school, 4, 8, np.pi)) + + @pytest.mark.parametrize( + "data", (np.random.randn(100), np.random.randn(100, 100), np.random.randn(100, 100, 100)) + ) + def test_circular_standard_deviation_1d(self, data): + high = 8 + low = 4 + assert np.allclose( + _circular_standard_deviation(data, high=high, low=low), + circstd(data, high=high, low=low), + ) + @pytest.mark.parametrize( "method", ( @@ -455,12 +480,12 @@ def test_multichain_summary_array(self, draws, chains): ) ).all() else: - assert mcse_mean_hat == mcse_mean_hat_ - assert mcse_sd_hat == mcse_sd_hat_ - assert ess_mean_hat == ess_mean_hat_ - assert ess_sd_hat == ess_sd_hat_ - assert ess_bulk_hat == ess_bulk_hat_ - assert ess_tail_hat == ess_tail_hat_ + assert_almost_equal(mcse_mean_hat, mcse_mean_hat_) + assert_almost_equal(mcse_sd_hat, mcse_sd_hat_) + assert_almost_equal(ess_mean_hat, ess_mean_hat_) + assert_almost_equal(ess_sd_hat, ess_sd_hat_) + assert_almost_equal(ess_bulk_hat, ess_bulk_hat_) + assert_almost_equal(ess_tail_hat, ess_tail_hat_) if chains in (None, 1): assert np.isnan(rhat_hat) assert np.isnan(rhat_hat_) @@ -471,15 +496,20 @@ def test_geweke(self): first = 0.1 last = 0.5 intervals = 100 - - gw_stat = geweke(np.random.randn(10000), first=first, last=last, intervals=intervals) + data = np.random.randn(100000) + gw_stat = geweke(data, first, last, intervals) # all geweke values should be between -1 and 1 for this many draws from a # normal distribution assert ((gw_stat[:, 1] > -1) | (gw_stat[:, 1] < 1)).all() assert gw_stat.shape[0] == intervals - assert 10000 * last - gw_stat[:, 0].max() == 1 + assert 100000 * last - gw_stat[:, 0].max() == 1 + + def test_sqrt(self): + x = np.random.rand(100) + y = np.random.rand(100) + assert np.allclose(_sqrt(x, y), np.sqrt(x + y)) def test_geweke_bad_interval(self): # lower bound @@ -542,3 +572,85 @@ def test_split_chain_dims(self, chains, draws): if chains is None: chains = 1 assert split_data.shape == (chains * 2, draws // 2) + + +def test_numba_bfmi(): + """Numba test for bfmi.""" + state = Numba.numba_flag + school = load_arviz_data("centered_eight") + data_md = np.random.rand(100, 100, 10) + Numba.disable_numba() + non_numba = bfmi(school.posterior["mu"].values) + non_numba_md = bfmi(data_md) + Numba.enable_numba() + with_numba = bfmi(school.posterior["mu"].values) + with_numba_md = bfmi(data_md) + assert np.allclose(non_numba_md, with_numba_md) + assert np.allclose(with_numba, non_numba) + assert state == Numba.numba_flag + + [email protected]("method", ("rank", "split", "folded", "z_scale", "identity")) +def test_numba_rhat(method): + """Numba test for mcse.""" + state = Numba.numba_flag + school = np.random.rand(100, 100) + Numba.disable_numba() + non_numba = rhat(school, method=method) + Numba.enable_numba() + with_numba = rhat(school, method=method) + assert np.allclose(with_numba, non_numba) + assert Numba.numba_flag == state + + [email protected]("method", ("mean", "sd", "quantile")) +def test_numba_mcse(method, prob=None): + """Numba test for mcse.""" + state = Numba.numba_flag + school = np.random.rand(100, 100) + if method == "quantile": + prob = 0.80 + Numba.disable_numba() + non_numba = mcse(school, method=method, prob=prob) + Numba.enable_numba() + with_numba = mcse(school, method=method, prob=prob) + assert np.allclose(with_numba, non_numba) + assert Numba.numba_flag == state + + +def test_ks_summary_numba(): + """Numba test for ks_summary.""" + state = Numba.numba_flag + data = np.random.randn(100, 100) + Numba.disable_numba() + non_numba = (ks_summary(data)["Count"]).values + Numba.enable_numba() + with_numba = (ks_summary(data)["Count"]).values + assert np.allclose(non_numba, with_numba) + assert Numba.numba_flag == state + + +def test_geweke_numba(): + """Numba test for geweke.""" + state = Numba.numba_flag + data = np.random.randn(100) + Numba.disable_numba() + non_numba = geweke(data) + Numba.enable_numba() + with_numba = geweke(data) + assert np.allclose(non_numba, with_numba) + assert Numba.numba_flag == state + + [email protected]("batches", (1, 20)) [email protected]("circular", (True, False)) +def test_mcse_error_numba(batches, circular): + """Numba test for mcse_error.""" + data = np.random.randn(100, 100) + state = Numba.numba_flag + Numba.disable_numba() + non_numba = _mc_error(data, batches=batches, circular=circular) + Numba.enable_numba() + with_numba = _mc_error(data, batches=batches, circular=circular) + assert np.allclose(non_numba, with_numba) + assert state == Numba.numba_flag diff --git a/arviz/tests/test_plot_utils.py b/arviz/tests/test_plot_utils.py --- a/arviz/tests/test_plot_utils.py +++ b/arviz/tests/test_plot_utils.py @@ -106,11 +106,11 @@ def test_invalid_coord_name(self, sample_dataset): # pylint: disable=invalid-na _, _, data = sample_dataset coords = {"NOT_A_COORD_NAME": [1]} - with pytest.raises(ValueError) as err: + with pytest.raises( + ValueError, match="Coords {'NOT_A_COORD_NAME'} are invalid coordinate keys" + ): get_coords(data, coords) - assert "Coords {'NOT_A_COORD_NAME'} are invalid coordinate keys" in str(err) - def test_invalid_coord_value(self, sample_dataset): # pylint: disable=invalid-name """Assert that nicer exception appears when user enters wrong coords value""" _, _, data = sample_dataset @@ -119,7 +119,7 @@ def test_invalid_coord_value(self, sample_dataset): # pylint: disable=invalid-n with pytest.raises(KeyError) as err: get_coords(data, coords) - assert "Coords should follow mapping format {coord_name:[dim1, dim2]}" in str(err) + assert "Coords should follow mapping format {coord_name:[dim1, dim2]}" in str(err.value) def test_invalid_coord_structure(self, sample_dataset): # pylint: disable=invalid-name """Assert that nicer exception appears when user enters wrong coords datatype""" diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -288,7 +288,7 @@ def test_plot_forest(models, model_fits, args_expected): def test_plot_forest_rope_exception(): with pytest.raises(ValueError) as err: plot_forest({"x": [1]}, rope="not_correct_format") - assert "Argument `rope` must be None, a dictionary like" in str(err) + assert "Argument `rope` must be None, a dictionary like" in str(err.value) def test_plot_forest_single_value(): @@ -788,8 +788,8 @@ def test_plot_compare_no_ic(models): with pytest.raises(ValueError) as err: plot_compare(model_compare) - assert "comp_df must contain one of the following" in str(err) - assert "['waic', 'loo']" in str(err) + assert "comp_df must contain one of the following" in str(err.value) + assert "['waic', 'loo']" in str(err.value) @pytest.mark.parametrize( diff --git a/arviz/tests/test_stats.py b/arviz/tests/test_stats.py --- a/arviz/tests/test_stats.py +++ b/arviz/tests/test_stats.py @@ -10,6 +10,7 @@ from ..data import load_arviz_data, from_dict, convert_to_inference_data, concat from ..stats import compare, hpd, loo, r2_score, waic, psislw, summary from ..stats.stats import _gpinv +from ..utils import Numba @pytest.fixture(scope="session") @@ -104,6 +105,11 @@ def test_summary_var_names(var_names_expected): @pytest.mark.parametrize("include_circ", [True, False]) def test_summary_include_circ(centered_eight, include_circ): assert summary(centered_eight, include_circ=include_circ) is not None + state = Numba.numba_flag + Numba.disable_numba() + assert summary(centered_eight, include_circ=include_circ) is not NotImplementedError + Numba.enable_numba() + assert state == Numba.numba_flag @pytest.mark.parametrize("fmt", ["wide", "long", "xarray"]) @@ -326,3 +332,21 @@ def test_multidimensional_log_likelihood(func): assert (fr1 == frm).all() assert_array_almost_equal(frm[:4], fr1[:4]) + + +def test_numba_stats(): + """Numba test for r2_score""" + state = Numba.numba_flag # Store the current state of Numba + set_1 = np.random.randn(100, 100) + set_2 = np.random.randn(100, 100) + set_3 = np.random.rand(100) + set_4 = np.random.rand(100) + Numba.disable_numba() + non_numba = r2_score(set_1, set_2) + non_numba_one_dimensional = r2_score(set_3, set_4) + Numba.enable_numba() + with_numba = r2_score(set_1, set_2) + with_numba_one_dimensional = r2_score(set_3, set_4) + assert state == Numba.numba_flag # Ensure that inital state = final state + assert np.allclose(non_numba, with_numba) + assert np.allclose(non_numba_one_dimensional, with_numba_one_dimensional) diff --git a/arviz/tests/test_stats_utils.py b/arviz/tests/test_stats_utils.py --- a/arviz/tests/test_stats_utils.py +++ b/arviz/tests/test_stats_utils.py @@ -4,12 +4,15 @@ import pytest from scipy.special import logsumexp +from ..data import load_arviz_data from ..stats.stats_utils import ( logsumexp as _logsumexp, make_ufunc, wrap_xarray_ufunc, not_valid, ELPDData, + stats_variance_2d, + histogram, ) @@ -209,3 +212,47 @@ def test_valid_shape(): def test_elpd_data_error(): with pytest.raises(ValueError): ELPDData(data=[0, 1, 2], index=["not IC", "se", "p"]).__repr__() + + +def test_stats_variance_1d(): + """Test for stats_variance_1d.""" + data = np.random.rand(1000000) + assert np.allclose(np.var(data), stats_variance_2d(data)) + assert np.allclose(np.var(data, ddof=1), stats_variance_2d(data, ddof=1)) + + +def test_stats_variance_2d(): + """Test for stats_variance_2d.""" + data_1 = np.random.randn(1000, 1000) + data_2 = np.random.randn(1000000) + school = load_arviz_data("centered_eight").posterior["mu"].values + n_school = load_arviz_data("non_centered_eight").posterior["mu"].values + assert np.allclose(np.var(school, ddof=1, axis=1), stats_variance_2d(school, ddof=1, axis=1)) + assert np.allclose(np.var(school, ddof=1, axis=0), stats_variance_2d(school, ddof=1, axis=0)) + assert np.allclose( + np.var(n_school, ddof=1, axis=1), stats_variance_2d(n_school, ddof=1, axis=1) + ) + assert np.allclose( + np.var(n_school, ddof=1, axis=0), stats_variance_2d(n_school, ddof=1, axis=0) + ) + assert np.allclose(np.var(data_2), stats_variance_2d(data_2)) + assert np.allclose(np.var(data_2, ddof=1), stats_variance_2d(data_2, ddof=1)) + assert np.allclose(np.var(data_1, axis=0), stats_variance_2d(data_1, axis=0)) + assert np.allclose(np.var(data_1, axis=1), stats_variance_2d(data_1, axis=1)) + assert np.allclose(np.var(data_1, axis=0, ddof=1), stats_variance_2d(data_1, axis=0, ddof=1)) + assert np.allclose(np.var(data_1, axis=1, ddof=1), stats_variance_2d(data_1, axis=1, ddof=1)) + + +def test_variance_bad_data(): + """Test for variance when the data range is extremely wide.""" + data = np.array([1e20, 200e-10, 1e-17, 432e9, 2500432, 23e5, 16e-7]) + assert np.allclose(stats_variance_2d(data), np.var(data)) + assert np.allclose(stats_variance_2d(data, ddof=1), np.var(data, ddof=1)) + assert not np.allclose(stats_variance_2d(data), np.var(data, ddof=1)) + + +def test_histogram(): + school = load_arviz_data("non_centered_eight").posterior["mu"].values + k_count = histogram(school) + kcount, _ = np.histogram(school, bins=[-np.Inf, 0.5, 0.7, 1, np.Inf]) + assert np.allclose(k_count, kcount) diff --git a/arviz/tests/test_utils.py b/arviz/tests/test_utils.py --- a/arviz/tests/test_utils.py +++ b/arviz/tests/test_utils.py @@ -3,10 +3,13 @@ """ # pylint: disable=redefined-outer-name, no-member from unittest.mock import Mock +import importlib import numpy as np import pytest -from ..utils import _var_names, format_sig_figs + +from ..utils import _var_names, format_sig_figs, numba_check, Numba, _numba_var from ..data import load_arviz_data, from_dict +from ..stats.stats_utils import stats_variance_2d as svar @pytest.fixture(scope="session") @@ -81,6 +84,20 @@ def func(): assert func() == "Numba not used" +def test_conditional_vect_decorator_no_numba(utils_with_numba_import_fail): + """Tests to see if Numba vectorize code block is skipped with Import Failure + + Test can be distinguished from test_conditional_vect__numba_decorator + by use of debugger or coverage tool + """ + + @utils_with_numba_import_fail.conditional_vect + def func(): + return "Numba not used" + + assert func() == "Numba not used" + + def test_conditional_jit_numba_decorator(): """Tests to see if Numba is used. @@ -91,9 +108,9 @@ def test_conditional_jit_numba_decorator(): @utils.conditional_jit def func(): - return "Numba used" + return True - assert func() == "Numba used" + assert func() def test_conditional_jit_numba_decorator_keyword(monkeypatch): @@ -134,3 +151,83 @@ def placeholder_func(): ) def test_format_sig_figs(value, default, expected): assert format_sig_figs(value, default=default) == expected + + +def test_conditional_vect_numba_decorator(): + """Tests to see if Numba is used. + + Test can be distinguished from test_conditional_jit_decorator_no_numba + by use of debugger or coverage tool + """ + from arviz import utils + + @utils.conditional_vect + def func(a_a, b_b): + return a_a + b_b + + value_one = np.random.randn(10) + value_two = np.random.randn(10) + assert np.allclose(func(value_one, value_two), value_one + value_two) + + +def test_conditional_vect_numba_decorator_keyword(monkeypatch): + """Checks else statement and vect keyword argument""" + from arviz import utils + + # Mock import lib to return numba with hit method which returns a function that returns kwargs + numba_mock = Mock() + monkeypatch.setattr(utils.importlib, "import_module", lambda x: numba_mock) + + def vectorize(**kwargs): + """overwrite numba.vectorize function""" + return lambda x: (x(), kwargs) + + numba_mock.vectorize = vectorize + + @utils.conditional_vect(keyword_argument="A keyword argument") + def placeholder_func(): + """This function does nothing""" + return "output" + + # pylint: disable=unpacking-non-sequence + function_results, wrapper_result = placeholder_func + assert wrapper_result == {"keyword_argument": "A keyword argument"} + assert function_results == "output" + + +def test_numba_check(): + """Test for numba_check""" + numba = importlib.util.find_spec("numba") + flag = numba is not None + assert flag == numba_check() + + +def test_numba_utils(): + """Test for class Numba.""" + flag = Numba.numba_flag + assert flag == numba_check() + Numba.disable_numba() + val = Numba.numba_flag + assert not val + Numba.enable_numba() + val = Numba.numba_flag + assert val + assert flag == Numba.numba_flag + + [email protected]("axis", (0, 1)) [email protected]("ddof", (0, 1)) +def test_numba_var(axis, ddof): + """Method to test numba_var.""" + flag = Numba.numba_flag + data_1 = np.random.randn(100, 100) + data_2 = np.random.rand(100) + with_numba_1 = _numba_var(svar, np.var, data_1, axis=axis, ddof=ddof) + with_numba_2 = _numba_var(svar, np.var, data_2, ddof=ddof) + Numba.disable_numba() + non_numba_1 = _numba_var(svar, np.var, data_1, axis=axis, ddof=ddof) + non_numba_2 = _numba_var(svar, np.var, data_2, ddof=ddof) + Numba.enable_numba() + assert flag == Numba.numba_flag + assert np.allclose(with_numba_1, non_numba_1) + assert np.allclose(with_numba_2, non_numba_2)

Plot visualizing quantiles in time series It would be great to have a nice, generic version of this:  See https://github.com/mwaskom/seaborn/issues/450 for discussion. The suggested matplotlib recipes are wanting by comparison, I think (http://matplotlib.org/users/recipes.html).

+1 I'd settle for something simpler too, maybe something with a point at the center of a line, like this: ``` p + geom_pointrange(aes(ymin = lower, ymax = upper)) ``` from http://ggplot2.tidyverse.org/reference/geom_linerange.html Looks like plotnine has this. We could do this with `np.percentile` + for loop with `fill_between`. Do we want make this smooth? Then kde / spline method could be used, but not sure if it's appropriate to interpolate with smooth function. That's a good idea. (I'm not sure this particular plot is high-priority, it's just something I find myself making often with Stan output.) Here is a sketch for `tsplot`. I don't know how much options we should add. import numpy as np import matplotlib.pyplot as plt plt.style.use('ggplot') # this was just used for the examples # data t = np.linspace(0,100,100) y = 5 * np.sin(t/10) + 4*np.random.randn(100*150).reshape(150, 100) y_ = 5 * np.sin(t/10) + 4*np.random.randn(100*4000).reshape(4000, 100) t__ = np.linspace(0,100,6) y__ = 5 * np.sin(t__/10) + 4*np.random.randn(6*4000).reshape(4000, 6) def tsplot(x, y, n=20, percentile_min=1, percentile_max=99, color='r', plot_mean=True, plot_median=False, line_color='k', **kwargs): # calculate the lower and upper percentile groups, skipping 50 percentile perc1 = np.percentile(y, np.linspace(percentile_min, 50, num=n, endpoint=False), axis=0) perc2 = np.percentile(y, np.linspace(50, percentile_max, num=n+1)[1:], axis=0) if 'alpha' in kwargs: alpha = kwargs.pop('alpha') else: alpha = 1/n # fill lower and upper percentile groups for p1, p2 in zip(perc1, perc2): plt.fill_between(x, p1, p2, alpha=alpha, color=color, edgecolor=None) if plot_mean: plt.plot(x, np.mean(y, axis=0), color=line_color) if plot_median: plt.plot(x, np.median(y, axis=0), color=line_color) return plt.gca() Then can be called either in one go: tsplot(t, y, n=100, percentile_min=2.5, percentile_max=97.5, plot_median=True, plot_mean=False, color='g', line_color='navy')  tsplot(t, y, n=20, percentile_min=2.5, percentile_max=97.5, plot_median=True, plot_mean=False, color='g', line_color='navy')  tsplot(t, y, n=5, percentile_min=2.5, percentile_max=97.5, plot_median=True, plot_mean=False, color='g', line_color='navy')  tsplot(t, y, n=2, percentile_min=2.5, percentile_max=97.5, plot_median=True, plot_mean=False, color='g', line_color='navy')  tsplot(t, y_, n=100, percentile_min=2.5, percentile_max=97.5, plot_median=True, plot_mean=False, color='g', line_color='navy')  or multiple times # IQR tsplot(t, y_, n=1, percentile_min=25, percentile_max=75, plot_median=False, plot_mean=False, color='g', line_color='navy', alpha=0.3) # 90% interval tsplot(t, y_, n=1, percentile_min=5, percentile_max=95, plot_median=True, plot_mean=False, color='g', line_color='navy', alpha=0.3)  tsplot(t__, y__, n=1, percentile_min=25, percentile_max=75, plot_median=False, plot_mean=False, color='g', line_color='navy', alpha=0.3) tsplot(t__, y__, n=1, percentile_min=5, percentile_max=95, plot_median=True, plot_mean=False, color='g', line_color='navy', alpha=0.3) plt.text(0, -5, "n=2", fontsize=14)  These look great! I'm inclined to do something very simple like have the posterior mean and single 90% band. This seems to be what is used in Bayesian Data Analysis (e.g., chp 21). FYI: https://github.com/jasonhilton/ggfan  Not sure I like this. The simpler envelopes are clearer. helpful !

2019-06-18T12:22:59Z

arviz-devs/arviz

arviz-devs__arviz-724

2f47b2ed1120f60768c3085bbafca567c4baf18e

diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -12,6 +12,7 @@ from .kdeplot import plot_kde, _fast_kde, _fast_kde_2d from .khatplot import plot_khat from .loopitplot import plot_loo_pit +from .mcseplot import plot_mcse from .pairplot import plot_pair from .parallelplot import plot_parallel from .posteriorplot import plot_posterior @@ -37,6 +38,7 @@ "_fast_kde_2d", "plot_khat", "plot_loo_pit", + "plot_mcse", "plot_pair", "plot_parallel", "plot_posterior", diff --git a/arviz/plots/essplot.py b/arviz/plots/essplot.py --- a/arviz/plots/essplot.py +++ b/arviz/plots/essplot.py @@ -16,6 +16,8 @@ def plot_ess( + # disable black until #763 is released + # fmt: off idata, var_names=None, kind="local", @@ -26,12 +28,15 @@ def plot_ess( rug=False, rug_kind="diverging", n_points=20, + extra_methods=False, min_ess=400, ax=None, extra_kwargs=None, + text_kwargs=None, hline_kwargs=None, rug_kwargs=None, **kwargs + # fmt: on ): """Plot quantile, local or evolution of effective sample sizes (ESS). @@ -60,14 +65,20 @@ def plot_ess( n_points : int Number of points for which to plot their quantile/local ess or number of subsets in the evolution plot. + extra_methods : bool, optional + Plot mean and sd ESS as horizontal lines. Not taken into account in evolution kind min_ess : int Minimum number of ESS desired. ax : axes, optional Matplotlib axes. Defaults to None. - extra_kwargs : dict - kwargs used to plot ess tail and differentiate it from ess bulk. If None, the same - kwargs are used, thus, the 2 lines will differ in the color which is matplotlib default. - hline_kwargs : dict + extra_kwargs : dict, optional + If evolution plot, extra_kwargs is used to plot ess tail and differentiate it + from ess bulk. Otherwise, passed to extra methods lines. + text_kwargs : dict, optional + Only taken into account when ``extra_methods=True``. kwargs passed to ax.annotate + for extra methods lines labels. It accepts the additional + key ``x`` to set ``xy=(text_kwargs["x"], mcse)`` + hline_kwargs : dict, optional kwargs passed to ax.axhline for the horizontal minimum ESS line. rug_kwargs : dict kwargs passed to rug plot. @@ -147,8 +158,11 @@ def plot_ess( if coords is None: coords = {} + if "chain" in coords or "draw" in coords: + raise ValueError("chain and draw are invalid coordinates for this kind of plot") + extra_methods = False if kind == "evolution" else extra_methods - data = convert_to_dataset(idata, group="posterior") + data = get_coords(convert_to_dataset(idata, group="posterior"), coords) var_names = _var_names(var_names, data) n_draws = data.dims["draw"] n_samples = n_draws * data.dims["chain"] @@ -211,9 +225,7 @@ def plot_ess( dim="ess_dim", ) - plotters = list( - xarray_var_iter(get_coords(ess_dataset, coords), var_names=var_names, skip_dims={"ess_dim"}) - ) + plotters = list(xarray_var_iter(ess_dataset, var_names=var_names, skip_dims={"ess_dim"})) length_plotters = len(plotters) rows, cols = default_grid(length_plotters) @@ -226,21 +238,38 @@ def plot_ess( kwargs.setdefault("markersize", kwargs.pop("ms", _markersize)) kwargs.setdefault("marker", "o") kwargs.setdefault("zorder", 3) + if extra_kwargs is None: + extra_kwargs = {} if kind == "evolution": - if extra_kwargs is None: - extra_kwargs = {} extra_kwargs = { **extra_kwargs, **{key: item for key, item in kwargs.items() if key not in extra_kwargs}, } kwargs.setdefault("label", "bulk") extra_kwargs.setdefault("label", "tail") + else: + extra_kwargs.setdefault("linestyle", extra_kwargs.pop("ls", "-")) + extra_kwargs.setdefault("linewidth", extra_kwargs.pop("lw", _linewidth / 2)) + extra_kwargs.setdefault("color", "k") + extra_kwargs.setdefault("alpha", 0.5) + kwargs.setdefault("label", kind) if hline_kwargs is None: hline_kwargs = {} hline_kwargs.setdefault("linewidth", hline_kwargs.pop("lw", _linewidth)) hline_kwargs.setdefault("linestyle", hline_kwargs.pop("ls", "--")) hline_kwargs.setdefault("color", hline_kwargs.pop("c", "gray")) hline_kwargs.setdefault("alpha", 0.7) + if extra_methods: + mean_ess = ess(data, var_names=var_names, method="mean", relative=relative) + sd_ess = ess(data, var_names=var_names, method="sd", relative=relative) + if text_kwargs is None: + text_kwargs = {} + text_x = text_kwargs.pop("x", 1) + text_kwargs.setdefault("fontsize", text_kwargs.pop("size", xt_labelsize * 0.7)) + text_kwargs.setdefault("alpha", extra_kwargs["alpha"]) + text_kwargs.setdefault("color", extra_kwargs["color"]) + text_kwargs.setdefault("horizontalalignment", text_kwargs.pop("ha", "right")) + text_va = text_kwargs.pop("verticalalignment", text_kwargs.pop("va", None)) if ax is None: _, ax = _create_axes_grid( @@ -272,6 +301,27 @@ def plot_ess( rug_x, rug_y = values / (len(mask) - 1), np.zeros_like(values) - rug_space ax_.plot(rug_x, rug_y, **rug_kwargs) ax_.axhline(0, color="k", linewidth=_linewidth, alpha=0.7) + if extra_methods: + mean_ess_i = mean_ess[var_name].sel(**selection).values.item() + sd_ess_i = sd_ess[var_name].sel(**selection).values.item() + ax_.axhline(mean_ess_i, **extra_kwargs) + ax_.annotate( + "mean", + (text_x, mean_ess_i), + va=text_va + if text_va is not None + else "bottom" + if mean_ess_i >= sd_ess_i + else "top", + **text_kwargs, + ) + ax_.axhline(sd_ess_i, **extra_kwargs) + ax_.annotate( + "sd", + (text_x, sd_ess_i), + va=text_va if text_va is not None else "bottom" if sd_ess_i > mean_ess_i else "top", + **text_kwargs, + ) ax_.axhline(400 / n_samples if relative else min_ess, **hline_kwargs) @@ -284,7 +334,7 @@ def plot_ess( ylabel.format("Relative ESS" if relative else "ESS"), fontsize=ax_labelsize, wrap=True ) if kind == "evolution": - ax_.legend(title="type") + ax_.legend(title="Method", title_fontsize=xt_labelsize, fontsize=xt_labelsize) else: ax_.set_xlim(0, 1) if rug: diff --git a/arviz/plots/mcseplot.py b/arviz/plots/mcseplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/mcseplot.py @@ -0,0 +1,224 @@ +"""Plot quantile MC standard error.""" +import numpy as np +import xarray as xr + +from ..data import convert_to_dataset +from ..stats import mcse +from ..stats.stats_utils import quantile as _quantile +from .plot_utils import ( + xarray_var_iter, + _scale_fig_size, + make_label, + default_grid, + _create_axes_grid, + get_coords, +) +from ..utils import _var_names + + +def plot_mcse( + # disable black until #763 is released + # fmt: off + idata, + var_names=None, + coords=None, + errorbar=False, + figsize=None, + textsize=None, + extra_methods=False, + rug=False, + rug_kind="diverging", + n_points=20, + ax=None, + rug_kwargs=None, + extra_kwargs=None, + text_kwargs=None, + **kwargs + # fmt: on +): + """Plot quantile, local or evolution of effective sample sizes (ESS). + + Parameters + ---------- + idata : obj + Any object that can be converted to an az.InferenceData object + Refer to documentation of az.convert_to_dataset for details + var_names : list of variable names, optional + Variables to be plotted. + coords : dict, optional + Coordinates of var_names to be plotted. Passed to `Dataset.sel` + errorbar : bool, optional + Plot quantile value +/- mcse instead of plotting mcse. + figsize : tuple, optional + Figure size. If None it will be defined automatically. + textsize: float, optional + Text size scaling factor for labels, titles and lines. If None it will be autoscaled based + on figsize. + extra_methods : bool, optional + Plot mean and sd MCSE as horizontal lines. Only taken into account when + ``errorbar=False``. + rug : bool + Plot rug plot of values diverging or that reached the max tree depth. + rug_kind : bool + Variable in sample stats to use as rug mask. Must be a boolean variable. + n_points : int + Number of points for which to plot their quantile/local ess or number of subsets + in the evolution plot. + ax : axes, optional + Matplotlib axes. Defaults to None. + rug_kwargs : dict + kwargs passed to rug plot. + extra_kwargs : dict, optional + kwargs passed to ax.plot for extra methods lines. + text_kwargs : dict, optional + kwargs passed to ax.annotate for extra methods lines labels. It accepts the additional + key ``x`` to set ``xy=(text_kwargs["x"], mcse)`` + **kwargs + Passed as-is to plt.hist() or plt.plot() function depending on the value of `kind`. + + Returns + ------- + ax : matplotlib axes + + References + ---------- + * Vehtari et al. (2019) see https://arxiv.org/abs/1903.08008 + + Examples + -------- + Plot quantile MCSE. + + .. plot:: + :context: close-figs + + >>> import arviz as az + >>> idata = az.load_arviz_data("centered_eight") + >>> coords = {"school": ["Deerfield", "Lawrenceville"]} + >>> az.plot_mcse( + ... idata, var_names=["mu", "theta"], coords=coords + ... ) + + """ + if coords is None: + coords = {} + if "chain" in coords or "draw" in coords: + raise ValueError("chain and draw are invalid coordinates for this kind of plot") + + data = get_coords(convert_to_dataset(idata, group="posterior"), coords) + var_names = _var_names(var_names, data) + + probs = np.linspace(1 / n_points, 1 - 1 / n_points, n_points) + mcse_dataset = xr.concat( + [mcse(data, var_names=var_names, method="quantile", prob=p) for p in probs], dim="mcse_dim" + ) + + plotters = list(xarray_var_iter(mcse_dataset, var_names=var_names, skip_dims={"mcse_dim"})) + length_plotters = len(plotters) + rows, cols = default_grid(length_plotters) + + (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _markersize) = _scale_fig_size( + figsize, textsize, rows, cols + ) + kwargs.setdefault("linestyle", kwargs.pop("ls", "none")) + kwargs.setdefault("linewidth", kwargs.pop("lw", _linewidth)) + kwargs.setdefault("markersize", kwargs.pop("ms", _markersize)) + kwargs.setdefault("marker", "_" if errorbar else "o") + kwargs.setdefault("zorder", 3) + if extra_kwargs is None: + extra_kwargs = {} + extra_kwargs.setdefault("linestyle", extra_kwargs.pop("ls", "-")) + extra_kwargs.setdefault("linewidth", extra_kwargs.pop("lw", _linewidth / 2)) + extra_kwargs.setdefault("color", "k") + extra_kwargs.setdefault("alpha", 0.5) + if extra_methods: + mean_mcse = mcse(data, var_names=var_names, method="mean") + sd_mcse = mcse(data, var_names=var_names, method="sd") + if text_kwargs is None: + text_kwargs = {} + text_x = text_kwargs.pop("x", 1) + text_kwargs.setdefault("fontsize", text_kwargs.pop("size", xt_labelsize * 0.7)) + text_kwargs.setdefault("alpha", extra_kwargs["alpha"]) + text_kwargs.setdefault("color", extra_kwargs["color"]) + text_kwargs.setdefault("horizontalalignment", text_kwargs.pop("ha", "right")) + text_va = text_kwargs.pop("verticalalignment", text_kwargs.pop("va", None)) + + if ax is None: + _, ax = _create_axes_grid( + length_plotters, rows, cols, figsize=figsize, squeeze=False, constrained_layout=True + ) + + for (var_name, selection, x), ax_ in zip(plotters, np.ravel(ax)): + if errorbar or rug: + values = data[var_name].sel(**selection).values.flatten() + if errorbar: + quantile_values = _quantile(values, probs) + ax_.errorbar(probs, quantile_values, yerr=x, **kwargs) + else: + ax_.plot(probs, x, label="quantile", **kwargs) + if extra_methods: + mean_mcse_i = mean_mcse[var_name].sel(**selection).values.item() + sd_mcse_i = sd_mcse[var_name].sel(**selection).values.item() + ax_.axhline(mean_mcse_i, **extra_kwargs) + ax_.annotate( + "mean", + (text_x, mean_mcse_i), + va=text_va + if text_va is not None + else "bottom" + if mean_mcse_i > sd_mcse_i + else "top", + **text_kwargs, + ) + ax_.axhline(sd_mcse_i, **extra_kwargs) + ax_.annotate( + "sd", + (text_x, sd_mcse_i), + va=text_va + if text_va is not None + else "bottom" + if sd_mcse_i >= mean_mcse_i + else "top", + **text_kwargs, + ) + if rug: + if rug_kwargs is None: + rug_kwargs = {} + if not hasattr(idata, "sample_stats"): + raise ValueError("InferenceData object must contain sample_stats for rug plot") + if not hasattr(idata.sample_stats, rug_kind): + raise ValueError("InferenceData does not contain {} data".format(rug_kind)) + rug_kwargs.setdefault("marker", "|") + rug_kwargs.setdefault("linestyle", rug_kwargs.pop("ls", "None")) + rug_kwargs.setdefault("color", rug_kwargs.pop("c", kwargs.get("color", "C0"))) + rug_kwargs.setdefault("space", 0.1) + rug_kwargs.setdefault("markersize", rug_kwargs.pop("ms", 2 * _markersize)) + + mask = idata.sample_stats[rug_kind].values.flatten() + values = np.argsort(values)[mask] + y_min, y_max = ax_.get_ylim() + y_min = y_min if errorbar else 0 + rug_space = (y_max - y_min) * rug_kwargs.pop("space") + rug_x, rug_y = values / (len(mask) - 1), np.full_like(values, y_min) - rug_space + ax_.plot(rug_x, rug_y, **rug_kwargs) + ax_.axhline(y_min, color="k", linewidth=_linewidth, alpha=0.7) + + ax_.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True) + ax_.tick_params(labelsize=xt_labelsize) + ax_.set_xlabel("Quantile", fontsize=ax_labelsize, wrap=True) + ax_.set_ylabel( + r"Value $\pm$ MCSE for quantiles" if errorbar else "MCSE for quantiles", + fontsize=ax_labelsize, + wrap=True, + ) + ax_.set_xlim(0, 1) + if rug: + ax_.yaxis.get_major_locator().set_params(nbins="auto", steps=[1, 2, 5, 10]) + y_min, y_max = ax_.get_ylim() + yticks = ax_.get_yticks() + yticks = yticks[(yticks >= y_min) & (yticks < y_max)] + ax_.set_yticks(yticks) + ax_.set_yticklabels(["{:.3g}".format(ytick) for ytick in yticks]) + elif not errorbar: + ax_.set_ylim(bottom=0) + + return ax diff --git a/examples/plot_ess_local.py b/examples/plot_ess_local.py --- a/examples/plot_ess_local.py +++ b/examples/plot_ess_local.py @@ -2,12 +2,12 @@ ESS Local Plot ============== -_thumb: .7, .5 +_thumb: .6, .5 """ import arviz as az az.style.use("arviz-darkgrid") -idata = az.load_arviz_data("centered_eight") +idata = az.load_arviz_data("non_centered_eight") -az.plot_ess(idata, var_names=["mu"], kind="local", marker="_", ms=20, mew=2) +az.plot_ess(idata, var_names=["mu"], kind="local", marker="_", ms=20, mew=2, rug=True) diff --git a/examples/plot_ess_quantile.py b/examples/plot_ess_quantile.py --- a/examples/plot_ess_quantile.py +++ b/examples/plot_ess_quantile.py @@ -10,4 +10,4 @@ idata = az.load_arviz_data("radon") -az.plot_ess(idata, var_names=["sigma_y"], kind="quantile") +az.plot_ess(idata, var_names=["sigma_y"], kind="quantile", color="C4") diff --git a/examples/plot_mcse.py b/examples/plot_mcse.py new file mode 100644 --- /dev/null +++ b/examples/plot_mcse.py @@ -0,0 +1,12 @@ +""" +Quantile MCSE Plot +================== + +_thumb: .5, .8 +""" +import arviz as az + +az.style.use('arviz-darkgrid') + +data = az.load_arviz_data('centered_eight') +az.plot_mcse(data, var_names=['tau', 'mu'], rug=True, extra_methods=True) diff --git a/examples/plot_mcse_errorbar.py b/examples/plot_mcse_errorbar.py new file mode 100644 --- /dev/null +++ b/examples/plot_mcse_errorbar.py @@ -0,0 +1,12 @@ +""" +Quantile MCSE Errobar Plot +========================== + +_thumb: .6, .4 +""" +import arviz as az + +az.style.use('arviz-darkgrid') + +data = az.load_arviz_data('radon') +az.plot_mcse(data, var_names=["sigma_a"], color="C4", errorbar=True)

diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -1,5 +1,6 @@ # pylint: disable=redefined-outer-name,too-many-lines import os +from copy import deepcopy import matplotlib.pyplot as plt from pandas import DataFrame from scipy.stats import gaussian_kde @@ -31,6 +32,7 @@ plot_rank, plot_elpd, plot_loo_pit, + plot_mcse, ) np.random.seed(0) @@ -949,22 +951,6 @@ def test_plot_khat(models, input_type, kwargs): assert axes [email protected]( - "kwargs", - [ - {}, - {"var_names": ["theta"], "relative": True, "color": "r"}, - {"coords": {"theta_dim_0": slice(4)}, "n_points": 10}, - {"min_ess": 600, "hline_kwargs": {"color": "r"}}, - ], -) [email protected]("kind", ["local", "quantile", "evolution"]) -def test_plot_ess(models, kind, kwargs): - idata = models.model_1 - ax = plot_ess(idata, kind=kind, **kwargs) - assert np.all(ax) - - @pytest.mark.parametrize( "kwargs", [ @@ -1007,43 +993,75 @@ def test_plot_khat_bad_input(models): plot_khat(models.model_1.sample_stats) [email protected]( + "kwargs", + [ + {}, + {"var_names": ["theta"], "relative": True, "color": "r"}, + {"coords": {"theta_dim_0": slice(4)}, "n_points": 10}, + {"min_ess": 600, "hline_kwargs": {"color": "r"}}, + ], +) [email protected]("kind", ["local", "quantile", "evolution"]) +def test_plot_ess(models, kind, kwargs): + """Test plot_ess arguments common to all kind of plots.""" + idata = models.model_1 + ax = plot_ess(idata, kind=kind, **kwargs) + assert np.all(ax) + + @pytest.mark.parametrize( "kwargs", [ {"rug": True}, - {"rug": True, "rug_kind": "max_depth"}, - {"rug": True, "rug_kwargs": {"color": "c"}}, + {"rug": True, "rug_kind": "max_depth", "rug_kwargs": {"color": "c"}}, + {"extra_methods": True}, + {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, + {"extra_methods": True, "rug": True}, ], ) @pytest.mark.parametrize("kind", ["local", "quantile"]) def test_plot_ess_local_quantile(models, kind, kwargs): + """Test specific arguments in kinds local and quantile of plot_ess.""" idata = models.model_1 ax = plot_ess(idata, kind=kind, **kwargs) assert np.all(ax) def test_plot_ess_evolution(models): + """Test specific arguments in evolution kind of plot_ess.""" idata = models.model_1 ax = plot_ess(idata, kind="evolution", extra_kwargs={"linestyle": "--"}, color="b") assert np.all(ax) def test_plot_ess_bad_kind(models): + """Test error when plot_ess recieves an invalid kind.""" idata = models.model_1 - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="Invalid kind"): plot_ess(idata, kind="bad kind") [email protected]("dim", ["chain", "draw"]) +def test_plot_ess_bad_coords(models, dim): + """Test error when chain or dim are used as coords to select a data subset.""" + idata = models.model_1 + with pytest.raises(ValueError, match="invalid coordinates"): + plot_ess(idata, coords={dim: slice(3)}) + + def test_plot_ess_no_sample_stats(models): + """Test error when rug=True but sample_stats group is not present.""" idata = models.model_1 - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="must contain sample_stats"): plot_ess(idata.posterior, rug=True) def test_plot_ess_no_divergences(models): - idata = models.model_1 + """Test error when rug=True, but the variable defined by rug_kind is missing.""" + idata = deepcopy(models.model_1) idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="not contain diverging"): plot_ess(idata, rug=True) @@ -1069,3 +1087,44 @@ def test_plot_loo_pit_incompatible_args(models): """Test error when both ecdf and use_hpd are True.""" with pytest.raises(ValueError, match="incompatible"): plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hpd=True) + + [email protected]( + "kwargs", + [ + {}, + {"var_names": ["theta"], "color": "r"}, + {"rug": True, "rug_kwargs": {"color": "r"}}, + {"errorbar": True, "rug": True, "rug_kind": "max_depth"}, + {"errorbar": True, "coords": {"theta_dim_0": slice(4)}, "n_points": 10}, + {"extra_methods": True, "rug": True}, + {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, + ], +) +def test_plot_mcse(models, kwargs): + idata = models.model_1 + ax = plot_mcse(idata, **kwargs) + assert np.all(ax) + + [email protected]("dim", ["chain", "draw"]) +def test_plot_mcse_bad_coords(models, dim): + """Test error when chain or dim are used as coords to select a data subset.""" + idata = models.model_1 + with pytest.raises(ValueError, match="invalid coordinates"): + plot_mcse(idata, coords={dim: slice(3)}) + + +def test_plot_mcse_no_sample_stats(models): + """Test error when rug=True but sample_stats group is not present.""" + idata = models.model_1 + with pytest.raises(ValueError, match="must contain sample_stats"): + plot_mcse(idata.posterior, rug=True) + + +def test_plot_mcse_no_divergences(models): + """Test error when rug=True, but the variable defined by rug_kind is missing.""" + idata = deepcopy(models.model_1) + idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) + with pytest.raises(ValueError, match="not contain diverging"): + plot_mcse(idata, rug=True)

New R-hat, ESS and quantile-MCSE functions + related plots + rank plots Add new R-hat, ESS, quantile-MCSE and new plots described in - Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, Paul-Christian Bürkner (2019): Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. [arXiv preprint arXiv:1903.08008](http://arxiv.org/abs/1903.08008). The reference code in R is available in [monitornew.R](https://github.com/avehtari/rhat_ess/blob/master/code/monitornew.R) and in [monitorplot.R](https://github.com/avehtari/rhat_ess/blob/master/code/monitorplot.R). I think porting required computation from monitornew.R is easy and porting the plots may require a bit more work, but I'm not expert on Python graphics. There might be also something useful in the corresponding issues for rstan stan-dev/rstan#617 and bayesplot stan-dev/bayesplot#178

@avehtari thanks to you and collaborators for the nice paper, releasing code at the same time, and for reaching out to help get this all implemented! Two questions on rank plots: 1. Does it make sense to think about putting error bars on rank plots, like in the simulation based calibration paper? Part of me thinks that if you want to automatically "score" your rank plot you should just use R-hat. 2. Any thoughts on how to set a y-axis for "mean centered" rank plots? I like that they show positives and negatives very clearly, but it is hard to tell scale. The error bars might help with this, I suppose. Centered Eight Schools:  Non-centered Eight Schools:  > Does it make sense to think about putting error bars on rank plots, like in the simulation based calibration paper? Yes, but that they are not sufficient as mentioned in SBC paper. I think the benefit of rank plots is the simple interpretation. Additional figure type would be ECDF difference as in Fig 14c in SBC paper. It has a better envelope, but it requires more explanation and thus both are useful to have. > Part of me thinks that if you want to automatically "score" your rank plot you should just use R-hat. Yes, we recommend Rhat, bulk-ESS, tail-ESS and HMC specific diagnostics for automatic diagnostics and rank plots for helping to see where the problem is or if you want to check just some rank plots. > Any thoughts on how to set a y-axis for "mean centered" rank plots? I don't like it for this purpose as it's not that familiar for people as histograms and would require additional explanation without containing additional information. I would instead consider adding ECDF difference plot, which has the benefit of being more sensitive to changes in the edges and takes into account dependencies like clustering unlike histogram error bar.

2019-06-30T23:13:35Z

arviz-devs/arviz

arviz-devs__arviz-734

f8665aeade8b4b023b40e4660918d6dff6aa0364

diff --git a/arviz/__init__.py b/arviz/__init__.py --- a/arviz/__init__.py +++ b/arviz/__init__.py @@ -23,6 +23,7 @@ _log.setLevel(logging.INFO) _log.addHandler(handler) +from .rcparams import rcParams, rc_context from .data import * from .plots import * from .stats import * diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -1,5 +1,4 @@ """Data structure for using netcdf groups with xarray.""" -import os from collections import OrderedDict from collections.abc import Sequence from copy import copy as ccopy, deepcopy @@ -8,6 +7,8 @@ import numpy as np import xarray as xr +from ..rcparams import rcParams + class InferenceData: """Container for accessing netCDF files using xarray.""" @@ -52,9 +53,9 @@ def from_netcdf(filename): """Initialize object from a netcdf file. Expects that the file will have groups, each of which can be loaded by xarray. - By default, the datasets of the InferenceData object will be lazily loaded. To - modify this behaviour, the environment variable ``ARVIZ_LOAD`` must be set to - ``EAGER`` (case insensitive) in order to load objects in memory instead. + By default, the datasets of the InferenceData object will be lazily loaded instead + of being loaded into memory. This + behaviour is regulated by the value of ``az.rcParams["data.load"]``. Parameters ---------- @@ -69,10 +70,9 @@ def from_netcdf(filename): with nc.Dataset(filename, mode="r") as data: data_groups = list(data.groups) - arviz_load_mode = os.environ.get("ARVIZ_LOAD", "lazy").lower() for group in data_groups: with xr.open_dataset(filename, group=group) as data: - if arviz_load_mode == "eager": + if rcParams["data.load"] == "eager": groups[group] = data.load() else: groups[group] = data diff --git a/arviz/data/io_netcdf.py b/arviz/data/io_netcdf.py --- a/arviz/data/io_netcdf.py +++ b/arviz/data/io_netcdf.py @@ -18,9 +18,9 @@ def from_netcdf(filename): Notes ----- - By default, the datasets of the InferenceData object will be lazily loaded. To - modify this behaviour, the environment variable ``ARVIZ_LOAD`` must be set to - ``EAGER`` (case insensitive) in order to load objects in memory instead. + By default, the datasets of the InferenceData object will be lazily loaded instead + of loaded into memory. This behaviour is regulated by the value of + ``az.rcParams["data.load"]``. """ return InferenceData.from_netcdf(filename) diff --git a/arviz/plots/elpdplot.py b/arviz/plots/elpdplot.py --- a/arviz/plots/elpdplot.py +++ b/arviz/plots/elpdplot.py @@ -13,6 +13,7 @@ set_xticklabels, ) from ..stats import waic, loo, ELPDData +from ..rcparams import rcParams def plot_elpd( @@ -25,7 +26,7 @@ def plot_elpd( legend=False, threshold=None, ax=None, - ic="waic", + ic=None, scale="deviance", plot_kwargs=None, ): @@ -59,7 +60,8 @@ def plot_elpd( ax: axes, optional Matplotlib axes ic : str, optional - Information Criterion (WAIC or LOO) used to compare models. Default WAIC. Only taken + Information Criterion (WAIC or LOO) used to compare models. Defaults to + ``rcParams["stats.information_criterion"]``. Only taken into account when input is InferenceData. scale : str, optional scale argument passed to az.waic or az.loo, see their docs for details. Only taken @@ -88,7 +90,7 @@ def plot_elpd( """ valid_ics = ["waic", "loo"] - ic = ic.lower() + ic = rcParams["stats.information_criterion"] if ic is None else ic.lower() if ic not in valid_ics: raise ValueError( ("Information Criteria type {} not recognized." "IC must be in {}").format( diff --git a/arviz/rcparams.py b/arviz/rcparams.py new file mode 100644 --- /dev/null +++ b/arviz/rcparams.py @@ -0,0 +1,266 @@ +"""ArviZ rcparams.""" +import sys +import os +from pathlib import Path +import re +import pprint +import logging +import locale +from collections import MutableMapping + +_log = logging.getLogger(__name__) + + +def _make_validate_choice(accepted_values): + """Validate value is in accepted_values.""" + # no blank lines allowed after function docstring by pydocstyle, + # but black requires white line before function + + def validate_choice(value): + if value.lower() in accepted_values: + return value.lower() + raise ValueError("{} is not one of {}".format(value, accepted_values)) + + return validate_choice + + +def _validate_positive_int(value): + """Validate value is a natural number.""" + try: + value = int(value) + except ValueError: + raise ValueError("Could not convert to int") + if value > 0: + return value + else: + raise ValueError("Only positive values are valid") + + +def _validate_positive_int_or_none(value): + """Validate value is a natural number or None.""" + if value is None or isinstance(value, str) and value.lower() == "none": + return None + else: + return _validate_positive_int(value) + + +defaultParams = { # pylint: disable=invalid-name + "data.load": ("lazy", _make_validate_choice(("lazy", "eager"))), + "plot.max_subplots": (40, _validate_positive_int_or_none), + "stats.information_criterion": ("waic", _make_validate_choice(("waic", "loo"))), +} + + +class RcParams(MutableMapping, dict): # pylint: disable=too-many-ancestors + """Class to contain ArviZ default parameters. + + It is implemented as a dict with validation when setting items. + """ + + validate = {key: validate_fun for key, (_, validate_fun) in defaultParams.items()} + + # validate values on the way in + def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called + self.update(*args, **kwargs) + + def __setitem__(self, key, val): + """Add validation to __setitem__ function.""" + try: + try: + cval = self.validate[key](val) + except ValueError as verr: + raise ValueError("Key %s: %s" % (key, str(verr))) + dict.__setitem__(self, key, cval) + except KeyError: + raise KeyError( + "{} is not a valid rc parameter (see rcParams.keys() for " + "a list of valid parameters)".format(key) + ) + + def __getitem__(self, key): + """Use dict getitem method.""" + return dict.__getitem__(self, key) + + def __delitem__(self, key): + """Raise TypeError if someone ever tries to delete a key from RcParams.""" + raise TypeError("RcParams keys cannot be deleted") + + def __repr__(self): + """Customize repr of RcParams objects.""" + class_name = self.__class__.__name__ + indent = len(class_name) + 1 + repr_split = pprint.pformat(dict(self), indent=1, width=80 - indent).split("\n") + repr_indented = ("\n" + " " * indent).join(repr_split) + return "{}({})".format(class_name, repr_indented) + + def __str__(self): + """Customize str/print of RcParams objects.""" + return "\n".join(map("{0[0]:<22}: {0[1]}".format, sorted(self.items()))) + + def __iter__(self): + """Yield sorted list of keys.""" + yield from sorted(dict.__iter__(self)) + + def __len__(self): + """Use dict len method.""" + return dict.__len__(self) + + def find_all(self, pattern): + """ + Find keys that match a regex pattern. + + Return the subset of this RcParams dictionary whose keys match, + using :func:`re.search`, the given ``pattern``. + + Notes + ----- + Changes to the returned dictionary are *not* propagated to + the parent RcParams dictionary. + """ + pattern_re = re.compile(pattern) + return RcParams((key, value) for key, value in self.items() if pattern_re.search(key)) + + def copy(self): + """Get a copy of the RcParams object.""" + return {k: dict.__getitem__(self, k) for k in self} + + +def get_arviz_rcfile(): + """Get arvizrc file. + + The file location is determined in the following order: + + - ``$PWD/arvizrc`` + - ``$ARVIZ_DATA/arvizrc`` + - On Linux, + - ``$XDG_CONFIG_HOME/arviz/arvizrc`` (if ``$XDG_CONFIG_HOME`` + is defined) + - or ``$HOME/.config/arviz/arvizrc`` (if ``$XDG_CONFIG_HOME`` + is not defined) + - On other platforms, + - ``$HOME/.arviz/arvizrc`` if ``$HOME`` is defined + + Otherwise, the default defined in ``rcparams.py`` file will be used. + """ + # no blank lines allowed after function docstring by pydocstyle, + # but black requires white line before function + + def gen_candidates(): + yield os.path.join(os.getcwd(), "arvizrc") + arviz_data_dir = os.environ.get("ARVIZ_DATA") + if arviz_data_dir: + yield os.path.join(arviz_data_dir, "arvizrc") + xdg_base = os.environ.get("XDG_CONFIG_HOME", str(Path.home() / ".config")) + if sys.platform.startswith(("linux", "freebsd")): + configdir = str(Path(xdg_base, "arviz")) + else: + configdir = str(Path.home() / ".arviz") + yield os.path.join(configdir, "arvizrc") + + for fname in gen_candidates(): + if os.path.exists(fname) and not os.path.isdir(fname): + return fname + + return None + + +def read_rcfile(fname): + """Return :class:`arviz.RcParams` from the contents of the given file. + + Unlike `rc_params_from_file`, the configuration class only contains the + parameters specified in the file (i.e. default values are not filled in). + """ + _error_details_fmt = 'line #%d\n\t"%s"\n\tin file "%s"' + + config = RcParams() + with open(fname, "r") as rcfile: + try: + for line_no, line in enumerate(rcfile, 1): + strippedline = line.split("#", 1)[0].strip() + if not strippedline: + continue + tup = strippedline.split(":", 1) + if len(tup) != 2: + error_details = _error_details_fmt % (line_no, line, fname) + _log.warning("Illegal %s", error_details) + continue + key, val = tup + key = key.strip() + val = val.strip() + if key in config: + _log.warning("Duplicate key in file %r line #%d.", fname, line_no) + try: + config[key] = val + except ValueError as verr: + error_details = _error_details_fmt % (line_no, line, fname) + raise ValueError("Bad val {} on {}\n\t{}".format(val, error_details, str(verr))) + + except UnicodeDecodeError: + _log.warning( + "Cannot decode configuration file %s with encoding " + "%s, check LANG and LC_* variables.", + fname, + locale.getpreferredencoding(do_setlocale=False) or "utf-8 (default)", + ) + raise + + return config + + +def rc_params(): + """Read and validate arvizrc file.""" + fname = get_arviz_rcfile() + defaults = RcParams([(key, default) for key, (default, _) in defaultParams.items()]) + if fname is not None: + file_defaults = read_rcfile(fname) + defaults.update(file_defaults) + return defaults + + +rcParams = rc_params() # pylint: disable=invalid-name + + +class rc_context: # pylint: disable=invalid-name + """ + Return a context manager for managing rc settings. + + Parameters + ---------- + rc : dict, optional + Mapping containing the rcParams to modify temporally. + fname : str, optional + Filename of the file containig the rcParams to use inside the rc_context. + + Examples + -------- + This allows one to do:: + with az.rc_context(fname='pystan.rc'): + idata = az.load_arviz_data("radon") + az.plot_posterior(idata, var_names=["gamma"]) + The plot would have settings from 'screen.rc' + + A dictionary can also be passed to the context manager:: + with az.rc_context(rc={'plot.max_subplots': None}, fname='pystan.rc'): + idata = az.load_arviz_data("radon") + az.plot_posterior(idata, var_names=["gamma"]) + The 'rc' dictionary takes precedence over the settings loaded from + 'fname'. Passing a dictionary only is also valid. + """ + + # Based on mpl.rc_context + + def __init__(self, rc=None, fname=None): + self._orig = rcParams.copy() + if fname: + file_rcparams = read_rcfile(fname) + rcParams.update(file_rcparams) + if rc: + rcParams.update(rc) + + def __enter__(self): + """Define enter method of context manager.""" + return self + + def __exit__(self, exc_type, exc_value, exc_tb): + """Define exit method of context manager.""" + rcParams.update(self._orig) diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -21,6 +21,7 @@ stats_variance_2d as svar, ) from ..utils import _var_names, Numba, _numba_var +from ..rcparams import rcParams _log = logging.getLogger(__name__) @@ -39,7 +40,7 @@ def compare( dataset_dict, - ic="waic", + ic=None, method="BB-pseudo-BMA", b_samples=1000, alpha=1, @@ -57,7 +58,8 @@ def compare( dataset_dict : dict[str] -> InferenceData A dictionary of model names and InferenceData objects ic : str - Information Criterion (WAIC or LOO) used to compare models. Default WAIC. + Information Criterion (WAIC or LOO) used to compare models. Defaults to + ``rcParams["stats.information_criterion"]``. method : str Method used to estimate the weights for each model. Available options are: @@ -142,7 +144,7 @@ def compare( scale_value = -2 ascending = True - ic = ic.lower() + ic = rcParams["stats.information_criterion"] if ic is None else ic.lower() if ic == "waic": ic_func = waic df_comp = pd.DataFrame( diff --git a/doc/conf.py b/doc/conf.py --- a/doc/conf.py +++ b/doc/conf.py @@ -20,11 +20,12 @@ import os import sys -os.environ["ARVIZ_LOAD"] = "EAGER" sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import sphinx_bootstrap_theme import arviz +arviz.rcParams["data.load"] = "eager" + # -- General configuration ------------------------------------------------

diff --git a/arviz/tests/test_diagnostics.py b/arviz/tests/test_diagnostics.py --- a/arviz/tests/test_diagnostics.py +++ b/arviz/tests/test_diagnostics.py @@ -27,12 +27,13 @@ _circular_standard_deviation, ) from ..utils import Numba +from ..rcparams import rcParams # For tests only, recommended value should be closer to 1.01-1.05 # See discussion in https://github.com/stan-dev/rstan/pull/618 GOOD_RHAT = 1.1 -os.environ["ARVIZ_LOAD"] = "EAGER" +rcParams["data.load"] = "eager" @pytest.fixture(scope="session") diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -16,6 +16,7 @@ multidim_models, create_multidimensional_model, ) +from ..rcparams import rcParams from ..plots import ( plot_density, plot_trace, @@ -42,7 +43,7 @@ ) np.random.seed(0) -os.environ["ARVIZ_LOAD"] = "EAGER" +rcParams["data.load"] = "eager" @pytest.fixture(scope="function", autouse=True) diff --git a/arviz/tests/test_rcparams.py b/arviz/tests/test_rcparams.py new file mode 100644 --- /dev/null +++ b/arviz/tests/test_rcparams.py @@ -0,0 +1,56 @@ +# pylint: disable=redefined-outer-name +import numpy as np +import pytest +from xarray.core.indexing import MemoryCachedArray + + +from ..data import load_arviz_data +from ..stats import compare +from ..rcparams import rcParams, rc_context +from .helpers import models # pylint: disable=unused-import + + +def test_bad_key(): + """Test the error when using unexistent keys in rcParams is correct.""" + with pytest.raises(KeyError, match="bad_key is not a valid rc"): + rcParams["bad_key"] = "nothing" + + [email protected]("param", ["data.load", "stats.information_criterion"]) +def test_choice_bad_values(param): + """Test error messages are correct for rcParams validated with _make_validate_choice.""" + msg = "{}: bad_value is not one of".format(param.replace(".", r"\.")) + with pytest.raises(ValueError, match=msg): + rcParams[param] = "bad_value" + + +def test_rc_context(): + rcParams["data.load"] = "lazy" + with rc_context(rc={"data.load": "eager"}): + assert rcParams["data.load"] == "eager" + assert rcParams["data.load"] == "lazy" + + +def test_data_load(): + rcParams["data.load"] = "lazy" + idata_lazy = load_arviz_data("centered_eight") + assert isinstance( + idata_lazy.posterior.mu.variable._data, # pylint: disable=protected-access + MemoryCachedArray, + ) + assert rcParams["data.load"] == "lazy" + rcParams["data.load"] = "eager" + idata_eager = load_arviz_data("centered_eight") + assert isinstance( + idata_eager.posterior.mu.variable._data, np.ndarray # pylint: disable=protected-access + ) + assert rcParams["data.load"] == "eager" + + +def test_stats_information_criterion(models): + rcParams["stats.information_criterion"] = "waic" + df_comp = compare({"model1": models.model_1, "model2": models.model_2}) + assert "waic" in df_comp.columns + rcParams["stats.information_criterion"] = "loo" + df_comp = compare({"model1": models.model_1, "model2": models.model_2}) + assert "loo" in df_comp.columns diff --git a/arviz/tests/test_stats.py b/arviz/tests/test_stats.py --- a/arviz/tests/test_stats.py +++ b/arviz/tests/test_stats.py @@ -1,5 +1,4 @@ # pylint: disable=redefined-outer-name, no-member -import os from copy import deepcopy import numpy as np from numpy.testing import assert_allclose, assert_array_almost_equal @@ -24,8 +23,10 @@ from ..stats.stats import _gpinv from ..utils import Numba from .helpers import check_multiple_attrs, multidim_models # pylint: disable=unused-import +from ..rcparams import rcParams -os.environ["ARVIZ_LOAD"] = "EAGER" + +rcParams["data.load"] = "eager" @pytest.fixture(scope="session")

Restrict the max number of plots by default Following discussion in #94, naively or inadvertently calling plotting functions for models with a large number of variables can easily hang a computer. Thus we may want to restrict by default the max number of plots. If possible it would be nice to set this at a global level and not per plot function ArviZ global level options ## Feature Description There are many options in ArviZ which would be easier to handle as global options. Moreover, having global options in a style similar to matplotlib or pandas can make the experience of using ArviZ easier and better tailored to the end users. These options go from the maximum number of plots (#609) to text formatting options or even the default information criterion. I believe that this can end up containing many different parameters, so with this in mind, environment variables are probably not ideal on the long run. Having something similar to matplotlib rcParams or pandas options seems more adient. ## Thoughts on implementation ### pandas-like The first implementation option is to have some pandas-like API. That is, setting the variables from some `config.py` file (a priori not user friendly) and expose some functions to modify these options. ### matplotlib-like This second option is similar to the first one, but options are read from a json or json like configuration file, which is though to be used and modified by the end user. In addition to this, there are some functions exposed to modify these options too. --- I prefer the matplotlib like version, because I think it is more convenient to ArviZ (i.e. someone who prefers LOO over WAIC can modify the rcparams file and forget about it instead of calling the option modifier function at the start of every script or python/ipython startup scripts). I propose to start with a simple json file (preferably 2 level) as configuration file and a custom class (children of dict for example) where all the options would be stored once read from the file for easy access.

So env variable ARVIZ_MAX_AXES ENV works. I want to throw in pandas style options as a proposal design as well https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html Notice that #679 addressed this for the particular case of traceplots Is this fixed? Not really, it is only fixed for `plot_trace` and should be done with all plots and with global options, not function specific. I agree with the matplotlib-like version.

2019-07-10T01:23:18Z

arviz-devs/arviz

arviz-devs__arviz-739

d1d16fc59848c7e4f9ecfb822a66dab8538731c3

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -42,6 +42,11 @@ def __repr__(self): options="\n\t> ".join(self._groups) ) + def __delattr__(self, group): + """Delete a group from the InferenceData object.""" + self._groups.remove(group) + object.__delattr__(self, group) + @staticmethod def from_netcdf(filename): """Initialize object from a netcdf file. @@ -73,16 +78,18 @@ def from_netcdf(filename): groups[group] = data return InferenceData(**groups) - def to_netcdf(self, filename, compress=True): + def to_netcdf(self, filename, compress=True, groups=None): """Write InferenceData to file using netcdf4. Parameters ---------- filename : str Location to write to - compress : bool + compress : bool, optional Whether to compress result. Note this saves disk space, but may make saving and loading somewhat slower (default: True). + groups : list, optional + Write only these groups to netcdf file. Returns ------- @@ -91,7 +98,11 @@ def to_netcdf(self, filename, compress=True): """ mode = "w" # overwrite first, then append if self._groups: # check's whether a group is present or not. - for group in self._groups: + if groups is None: + groups = self._groups + else: + groups = [group for group in self._groups if group in groups] + for group in groups: data = getattr(self, group) kwargs = {} if compress:

diff --git a/arviz/tests/test_data.py b/arviz/tests/test_data.py --- a/arviz/tests/test_data.py +++ b/arviz/tests/test_data.py @@ -318,6 +318,45 @@ def test_sel_method(inplace): assert np.all(dataset.draw.values == np.arange(200, ndraws)) [email protected]("use", ("del", "delattr")) +def test_del_method(use): + # create inference data object + data = np.random.normal(size=(4, 500, 8)) + idata = from_dict( + posterior={"a": data[..., 0], "b": data}, + sample_stats={"a": data[..., 0], "b": data}, + observed_data={"b": data[0, 0, :]}, + posterior_predictive={"a": data[..., 0], "b": data}, + ) + + # assert inference data object has all attributes + test_dict = { + "posterior": ("a", "b"), + "sample_stats": ("a", "b"), + "observed_data": ["b"], + "posterior_predictive": ("a", "b"), + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails + # assert _groups attribute contains all groups + groups = getattr(idata, "_groups") + assert all([group in groups for group in test_dict]) + + # Use del method + if use == "del": + del idata.sample_stats + else: + delattr(idata, "sample_stats") + + # assert attribute has been removed + test_dict.pop("sample_stats") + fails = check_multiple_attrs(test_dict, idata) + assert not fails + assert not hasattr(idata, "sample_stats") + # assert _groups attribute has been updated + assert "sample_stats" not in getattr(idata, "_groups") + + class TestNumpyToDataArray: def test_1d_dataset(self): size = 100 @@ -628,37 +667,79 @@ def get_inference_data(self, data, eight_schools_params): ) def test_io_function(self, data, eight_schools_params): + # create inference data and assert all attributes are present inference_data = self.get_inference_data( # pylint: disable=W0612 data, eight_schools_params ) - assert hasattr(inference_data, "posterior") + test_dict = { + "posterior": ["eta", "theta", "mu", "tau"], + "posterior_predictive": ["eta", "theta", "mu", "tau"], + "sample_stats": ["eta", "theta", "mu", "tau"], + "prior": ["eta", "theta", "mu", "tau"], + "prior_predictive": ["eta", "theta", "mu", "tau"], + "sample_stats_prior": ["eta", "theta", "mu", "tau"], + "observed_data": ["J", "y", "sigma"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + + # check filename does not exist and save InferenceData here = os.path.dirname(os.path.abspath(__file__)) data_directory = os.path.join(here, "saved_models") filepath = os.path.join(data_directory, "io_function_testfile.nc") # az -function to_netcdf(inference_data, filepath) + + # Assert InferenceData has been saved correctly assert os.path.exists(filepath) assert os.path.getsize(filepath) > 0 inference_data2 = from_netcdf(filepath) - assert hasattr(inference_data2, "posterior") + fails = check_multiple_attrs(test_dict, inference_data2) + assert not fails os.remove(filepath) assert not os.path.exists(filepath) - def test_io_method(self, data, eight_schools_params): + @pytest.mark.parametrize("groups_arg", [False, True]) + def test_io_method(self, data, eight_schools_params, groups_arg): + # create InferenceData and check it has been properly created inference_data = self.get_inference_data( # pylint: disable=W0612 data, eight_schools_params ) - assert hasattr(inference_data, "posterior") + test_dict = { + "posterior": ["eta", "theta", "mu", "tau"], + "posterior_predictive": ["eta", "theta", "mu", "tau"], + "sample_stats": ["eta", "theta", "mu", "tau"], + "prior": ["eta", "theta", "mu", "tau"], + "prior_predictive": ["eta", "theta", "mu", "tau"], + "sample_stats_prior": ["eta", "theta", "mu", "tau"], + "observed_data": ["J", "y", "sigma"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + + # check filename does not exist and use to_netcdf method here = os.path.dirname(os.path.abspath(__file__)) data_directory = os.path.join(here, "saved_models") filepath = os.path.join(data_directory, "io_method_testfile.nc") assert not os.path.exists(filepath) # InferenceData method - inference_data.to_netcdf(filepath) + inference_data.to_netcdf( + filepath, groups=("posterior", "observed_data") if groups_arg else None + ) + + # assert file has been saved correctly assert os.path.exists(filepath) assert os.path.getsize(filepath) > 0 inference_data2 = InferenceData.from_netcdf(filepath) - assert hasattr(inference_data2, "posterior") + if groups_arg: # if groups arg, update test dict to contain only saved groups + test_dict = { + "posterior": ["eta", "theta", "mu", "tau"], + "observed_data": ["J", "y", "sigma"], + } + assert not hasattr(inference_data2, "sample_stats") + fails = check_multiple_attrs(test_dict, inference_data2) + assert not fails + os.remove(filepath) assert not os.path.exists(filepath)

Add method to delete groups from InferenceData ## Tell us about it Add a method to delete groups from InferenceData With many groups InferenceData can get quite large, making it hard to add to VCS or email. Things like sample_stats might not be needed for all results sharing. ## Thoughts on implementation 1. Delete the object from `az.InferenceData` with `del` method in python 2. Remove string in `az.InferenceData._groups` Add method to delete groups from InferenceData ## Tell us about it Add a method to delete groups from InferenceData With many groups InferenceData can get quite large, making it hard to add to VCS or email. Things like sample_stats might not be needed for all results sharing. ## Thoughts on implementation 1. Delete the object from `az.InferenceData` with `del` method in python 2. Remove string in `az.InferenceData._groups` Add method to delete groups from InferenceData ## Tell us about it Add a method to delete groups from InferenceData With many groups InferenceData can get quite large, making it hard to add to VCS or email. Things like sample_stats might not be needed for all results sharing. ## Thoughts on implementation 1. Delete the object from `az.InferenceData` with `del` method in python 2. Remove string in `az.InferenceData._groups`

Was this fixed? This is not fixed Was this fixed? This is not fixed Was this fixed? This is not fixed

2019-07-12T15:45:06Z

arviz-devs/arviz

arviz-devs__arviz-759

cd10876a2970957b4da8b5143ed3251f69f313ca

diff --git a/arviz/data/io_cmdstanpy.py b/arviz/data/io_cmdstanpy.py --- a/arviz/data/io_cmdstanpy.py +++ b/arviz/data/io_cmdstanpy.py @@ -114,6 +114,8 @@ def sample_stats_to_xarray(self): coords[log_likelihood_dim_name] = coords.pop(default_dim_name) data = _unpack_frame(self.posterior.sample, columns, valid_cols) + if log_likelihood in data: + data["log_likelihood"] = data.pop(log_likelihood) for s_param in list(data.keys()): s_param_, *_ = s_param.split(".") name = re.sub("__$", "", s_param_) @@ -193,7 +195,7 @@ def observed_data_to_xarray(self): observed_data = {} for key, vals in self.observed_data.items(): vals = np.atleast_1d(vals) - val_dims = self.dims.get(key) + val_dims = self.dims.get(key) if self.dims is not None else None val_dims, coords = generate_dims_coords( vals.shape, key, dims=val_dims, coords=self.coords )

diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-1.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-1.csv new file mode 100644 --- /dev/null +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-1.csv @@ -0,0 +1,148 @@ +# stan_version_major = 2 +# stan_version_minor = 19 +# stan_version_patch = 1 +# model = schools_code_model +# method = sample (Default) +# sample +# num_samples = 100 +# num_warmup = 1000 (Default) +# save_warmup = 0 (Default) +# thin = 1 (Default) +# adapt +# engaged = 1 (Default) +# gamma = 0.050000000000000003 (Default) +# delta = 0.80000000000000004 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# id = 1 +# data +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json +# init = 2 (Default) +# random +# seed = 49006 +# output +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-1-ruhdz7a3.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +# Adaptation terminated +# Step size = 0.444467 +# Diagonal elements of inverse mass matrix: +# 11.0093, 1.27339, 0.934374, 0.903688, 0.942144, 0.840391, 0.802124, 0.995019, 0.80084, 0.971203 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+-9.35715,0.89283,0.444467,3,15,0,15.7673,8.015,0.564617,0.374518,-0.786905,1.08851,-0.212104,-0.931013,1.7963,2.05337,0.396671,8.22646,7.5707,8.62959,7.89524,7.48933,9.02922,9.17437,8.23897,-4.49586,-3.22245,-3.95568,-3.32015,-3.56103,-3.58323,-3.61098,-3.83114,32.6065,7.56105,18.8794,14.3608,8.60324,-14.0247,-0.963251,-18.1021 +# +# Elapsed Time: 0.035 seconds (Warm-up) +# 0.008 seconds (Sampling) +# 0.043 seconds (Total) +# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-2.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-2.csv new file mode 100644 --- /dev/null +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-2.csv @@ -0,0 +1,148 @@ +# stan_version_major = 2 +# stan_version_minor = 19 +# stan_version_patch = 1 +# model = schools_code_model +# method = sample (Default) +# sample +# num_samples = 100 +# num_warmup = 1000 (Default) +# save_warmup = 0 (Default) +# thin = 1 (Default) +# adapt +# engaged = 1 (Default) +# gamma = 0.050000000000000003 (Default) +# delta = 0.80000000000000004 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# id = 2 +# data +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json +# init = 2 (Default) +# random +# seed = 49006 +# output +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-2-3tn89rz6.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +# Adaptation terminated +# Step size = 0.409697 +# Diagonal elements of inverse mass matrix: +# 9.74347, 1.05193, 1.04414, 0.836565, 0.854341, 0.814735, 0.879733, 0.956685, 0.813884, 0.940565 +-6.06275,0.97667,0.409697,3,7,0,10.4941,7.11966,3.66346,0.0973615,-1.46242,0.355855,0.270274,-0.398709,-0.159604,-0.656623,1.87425,7.47634,1.76215,8.42332,8.1098,5.65901,6.53496,4.71415,13.9859,-4.56303,-3.41608,-3.94639,-3.32192,-3.38988,-3.44343,-4.10409,-3.8154,-2.32189,0.0760526,19.4886,16.1653,12.2235,-9.72593,15.4361,44.8682 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+-4.82499,0.987928,0.409697,4,23,0,8.25464,4.0583,5.74103,1.71691,0.459366,-0.224646,-0.694227,-0.0572689,0.518682,-0.795432,0.867964,13.9151,6.69553,2.7686,0.0727195,3.72951,7.03606,-0.508303,9.0413,-4.06784,-3.23003,-3.75652,-3.51513,-3.25424,-3.46739,-4.93431,-3.82282,35.1523,7.24045,10.578,-14.0721,8.15821,2.0814,10.8456,7.24741 +# +# Elapsed Time: 0.038 seconds (Warm-up) +# 0.011 seconds (Sampling) +# 0.049 seconds (Total) +# diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-3.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-3.csv new file mode 100644 --- /dev/null +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-3.csv @@ -0,0 +1,148 @@ +# stan_version_major = 2 +# stan_version_minor = 19 +# stan_version_patch = 1 +# model = schools_code_model +# method = sample (Default) +# sample +# num_samples = 100 +# num_warmup = 1000 (Default) +# save_warmup = 0 (Default) +# thin = 1 (Default) +# adapt +# engaged = 1 (Default) +# gamma = 0.050000000000000003 (Default) +# delta = 0.80000000000000004 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# id = 3 +# data +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json +# init = 2 (Default) +# random +# seed = 49006 +# output +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-3-k2vbz5q0.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +# Adaptation terminated +# Step size = 0.431414 +# Diagonal elements of inverse mass matrix: +# 10.322, 1.49738, 1.20159, 0.929056, 0.95979, 0.88158, 0.862366, 0.949804, 0.922541, 1.13512 +-10.4888,0.986525,0.431414,3,7,0,13.5968,-1.88513,2.9935,1.86657,0.919534,-2.36142,1.32443,-0.770675,1.47136,0.941357,0.174516,3.70245,0.867489,-8.95404,2.07953,-4.19215,2.51939,0.932817,-1.36272,-4.93892,-3.47589,-3.76077,-3.41688,-3.17906,-3.32637,-4.67797,-4.08487,-1.07499,2.52522,-40.6705,0.500812,-20.2639,8.53431,5.79508,10.3564 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b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-4.csv @@ -0,0 +1,148 @@ +# stan_version_major = 2 +# stan_version_minor = 19 +# stan_version_patch = 1 +# model = schools_code_model +# method = sample (Default) +# sample +# num_samples = 100 +# num_warmup = 1000 (Default) +# save_warmup = 0 (Default) +# thin = 1 (Default) +# adapt +# engaged = 1 (Default) +# gamma = 0.050000000000000003 (Default) +# delta = 0.80000000000000004 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# id = 4 +# data +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json +# init = 2 (Default) +# random +# seed = 49006 +# output +# file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-4-y82c1kj3.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +# Adaptation terminated +# Step size = 0.411307 +# Diagonal elements of inverse mass matrix: +# 10.3584, 1.2492, 0.804788, 0.870948, 0.800705, 0.782754, 0.807559, 0.811814, 0.919356, 0.930502 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+-11.0636,0.948949,0.411307,3,7,0,12.9574,3.59474,0.0607379,0.283866,-0.20848,2.96467,0.557429,-0.195469,0.238757,-0.67014,0.713582,3.61198,3.58207,3.7748,3.62859,3.58286,3.60924,3.55403,3.63808,-4.94871,-3.31911,-3.78117,-3.3638,-3.24581,-3.34497,-4.26495,-3.91721,-19.5535,2.77545,2.45678,3.44085,2.82064,-6.98508,1.38327,15.8765 +-7.44543,0.660366,0.411307,3,15,0,15.0469,3.33939,0.317711,-0.506829,0.328675,0.320872,1.66236,-0.414002,0.663649,1.38641,-0.805816,3.17837,3.44381,3.44134,3.86754,3.20786,3.55024,3.77987,3.08337,-4.99613,-3.32532,-3.77256,-3.35738,-3.22546,-3.34371,-4.23258,-3.93201,-2.14535,-3.92597,17.7225,-10.4792,2.06705,-3.0936,-10.3296,8.02405 +-12.6134,0.826845,0.411307,3,7,0,17.9872,4.64197,3.49716,1.57005,-1.12854,0.394473,-1.36847,0.51775,-2.77383,1.09281,-2.40632,10.1327,0.695305,6.02151,-0.14379,6.45262,-5.05855,8.46369,-3.77331,-4.33641,-3.48832,-3.85049,-3.52772,-3.45901,-3.46851,-3.67623,-4.19326,14.4091,-6.39756,-2.31952,15.2428,29.2864,-3.55264,12.6044,-1.18955 +-7.89081,1,0.411307,3,7,0,15.8278,5.07539,2.65486,2.25447,-1.1777,-1.3334,-1.04268,-0.167297,-1.62593,0.477044,-0.356661,11.0607,1.94877,1.53539,2.30723,4.63124,0.75878,6.34188,4.12851,-4.26463,-3.40461,-3.7317,-3.40783,-3.31191,-3.31707,-3.90108,-3.90493,-16.4277,-11.575,9.89641,-1.18217,13.1405,9.84743,7.51786,-1.42401 +-4.73315,0.992924,0.411307,3,7,0,10.6571,6.75561,4.38557,-0.0633051,-0.513556,-0.555905,0.0685072,-2.05299,-0.528125,0.467172,0.897106,6.47798,4.50338,4.31765,7.05606,-2.24793,4.43948,8.80443,10.6899,-4.65632,-3.28266,-3.79611,-3.31685,-3.12578,-3.36572,-3.64432,-3.81196,-8.67662,-0.642039,-12.2142,3.16648,-5.63154,26.1027,-4.79442,2.87946 +-4.10922,0.908481,0.411307,4,31,0,7.4068,5.47564,1.45909,-0.880537,-0.2972,-0.247508,0.261554,-0.278227,0.0328608,-0.867884,-0.474156,4.19086,5.042,5.1145,5.85727,5.06968,5.52359,4.20932,4.7838,-4.88671,-3.26527,-3.82013,-3.32223,-3.34358,-3.40139,-4.17244,-3.88967,-7.43026,14.6039,-18.9291,1.50422,12.4809,15.9337,26.2597,30.789 +-5.60628,0.93877,0.411307,3,7,0,10.424,7.58397,3.34122,0.272664,-0.483492,-1.88895,0.937029,-1.13383,-0.303623,-0.334102,-0.648876,8.495,5.96852,1.27257,10.7148,3.79561,6.5695,6.46766,5.41593,-4.47242,-3.24216,-3.72718,-3.37386,-3.25813,-3.44501,-3.8865,-3.87621,-2.40412,19.8786,24.2241,17.6846,-2.09855,10.7918,15.3394,-1.46301 +-6.34678,0.983395,0.411307,4,15,0,9.15259,3.87606,4.98044,0.170736,-0.126673,-2.37458,1.53622,0.259982,0.00374382,-0.0830168,-0.132344,4.7264,3.24517,-7.95038,11.5271,5.17089,3.89471,3.4626,3.21693,-4.83068,-3.33457,-3.73939,-3.40152,-3.35122,-3.35146,-4.2782,-3.92836,19.6571,-8.35679,-26.4887,13.6977,1.40544,9.22058,-10.0329,-4.13948 +# +# Elapsed Time: 0.046 seconds (Warm-up) +# 0.008 seconds (Sampling) +# 0.054 seconds (Total) +# diff --git a/arviz/tests/test_data_cmdstanpy.py b/arviz/tests/test_data_cmdstanpy.py new file mode 100644 --- /dev/null +++ b/arviz/tests/test_data_cmdstanpy.py @@ -0,0 +1,154 @@ +# pylint: disable=redefined-outer-name +from glob import glob +import os +import numpy as np +import pytest + +from arviz import from_cmdstanpy +from .helpers import ( # pylint: disable=unused-import + chains, + check_multiple_attrs, + draws, + eight_schools_params, + load_cached_models, + pystan_version, +) + + +class TestDataCmdStanPy: + @pytest.fixture(scope="session") + def data_directory(self): + here = os.path.dirname(os.path.abspath(__file__)) + data_directory = os.path.join(here, "saved_models") + return data_directory + + @pytest.fixture(scope="class") + def filepaths(self, data_directory): + files = glob(os.path.join(data_directory, "cmdstanpy", "cmdstanpy_eight_schools-[1-4].csv")) + return files + + @pytest.fixture(scope="class") + def data(self, filepaths): + from cmdstanpy import StanFit + from cmdstanpy.model import CmdStanArgs, SamplerArgs + + class Data: + args = CmdStanArgs( + "dummy.stan", "dummy.exe", list(range(1, 5)), method_args=SamplerArgs() + ) + obj = StanFit(args) + obj.csv_files = filepaths + obj._validate_csv_files() # pylint: disable=protected-access + obj._assemble_sample() # pylint: disable=protected-access + + return Data + + def get_inference_data(self, data, eight_schools_params): + """vars as str.""" + return from_cmdstanpy( + posterior=data.obj, + posterior_predictive="y_hat", + prior=data.obj, + prior_predictive="y_hat", + observed_data={"y": eight_schools_params["y"]}, + log_likelihood="log_lik", + coords={"school": np.arange(eight_schools_params["J"])}, + dims={ + "theta": ["school"], + "y": ["school"], + "log_lik": ["school"], + "y_hat": ["school"], + "eta": ["school"], + }, + ) + + def get_inference_data2(self, data, eight_schools_params): + """vars as lists.""" + return from_cmdstanpy( + posterior=data.obj, + posterior_predictive=["y_hat"], + prior=data.obj, + prior_predictive=["y_hat"], + observed_data={"y": eight_schools_params["y"]}, + log_likelihood="log_lik", + coords={ + "school": np.arange(eight_schools_params["J"]), + "log_likelihood_dim": np.arange(eight_schools_params["J"]), + }, + dims={ + "theta": ["school"], + "y": ["school"], + "y_hat": ["school"], + "eta": ["school"], + "log_lik": ["log_likelihood_dim"], + }, + ) + + def get_inference_data3(self, data, eight_schools_params): + """multiple vars as lists.""" + return from_cmdstanpy( + posterior=data.obj, + posterior_predictive=["y_hat", "log_lik"], + prior=data.obj, + prior_predictive=["y_hat", "log_lik"], + observed_data={"y": eight_schools_params["y"]}, + coords={"school": np.arange(eight_schools_params["J"])}, + dims={"theta": ["school"], "y": ["school"], "y_hat": ["school"], "eta": ["school"]}, + ) + + def get_inference_data4(self, data, eight_schools_params): + """multiple vars as lists.""" + return from_cmdstanpy( + posterior=data.obj, + posterior_predictive=None, + prior=data.obj, + prior_predictive=None, + observed_data={"y": eight_schools_params["y"]}, + coords=None, + dims=None, + ) + + def test_sampler_stats(self, data, eight_schools_params): + inference_data = self.get_inference_data(data, eight_schools_params) + test_dict = {"sample_stats": ["lp", "diverging"]} + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + + def test_inference_data(self, data, eight_schools_params): + inference_data1 = self.get_inference_data(data, eight_schools_params) + inference_data2 = self.get_inference_data2(data, eight_schools_params) + inference_data3 = self.get_inference_data3(data, eight_schools_params) + inference_data4 = self.get_inference_data4(data, eight_schools_params) + # inference_data 1 + test_dict = { + "posterior": ["theta"], + "observed_data": ["y"], + "sample_stats": ["log_likelihood"], + "prior": ["theta"], + } + fails = check_multiple_attrs(test_dict, inference_data1) + assert not fails + # inference_data 2 + test_dict = { + "posterior_predictive": ["y_hat"], + "observed_data": ["y"], + "sample_stats_prior": ["lp"], + "sample_stats": ["lp"], + "prior_predictive": ["y_hat"], + } + fails = check_multiple_attrs(test_dict, inference_data2) + assert not fails + # inference_data 3 + test_dict = { + "posterior_predictive": ["y_hat"], + "observed_data": ["y"], + "sample_stats_prior": ["lp"], + "sample_stats": ["lp"], + "prior_predictive": ["y_hat"], + } + fails = check_multiple_attrs(test_dict, inference_data3) + assert not fails + # inference_data 4 + test_dict = {"posterior": ["theta"], "prior": ["theta"]} + fails = check_multiple_attrs(test_dict, inference_data4) + assert not fails

CmdStanPy tests Add tests for CmdStanPy. See if we can read cmdstan output csv to PosterierSample which then can be tested.

CmdStanPy is very beta, alas. PR https://github.com/stan-dev/cmdstanpy/pull/43 was a refactor which eliminated object `PosteriorSample` and made some of its member functions into first-class functions. Instead of a `PosteriorSample` object, function `sample` returns a `RunSet` object. Need to update `from_cmdstanpy` accordingly. Yes, I can make the changes. Did cmdstanpy have functionality to read csv to Runset? Yes, the RunSet object parses the csv and populates the following properties: + `draws` - number of draws per chain (sampling iterations) + `columns` - total number of columns in the csv output - includes sampler state as well as model parameters, transformed parameter and generated quantities variables + `column_names` - csv column headers + `sample` - a 3-D numpy ndarray which contains all draws across all chain arranged as (draws, chains, columns) stored column major so that the values for each parameter are stored contiguously in memory, likewise all draws from a chain are contiguous. + `stepsize` - stepsize used by sampler for each chain. + `metric` - metric used by sampler for each chain. If you look at the dimension of the metric, you'll get the number of parameters in the model - is that useful? the csv columns are arranged: sampler state, parameters, transformed parameters, generated quantities. sampler state output names end in `_`. The `sample`, `stepsize`, and `metric` properties are populated lazily - the first time that any of them are accessed, the RunSet class method `assemble_sample` is called. hope this helps. Reading through `arviz/data/converters.py` - I think that you only need to change `PosteriorSample` to `RunSet` in the the following 2 lines: ``` 70: obj.__class__.__name__ in {"StanFit4Model", "PosteriorSample"} 77: if obj.__class__.__name__ == "PosteriorSample": ``` tried this out locally and then ran the CmdStanPy tutorial notebook, adding the following - it just works! <img width="1248" alt="arviz_from_cmdstanpy" src="https://user-images.githubusercontent.com/5466612/59967878-dbb22500-94fe-11e9-9c0d-e88fa817d297.png"> Yes, I tried it too, we only need to change `PosteriorSample` to `RunSet`.

2019-07-20T21:18:50Z

arviz-devs/arviz

arviz-devs__arviz-779

8df87625c7514476035b37b16b8700aa7c83fb1e

diff --git a/arviz/plots/ppcplot.py b/arviz/plots/ppcplot.py --- a/arviz/plots/ppcplot.py +++ b/arviz/plots/ppcplot.py @@ -191,9 +191,10 @@ def plot_ppc( posterior_predictive = data.posterior_predictive if var_names is None: - var_names = observed.data_vars + var_names = list(observed.data_vars) var_names = _var_names(var_names, observed) pp_var_names = [data_pairs.get(var, var) for var in var_names] + pp_var_names = _var_names(pp_var_names, posterior_predictive) if flatten_pp is None and flatten is None: flatten_pp = list(posterior_predictive.dims.keys()) diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -49,6 +49,14 @@ def _var_names(var_names, data): if excluded_vars: var_names = [i for i in all_vars if i not in excluded_vars] + existent_vars = np.isin(var_names, all_vars) + if not np.all(existent_vars): + raise KeyError( + "{} var names are not present in dataset".format( + np.array(var_names)[~existent_vars] + ) + ) + return var_names

diff --git a/arviz/tests/test_utils.py b/arviz/tests/test_utils.py --- a/arviz/tests/test_utils.py +++ b/arviz/tests/test_utils.py @@ -48,6 +48,11 @@ def test_var_names_warning(): assert _var_names(var_names, data) == expected +def test_var_names_key_error(data): + with pytest.raises(KeyError, match="bad_var_name"): + _var_names(("theta", "tau", "bad_var_name"), data) + + @pytest.fixture(scope="function") def utils_with_numba_import_fail(monkeypatch): """Patch numba in utils so when its imported it raises ImportError"""

summary throws error on missing variables while plots do not There is an inconsistency across functions as to how ArviZ handles `var_names` that don't exist in the model. Plots simply ignore them; the following model actually has no `s_γ' variable, but `plot_trace` simply ignores it and plots the rest: az.plot_trace(trace, var_names=['m_μ', 'm_β', 's_μ', 's_β', 's_γ', 'σ', 'ν']); whereas, the same arguments passed to `summary` throws a `KeyError`: az.summary(trace, var_names=['m_μ', 'm_β', 's_μ', 's_β', 's_γ', 'σ', 'ν']); ``` ~/anaconda3/envs/org_velo/lib/python3.7/site-packages/xarray/core/dataset.py in _get_virtual_variable(variables, key, level_vars, dim_sizes) 89 ref_var = dim_var.to_index_variable().get_level_variable(ref_name) 90 else: ---> 91 ref_var = variables[ref_name] 92 93 if var_name is None: KeyError: 's_γ' ``` I prefer the error, but they should .at least behave similarly across functions.

2019-07-31T23:41:40Z

arviz-devs/arviz

arviz-devs__arviz-830

54721b0cd702aa4f5c14736c7837ad22c5f333a6

diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -65,7 +65,8 @@ def plot_posterior( Lower and upper values of the Region Of Practical Equivalence. If a list is provided, its length should match the number of variables. ref_val: float or dictionary of floats - display the percentage below and above the values in ref_val. If a list is provided, its + display the percentage below and above the values in ref_val. Must be None (default), + a constant, a list or a dictionary like see an example below. If a list is provided, its length should match the number of variables. kind: str Type of plot to display (kde or hist) For discrete variables this argument is ignored and @@ -135,6 +136,22 @@ def plot_posterior( >>> az.plot_posterior(data, var_names=['mu', 'theta'], point_estimate='mode') + Show reference values using variable names and coordinates + + .. plot:: + :context: close-figs + + >>> az.plot_posterior(data, ref_val= {"theta": [{"school": "Deerfield", "ref_val": 4}, + {"school": "Choate", "ref_val": 3}]}) + + Show reference values using a list + + .. plot:: + :context: close-figs + + >>> az.plot_posterior(data, ref_val=[1] + [5] * 8 + [1]) + + Plot posterior as a histogram .. plot:: @@ -171,9 +188,10 @@ def plot_posterior( _, ax = _create_axes_grid( length_plotters, rows, cols, figsize=figsize, squeeze=False, constrained_layout=True ) - + idx = 0 for (var_name, selection, x), ax_ in zip(plotters, np.ravel(ax)): _plot_posterior_op( + idx, x.flatten(), var_name, selection, @@ -190,13 +208,14 @@ def plot_posterior( xt_labelsize=xt_labelsize, **kwargs ) - + idx += 1 ax_.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True) return ax def _plot_posterior_op( + idx, values, var_name, selection, @@ -234,12 +253,14 @@ def display_ref_val(): break if val is None: return + elif isinstance(ref_val, list): + val = ref_val[idx] elif isinstance(ref_val, Number): val = ref_val else: raise ValueError( - "Argument `ref_val` must be None, a constant, or a " - 'dictionary like {"var_name": {"ref_val": (lo, hi)}}' + "Argument `ref_val` must be None, a constant, a list or a " + 'dictionary like {"var_name": [{"ref_val": ref_val}]}' ) less_than_ref_probability = (values < val).mean() greater_than_ref_probability = (values >= val).mean()

diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -653,8 +653,8 @@ def test_plot_rank(models, kwargs): { "ref_val": { "theta": [ - {"school": ["Choate", "Deerfield"], "ref_val": -1}, - {"school": "Lawrenceville", "ref_val": 3}, + # {"school": ["Choate", "Deerfield"], "ref_val": -1}, this is not working + {"school": "Lawrenceville", "ref_val": 3} ] } },

plot_posterior ref_val (and rope) format is weird I don't get this format. Can someone explain what is going on? ~~ref_val = {"mu" : {"school" : "Choate", "ref_val" : 0.3}~~ ref_val = {"theta" : {"school" : "Choate", "ref_val" : 0.3} It is not even possible to pass that `all`? elif isinstance(ref_val, dict): for sel in ref_val.get(var_name, []): if all(k in selection and selection[k] == v for k, v in sel.items()): val = sel["ref_val"] break

@ColCarroll for this one Im planning on applying to Recurse Center. I'd like to use this issue as the pair program exercise. Is that alright by everyone? Sounds good. I think there could be different ways to do this ref_val and rope should probably follow the same format (instead of number it uses (number, number) number <-- numbers.Number() # so any number var <-- variable name # global setting ref_val --> number --> all variables ref_val --> (var, number) --> var only (for all coords) ref_val --> (var, (coord, coord_val), number) --> var with specific coord ref_val --> (var, ((coord1, coord1_val), (coord2, coord2_val), ... ), number) --> var with specific coord combination Then it could support multiple vars ref_val --> ((var1, var2, ...), number) --> (var1, var2, ... ) only (for all coords) ref_val --> ((var1, var2, ...), (coord, coord_val), number) --> (var1, var2, ... ) with specific coord ref_val --> ((var1, var2, ...), ((coord1, coord1_val), (coord2, coord2_val), ... ), number) --> (var1, var2, ... ) with specific coord combination Then there is the problem how to combine these in a meaningful way. Like what if user wants to have a global setting + some specific ones... Can we use dictionary, and what if user uses the default key? I got rejected from Recurse Center so this one is up for grabs again. I may work on it when time allows Oops, just saw I was tagged on this 😬 . I will take a look again! pymc-devs/pymc3#3498

2019-09-25T21:07:16Z

arviz-devs/arviz

arviz-devs__arviz-835

a77acb0f271157dd3e6a3dceb866e8f7c065aba8

diff --git a/arviz/plots/distplot.py b/arviz/plots/distplot.py --- a/arviz/plots/distplot.py +++ b/arviz/plots/distplot.py @@ -8,7 +8,7 @@ def plot_dist( values, values2=None, - color="C0", + color=None, kind="auto", cumulative=False, label=None, diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -9,7 +9,7 @@ from ..data import convert_to_dataset from ..stats import hpd from ..stats.diagnostics import _ess, _rhat -from .plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins +from .plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins, get_coords from .kdeplot import _fast_kde from ..utils import _var_names, conditional_jit @@ -26,6 +26,7 @@ def plot_forest( kind="forestplot", model_names=None, var_names=None, + coords=None, combined=False, credible_interval=0.94, rope=None, @@ -40,6 +41,7 @@ def plot_forest( ridgeplot_overlap=2, ridgeplot_kind="auto", figsize=None, + ax=None, ): """Forest plot to compare credible intervals from a number of distributions. @@ -59,6 +61,8 @@ def plot_forest( var_names: list[str], optional List of variables to plot (defaults to None, which results in all variables plotted) + coords : dict, optional + Coordinates of var_names to be plotted. Passed to `Dataset.sel` combined : bool Flag for combining multiple chains into a single chain. If False (default), chains will be plotted separately. @@ -97,6 +101,8 @@ def plot_forest( histograms. To override this use "hist" to plot histograms and "density" for KDEs figsize : tuple Figure size. If None it will be defined automatically. + ax : axes, optional + Matplotlib axes. Defaults to None. Returns ------- @@ -111,7 +117,7 @@ def plot_forest( >>> import arviz as az >>> non_centered_data = az.load_arviz_data('non_centered_eight') - >>> fig, axes = az.plot_forest(non_centered_data, + >>> axes = az.plot_forest(non_centered_data, >>> kind='forestplot', >>> var_names=['theta'], >>> combined=True, @@ -124,7 +130,7 @@ def plot_forest( .. plot:: :context: close-figs - >>> fig, axes = az.plot_forest(non_centered_data, + >>> axes = az.plot_forest(non_centered_data, >>> kind='ridgeplot', >>> var_names=['theta'], >>> combined=True, @@ -136,7 +142,12 @@ def plot_forest( if not isinstance(data, (list, tuple)): data = [data] - datasets = [convert_to_dataset(datum) for datum in reversed(data)] + if coords is None: + coords = {} + datasets = get_coords( + [convert_to_dataset(datum) for datum in reversed(data)], + list(reversed(coords)) if isinstance(coords, (list, tuple)) else coords, + ) var_names = _var_names(var_names, datasets) @@ -167,14 +178,17 @@ def plot_forest( if markersize is None: markersize = auto_markersize - fig, axes = plt.subplots( - nrows=1, - ncols=ncols, - figsize=figsize, - gridspec_kw={"width_ratios": width_ratios}, - sharey=True, - constrained_layout=True, - ) + if ax is None: + _, axes = plt.subplots( + nrows=1, + ncols=ncols, + figsize=figsize, + gridspec_kw={"width_ratios": width_ratios}, + sharey=True, + constrained_layout=True, + ) + else: + axes = ax axes = np.atleast_1d(axes) if kind == "forestplot": @@ -207,20 +221,20 @@ def plot_forest( plot_handler.plot_rhat(axes[idx], xt_labelsize, titlesize, markersize) idx += 1 - for ax in axes: + for ax_ in axes: if kind == "ridgeplot": - ax.grid(False) + ax_.grid(False) else: - ax.grid(False, axis="y") + ax_.grid(False, axis="y") # Remove ticklines on y-axes - ax.tick_params(axis="y", left=False, right=False) + ax_.tick_params(axis="y", left=False, right=False) - for loc, spine in ax.spines.items(): + for loc, spine in ax_.spines.items(): if loc in ["left", "right"]: spine.set_visible(False) if len(plot_handler.data) > 1: - plot_handler.make_bands(ax) + plot_handler.make_bands(ax_) labels, ticks = plot_handler.labels_and_ticks() axes[0].set_yticks(ticks) @@ -231,7 +245,7 @@ def plot_forest( y_max += ridgeplot_overlap axes[0].set_ylim(-all_plotters[0].group_offset, y_max) - return fig, axes + return axes class PlotHandler: diff --git a/arviz/plots/jointplot.py b/arviz/plots/jointplot.py --- a/arviz/plots/jointplot.py +++ b/arviz/plots/jointplot.py @@ -20,6 +20,7 @@ def plot_joint( fill_last=True, joint_kwargs=None, marginal_kwargs=None, + ax=None, ): """ Plot a scatter or hexbin of two variables with their respective marginals distributions. @@ -51,6 +52,9 @@ def plot_joint( Additional keywords modifying the join distribution (central subplot) marginal_kwargs : dicts, optional Additional keywords modifying the marginals distributions (top and right subplot) + ax : tuple of axes, optional + Tuple containing (axjoin, ax_hist_x, ax_hist_y). If None, a new figure and axes + will be created. Returns ------- @@ -95,6 +99,23 @@ def plot_joint( >>> kind='kde', >>> figsize=(6, 6)) + Overlayed plots: + + .. plot:: + :context: close-figs + + >>> data2 = az.load_arviz_data("centered_eight") + >>> kde_kwargs = {"contourf_kwargs": {"alpha": 0}, "contour_kwargs": {"colors": "k"}} + >>> ax = az.plot_joint( + ... data, var_names=("mu", "tau"), kind="kde", fill_last=False, + ... joint_kwargs=kde_kwargs, marginal_kwargs={"color": "k"} + ... ) + >>> kde_kwargs["contour_kwargs"]["colors"] = "r" + >>> az.plot_joint( + ... data2, var_names=("mu", "tau"), kind="kde", fill_last=False, + ... joint_kwargs=kde_kwargs, marginal_kwargs={"color": "r"}, ax=ax + ... ) + """ valid_kinds = ["scatter", "kde", "hexbin"] if kind not in valid_kinds: @@ -126,19 +147,24 @@ def plot_joint( marginal_kwargs.setdefault("plot_kwargs", {}) marginal_kwargs["plot_kwargs"]["linewidth"] = linewidth - # Instantiate figure and grid - fig, _ = plt.subplots(0, 0, figsize=figsize, constrained_layout=True) - grid = plt.GridSpec(4, 4, hspace=0.1, wspace=0.1, figure=fig) - - # Set up main plot - axjoin = fig.add_subplot(grid[1:, :-1]) + if ax is None: + # Instantiate figure and grid + fig, _ = plt.subplots(0, 0, figsize=figsize, constrained_layout=True) + grid = plt.GridSpec(4, 4, hspace=0.1, wspace=0.1, figure=fig) + + # Set up main plot + axjoin = fig.add_subplot(grid[1:, :-1]) + # Set up top KDE + ax_hist_x = fig.add_subplot(grid[0, :-1], sharex=axjoin) + # Set up right KDE + ax_hist_y = fig.add_subplot(grid[1:, -1], sharey=axjoin) + elif len(ax) == 3: + axjoin, ax_hist_x, ax_hist_y = ax + else: + raise ValueError("ax must be of lenght 3 but found {}".format(len(ax))) - # Set up top KDE - ax_hist_x = fig.add_subplot(grid[0, :-1], sharex=axjoin) + # Personalize axes ax_hist_x.tick_params(labelleft=False, labelbottom=False) - - # Set up right KDE - ax_hist_y = fig.add_subplot(grid[1:, -1], sharey=axjoin) ax_hist_y.tick_params(labelleft=False, labelbottom=False) # Set labels for axes @@ -163,8 +189,8 @@ def plot_joint( axjoin.hexbin(x, y, mincnt=1, gridsize=gridsize, **joint_kwargs) axjoin.grid(False) - for val, ax, rotate in ((x, ax_hist_x, False), (y, ax_hist_y, True)): - plot_dist(val, textsize=xt_labelsize, rotated=rotate, ax=ax, **marginal_kwargs) + for val, ax_, rotate in ((x, ax_hist_x, False), (y, ax_hist_y, True)): + plot_dist(val, textsize=xt_labelsize, rotated=rotate, ax=ax_, **marginal_kwargs) ax_hist_x.set_xlim(axjoin.get_xlim()) ax_hist_y.set_ylim(axjoin.get_ylim()) diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -456,21 +456,33 @@ def get_coords(data, coords): data: xarray xarray.DataSet or xarray.DataArray object, same type as input """ - try: - return data.sel(**coords) - - except ValueError: - invalid_coords = set(coords.keys()) - set(data.coords.keys()) - raise ValueError("Coords {} are invalid coordinate keys".format(invalid_coords)) - - except KeyError as err: - raise KeyError( - ( - "Coords should follow mapping format {{coord_name:[dim1, dim2]}}. " - "Check that coords structure is correct and" - " dimensions are valid. {}" - ).format(err) - ) + if not isinstance(data, (list, tuple)): + try: + return data.sel(**coords) + + except ValueError: + invalid_coords = set(coords.keys()) - set(data.coords.keys()) + raise ValueError("Coords {} are invalid coordinate keys".format(invalid_coords)) + + except KeyError as err: + raise KeyError( + ( + "Coords should follow mapping format {{coord_name:[dim1, dim2]}}. " + "Check that coords structure is correct and" + " dimensions are valid. {}" + ).format(err) + ) + if not isinstance(coords, (list, tuple)): + coords = [coords] * len(data) + data_subset = [] + for idx, (datum, coords_dict) in enumerate(zip(data, coords)): + try: + data_subset.append(get_coords(datum, coords_dict)) + except ValueError as err: + raise ValueError("Error in data[{}]: {}".format(idx, err)) + except KeyError as err: + raise KeyError("Error in data[{}]: {}".format(idx, err)) + return data_subset def color_from_dim(dataarray, dim_name): diff --git a/examples/plot_forest.py b/examples/plot_forest.py --- a/examples/plot_forest.py +++ b/examples/plot_forest.py @@ -10,7 +10,7 @@ centered_data = az.load_arviz_data("centered_eight") non_centered_data = az.load_arviz_data("non_centered_eight") -_, axes = az.plot_forest( +axes = az.plot_forest( [centered_data, non_centered_data], model_names=["Centered", "Non Centered"], var_names=["mu"] ) axes[0].set_title("Estimated theta for eight schools model") diff --git a/examples/plot_forest_ridge.py b/examples/plot_forest_ridge.py --- a/examples/plot_forest_ridge.py +++ b/examples/plot_forest_ridge.py @@ -9,7 +9,7 @@ az.style.use("arviz-darkgrid") rugby_data = az.load_arviz_data("rugby") -fig, axes = az.plot_forest( +axes = az.plot_forest( rugby_data, kind="ridgeplot", var_names=["defs"],

diff --git a/arviz/tests/test_plot_utils.py b/arviz/tests/test_plot_utils.py --- a/arviz/tests/test_plot_utils.py +++ b/arviz/tests/test_plot_utils.py @@ -125,6 +125,7 @@ def test_xarray_var_data_array(sample_dataset): # pylint: disable=invalid-name class TestCoordsExceptions: + # test coord exceptions on datasets def test_invalid_coord_name(self, sample_dataset): # pylint: disable=invalid-name """Assert that nicer exception appears when user enters wrong coords name""" _, _, data = sample_dataset @@ -140,11 +141,11 @@ def test_invalid_coord_value(self, sample_dataset): # pylint: disable=invalid-n _, _, data = sample_dataset coords = {"draw": [1234567]} - with pytest.raises(KeyError) as err: + with pytest.raises( + KeyError, match=r"Coords should follow mapping format {coord_name:\[dim1, dim2\]}" + ): get_coords(data, coords) - assert "Coords should follow mapping format {coord_name:[dim1, dim2]}" in str(err.value) - def test_invalid_coord_structure(self, sample_dataset): # pylint: disable=invalid-name """Assert that nicer exception appears when user enters wrong coords datatype""" _, _, data = sample_dataset @@ -153,6 +154,28 @@ def test_invalid_coord_structure(self, sample_dataset): # pylint: disable=inval with pytest.raises(TypeError): get_coords(data, coords) + # test coord exceptions on dataset list + def test_invalid_coord_name_list(self, sample_dataset): # pylint: disable=invalid-name + """Assert that nicer exception appears when user enters wrong coords name""" + _, _, data = sample_dataset + coords = {"NOT_A_COORD_NAME": [1]} + + with pytest.raises( + ValueError, match=r"data\[1\]:.+Coords {'NOT_A_COORD_NAME'} are invalid coordinate keys" + ): + get_coords((data, data), ({"draw": [0, 1]}, coords)) + + def test_invalid_coord_value_list(self, sample_dataset): # pylint: disable=invalid-name + """Assert that nicer exception appears when user enters wrong coords value""" + _, _, data = sample_dataset + coords = {"draw": [1234567]} + + with pytest.raises( + KeyError, + match=r"data\[0\]:.+Coords should follow mapping format {coord_name:\[dim1, dim2\]}", + ): + get_coords((data, data), (coords, {"draw": [0, 1]})) + def test_filter_plotter_list(): plotters = list(range(7)) diff --git a/arviz/tests/test_plots.py b/arviz/tests/test_plots.py --- a/arviz/tests/test_plots.py +++ b/arviz/tests/test_plots.py @@ -179,7 +179,7 @@ def test_plot_trace_max_subplots_warning(models): def test_plot_forest(models, model_fits, args_expected): obj = [getattr(models, model_fit) for model_fit in model_fits] args, expected = args_expected - _, axes = plot_forest(obj, **args) + axes = plot_forest(obj, **args) assert axes.shape == (expected,) @@ -190,7 +190,7 @@ def test_plot_forest_rope_exception(): def test_plot_forest_single_value(): - _, axes = plot_forest({"x": [1]}) + axes = plot_forest({"x": [1]}) assert axes.shape @@ -237,6 +237,12 @@ def test_plot_joint(models, kind): assert axjoin +def test_plot_joint_ax_tuple(models): + ax = plot_joint(models.model_1, var_names=("mu", "tau")) + axjoin, _, _ = plot_joint(models.model_2, var_names=("mu", "tau"), ax=ax) + assert axjoin + + def test_plot_joint_discrete(discrete_model): axjoin, _, _ = plot_joint(discrete_model) assert axjoin @@ -249,6 +255,10 @@ def test_plot_joint_bad(models): with pytest.raises(Exception): plot_joint(models.model_1, var_names=("mu", "tau", "eta")) + with pytest.raises(ValueError, match="ax.+3.+5"): + _, axes = plt.subplots(5, 1) + plot_joint(models.model_1, var_names=("mu", "tau"), ax=axes) + @pytest.mark.parametrize( "kwargs",

[FR] Add ax argument to plot_forest ## Tell us about it I've seen a couple of pull requests adding an `ax` argument to various `plot_X` functions (#773, #653, #162). This enables plotting on existing axes, giving users greater control of the plots (e.g., putting multiple plots in the same figure). Would it be possible to do this for `plot_forest` as well? ## Thoughts on implementation I don't have much here. I don't think that I would be able to create PR for this myself, since I'm pretty unfamiliar with the arviz codebase, but I'd greatly appreciate this if this were eventually added. edit: a complication might be that `plot_forest` can create several axes, not just one, but it's possible that other plots deal with this as well.

2019-09-29T14:35:44Z

arviz-devs/arviz

arviz-devs__arviz-934

549ed97ebfa47d2fa473453b765d738aecfe14d0

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -304,8 +304,8 @@ def concat(*args, dim=None, copy=True, inplace=False, reset_dim=True): ) msg += " Valid dimensions are `chain` and `draw`." raise TypeError(msg) - group_data = getattr(arg, group) - args_groups[group] = deepcopy(group_data) if copy else group_data + group_data = getattr(arg, group) + args_groups[group] = deepcopy(group_data) if copy else group_data # add arg0 to args_groups if inplace is False if not inplace: for group in arg0_groups:

diff --git a/arviz/tests/test_data.py b/arviz/tests/test_data.py --- a/arviz/tests/test_data.py +++ b/arviz/tests/test_data.py @@ -303,6 +303,22 @@ def test_concat_bad(): concat(idata3, idata, dim="chain") +def test_inference_concat_keeps_all_fields(): + """From failures observed in issue #907""" + idata1 = from_dict(posterior={"A": [1, 2, 3, 4]}, sample_stats={"B": [2, 3, 4, 5]}) + idata2 = from_dict(prior={"C": [1, 2, 3, 4]}, observed_data={"D": [2, 3, 4, 5]}) + + idata_c1 = concat(idata1, idata2) + idata_c2 = concat(idata2, idata1) + + test_dict = {"posterior": ["A"], "sample_stats": ["B"], "prior": ["C"], "observed_data": ["D"]} + + fails_c1 = check_multiple_attrs(test_dict, idata_c1) + assert not fails_c1 + fails_c2 = check_multiple_attrs(test_dict, idata_c2) + assert not fails_c2 + + @pytest.mark.parametrize("inplace", [True, False]) def test_sel_method(inplace): data = np.random.normal(size=(4, 500, 8))

Concat fails with empty InferenceData **Describe the bug** `arviz.concat` produces incorrect outputs when one of the arguments is an `InferenceData` with no groups. **To Reproduce** ```python >>> import arviz as az >>> idata = az.load_arviz_data("centered_eight") >>> idata Inference data with groups: > posterior > sample_stats > posterior_predictive > prior > observed_data >>> az.concat(idata, az.InferenceData()) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.7/site-packages/arviz/data/inference_data.py", line 307, in concat group_data = getattr(arg, group) UnboundLocalError: local variable 'group' referenced before assignment >>> az.concat(az.InferenceData(), idata) Inference data with groups: > observed_data ``` **Expected behavior** Both `concat` commands should have produced an `InferenceData` with the same groups and data as `idata`. **Additional context** Using master branch on OS X.

I suspect the issue is that these 2 lines need to be indented https://github.com/arviz-devs/arviz/blob/41279495780f2a3a6f0a8908253753078565c490/arviz/data/inference_data.py#L307-L308 I've replicated the problem on my MacOSX computer, and it appears @sethaxen 's solution will fix. Will submit a PR.

2019-12-03T23:42:12Z

arviz-devs/arviz

arviz-devs__arviz-991

c3158613f9e793902eddd0e5ecd715595c31c824

diff --git a/arviz/stats/diagnostics.py b/arviz/stats/diagnostics.py --- a/arviz/stats/diagnostics.py +++ b/arviz/stats/diagnostics.py @@ -46,6 +46,7 @@ def bfmi(data): Examples -------- Compute the BFMI of an InferenceData object + .. ipython:: In [1]: import arviz as az @@ -526,9 +527,9 @@ def _bfmi(energy): energy_mat = np.atleast_2d(energy) num = np.square(np.diff(energy_mat, axis=1)).mean(axis=1) # pylint: disable=no-member if energy_mat.ndim == 2: - den = _numba_var(svar, np.var, energy_mat, axis=1, ddof=0) + den = _numba_var(svar, np.var, energy_mat, axis=1, ddof=1) else: - den = np.var(energy, axis=1) + den = np.var(energy, axis=1, ddof=1) return num / den

diff --git a/arviz/tests/test_diagnostics.py b/arviz/tests/test_diagnostics.py --- a/arviz/tests/test_diagnostics.py +++ b/arviz/tests/test_diagnostics.py @@ -45,7 +45,7 @@ def data(): class TestDiagnostics: def test_bfmi(self): energy = np.array([1, 2, 3, 4]) - assert_almost_equal(bfmi(energy), 0.8) + assert_almost_equal(bfmi(energy), 0.6) def test_bfmi_dataset(self): data = load_arviz_data("centered_eight")

Incorrect denominator in bfmi The original equation for `bfmi` is at the bottom of page 7 in [this paper](https://arxiv.org/pdf/1604.00695v1.pdf). The numerator in numpy for a vector `E` is `(length(E) - 1) * np.mean(np.square(np.diff(E)))`. The denominator is `(length(E) - 1) * np.var(E, ddof = 1)`. The two scaling factors cancel and we get ```python def bfmi(E): return np.mean(np.square(np.diff(E))) / np.var(E, ddof = 1) ``` The current implementation uses `ddof=0`, so it's off from the equation in the paper by `length(E) / (length(E) - 1)` **Additional context** PyStan's implementation [here](https://github.com/stan-dev/pystan/blob/cbc5c0fc8597504776c08a138adc9ae63329e6a1/pystan/diagnostics.py#L213-L217) is equivalent to the paper's. RStan's implementation of `get_bfmi` [here](https://rdrr.io/cran/rstan/src/R/check_hmc_diagnostics.R) has a different implementation than either ArviZ's current one or PyStan's. Theirs amounts to the original paper's equation multiplied by `(length(E) - 1) / length(E)`. Incorrect denominator in bfmi The original equation for `bfmi` is at the bottom of page 7 in [this paper](https://arxiv.org/pdf/1604.00695v1.pdf). The numerator in numpy for a vector `E` is `(length(E) - 1) * np.mean(np.square(np.diff(E)))`. The denominator is `(length(E) - 1) * np.var(E, ddof = 1)`. The two scaling factors cancel and we get ```python def bfmi(E): return np.mean(np.square(np.diff(E))) / np.var(E, ddof = 1) ``` The current implementation uses `ddof=0`, so it's off from the equation in the paper by `length(E) / (length(E) - 1)` **Additional context** PyStan's implementation [here](https://github.com/stan-dev/pystan/blob/cbc5c0fc8597504776c08a138adc9ae63329e6a1/pystan/diagnostics.py#L213-L217) is equivalent to the paper's. RStan's implementation of `get_bfmi` [here](https://rdrr.io/cran/rstan/src/R/check_hmc_diagnostics.R) has a different implementation than either ArviZ's current one or PyStan's. Theirs amounts to the original paper's equation multiplied by `(length(E) - 1) / length(E)`.

Hi, can I work on this issue? That would be nice! There's was an offline discussion that it would be good to check with the Rstan team and post on Stan's discourse to see if there's a reason for the discrepancy from the paper. I filed https://github.com/stan-dev/rstan/issues/712, but that's as far as I got. Hi, can I work on this issue? That would be nice! There's was an offline discussion that it would be good to check with the Rstan team and post on Stan's discourse to see if there's a reason for the discrepancy from the paper. I filed https://github.com/stan-dev/rstan/issues/712, but that's as far as I got.

2020-01-13T07:19:11Z

arviz-devs/arviz

arviz-devs__arviz-993

4cfe800dff78dbd6ce4b625693477ee0deb3a68d

diff --git a/arviz/plots/elpdplot.py b/arviz/plots/elpdplot.py --- a/arviz/plots/elpdplot.py +++ b/arviz/plots/elpdplot.py @@ -21,7 +21,7 @@ def plot_elpd( threshold=None, ax=None, ic=None, - scale="deviance", + scale=None, plot_kwargs=None, backend=None, backend_kwargs=None, @@ -109,6 +109,7 @@ def plot_elpd( """ valid_ics = ["waic", "loo"] ic = rcParams["stats.information_criterion"] if ic is None else ic.lower() + scale = rcParams["stats.ic_scale"] if scale is None else scale.lower() if ic not in valid_ics: raise ValueError( ("Information Criteria type {} not recognized." "IC must be in {}").format( diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -42,13 +42,7 @@ def compare( - dataset_dict, - ic=None, - method="BB-pseudo-BMA", - b_samples=1000, - alpha=1, - seed=None, - scale="deviance", + dataset_dict, ic=None, method="BB-pseudo-BMA", b_samples=1000, alpha=1, seed=None, scale=None ): r"""Compare models based on WAIC or LOO cross-validation. @@ -136,7 +130,7 @@ def compare( """ names = list(dataset_dict.keys()) - scale = scale.lower() + scale = rcParams["stats.ic_scale"] if scale is None else scale.lower() if scale == "log": scale_value = 1 ascending = False @@ -421,7 +415,7 @@ def hpd(ary, credible_interval=0.94, circular=False, multimodal=False): return hpd_intervals -def loo(data, pointwise=False, reff=None, scale="deviance"): +def loo(data, pointwise=False, reff=None, scale=None): """Pareto-smoothed importance sampling leave-one-out cross-validation. Calculates leave-one-out (LOO) cross-validation for out of sample predictive model fit, @@ -493,12 +487,13 @@ def loo(data, pointwise=False, reff=None, scale="deviance"): shape = log_likelihood.shape n_samples = shape[-1] n_data_points = np.product(shape[:-1]) + scale = rcParams["stats.ic_scale"] if scale is None else scale.lower() - if scale.lower() == "deviance": + if scale == "deviance": scale_value = -2 - elif scale.lower() == "log": + elif scale == "log": scale_value = 1 - elif scale.lower() == "negative_log": + elif scale == "negative_log": scale_value = -1 else: raise TypeError('Valid scale values are "deviance", "log", "negative_log"') @@ -1101,7 +1096,7 @@ def summary( return summary_df -def waic(data, pointwise=False, scale="deviance"): +def waic(data, pointwise=False, scale=None): """Calculate the widely available information criterion. Also calculates the WAIC's standard error and the effective number of @@ -1165,12 +1160,13 @@ def waic(data, pointwise=False, scale="deviance"): if "log_likelihood" not in inference_data.sample_stats: raise TypeError("Data must include log_likelihood in sample_stats") log_likelihood = inference_data.sample_stats.log_likelihood + scale = rcParams["stats.ic_scale"] if scale is None else scale.lower() - if scale.lower() == "deviance": + if scale == "deviance": scale_value = -2 - elif scale.lower() == "log": + elif scale == "log": scale_value = 1 - elif scale.lower() == "negative_log": + elif scale == "negative_log": scale_value = -1 else: raise TypeError('Valid scale values are "deviance", "log", "negative_log"') diff --git a/examples/bokeh/bokeh_plot_dist.py b/examples/bokeh/bokeh_plot_dist.py --- a/examples/bokeh/bokeh_plot_dist.py +++ b/examples/bokeh/bokeh_plot_dist.py @@ -16,9 +16,7 @@ ax_poisson = bkp.figure(**figure_kwargs) ax_normal = bkp.figure(**figure_kwargs) -az.plot_dist( - a, color="black", label="Poisson", ax=ax_poisson, backend="bokeh", show=False, -) +az.plot_dist(a, color="black", label="Poisson", ax=ax_poisson, backend="bokeh", show=False) az.plot_dist(b, color="red", label="Gaussian", ax=ax_normal, backend="bokeh", show=False) ax = row(ax_poisson, ax_normal) diff --git a/examples/bokeh/bokeh_plot_loo_pit_ecdf.py b/examples/bokeh/bokeh_plot_loo_pit_ecdf.py --- a/examples/bokeh/bokeh_plot_loo_pit_ecdf.py +++ b/examples/bokeh/bokeh_plot_loo_pit_ecdf.py @@ -11,5 +11,5 @@ log_weights = az.psislw(-log_like)[0] ax = az.plot_loo_pit( - idata, y="y_like", log_weights=log_weights, ecdf=True, color="orange", backend="bokeh", + idata, y="y_like", log_weights=log_weights, ecdf=True, color="orange", backend="bokeh" ) diff --git a/examples/bokeh/bokeh_plot_pair.py b/examples/bokeh/bokeh_plot_pair.py --- a/examples/bokeh/bokeh_plot_pair.py +++ b/examples/bokeh/bokeh_plot_pair.py @@ -10,5 +10,5 @@ coords = {"school": ["Choate", "Deerfield"]} ax = az.plot_pair( - centered, var_names=["theta", "mu", "tau"], coords=coords, divergences=True, backend="bokeh", + centered, var_names=["theta", "mu", "tau"], coords=coords, divergences=True, backend="bokeh" )

diff --git a/arviz/tests/test_plots_bokeh.py b/arviz/tests/test_plots_bokeh.py --- a/arviz/tests/test_plots_bokeh.py +++ b/arviz/tests/test_plots_bokeh.py @@ -189,7 +189,7 @@ def test_plot_kde_1d(continuous_model): "kwargs", [ {"contour": True, "fill_last": False}, - {"contour": True, "contourf_kwargs": {"cmap": "plasma"},}, + {"contour": True, "contourf_kwargs": {"cmap": "plasma"}}, {"contour": False}, {"contour": False, "pcolormesh_kwargs": {"cmap": "plasma"}}, ], @@ -275,9 +275,7 @@ def test_plot_compare_no_ic(models): assert "['waic', 'loo']" in str(err.value) [email protected]( - "kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"},], -) [email protected]("kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"}]) @pytest.mark.parametrize("add_model", [False, True]) @pytest.mark.parametrize("use_elpddata", [False, True]) def test_plot_elpd(models, add_model, use_elpddata, kwargs): @@ -300,9 +298,7 @@ def test_plot_elpd(models, add_model, use_elpddata, kwargs): assert axes.shape[0] == len(model_dict) - 1 [email protected]( - "kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"},], -) [email protected]("kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"}]) @pytest.mark.parametrize("add_model", [False, True]) @pytest.mark.parametrize("use_elpddata", [False, True]) def test_plot_elpd_multidim(multidim_models, add_model, use_elpddata, kwargs): @@ -443,9 +439,7 @@ def test_plot_forest(models, model_fits, args_expected): def test_plot_forest_rope_exception(): with pytest.raises(ValueError) as err: - plot_forest( - {"x": [1]}, rope="not_correct_format", backend="bokeh", show=False, - ) + plot_forest({"x": [1]}, rope="not_correct_format", backend="bokeh", show=False) assert "Argument `rope` must be None, a dictionary like" in str(err.value) @@ -508,19 +502,15 @@ def test_plot_joint_discrete(discrete_model): def test_plot_joint_bad(models): with pytest.raises(ValueError): plot_joint( - models.model_1, var_names=("mu", "tau"), kind="bad_kind", backend="bokeh", show=False, + models.model_1, var_names=("mu", "tau"), kind="bad_kind", backend="bokeh", show=False ) with pytest.raises(Exception): - plot_joint( - models.model_1, var_names=("mu", "tau", "eta"), backend="bokeh", show=False, - ) + plot_joint(models.model_1, var_names=("mu", "tau", "eta"), backend="bokeh", show=False) with pytest.raises(ValueError): _, axes = list(range(5)) - plot_joint( - models.model_1, var_names=("mu", "tau"), ax=axes, backend="bokeh", show=False, - ) + plot_joint(models.model_1, var_names=("mu", "tau"), ax=axes, backend="bokeh", show=False) @pytest.mark.parametrize( @@ -614,7 +604,7 @@ def test_plot_loo_pit_incompatible_args(models): """Test error when both ecdf and use_hpd are True.""" with pytest.raises(ValueError, match="incompatible"): plot_loo_pit( - idata=models.model_1, y="y", ecdf=True, use_hpd=True, backend="bokeh", show=False, + idata=models.model_1, y="y", ecdf=True, use_hpd=True, backend="bokeh", show=False ) @@ -694,8 +684,8 @@ def test_plot_mcse_no_divergences(models): @pytest.mark.parametrize( "kwargs", [ - {"var_names": "theta", "divergences": True, "coords": {"theta_dim_0": [0, 1]},}, - {"divergences": True, "var_names": ["theta", "mu"],}, + {"var_names": "theta", "divergences": True, "coords": {"theta_dim_0": [0, 1]}}, + {"divergences": True, "var_names": ["theta", "mu"]}, {"kind": "kde", "var_names": ["theta"]}, {"kind": "hexbin", "var_names": ["theta"]}, {"kind": "hexbin", "var_names": ["theta"]}, @@ -760,7 +750,7 @@ def test_plot_parallel_exception(models, var_names): """Ensure that correct exception is raised when one variable is passed.""" with pytest.raises(ValueError): assert plot_parallel( - models.model_1, var_names=var_names, norm_method="foo", backend="bokeh", show=False, + models.model_1, var_names=var_names, norm_method="foo", backend="bokeh", show=False ) @@ -860,9 +850,7 @@ def test_plot_ppc_bad(models, kind): with pytest.raises(TypeError): plot_ppc(models.model_1, kind="bad_val", backend="bokeh", show=False) with pytest.raises(TypeError): - plot_ppc( - models.model_1, num_pp_samples="bad_val", backend="bokeh", show=False, - ) + plot_ppc(models.model_1, num_pp_samples="bad_val", backend="bokeh", show=False) @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) @@ -913,13 +901,9 @@ def test_plot_posterior_bad(models): with pytest.raises(ValueError): plot_posterior(models.model_1, backend="bokeh", show=False, rope="bad_value") with pytest.raises(ValueError): - plot_posterior( - models.model_1, ref_val="bad_value", backend="bokeh", show=False, - ) + plot_posterior(models.model_1, ref_val="bad_value", backend="bokeh", show=False) with pytest.raises(ValueError): - plot_posterior( - models.model_1, point_estimate="bad_value", backend="bokeh", show=False, - ) + plot_posterior(models.model_1, point_estimate="bad_value", backend="bokeh", show=False) @pytest.mark.parametrize("point_estimate", ("mode", "mean", "median"))

Integrate ic_scale rcParam ## Tell us about it `stats.ic_scale` has a default value in rcParams which is not yet used. Part of #792 ## Thoughts on implementation `compare`, `waic` and `loo` should have `None` as default for `ic_scale` and use the default set in rcParams. The implementation should be very similar to `stats.information_criterion` integration with `ic` argument

@OriolAbril Can you assign me this issue I would be happy to work on that @Shashankjain12 you can work on that, there is no need to be assigned. It is great that you comment on the issue you plan to work on. One useful piece of advise in order to make sure you are not working on the same topic as somebody else (this is especially useful now that many of you are working on ArviZ to familiarize yourselves with it before GSoC), is to start by commenting on the issue just like you did and then once you start coding, send the pull request right away (even though it will probably not be finished). As said in the [contributing guide](https://github.com/arviz-devs/arviz/blob/master/CONTRIBUTING.md), if you add `[WIP]` to the PR title and indicate the issue it fixes, it shows that work is currently ongoing. Regarding your comment on Gitter, it looks like you are confusing `ic` (acronym for information criterion) with `ic_scale`. Working on rcParams can be quite confusing because the name of the rcParam may not always match exactly the argument used when calling ArviZ functions. I will try to clarify the issue description below, sorry about that. In this case, the two arg/rcParam pairs are `ic`/`information_criterion` and `scale`/`ic_scale`. These arguments are relevant for several functions like `loo`, `waic`, `compare` and `plot_elpd`. `ic` already uses `None` as default, which means that the used can customize the default using the `stats.information_criterion` rcParam. However, `scale` has `"deviance"` hardcoded as a default, instead of using the `stats.ic_scale` rcParam. The aim of the issue is to update `scale`/`ic_scale` to mimic the behaviour of `ic`/`information_criterion` Sure @OriolAbril Thanks for sorting that out I will start working on this issue..👍

2020-01-13T18:21:31Z

arviz-devs/arviz

arviz-devs__arviz-994

c3158613f9e793902eddd0e5ecd715595c31c824

diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -9,7 +9,7 @@ from .forestplot import plot_forest from .hpdplot import plot_hpd from .jointplot import plot_joint -from .kdeplot import plot_kde, _fast_kde, _fast_kde_2d +from .kdeplot import plot_kde from .khatplot import plot_khat from .loopitplot import plot_loo_pit from .mcseplot import plot_mcse @@ -20,6 +20,7 @@ from .rankplot import plot_rank from .traceplot import plot_trace from .violinplot import plot_violin +from .plot_utils import _fast_kde, _fast_kde_2d __all__ = [ diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py --- a/arviz/plots/backends/bokeh/densityplot.py +++ b/arviz/plots/backends/bokeh/densityplot.py @@ -5,8 +5,12 @@ from bokeh.models.annotations import Title from . import backend_kwarg_defaults, backend_show -from ...kdeplot import _fast_kde -from ...plot_utils import _create_axes_grid, make_label +from ...plot_utils import ( + make_label, + _create_axes_grid, + calculate_point_estimate, + _fast_kde, +) from ....stats import hpd from ....stats.stats_utils import histogram @@ -186,10 +190,7 @@ def _d_helper( ax.diamond(xmax, 0, line_color="black", fill_color=color, size=markersize) if point_estimate is not None: - if point_estimate == "mean": - est = np.mean(vec) - elif point_estimate == "median": - est = np.median(vec) + est = calculate_point_estimate(point_estimate, vec, bw) ax.circle(est, 0, fill_color=color, line_color="black", size=markersize) _title = Title() diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -12,8 +12,7 @@ from bokeh.models.tickers import FixedTicker from . import backend_kwarg_defaults, backend_show -from ...kdeplot import _fast_kde -from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins +from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins, _fast_kde from ....rcparams import rcParams from ....stats import hpd from ....stats.diagnostics import _ess, _rhat diff --git a/arviz/plots/backends/bokeh/posteriorplot.py b/arviz/plots/backends/bokeh/posteriorplot.py --- a/arviz/plots/backends/bokeh/posteriorplot.py +++ b/arviz/plots/backends/bokeh/posteriorplot.py @@ -6,15 +6,15 @@ import numpy as np from bokeh.layouts import gridplot from bokeh.models.annotations import Title -from scipy.stats import mode from . import backend_kwarg_defaults, backend_show -from ...kdeplot import plot_kde, _fast_kde +from ...kdeplot import plot_kde from ...plot_utils import ( make_label, _create_axes_grid, format_sig_figs, round_num, + calculate_point_estimate, ) from ....stats import hpd @@ -189,19 +189,7 @@ def display_rope(max_data): def display_point_estimate(max_data): if not point_estimate: return - if point_estimate not in ("mode", "mean", "median"): - raise ValueError("Point Estimate should be in ('mode','mean','median')") - if point_estimate == "mean": - point_value = values.mean() - elif point_estimate == "mode": - if isinstance(values[0], float): - density, lower, upper = _fast_kde(values, bw=bw) - x = np.linspace(lower, upper, len(density)) - point_value = x[np.argmax(density)] - else: - point_value = mode(values)[0][0] - elif point_estimate == "median": - point_value = np.median(values) + point_value = calculate_point_estimate(point_estimate, values, bw) sig_figs = format_sig_figs(point_value, round_to) point_text = "{point_estimate}={point_value:.{sig_figs}g}".format( point_estimate=point_estimate, point_value=point_value, sig_figs=sig_figs diff --git a/arviz/plots/backends/matplotlib/densityplot.py b/arviz/plots/backends/matplotlib/densityplot.py --- a/arviz/plots/backends/matplotlib/densityplot.py +++ b/arviz/plots/backends/matplotlib/densityplot.py @@ -4,8 +4,12 @@ from . import backend_show from ....stats import hpd -from ...kdeplot import _fast_kde -from ...plot_utils import _create_axes_grid, make_label +from ...plot_utils import ( + make_label, + _create_axes_grid, + calculate_point_estimate, + _fast_kde, +) def plot_density( @@ -115,8 +119,9 @@ def _d_helper( Size of markers credible_interval : float Credible intervals. Defaults to 0.94 - point_estimate : str or None - 'mean' or 'median' + point_estimate : Optional[str] + Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. + Defaults to 'auto' i.e. it falls back to default set in rcParams. shade : float Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 (opaque). Defaults to 0. @@ -156,10 +161,7 @@ def _d_helper( ax.plot(xmax, 0, hpd_markers, color=color, markeredgecolor="k", markersize=markersize) if point_estimate is not None: - if point_estimate == "mean": - est = np.mean(vec) - elif point_estimate == "median": - est = np.median(vec) + est = calculate_point_estimate(point_estimate, vec, bw) ax.plot(est, 0, "o", color=color, markeredgecolor="k", markersize=markersize) ax.set_yticks([]) diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -10,8 +10,7 @@ from ....stats import hpd from ....stats.diagnostics import _ess, _rhat from ....stats.stats_utils import histogram -from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins -from ...kdeplot import _fast_kde +from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins, _fast_kde from ....utils import conditional_jit diff --git a/arviz/plots/backends/matplotlib/loopitplot.py b/arviz/plots/backends/matplotlib/loopitplot.py --- a/arviz/plots/backends/matplotlib/loopitplot.py +++ b/arviz/plots/backends/matplotlib/loopitplot.py @@ -3,7 +3,7 @@ import numpy as np from . import backend_kwarg_defaults, backend_show -from ...kdeplot import _fast_kde +from ...plot_utils import _fast_kde from ...hpdplot import plot_hpd diff --git a/arviz/plots/backends/matplotlib/posteriorplot.py b/arviz/plots/backends/matplotlib/posteriorplot.py --- a/arviz/plots/backends/matplotlib/posteriorplot.py +++ b/arviz/plots/backends/matplotlib/posteriorplot.py @@ -3,16 +3,16 @@ from numbers import Number import matplotlib.pyplot as plt import numpy as np -from scipy.stats import mode from . import backend_show from ....stats import hpd -from ...kdeplot import plot_kde, _fast_kde +from ...kdeplot import plot_kde from ...plot_utils import ( make_label, _create_axes_grid, format_sig_figs, round_num, + calculate_point_estimate, ) @@ -181,19 +181,7 @@ def display_rope(): def display_point_estimate(): if not point_estimate: return - if point_estimate not in ("mode", "mean", "median"): - raise ValueError("Point Estimate should be in ('mode','mean','median')") - if point_estimate == "mean": - point_value = values.mean() - elif point_estimate == "mode": - if isinstance(values[0], float): - density, lower, upper = _fast_kde(values, bw=bw) - x = np.linspace(lower, upper, len(density)) - point_value = x[np.argmax(density)] - else: - point_value = mode(values)[0][0] - elif point_estimate == "median": - point_value = np.median(values) + point_value = calculate_point_estimate(point_estimate, values, bw) sig_figs = format_sig_figs(point_value, round_to) point_text = "{point_estimate}={point_value:.{sig_figs}g}".format( point_estimate=point_estimate, point_value=point_value, sig_figs=sig_figs diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -4,11 +4,12 @@ import numpy as np from . import backend_show -from ...kdeplot import plot_kde, _fast_kde +from ...kdeplot import plot_kde from ...plot_utils import ( make_label, _create_axes_grid, get_bins, + _fast_kde, ) from ....stats.stats_utils import histogram diff --git a/arviz/plots/backends/matplotlib/violinplot.py b/arviz/plots/backends/matplotlib/violinplot.py --- a/arviz/plots/backends/matplotlib/violinplot.py +++ b/arviz/plots/backends/matplotlib/violinplot.py @@ -5,8 +5,7 @@ from . import backend_show from ....stats import hpd from ....stats.stats_utils import histogram -from ...kdeplot import _fast_kde -from ...plot_utils import get_bins, make_label, _create_axes_grid +from ...plot_utils import get_bins, make_label, _create_axes_grid, _fast_kde def plot_violin( diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py --- a/arviz/plots/densityplot.py +++ b/arviz/plots/densityplot.py @@ -23,7 +23,7 @@ def plot_density( data_labels=None, var_names=None, credible_interval=0.94, - point_estimate="mean", + point_estimate="auto", colors="cycle", outline=True, hpd_markers="", @@ -61,8 +61,8 @@ def plot_density( credible_interval : float Credible intervals. Should be in the interval (0, 1]. Defaults to 0.94. point_estimate : Optional[str] - Plot point estimate per variable. Values should be 'mean', 'median' or None. - Defaults to 'mean'. + Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. + Defaults to 'auto' i.e. it falls back to default set in rcParams. colors : Optional[Union[List[str],str]] List with valid matplotlib colors, one color per model. Alternative a string can be passed. If the string is `cycle`, it will automatically choose a color per model from matplotlib's @@ -153,12 +153,6 @@ def plot_density( datasets = [convert_to_dataset(datum, group=group) for datum in data] var_names = _var_names(var_names, datasets) - - if point_estimate not in ("mean", "median", None): - raise ValueError( - "Point estimate should be 'mean'," "median' or None, not {}".format(point_estimate) - ) - n_data = len(datasets) if data_labels is None: diff --git a/arviz/plots/kdeplot.py b/arviz/plots/kdeplot.py --- a/arviz/plots/kdeplot.py +++ b/arviz/plots/kdeplot.py @@ -1,15 +1,9 @@ # pylint: disable=unexpected-keyword-arg """One-dimensional kernel density estimate plots.""" -import warnings -import numpy as np -from scipy.signal import gaussian, convolve, convolve2d # pylint: disable=no-name-in-module -from scipy.sparse import coo_matrix import xarray as xr from ..data import InferenceData -from ..utils import conditional_jit, _stack -from ..stats.stats_utils import histogram -from .plot_utils import get_plotting_function +from .plot_utils import get_plotting_function, _fast_kde, _fast_kde_2d def plot_kde( @@ -226,171 +220,3 @@ def plot_kde( ax = plot(**kde_plot_args) return ax - - -def _fast_kde(x, cumulative=False, bw=4.5, xmin=None, xmax=None): - """Fast Fourier transform-based Gaussian kernel density estimate (KDE). - - The code was adapted from https://github.com/mfouesneau/faststats - - Parameters - ---------- - x : Numpy array or list - cumulative : bool - If true, estimate the cdf instead of the pdf - bw : float - Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the - smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule - of thumb (the default rule used by SciPy). - xmin : float - Manually set lower limit. - xmax : float - Manually set upper limit. - - Returns - ------- - density: A gridded 1D KDE of the input points (x) - xmin: minimum value of x - xmax: maximum value of x - """ - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - if x.size == 0: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - len_x = len(x) - n_points = 200 if (xmin or xmax) is None else 500 - - if xmin is None: - xmin = np.min(x) - if xmax is None: - xmax = np.max(x) - - assert np.min(x) >= xmin - assert np.max(x) <= xmax - - log_len_x = np.log(len_x) * bw - - n_bins = min(int(len_x ** (1 / 3) * log_len_x * 2), n_points) - if n_bins < 2: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - _, grid, _ = histogram(x, n_bins, range_hist=(xmin, xmax)) - - scotts_factor = len_x ** (-0.2) - kern_nx = int(scotts_factor * 2 * np.pi * log_len_x) - kernel = gaussian(kern_nx, scotts_factor * log_len_x) - - npad = min(n_bins, 2 * kern_nx) - grid = np.concatenate([grid[npad:0:-1], grid, grid[n_bins : n_bins - npad : -1]]) - density = convolve(grid, kernel, mode="same", method="direct")[npad : npad + n_bins] - norm_factor = (2 * np.pi * log_len_x ** 2 * scotts_factor ** 2) ** 0.5 - - density /= norm_factor - - if cumulative: - density = density.cumsum() / density.sum() - - return density, xmin, xmax - - -def _cov_1d(x): - x = x - x.mean(axis=0) - ddof = x.shape[0] - 1 - return np.dot(x.T, x.conj()) / ddof - - -def _cov(data): - if data.ndim == 1: - return _cov_1d(data) - elif data.ndim == 2: - x = data.astype(float) - avg, _ = np.average(x, axis=1, weights=None, returned=True) - ddof = x.shape[1] - 1 - if ddof <= 0: - warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) - ddof = 0.0 - x -= avg[:, None] - prod = _dot(x, x.T.conj()) - prod *= np.true_divide(1, ddof) - return prod.squeeze() - else: - raise ValueError("{} dimension arrays are not supported".format(data.ndim)) - - -@conditional_jit(cache=True) -def _dot(x, y): - return np.dot(x, y) - - -def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): - """ - 2D fft-based Gaussian kernel density estimate (KDE). - - The code was adapted from https://github.com/mfouesneau/faststats - - Parameters - ---------- - x : Numpy array or list - y : Numpy array or list - gridsize : tuple - Number of points used to discretize data. Use powers of 2 for fft optimization - circular: bool - If True, use circular boundaries. Defaults to False - Returns - ------- - grid: A gridded 2D KDE of the input points (x, y) - xmin: minimum value of x - xmax: maximum value of x - ymin: minimum value of y - ymax: maximum value of y - """ - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - y = np.asarray(y, dtype=float) - y = y[np.isfinite(y)] - - xmin, xmax = x.min(), x.max() - ymin, ymax = y.min(), y.max() - - len_x = len(x) - weights = np.ones(len_x) - n_x, n_y = gridsize - - d_x = (xmax - xmin) / (n_x - 1) - d_y = (ymax - ymin) / (n_y - 1) - - xyi = _stack(x, y).T - xyi -= [xmin, ymin] - xyi /= [d_x, d_y] - xyi = np.floor(xyi, xyi).T - - scotts_factor = len_x ** (-1 / 6) - cov = _cov(xyi) - std_devs = np.diag(cov ** 0.5) - kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) - - inv_cov = np.linalg.inv(cov * scotts_factor ** 2) - - x_x = np.arange(kern_nx) - kern_nx / 2 - y_y = np.arange(kern_ny) - kern_ny / 2 - x_x, y_y = np.meshgrid(x_x, y_y) - - kernel = _stack(x_x.flatten(), y_y.flatten()) - kernel = _dot(inv_cov, kernel) * kernel - kernel = np.exp(-kernel.sum(axis=0) / 2) - kernel = kernel.reshape((int(kern_ny), int(kern_nx))) - - boundary = "wrap" if circular else "symm" - - grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() - grid = convolve2d(grid, kernel, mode="same", boundary=boundary) - - norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor ** 2) - norm_factor = len_x * d_x * d_y * norm_factor ** 0.5 - - grid /= norm_factor - - return grid, xmin, xmax, ymin, ymax diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py --- a/arviz/plots/loopitplot.py +++ b/arviz/plots/loopitplot.py @@ -5,8 +5,11 @@ from xarray import DataArray from ..stats import loo_pit as _loo_pit -from .plot_utils import _scale_fig_size, get_plotting_function -from .kdeplot import _fast_kde +from .plot_utils import ( + _scale_fig_size, + get_plotting_function, + _fast_kde, +) def plot_loo_pit( diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -2,6 +2,10 @@ import warnings from itertools import product, tee import importlib +from scipy.signal import convolve, convolve2d +from scipy.sparse import coo_matrix +from scipy.signal.windows import gaussian +from scipy.stats import mode import packaging import numpy as np @@ -10,7 +14,7 @@ import xarray as xr -from ..utils import conditional_jit +from ..utils import conditional_jit, _stack from ..rcparams import rcParams @@ -674,3 +678,217 @@ def get_plotting_function(plot_name, plot_module, backend): plotting_method = getattr(module, plot_name) return plotting_method + + +def calculate_point_estimate(point_estimate, values, bw): + """Validate and calculate the point estimate. + + Parameters + ---------- + point_estimate : Optional[str] + Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. + Defaults to 'auto' i.e. it falls back to default set in rcParams. + values : 1-d array + bw : float + Bandwidth scaling factor. Should be larger than 0. The higher this number the smoother the + KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule of thumb + (the default used rule by SciPy). + + Returns + ------- + point_value : float + best estimate of data distribution + """ + point_value = None + if point_estimate == "auto": + point_estimate = rcParams["plot.point_estimate"] + elif point_estimate not in ("mean", "median", "mode", None): + raise ValueError( + "Point estimate should be 'mean', 'median', 'mode' or None, not {}".format( + point_estimate + ) + ) + if point_estimate == "mean": + point_value = values.mean() + elif point_estimate == "mode": + if isinstance(values[0], float): + density, lower, upper = _fast_kde(values, bw=bw) + x = np.linspace(lower, upper, len(density)) + point_value = x[np.argmax(density)] + else: + point_value = mode(values)[0][0] + elif point_estimate == "median": + point_value = np.median(values) + + return point_value + + +def _fast_kde(x, cumulative=False, bw=4.5, xmin=None, xmax=None): + """Fast Fourier transform-based Gaussian kernel density estimate (KDE). + + The code was adapted from https://github.com/mfouesneau/faststats + + Parameters + ---------- + x : Numpy array or list + cumulative : bool + If true, estimate the cdf instead of the pdf + bw : float + Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the + smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule + of thumb (the default rule used by SciPy). + xmin : float + Manually set lower limit. + xmax : float + Manually set upper limit. + + Returns + ------- + density: A gridded 1D KDE of the input points (x) + xmin: minimum value of x + xmax: maximum value of x + """ + x = np.asarray(x, dtype=float) + x = x[np.isfinite(x)] + if x.size == 0: + warnings.warn("kde plot failed, you may want to check your data") + return np.array([np.nan]), np.nan, np.nan + + len_x = len(x) + n_points = 200 if (xmin or xmax) is None else 500 + + if xmin is None: + xmin = np.min(x) + if xmax is None: + xmax = np.max(x) + + assert np.min(x) >= xmin + assert np.max(x) <= xmax + + log_len_x = np.log(len_x) * bw + + n_bins = min(int(len_x ** (1 / 3) * log_len_x * 2), n_points) + if n_bins < 2: + warnings.warn("kde plot failed, you may want to check your data") + return np.array([np.nan]), np.nan, np.nan + + hist, bin_edges = np.histogram(x, bins=n_bins, range=(xmin, xmax)) + grid = hist / (hist.sum() * np.diff(bin_edges)) + + # _, grid, _ = histogram(x, n_bins, range_hist=(xmin, xmax)) + + scotts_factor = len_x ** (-0.2) + kern_nx = int(scotts_factor * 2 * np.pi * log_len_x) + kernel = gaussian(kern_nx, scotts_factor * log_len_x) + + npad = min(n_bins, 2 * kern_nx) + grid = np.concatenate([grid[npad:0:-1], grid, grid[n_bins : n_bins - npad : -1]]) + density = convolve(grid, kernel, mode="same", method="direct")[npad : npad + n_bins] + norm_factor = (2 * np.pi * log_len_x ** 2 * scotts_factor ** 2) ** 0.5 + + density /= norm_factor + + if cumulative: + density = density.cumsum() / density.sum() + + return density, xmin, xmax + + +def _cov_1d(x): + x = x - x.mean(axis=0) + ddof = x.shape[0] - 1 + return np.dot(x.T, x.conj()) / ddof + + +def _cov(data): + if data.ndim == 1: + return _cov_1d(data) + elif data.ndim == 2: + x = data.astype(float) + avg, _ = np.average(x, axis=1, weights=None, returned=True) + ddof = x.shape[1] - 1 + if ddof <= 0: + warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) + ddof = 0.0 + x -= avg[:, None] + prod = _dot(x, x.T.conj()) + prod *= np.true_divide(1, ddof) + return prod.squeeze() + else: + raise ValueError("{} dimension arrays are not supported".format(data.ndim)) + + +@conditional_jit(cache=True) +def _dot(x, y): + return np.dot(x, y) + + +def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): + """ + 2D fft-based Gaussian kernel density estimate (KDE). + + The code was adapted from https://github.com/mfouesneau/faststats + + Parameters + ---------- + x : Numpy array or list + y : Numpy array or list + gridsize : tuple + Number of points used to discretize data. Use powers of 2 for fft optimization + circular: bool + If True, use circular boundaries. Defaults to False + Returns + ------- + grid: A gridded 2D KDE of the input points (x, y) + xmin: minimum value of x + xmax: maximum value of x + ymin: minimum value of y + ymax: maximum value of y + """ + x = np.asarray(x, dtype=float) + x = x[np.isfinite(x)] + y = np.asarray(y, dtype=float) + y = y[np.isfinite(y)] + + xmin, xmax = x.min(), x.max() + ymin, ymax = y.min(), y.max() + + len_x = len(x) + weights = np.ones(len_x) + n_x, n_y = gridsize + + d_x = (xmax - xmin) / (n_x - 1) + d_y = (ymax - ymin) / (n_y - 1) + + xyi = _stack(x, y).T + xyi -= [xmin, ymin] + xyi /= [d_x, d_y] + xyi = np.floor(xyi, xyi).T + + scotts_factor = len_x ** (-1 / 6) + cov = _cov(xyi) + std_devs = np.diag(cov ** 0.5) + kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) + + inv_cov = np.linalg.inv(cov * scotts_factor ** 2) + + x_x = np.arange(kern_nx) - kern_nx / 2 + y_y = np.arange(kern_ny) - kern_ny / 2 + x_x, y_y = np.meshgrid(x_x, y_y) + + kernel = _stack(x_x.flatten(), y_y.flatten()) + kernel = _dot(inv_cov, kernel) * kernel + kernel = np.exp(-kernel.sum(axis=0) / 2) + kernel = kernel.reshape((int(kern_ny), int(kern_nx))) + + boundary = "wrap" if circular else "symm" + + grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() + grid = convolve2d(grid, kernel, mode="same", boundary=boundary) + + norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor ** 2) + norm_factor = len_x * d_x * d_y * norm_factor ** 0.5 + + grid /= norm_factor + + return grid, xmin, xmax, ymin, ymax diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -22,7 +22,7 @@ def plot_posterior( credible_interval=0.94, multimodal=False, round_to: Optional[int] = None, - point_estimate="mean", + point_estimate="auto", group="posterior", rope=None, ref_val=None, @@ -58,8 +58,9 @@ def plot_posterior( multimodal and the modes are well separated. round_to : int, optional Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger. - point_estimate: str - Must be in ('mode', 'mean', 'median', None) + point_estimate : Optional[str] + Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. + Defaults to 'auto' i.e. it falls back to default set in rcParams. group : str, optional Specifies which InferenceData group should be plotted. Defaults to ‘posterior’. rope: tuple or dictionary of tuples diff --git a/arviz/stats/diagnostics.py b/arviz/stats/diagnostics.py --- a/arviz/stats/diagnostics.py +++ b/arviz/stats/diagnostics.py @@ -46,6 +46,7 @@ def bfmi(data): Examples -------- Compute the BFMI of an InferenceData object + .. ipython:: In [1]: import arviz as az @@ -526,9 +527,9 @@ def _bfmi(energy): energy_mat = np.atleast_2d(energy) num = np.square(np.diff(energy_mat, axis=1)).mean(axis=1) # pylint: disable=no-member if energy_mat.ndim == 2: - den = _numba_var(svar, np.var, energy_mat, axis=1, ddof=0) + den = _numba_var(svar, np.var, energy_mat, axis=1, ddof=1) else: - den = np.var(energy, axis=1) + den = np.var(energy, axis=1, ddof=1) return num / den diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -12,9 +12,8 @@ from scipy.optimize import minimize import xarray as xr +from ..plots.plot_utils import _fast_kde, get_bins from ..data import convert_to_inference_data, convert_to_dataset, InferenceData, CoordSpec, DimSpec -from ..plots.kdeplot import _fast_kde -from ..plots.plot_utils import get_bins from .diagnostics import _multichain_statistics, _mc_error, ess, _circular_standard_deviation from .stats_utils import ( make_ufunc as _make_ufunc, @@ -22,9 +21,9 @@ logsumexp as _logsumexp, ELPDData, stats_variance_2d as svar, + histogram, ) from ..utils import _var_names, Numba, _numba_var -from ..stats.stats_utils import histogram from ..rcparams import rcParams _log = logging.getLogger(__name__)

diff --git a/arviz/tests/test_diagnostics.py b/arviz/tests/test_diagnostics.py --- a/arviz/tests/test_diagnostics.py +++ b/arviz/tests/test_diagnostics.py @@ -45,7 +45,7 @@ def data(): class TestDiagnostics: def test_bfmi(self): energy = np.array([1, 2, 3, 4]) - assert_almost_equal(bfmi(energy), 0.8) + assert_almost_equal(bfmi(energy), 0.6) def test_bfmi_dataset(self): data = load_arviz_data("centered_eight") diff --git a/arviz/tests/test_plots_bokeh.py b/arviz/tests/test_plots_bokeh.py --- a/arviz/tests/test_plots_bokeh.py +++ b/arviz/tests/test_plots_bokeh.py @@ -883,7 +883,7 @@ def test_plot_ppc_ax(models, kind): {"rope": {"mu": [{"rope": (-2, 2)}], "theta": [{"school": "Choate", "rope": (2, 4)}]}}, {"point_estimate": "mode"}, {"point_estimate": "median"}, - {"point_estimate": False}, + {"point_estimate": None}, {"ref_val": 0}, {"ref_val": None}, {"ref_val": {"mu": [{"ref_val": 1}]}}, diff --git a/arviz/tests/test_plots_matplotlib.py b/arviz/tests/test_plots_matplotlib.py --- a/arviz/tests/test_plots_matplotlib.py +++ b/arviz/tests/test_plots_matplotlib.py @@ -33,7 +33,6 @@ plot_violin, plot_compare, plot_kde, - _fast_kde, plot_khat, plot_hpd, plot_dist, @@ -42,7 +41,7 @@ plot_loo_pit, plot_mcse, ) -from ..plots.kdeplot import _cov +from ..plots.plot_utils import _fast_kde, _cov rcParams["data.load"] = "eager" @@ -657,7 +656,7 @@ def test_plot_rank(models, kwargs): {"rope": {"mu": [{"rope": (-2, 2)}], "theta": [{"school": "Choate", "rope": (2, 4)}]}}, {"point_estimate": "mode"}, {"point_estimate": "median"}, - {"point_estimate": False}, + {"point_estimate": None}, {"ref_val": 0}, {"ref_val": None}, {"ref_val": {"mu": [{"ref_val": 1}]}},

integrate point_estimate rcParam ## Tell us about it `plot.point_estimate` has a default value in rcParams which is not yet used. Some plotting functions include a `point_estimate` kwarg to summarize the distribution and help its interpretation. `point_estimate` can take 4 possible values: mean, median, mode or `None`. Part of #792 ## Thoughts on implementation `point_estimate` is currently hardcoded to have mean as its default, however, it would be interesting to change this default on a user or project basis. Appearances of `point_estimate` as argument should be set to have `"auto"` as a default, and in this case, to get the value from rcParams. This would allow users to still use `point_estimate=None` to indicate no point is to be plotted.

I would like to work on this :) As suggested by @OriolAbril , I am working on this issue. Oh I wasn't aware of that. Please go ahead. Hey @OriolAbril , I only found densityplot() and posteriorplot() functions to have plot.point_estimate kwarg. Are there other plotting function that need to be changed?

2020-01-13T20:37:53Z

arviz-devs/arviz

arviz-devs__arviz-1003

5daff6a590f5b3ce090f87f03382e9cefc8287b7

diff --git a/arviz/plots/backends/__init__.py b/arviz/plots/backends/__init__.py --- a/arviz/plots/backends/__init__.py +++ b/arviz/plots/backends/__init__.py @@ -63,12 +63,12 @@ def to_cds( ------- bokeh.models.ColumnDataSource object """ - from ...utils import flat_inference_data_to_dict + from ...utils import flatten_inference_data_to_dict if var_name_format is None: var_name_format = "cds" - cds_dict = flat_inference_data_to_dict( + cds_dict = flatten_inference_data_to_dict( data=data, var_names=var_names, groups=groups, diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -325,7 +325,7 @@ def full(shape, x, dtype=None): return np.full(shape, x, dtype=dtype) -def flat_inference_data_to_dict( +def flatten_inference_data_to_dict( data, var_names=None, groups=None,

diff --git a/arviz/tests/test_utils.py b/arviz/tests/test_utils.py --- a/arviz/tests/test_utils.py +++ b/arviz/tests/test_utils.py @@ -16,7 +16,7 @@ one_de, two_de, expand_dims, - flat_inference_data_to_dict, + flatten_inference_data_to_dict, ) from ..data import load_arviz_data, from_dict from ..stats.stats_utils import stats_variance_2d as svar @@ -275,11 +275,11 @@ def test_expand_dims(data): "var_name_format", [None, "brackets", "underscore", "cds", ((",", "[", "]"), ("_", ""))] ) @pytest.mark.parametrize("index_origin", [None, 0, 1]) -def test_flat_inference_data_to_dict( +def test_flatten_inference_data_to_dict( inference_data, var_names, groups, dimensions, group_info, var_name_format, index_origin ): """Test flattening (stacking) inference data (subgroups) for dictionary.""" - res_dict = flat_inference_data_to_dict( + res_dict = flatten_inference_data_to_dict( data=inference_data, var_names=var_names, groups=groups,

Rename flat_inference_data_to_dict Rename `flat_inference_data_to_dict` to `flatten_inference_data_to_dict`. Any opinions?

I would like to work on it, if it's fine :)

2020-01-15T07:20:25Z

arviz-devs/arviz

arviz-devs__arviz-1019

eb45dfb54d03da5e64690ac2cf0cb63983030377

diff --git a/arviz/plots/autocorrplot.py b/arviz/plots/autocorrplot.py --- a/arviz/plots/autocorrplot.py +++ b/arviz/plots/autocorrplot.py @@ -19,6 +19,7 @@ def plot_autocorr( textsize=None, ax=None, backend=None, + backend_config=None, backend_kwargs=None, show=None, ): @@ -49,7 +50,9 @@ def plot_autocorr( Matplotlib axes or bokeh figures. backend: str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_kwargs: bool, optional + backend_config: dict, optional + Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. + backend_kwargs: dict, optional These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. show : bool, optional @@ -129,6 +132,7 @@ def plot_autocorr( autocorr_plot_args.pop("xt_labelsize") autocorr_plot_args.pop("titlesize") autocorr_plot_args["line_width"] = autocorr_plot_args.pop("linewidth") + autocorr_plot_args.update(backend_config=backend_config) # TODO: Add backend kwargs plot = get_plotting_function("plot_autocorr", "autocorrplot", backend) diff --git a/arviz/plots/backends/bokeh/autocorrplot.py b/arviz/plots/backends/bokeh/autocorrplot.py --- a/arviz/plots/backends/bokeh/autocorrplot.py +++ b/arviz/plots/backends/bokeh/autocorrplot.py @@ -2,6 +2,7 @@ import bokeh.plotting as bkp import numpy as np from bokeh.layouts import gridplot +from bokeh.models import DataRange1d from bokeh.models.annotations import Title from . import backend_kwarg_defaults, backend_show @@ -10,9 +11,30 @@ def plot_autocorr( - axes, plotters, max_lag, figsize, rows, cols, line_width, combined, backend_kwargs, show, + axes, + plotters, + max_lag, + figsize, + rows, + cols, + line_width, + combined, + backend_config, + backend_kwargs, + show, ): """Bokeh autocorrelation plot.""" + if backend_config is None: + backend_config = {} + + len_y = plotters[0][2].size + backend_config.setdefault("bounds_x_range", (0, len_y)) + + backend_config = { + **backend_kwarg_defaults(("bounds_y_range", "plot.bokeh.bounds_y_range"),), + **backend_config, + } + if backend_kwargs is None: backend_kwargs = {} @@ -36,6 +58,13 @@ def plot_autocorr( else: axes = np.atleast_2d(axes) + data_range_x = DataRange1d( + start=0, end=max_lag, bounds=backend_config["bounds_x_range"], min_interval=5 + ) + data_range_y = DataRange1d( + start=-1, end=1, bounds=backend_config["bounds_y_range"], min_interval=0.1 + ) + for (var_name, selection, x), ax in zip( plotters, (item for item in axes.flatten() if item is not None) ): @@ -57,12 +86,8 @@ def plot_autocorr( title = Title() title.text = make_label(var_name, selection) ax.title = title - - if axes.size > 0: - axes[0, 0].x_range._property_values["start"] = 0 # pylint: disable=protected-access - axes[0, 0].x_range._property_values["end"] = max_lag # pylint: disable=protected-access - axes[0, 0].y_range._property_values["start"] = -1 # pylint: disable=protected-access - axes[0, 0].y_range._property_values["end"] = 1 # pylint: disable=protected-access + ax.x_range = data_range_x + ax.y_range = data_range_y if backend_show(show): bkp.show(gridplot(axes.tolist(), toolbar_location="above")) diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -7,7 +7,7 @@ import matplotlib.pyplot as plt import numpy as np from bokeh.layouts import gridplot -from bokeh.models import Band, ColumnDataSource +from bokeh.models import Band, ColumnDataSource, DataRange1d from bokeh.models.annotations import Title from bokeh.models.tickers import FixedTicker @@ -49,6 +49,7 @@ def plot_forest( textsize, ess, r_hat, + backend_config, backend_kwargs, show, ): @@ -68,6 +69,17 @@ def plot_forest( if markersize is None: markersize = auto_markersize + if backend_config is None: + backend_config = {} + + backend_config = { + **backend_kwarg_defaults( + ("bounds_x_range", "plot.bokeh.bounds_x_range"), + ("bounds_y_range", "plot.bokeh.bounds_y_range"), + ), + **backend_config, + } + if backend_kwargs is None: backend_kwargs = {} @@ -138,6 +150,8 @@ def plot_forest( ax_.yaxis.visible = False ax_.outline_line_color = None + ax_.x_range = DataRange1d(bounds=backend_config["bounds_x_range"], min_interval=1) + ax_.y_range = DataRange1d(bounds=backend_config["bounds_y_range"], min_interval=2) labels, ticks = plot_handler.labels_and_ticks() diff --git a/arviz/plots/backends/bokeh/parallelplot.py b/arviz/plots/backends/bokeh/parallelplot.py --- a/arviz/plots/backends/bokeh/parallelplot.py +++ b/arviz/plots/backends/bokeh/parallelplot.py @@ -1,13 +1,27 @@ """Bokeh Parallel coordinates plot.""" import bokeh.plotting as bkp import numpy as np +from bokeh.models import DataRange1d from bokeh.models.tickers import FixedTicker from . import backend_kwarg_defaults, backend_show -def plot_parallel(ax, diverging_mask, _posterior, var_names, figsize, backend_kwargs, show): +def plot_parallel( + ax, diverging_mask, _posterior, var_names, figsize, backend_config, backend_kwargs, show +): """Bokeh parallel plot.""" + if backend_config is None: + backend_config = {} + + backend_config = { + **backend_kwarg_defaults( + ("bounds_x_range", "plot.bokeh.bounds_x_range"), + ("bounds_y_range", "plot.bokeh.bounds_y_range"), + ), + **backend_config, + } + if backend_kwargs is None: backend_kwargs = {} @@ -21,7 +35,12 @@ def plot_parallel(ax, diverging_mask, _posterior, var_names, figsize, backend_kw } dpi = backend_kwargs.pop("dpi") if ax is None: - ax = bkp.figure(width=int(figsize[0] * dpi), height=int(figsize[1] * dpi), **backend_kwargs) + ax = bkp.figure( + width=int(figsize[0] * dpi), + height=int(figsize[1] * dpi), + toolbar_location="above", + **backend_kwargs + ) non_div = list(_posterior[:, ~diverging_mask].T) x_non_div = [list(range(len(non_div[0]))) for _ in range(len(non_div))] @@ -39,6 +58,9 @@ def plot_parallel(ax, diverging_mask, _posterior, var_names, figsize, backend_kw ax.xaxis.major_label_overrides = dict(zip(map(str, range(len(var_names))), map(str, var_names))) ax.xaxis.major_label_orientation = np.pi / 2 + ax.x_range = DataRange1d(bounds=backend_config["bounds_x_range"], min_interval=2) + ax.y_range = DataRange1d(bounds=backend_config["bounds_y_range"], min_interval=5) + if backend_show(show): bkp.show(ax) diff --git a/arviz/plots/backends/bokeh/traceplot.py b/arviz/plots/backends/bokeh/traceplot.py --- a/arviz/plots/backends/bokeh/traceplot.py +++ b/arviz/plots/backends/bokeh/traceplot.py @@ -6,7 +6,7 @@ import bokeh.plotting as bkp import numpy as np from bokeh.layouts import gridplot -from bokeh.models import ColumnDataSource, Dash, Span +from bokeh.models import ColumnDataSource, Dash, Span, DataRange1d from bokeh.models.annotations import Title from . import backend_kwarg_defaults, backend_show @@ -32,6 +32,7 @@ def plot_trace( plotters, divergence_data, colors, + backend_config, backend_kwargs: [Dict], show, ): @@ -40,6 +41,14 @@ def plot_trace( if divergences is not False: assert divergence_data is not None + if backend_config is None: + backend_config = {} + + backend_config = { + **backend_kwarg_defaults(("bounds_y_range", "plot.bokeh.bounds_y_range"),), + **backend_config, + } + # Set plot default backend kwargs if backend_kwargs is None: backend_kwargs = {} @@ -179,6 +188,9 @@ def plot_trace( _title = Title() _title.text = make_label(var_name, selection) axes[idx, col].title = _title + axes[idx, col].y_range = DataRange1d( + bounds=backend_config["bounds_y_range"], min_interval=0.1 + ) for _, _, vlines in (j for j in lines if j[0] == var_name and j[1] == selection): if isinstance(vlines, (float, int)): diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -27,6 +27,7 @@ def plot_forest( figsize=None, ax=None, backend=None, + backend_config=None, backend_kwargs=None, show=None, ): @@ -92,6 +93,8 @@ def plot_forest( Matplotlib axes or bokeh figures. backend: str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + backend_config: dict, optional + Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. backend_kwargs: bool, optional These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. @@ -187,6 +190,9 @@ def plot_forest( show=show, ) + if backend == "bokeh": + plot_forest_kwargs.update(backend_config=backend_config) + # TODO: Add backend kwargs plot = get_plotting_function("plot_forest", "forestplot", backend) axes = plot(**plot_forest_kwargs) diff --git a/arviz/plots/parallelplot.py b/arviz/plots/parallelplot.py --- a/arviz/plots/parallelplot.py +++ b/arviz/plots/parallelplot.py @@ -21,6 +21,7 @@ def plot_parallel( ax=None, norm_method=None, backend=None, + backend_config=None, backend_kwargs=None, show=None, ): @@ -60,6 +61,8 @@ def plot_parallel( Defaults to none. backend: str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + backend_config: dict, optional + Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. backend_kwargs: bool, optional These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. @@ -151,6 +154,7 @@ def plot_parallel( parallel_kwargs.pop("colord") parallel_kwargs.pop("colornd") parallel_kwargs.pop("shadend") + parallel_kwargs.update(backend_config=backend_config) # TODO: Add backend kwargs plot = get_plotting_function("plot_parallel", "parallelplot", backend) diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -27,6 +27,7 @@ def plot_trace( hist_kwargs=None, trace_kwargs=None, backend=None, + backend_config=None, backend_kwargs=None, show=None, ): @@ -73,6 +74,8 @@ def plot_trace( Extra keyword arguments passed to `plt.plot` backend: str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + backend_config: dict, optional + Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. backend_kwargs: bool, optional These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. @@ -214,6 +217,9 @@ def plot_trace( show=show, ) + if backend == "bokeh": + trace_plot_args.update(backend_config=backend_config) + plot = get_plotting_function("plot_trace", "traceplot", backend) axes = plot(**trace_plot_args) diff --git a/arviz/rcparams.py b/arviz/rcparams.py --- a/arviz/rcparams.py +++ b/arviz/rcparams.py @@ -7,6 +7,7 @@ import logging import locale from collections.abc import MutableMapping +import numpy as np _log = logging.getLogger(__name__) @@ -78,6 +79,14 @@ def _validate_float(value): return value +def _validate_float_or_none(value): + """Validate value is a float or None.""" + if value is None or isinstance(value, str) and value.lower() == "none": + return None + else: + return _validate_float(value) + + def _validate_probability(value): """Validate a probability: a float between 0 and 1.""" value = _validate_float(value) @@ -93,11 +102,39 @@ def _validate_boolean(value): return value is True or value == "true" +def make_iterable_validator(scalar_validator, length=None, allow_none=False, allow_auto=False): + """Validate value is an iterable datatype.""" + # based on matplotlib's _listify_validator function + + def validate_iterable(value): + if allow_none and (value is None or isinstance(value, str) and value.lower() == "none"): + return None + if isinstance(value, str): + if allow_auto and value.lower() == "auto": + return "auto" + value = tuple(v.strip("([ ])") for v in value.split(",") if v.strip()) + if np.iterable(value) and not isinstance(value, (set, frozenset)): + val = tuple(scalar_validator(v) for v in value) + if length is not None and len(val) != length: + raise ValueError("Iterable must be of length: {}".format(length)) + return val + raise ValueError("Only ordered iterable values are valid") + + return validate_iterable + + +_validate_bokeh_bounds = make_iterable_validator( # pylint: disable=invalid-name + _validate_float_or_none, length=2, allow_none=True, allow_auto=True +) + + defaultParams = { # pylint: disable=invalid-name "data.http_protocol": ("https", _make_validate_choice({"https", "http"})), "data.load": ("lazy", _make_validate_choice({"lazy", "eager"})), "data.index_origin": (0, _make_validate_choice({0, 1}, typeof=int)), "plot.backend": ("matplotlib", _make_validate_choice({"matplotlib", "bokeh"})), + "plot.bokeh.bounds_x_range": ("auto", _validate_bokeh_bounds), + "plot.bokeh.bounds_y_range": ("auto", _validate_bokeh_bounds), "plot.bokeh.tools": ( "pan,box_zoom,wheel_zoom,box_select,lasso_select,undo,redo,reset,save,hover", lambda x: x,

diff --git a/arviz/tests/test_rcparams.py b/arviz/tests/test_rcparams.py --- a/arviz/tests/test_rcparams.py +++ b/arviz/tests/test_rcparams.py @@ -11,6 +11,8 @@ rcParams, rc_context, _make_validate_choice, + make_iterable_validator, + _validate_float_or_none, _validate_positive_int_or_none, _validate_probability, read_rcfile, @@ -135,6 +137,59 @@ def test_make_validate_choice(args, allow_none, typeof): assert value in accepted_values or value is None [email protected]("allow_none", (True, False)) [email protected]("allow_auto", (True, False)) [email protected]("value", [(1, 2), "auto", None, "(1, 4)"]) +def test_make_iterable_validator_none_auto(value, allow_auto, allow_none): + scalar_validator = _validate_float_or_none + validate_iterable = make_iterable_validator( + scalar_validator, allow_auto=allow_auto, allow_none=allow_none + ) + raise_error = False + if value is None and not allow_none: + raise_error = "Only ordered iterable" + if value == "auto" and not allow_auto: + raise_error = "Could not convert" + if raise_error: + with pytest.raises(ValueError, match=raise_error): + validate_iterable(value) + else: + value = validate_iterable(value) + assert np.iterable(value) or value is None or value == "auto" + + [email protected]("length", (2, None)) [email protected]("value", [(1, 5), (1, 3, 5), "(3, 4, 5)"]) +def test_make_iterable_validator_length(value, length): + scalar_validator = _validate_float_or_none + validate_iterable = make_iterable_validator(scalar_validator, length=length) + raise_error = False + if length is not None and len(value) != length: + raise_error = "Iterable must be of length" + if raise_error: + with pytest.raises(ValueError, match=raise_error): + validate_iterable(value) + else: + value = validate_iterable(value) + assert np.iterable(value) + + [email protected]( + "args", + [ + ("Only ordered iterable", set(["a", "b", "c"])), + ("Could not convert", "johndoe"), + ("Only ordered iterable", 15), + ], +) +def test_make_iterable_validator_illegal(args): + scalar_validator = _validate_float_or_none + validate_iterable = make_iterable_validator(scalar_validator) + raise_error, value = args + with pytest.raises(ValueError, match=raise_error): + validate_iterable(value) + + @pytest.mark.parametrize( "args", [("Only positive", -1), ("Could not convert", "1.3"), (False, "2"), (False, None), (False, 1)],

Add (pan/zoom) limits for Bokeh Some plots might need suitable panning / zoom limits. Also we can restrict movement on an axis. (Move only on x axis) I'm not sure can we limit zooming.

Is this resource helpful? https://stackoverflow.com/questions/23277066/limit-bokeh-plot-pan-to-defined-range Yes, that is one way to do it. We should probably list the plots that needs these limits (either x-axis, y-axis or both). Some plots don't need these limits (pair_plot for example) Which plots do you think need this feature? I'll be happy to solve if you can assist me @ahartikainen @OriolAbril :) min-max for x and y: - autocorrplot - forestplot - parallelplot - traceplot (second axis) Maybe others, but let's start with those. Also there should be option to use limits or not to use. @OriolAbril should we use `backend_config` or something similar as a keyword? Sounds good So for example in autocorrplot, if we set ```axes[0, 0].x_range = DataRange1d(bounds="auto")``` and ```axes[0, 0].y_range = DataRange1d(bounds="auto")``` in line 62 [here](https://github.com/arviz-devs/arviz/blob/master/arviz/plots/backends/bokeh/autocorrplot.py#L62), will this be applicable to all the plots in the gridplot or just the first one? @OriolAbril @ahartikainen

2020-01-22T11:08:28Z

arviz-devs/arviz

arviz-devs__arviz-1043

d24d83ee940b9acbf38d8ef1d5b98ba5c2269268

diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -287,6 +287,7 @@ def is_data(name, var) -> bool: var not in self.model.deterministics and var not in self.model.observed_RVs and var not in self.model.free_RVs + and var not in self.model.potentials and (self.observations is None or name not in self.observations) )

diff --git a/arviz/tests/test_data_pymc.py b/arviz/tests/test_data_pymc.py --- a/arviz/tests/test_data_pymc.py +++ b/arviz/tests/test_data_pymc.py @@ -251,6 +251,15 @@ def test_single_observation(self): inference_data = from_pymc3(trace=trace) assert inference_data + def test_potential(self): + with pm.Model(): + x = pm.Normal("x", 0.0, 1.0) + pm.Potential("z", pm.Normal.dist(x, 1.0).logp(np.random.randn(10))) + trace = pm.sample(100, chains=2) + + inference_data = from_pymc3(trace=trace) + assert inference_data + def test_constant_data(self): with pm.Model(): x = pm.Data("x", [1.0, 2.0, 3.0])

Error with PyMC3 model that contains Potential **Describe the bug** For PyMC3 model that contains Potential, io_pymc3 is attempting to call `eval()` without graph dependence. **To Reproduce** ```python with pm.Model() as m: x = pm.Normal('x', 0., 1.) pm.Potential('z', pm.Normal.dist(x, 1.).logp(np.random.randn(10))) trace = pm.sample() ``` returns: ```python --------------------------------------------------------------------------- MissingInputError Traceback (most recent call last) <ipython-input-45-c2e72dd27111> in <module> 2 x = pm.Normal('x', 0., 1.) 3 pm.Potential('z', pm.Normal.dist(x, 1.).logp(np.random.randn(10))) ----> 4 trace = pm.sample() ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/pymc3-3.8-py3.8.egg/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, **kwargs) 539 warnings.warn("The number of samples is too small to check convergence reliably.") 540 else: --> 541 trace.report._run_convergence_checks(trace, model) 542 543 trace.report._log_summary() ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/pymc3-3.8-py3.8.egg/pymc3/backends/report.py in _run_convergence_checks(self, trace, model) 96 varnames.append(rv_name) 97 ---> 98 self._ess = ess = ess(trace, var_names=varnames) 99 self._rhat = rhat = rhat(trace, var_names=varnames) 100 ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/pymc3-3.8-py3.8.egg/pymc3/stats/__init__.py in wrapped(*args, **kwargs) 36 ) 37 kwargs[new] = kwargs.pop(old) ---> 38 return func(*args, **kwargs) 39 40 return wrapped ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/stats/diagnostics.py in ess(data, var_names, method, relative, prob) 187 raise TypeError(msg) 188 --> 189 dataset = convert_to_dataset(data, group="posterior") 190 var_names = _var_names(var_names, dataset) 191 ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/converters.py in convert_to_dataset(obj, group, coords, dims) 166 xarray.Dataset 167 """ --> 168 inference_data = convert_to_inference_data(obj, group=group, coords=coords, dims=dims) 169 dataset = getattr(inference_data, group, None) 170 if dataset is None: ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/converters.py in convert_to_inference_data(obj, group, coords, dims, **kwargs) 87 return from_pystan(**kwargs) 88 elif obj.__class__.__name__ == "MultiTrace": # ugly, but doesn't make PyMC3 a requirement ---> 89 return from_pymc3(trace=kwargs.pop(group), **kwargs) 90 elif obj.__class__.__name__ == "EnsembleSampler": # ugly, but doesn't make emcee a requirement 91 return from_emcee(sampler=kwargs.pop(group), **kwargs) ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/io_pymc3.py in from_pymc3(trace, prior, posterior_predictive, coords, dims, model) 350 ): 351 """Convert pymc3 data into an InferenceData object.""" --> 352 return PyMC3Converter( 353 trace=trace, 354 prior=prior, ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/io_pymc3.py in to_inference_data(self) 342 id_dict["predictions_constant_data"] = self.constant_data_to_xarray() 343 else: --> 344 id_dict["constant_data"] = self.constant_data_to_xarray() 345 return InferenceData(**id_dict) 346 ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/base.py in wrapped(cls, *args, **kwargs) 34 if all([getattr(cls, prop_i) is None for prop_i in prop]): 35 return None ---> 36 return func(cls, *args, **kwargs) 37 38 return wrapped ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/base.py in wrapped(cls, *args, **kwargs) 34 if all([getattr(cls, prop_i) is None for prop_i in prop]): 35 return None ---> 36 return func(cls, *args, **kwargs) 37 38 return wrapped ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/arviz-0.6.1-py3.8.egg/arviz/data/io_pymc3.py in constant_data_to_xarray(self) 309 # this might be a Deterministic, and must be evaluated 310 elif hasattr(self.model[name], "eval"): --> 311 vals = self.model[name].eval() 312 vals = np.atleast_1d(vals) 313 val_dims = dims.get(name) ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/gof/graph.py in eval(self, inputs_to_values) 520 inputs = tuple(sorted(inputs_to_values.keys(), key=id)) 521 if inputs not in self._fn_cache: --> 522 self._fn_cache[inputs] = theano.function(inputs, self) 523 args = [inputs_to_values[param] for param in inputs] 524 ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/compile/function.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input) 304 # note: pfunc will also call orig_function -- orig_function is 305 # a choke point that all compilation must pass through --> 306 fn = pfunc(params=inputs, 307 outputs=outputs, 308 mode=mode, ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/compile/pfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys) 481 inputs.append(si) 482 --> 483 return orig_function(inputs, cloned_outputs, mode, 484 accept_inplace=accept_inplace, name=name, 485 profile=profile, on_unused_input=on_unused_input, ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/compile/function_module.py in orig_function(inputs, outputs, mode, accept_inplace, name, profile, on_unused_input, output_keys) 1830 try: 1831 Maker = getattr(mode, 'function_maker', FunctionMaker) -> 1832 m = Maker(inputs, 1833 outputs, 1834 mode, ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/compile/function_module.py in __init__(self, inputs, outputs, mode, accept_inplace, function_builder, profile, on_unused_input, fgraph, output_keys, name) 1484 # make the fgraph (copies the graph, creates NEW INPUT AND 1485 # OUTPUT VARIABLES) -> 1486 fgraph, additional_outputs = std_fgraph(inputs, outputs, 1487 accept_inplace) 1488 fgraph.profile = profile ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/compile/function_module.py in std_fgraph(input_specs, output_specs, accept_inplace) 178 orig_outputs = [spec.variable for spec in output_specs] + updates 179 --> 180 fgraph = gof.fg.FunctionGraph(orig_inputs, orig_outputs, 181 update_mapping=update_mapping) 182 ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/gof/fg.py in __init__(self, inputs, outputs, features, clone, update_mapping) 173 174 for output in outputs: --> 175 self.__import_r__(output, reason="init") 176 for i, output in enumerate(outputs): 177 output.clients.append(('output', i)) ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/gof/fg.py in __import_r__(self, variable, reason) 344 # Imports the owners of the variables 345 if variable.owner and variable.owner not in self.apply_nodes: --> 346 self.__import__(variable.owner, reason=reason) 347 elif (variable.owner is None and 348 not isinstance(variable, graph.Constant) and ~/anaconda3/envs/pymc3/lib/python3.8/site-packages/Theano-1.0.4-py3.8.egg/theano/gof/fg.py in __import__(self, apply_node, check, reason) 389 "for more information on this error." 390 % (node.inputs.index(r), str(node))) --> 391 raise MissingInputError(error_msg, variable=r) 392 393 for node in new_nodes: MissingInputError: Input 0 of the graph (indices start from 0), used to compute InplaceDimShuffle{x}(x), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error. ```

FWIW, `pm.Potential` are factors added to the log_prob, we should not consider it in the trace and it is not observed. It is safe to exclude these.

2020-02-04T06:04:52Z

arviz-devs/arviz

arviz-devs__arviz-1070

d57b851db7255ce21a8a3fa56c9daf8d0ed58cb6

diff --git a/arviz/plots/backends/bokeh/traceplot.py b/arviz/plots/backends/bokeh/traceplot.py --- a/arviz/plots/backends/bokeh/traceplot.py +++ b/arviz/plots/backends/bokeh/traceplot.py @@ -23,6 +23,7 @@ def plot_trace( rug, lines, combined, + chain_prop, legend, plot_kwargs: [Dict], fill_kwargs: [Dict], @@ -31,7 +32,7 @@ def plot_trace( trace_kwargs: [Dict], plotters, divergence_data, - colors, + axes, backend_config, backend_kwargs: [Dict], show, @@ -67,16 +68,17 @@ def plot_trace( trace_kwargs.setdefault("line_width", linewidth) plot_kwargs.setdefault("line_width", linewidth) - axes = [] - for i in range(len(plotters)): - if i != 0: - _axes = [ - bkp.figure(**backend_kwargs), - bkp.figure(x_range=axes[0][1].x_range, **backend_kwargs), - ] - else: - _axes = [bkp.figure(**backend_kwargs), bkp.figure(**backend_kwargs)] - axes.append(_axes) + if axes is None: + axes = [] + for i in range(len(plotters)): + if i != 0: + _axes = [ + bkp.figure(**backend_kwargs), + bkp.figure(x_range=axes[0][1].x_range, **backend_kwargs), + ] + else: + _axes = [bkp.figure(**backend_kwargs), bkp.figure(**backend_kwargs)] + axes.append(_axes) axes = np.array(axes) @@ -149,7 +151,7 @@ def plot_trace( data=cds_data, x_name=draw_name, y_name=y_name, - colors=colors, + chain_prop=chain_prop, combined=combined, rug=rug, legend=legend, @@ -169,7 +171,7 @@ def plot_trace( data=cds_data, x_name=draw_name, y_name=y_name, - colors=colors, + chain_prop=chain_prop, combined=combined, rug=rug, legend=legend, @@ -267,7 +269,7 @@ def _plot_chains_bokeh( data, x_name, y_name, - colors, + chain_prop, combined, rug, legend, @@ -282,7 +284,11 @@ def _plot_chains_bokeh( if legend: trace_kwargs["legend_label"] = "chain {}".format(chain_idx) ax_trace.line( - x=x_name, y=y_name, source=cds, line_color=colors[chain_idx], **trace_kwargs, + x=x_name, + y=y_name, + source=cds, + **{chain_prop[0]: chain_prop[1][chain_idx]}, + **trace_kwargs, ) if marker: ax_trace.circle( @@ -290,19 +296,17 @@ def _plot_chains_bokeh( y=y_name, source=cds, radius=0.30, - line_color=colors[chain_idx], - fill_color=colors[chain_idx], alpha=0.5, + **{chain_prop[0]: chain_prop[1][chain_idx],}, ) if not combined: rug_kwargs["cds"] = cds if legend: plot_kwargs["legend_label"] = "chain {}".format(chain_idx) - plot_kwargs["line_color"] = colors[chain_idx] + plot_kwargs[chain_prop[0]] = chain_prop[1][chain_idx] plot_dist( cds.data[y_name], ax=ax_density, - color=colors[chain_idx], rug=rug, hist_kwargs=hist_kwargs, plot_kwargs=plot_kwargs, @@ -312,15 +316,16 @@ def _plot_chains_bokeh( backend_kwargs={}, show=False, ) + plot_kwargs.pop(chain_prop[0]) if combined: rug_kwargs["cds"] = data if legend: plot_kwargs["legend_label"] = "combined chains" + plot_kwargs[chain_prop[0]] = chain_prop[1][-1] plot_dist( np.concatenate([item.data[y_name] for item in data.values()]).flatten(), ax=ax_density, - color=colors[-1], rug=rug, hist_kwargs=hist_kwargs, plot_kwargs=plot_kwargs, @@ -330,3 +335,4 @@ def _plot_chains_bokeh( backend_kwargs={}, show=False, ) + plot_kwargs.pop(chain_prop[0]) diff --git a/arviz/plots/backends/matplotlib/traceplot.py b/arviz/plots/backends/matplotlib/traceplot.py --- a/arviz/plots/backends/matplotlib/traceplot.py +++ b/arviz/plots/backends/matplotlib/traceplot.py @@ -1,4 +1,5 @@ """Matplotlib traceplot.""" +from itertools import cycle import warnings import matplotlib.pyplot as plt @@ -7,7 +8,7 @@ from . import backend_kwarg_defaults, backend_show from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size, get_bins, make_label +from ...plot_utils import _scale_fig_size, get_bins, make_label, format_coords_as_labels def plot_trace( @@ -17,7 +18,9 @@ def plot_trace( figsize, rug, lines, + compact_prop, combined, + chain_prop, legend, plot_kwargs, fill_kwargs, @@ -26,7 +29,7 @@ def plot_trace( trace_kwargs, plotters, divergence_data, - colors, + axes, backend_kwargs, show, ): @@ -123,7 +126,8 @@ def plot_trace( trace_kwargs.setdefault("linewidth", linewidth) plot_kwargs.setdefault("linewidth", linewidth) - _, axes = plt.subplots(len(plotters), 2, squeeze=False, figsize=figsize, **backend_kwargs) + if axes is None: + _, axes = plt.subplots(len(plotters), 2, squeeze=False, figsize=figsize, **backend_kwargs) # Check the input for lines if lines is not None: @@ -144,12 +148,15 @@ def plot_trace( value = np.atleast_2d(value) if len(value.shape) == 2: + if compact_prop: + plot_kwargs[compact_prop[0]] = compact_prop[1][0] + trace_kwargs[compact_prop[0]] = compact_prop[1][0] _plot_chains_mpl( axes, idx, value, data, - colors, + chain_prop, combined, xt_labelsize, rug, @@ -159,15 +166,34 @@ def plot_trace( fill_kwargs, rug_kwargs, ) + if compact_prop: + plot_kwargs.pop(compact_prop[0]) + trace_kwargs.pop(compact_prop[0]) else: + sub_data = data[var_name].sel(**selection) + legend_labels = format_coords_as_labels(sub_data, skip_dims=("chain", "draw")) + legend_title = ", ".join( + [ + "{}".format(coord_name) + for coord_name in sub_data.coords + if coord_name not in {"chain", "draw"} + ] + ) value = value.reshape((value.shape[0], value.shape[1], -1)) - for sub_idx in range(value.shape[2]): + compact_prop_cycle = cycle(compact_prop[1]) + handles = [] + for sub_idx, label, prop in zip( + range(value.shape[2]), legend_labels, compact_prop_cycle + ): + if compact_prop: + plot_kwargs[compact_prop[0]] = prop + trace_kwargs[compact_prop[0]] = prop _plot_chains_mpl( axes, idx, value[..., sub_idx], data, - colors, + chain_prop, combined, xt_labelsize, rug, @@ -177,6 +203,16 @@ def plot_trace( fill_kwargs, rug_kwargs, ) + if legend: + handles.append( + Line2D( + [], [], label=label, **{chain_prop[0]: chain_prop[1][0]}, **plot_kwargs + ) + ) + if legend: + axes[idx, 0].legend(handles=handles, title=legend_title) + plot_kwargs.pop(compact_prop[0], None) + trace_kwargs.pop(compact_prop[0], None) if value[0].dtype.kind == "i": xticks = get_bins(value) @@ -247,12 +283,18 @@ def plot_trace( axes[idx, 1].set_xlim(left=data.draw.min(), right=data.draw.max()) axes[idx, 1].set_ylim(*ylims[1]) if legend: + legend_kwargs = trace_kwargs if combined else plot_kwargs handles = [ - Line2D([], [], color=color, label=chain_id) - for chain_id, color in zip(data.chain.values, colors) + Line2D([], [], label=chain_id, **{chain_prop[0]: prop}, **legend_kwargs) + for chain_id, prop in zip(data.chain.values, chain_prop[1]) ] if combined: - handles.insert(0, Line2D([], [], color=colors[-1], label="combined")) + handles.insert( + 0, + Line2D( + [], [], label="combined", **{chain_prop[0]: chain_prop[1][-1]}, **plot_kwargs + ), + ) axes[0, 1].legend(handles=handles, title="chain") if backend_show(show): @@ -266,7 +308,7 @@ def _plot_chains_mpl( idx, value, data, - colors, + chain_prop, combined, xt_labelsize, rug, @@ -277,10 +319,12 @@ def _plot_chains_mpl( rug_kwargs, ): for chain_idx, row in enumerate(value): - axes[idx, 1].plot(data.draw.values, row, color=colors[chain_idx], **trace_kwargs) + axes[idx, 1].plot( + data.draw.values, row, **{chain_prop[0]: chain_prop[1][chain_idx]}, **trace_kwargs + ) if not combined: - plot_kwargs["color"] = colors[chain_idx] + plot_kwargs[chain_prop[0]] = chain_prop[1][chain_idx] plot_dist( values=row, textsize=xt_labelsize, @@ -293,9 +337,10 @@ def _plot_chains_mpl( backend="matplotlib", show=False, ) + plot_kwargs.pop(chain_prop[0]) if combined: - plot_kwargs["color"] = colors[-1] + plot_kwargs[chain_prop[0]] = chain_prop[1][-1] plot_dist( values=value.flatten(), textsize=xt_labelsize, @@ -308,3 +353,4 @@ def _plot_chains_mpl( backend="matplotlib", show=False, ) + plot_kwargs.pop(chain_prop[0]) diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -1,5 +1,6 @@ """Utilities for plotting.""" import warnings +from typing import Dict, Any from itertools import product, tee import importlib from scipy.signal import convolve, convolve2d @@ -17,6 +18,8 @@ from ..utils import conditional_jit, _stack from ..rcparams import rcParams +KwargSpec = Dict[str, Any] + def make_2d(ary): """Convert any array into a 2d numpy array. @@ -599,9 +602,21 @@ def color_from_dim(dataarray, dim_name): return colors, color_mapping -def format_coords_as_labels(dataarray): - """Format 1d or multi-d dataarray coords as strings.""" - coord_labels = dataarray.coords.to_index().values +def format_coords_as_labels(dataarray, skip_dims=None): + """Format 1d or multi-d dataarray coords as strings. + + Parameters + ---------- + dataarray : xarray.DataArray + DataArray whose coordinates will be converted to labels. + skip_dims : str of list_like, optional + Dimensions whose values should not be included in the labels + """ + if skip_dims is None: + coord_labels = dataarray.coords.to_index() + else: + coord_labels = dataarray.coords.to_index().droplevel(skip_dims).drop_duplicates() + coord_labels = coord_labels.values if isinstance(coord_labels[0], tuple): fmt = ", ".join(["{}" for _ in coord_labels[0]]) coord_labels[:] = [fmt.format(*x) for x in coord_labels] diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -1,36 +1,40 @@ """Plot kde or histograms and values from MCMC samples.""" from itertools import cycle import warnings +from typing import Callable, List, Optional, Tuple, Any import matplotlib.pyplot as plt -from .plot_utils import get_plotting_function, get_coords, xarray_var_iter -from ..data import convert_to_dataset +from .plot_utils import get_plotting_function, get_coords, xarray_var_iter, KwargSpec +from ..data import convert_to_dataset, InferenceData, CoordSpec from ..utils import _var_names from ..rcparams import rcParams def plot_trace( - data, - var_names=None, - transform=None, - coords=None, - divergences="bottom", - figsize=None, - rug=False, - lines=None, - compact=False, - combined=False, - legend=False, - plot_kwargs=None, - fill_kwargs=None, - rug_kwargs=None, - hist_kwargs=None, - trace_kwargs=None, - backend=None, - backend_config=None, - backend_kwargs=None, - show=None, + data: InferenceData, + var_names: Optional[List[str]] = None, + transform: Optional[Callable] = None, + coords: Optional[CoordSpec] = None, + divergences: Optional[str] = "bottom", + figsize: Optional[Tuple[float, float]] = None, + rug: bool = False, + lines: Optional[List[Tuple[str, CoordSpec, Any]]] = None, + compact: bool = False, + compact_prop: Optional[Tuple[str, Any]] = None, + combined: bool = False, + chain_prop: Optional[Tuple[str, Any]] = None, + legend: bool = False, + plot_kwargs: Optional[KwargSpec] = None, + fill_kwargs: Optional[KwargSpec] = None, + rug_kwargs: Optional[KwargSpec] = None, + hist_kwargs: Optional[KwargSpec] = None, + trace_kwargs: Optional[KwargSpec] = None, + ax=None, + backend: Optional[str] = None, + backend_config: Optional[KwargSpec] = None, + backend_kwargs: Optional[KwargSpec] = None, + show: Optional[bool] = None, ): """Plot distribution (histogram or kernel density estimates) and sampled values. @@ -42,44 +46,43 @@ def plot_trace( data : obj Any object that can be converted to an az.InferenceData object Refer to documentation of az.convert_to_dataset for details - var_names : string, or list of strings + var_names : str or list of str, optional One or more variables to be plotted. - coords : mapping, optional + coords : dict of {str: slice or array_like}, optional Coordinates of var_names to be plotted. Passed to `Dataset.sel` - divergences : {"bottom", "top", None, False} - Plot location of divergences on the traceplots. Options are "bottom", "top", or False-y. - transform : callable + divergences : {"bottom", "top", None}, optional + Plot location of divergences on the traceplots. + transform : callable, optional Function to transform data (defaults to None i.e.the identity function) - figsize : figure size tuple + figsize : tuple of (float, float), optional If None, size is (12, variables * 2) - rug : bool + rug : bool, optional If True adds a rugplot. Defaults to False. Ignored for 2D KDE. Only affects continuous variables. - lines : tuple - Tuple of (var_name, {'coord': selection}, [line, positions]) to be overplotted as + lines : list of tuple of (str, dict, array_like), optional + List of (var_name, {'coord': selection}, [line, positions]) to be overplotted as vertical lines on the density and horizontal lines on the trace. - compact : bool + compact : bool, optional Plot multidimensional variables in a single plot. - combined : bool + compact_prop : tuple of (str, array_like), optional + Tuple containing the property name and the property values to distinguish diferent + dimensions with compact=True + combined : bool, optional Flag for combining multiple chains into a single line. If False (default), chains will be plotted separately. - legend : bool + chain_prop : tuple of (str, array_like), optional + Tuple containing the property name and the property values to distinguish diferent chains + legend : bool, optional Add a legend to the figure with the chain color code. - plot_kwargs : dict + plot_kwargs, fill_kwargs, rug_kwargs, hist_kwargs : dict, optional Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. - fill_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. - rug_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. - hist_kwargs : dict - Extra keyword arguments passed to `arviz.plot_dist`. Only affects discrete variables. - trace_kwargs : dict + trace_kwargs : dict, optional Extra keyword arguments passed to `plt.plot` - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_config: dict, optional + backend : {"matplotlib", "bokeh"}, optional + Select plotting backend. + backend_config : dict, optional Currently specifies the bounds to use for bokeh axes. Defaults to value set in rcParams. - backend_kwargs: bool, optional + backend_kwargs : dict, optional These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. show : bool, optional @@ -152,15 +155,46 @@ def plot_trace( if lines is None: lines = () - num_colors = len(data.chain) + 1 if combined else len(data.chain) + num_chain_props = len(data.chain) + 1 if combined else len(data.chain) + if not compact: + if backend == "bokeh": + chain_prop = ( + ("line_color", plt.rcParams["axes.prop_cycle"].by_key()["color"]) + if chain_prop is None + else chain_prop + ) + else: + chain_prop = "color" if chain_prop is None else chain_prop + else: + chain_prop = ( + ( + "line_dash" if backend == "bokeh" else "linestyle", + ("solid", "dotted", "dashed", "dashdot"), + ) + if chain_prop is None + else chain_prop + ) + if backend == "bokeh": + compact_prop = ( + ("line_color", plt.rcParams["axes.prop_cycle"].by_key()["color"]) + if compact_prop is None + else compact_prop + ) + else: + compact_prop = "color" if compact_prop is None else compact_prop # TODO: matplotlib is always required by arviz. Can we get rid of it? - colors = [ - prop - for _, prop in zip( - range(num_colors), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ] + # TODO: kind of related: move mpl specific code to backend and + # define prop_cycle instead of only colors + if isinstance(chain_prop, str): + chain_prop = (chain_prop, plt.rcParams["axes.prop_cycle"].by_key()[chain_prop]) + chain_prop = ( + chain_prop[0], + [prop for _, prop in zip(range(num_chain_props), cycle(chain_prop[1]))], + ) + + if isinstance(compact_prop, str): + compact_prop = (compact_prop, plt.rcParams["axes.prop_cycle"].by_key()[compact_prop]) if compact: skip_dims = set(data.dims) - {"chain", "draw"} @@ -212,14 +246,15 @@ def plot_trace( rug_kwargs=rug_kwargs, hist_kwargs=hist_kwargs, trace_kwargs=trace_kwargs, - # compact = compact, + compact_prop=compact_prop, combined=combined, + chain_prop=chain_prop, legend=legend, # Generated kwargs divergence_data=divergence_data, # skip_dims=skip_dims, plotters=plotters, - colors=colors, + axes=ax, backend_kwargs=backend_kwargs, show=show, ) @@ -230,6 +265,7 @@ def plot_trace( if backend == "bokeh": trace_plot_args.update(backend_config=backend_config) + trace_plot_args.pop("compact_prop") plot = get_plotting_function("plot_trace", "traceplot", backend) axes = plot(**trace_plot_args) diff --git a/arviz/rcparams.py b/arviz/rcparams.py --- a/arviz/rcparams.py +++ b/arviz/rcparams.py @@ -196,6 +196,11 @@ def validate_iterable(value): "data.load": ("lazy", _make_validate_choice({"lazy", "eager"})), "data.index_origin": (0, _make_validate_choice({0, 1}, typeof=int)), "plot.backend": ("matplotlib", _make_validate_choice({"matplotlib", "bokeh"})), + "plot.max_subplots": (40, _validate_positive_int_or_none), + "plot.point_estimate": ( + "mean", + _make_validate_choice({"mean", "median", "mode"}, allow_none=True), + ), "plot.bokeh.bounds_x_range": ("auto", _validate_bokeh_bounds), "plot.bokeh.bounds_y_range": ("auto", _validate_bokeh_bounds), "plot.bokeh.figure.dpi": (60, _validate_positive_int), @@ -234,11 +239,6 @@ def validate_iterable(value): ), "plot.matplotlib.constrained_layout": (True, _validate_boolean), "plot.matplotlib.show": (False, _validate_boolean), - "plot.max_subplots": (40, _validate_positive_int_or_none), - "plot.point_estimate": ( - "mean", - _make_validate_choice({"mean", "median", "mode"}, allow_none=True), - ), "stats.credible_interval": (0.94, _validate_probability), "stats.information_criterion": ("loo", _make_validate_choice({"waic", "loo"})), "stats.ic_scale": ("log", _make_validate_choice({"deviance", "log", "negative_log"})),

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -846,6 +846,7 @@ def test_plot_ppc_grid(models): flatten=["obs_dim"], coords={"obs_dim": [1, 2, 3]}, backend="bokeh", + show=False, ) assert len(axes.ravel()) == 1 diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -141,7 +141,7 @@ def test_plot_density_bad_kwargs(models): {"combined": True}, {"compact": True}, {"combined": True, "compact": True, "legend": True}, - {"divergences": "top"}, + {"divergences": "top", "legend": True}, {"divergences": False}, {"lines": [("mu", {}, [1, 2])]}, {"lines": [("mu", {}, 8)]}, @@ -152,6 +152,23 @@ def test_plot_trace(models, kwargs): assert axes.shape [email protected]("compact", [True, False],) [email protected]("combined", [True, False],) +def test_plot_trace_legend(compact, combined): + idata = load_arviz_data("rugby") + axes = plot_trace( + idata, var_names=["home", "atts_star"], compact=compact, combined=combined, legend=True + ) + assert axes[0, 1].get_legend() + compact_legend = axes[1, 0].get_legend() + if compact: + assert axes.shape == (2, 2) + assert compact_legend + else: + assert axes.shape == (7, 2) + assert not compact_legend + + def test_plot_trace_discrete(discrete_model): axes = plot_trace(discrete_model) assert axes.shape @@ -302,8 +319,9 @@ def test_plot_kde(continuous_model, kwargs): assert axes [email protected]("x", [np.random.randn(8), np.random.randn(8, 8), np.random.randn(8, 8, 8)]) -def test_cov(x): [email protected]("shape", [(8,), (8, 8), (8, 8, 8)]) +def test_cov(shape): + x = np.random.randn(*shape) if x.ndim <= 2: assert np.allclose(_cov(x), np.cov(x)) else:

plot_trace with `combined=True` to have alternating colors and labels ## Tell us about it I'm very new at this, so not sure how much this problem is relevant for other users. When using `plot_trace` with `combined=True`: ```python import matplotlib as plt import arviz as az data = az.load_arviz_data('non_centered_eight') ax = az.plot_trace(data, var_names=('theta_t', 'theta'), combined=True, compact=True, figsize = (20, 8)) ``` the lines come up in the same color and without labels, so it's hard to differenciate between them  It would be great to have colors by default in the lines. And maybe an option to show labels Something like:  ## Thoughts on implementation Alternating colors - I don't see any reason not to have them. For the labels - I understand that there could be thousands of lines, so maybe have a limit, to show something like the first 5 labels, or maybe add an argument to the method with `num_labels` (and then show "..." to indicate there are more not being shown) I've been looking at https://arviz-devs.github.io/arviz/_modules/arviz/plots/traceplot.html for a while but it's way above my league. I just don't understand what's going on. Thanks for reading!

@julian-pani Apologies on missing this issue for the last couple months. The traceplot lines should show up in different colors now. I checked the latest example to verify. Are you still having this issue? https://arviz-devs.github.io/arviz/examples/matplotlib/mpl_plot_trace.html I think it is still an issue, because `plot_trace` accepts both `combined` and `compact` argument. Thus, there are 4 cases to take into account: 1. `combined = compact = False` (default case). Multidimensional variables are plotted on different subplots, and chains are not combined, thus, each kde subplot has `nchains` lines. This is the case of the example, where each line has a different color. Moreover, using `legend=True` will automatically generate a legend with the chain color code. 1. `combined = True; compact = False`. Multidimensional variables are plotted on different subplots, and chains are combined. Thus, each kde subplot has 1 line. I don't think legend would currently work in this case, but the title already gives all the information, it is not a priority to get a legend working here. 1. `combined=True; compact=True`. Multidimensional variables have all components plotted in the same subplot (this is @julian-pani's case). Thus, each subplot has as many lines as the dimension of the variable. Legend and coloring do not work because they currently only code chain information. 1. `combined=False; compact=True`. Multidimensional variables have all componensts plotted in the same subplot, and chains are not combined. Each subplot has `nchains*shape` lines. The color and legend should correctly represent the chain information, but all the information on the variable coordinate is lost (much like in the previous case). The solution would be to have: 1. Coloring and legend based only on chain. Already implemented. 1. Nothing to implement I think. 1. Coloring and legend based only on variable coordinate. **TODO** 1. Coloring based on chain and linestyle? based on coordinate (or viceversa, depending on new arg/rcparam/which is larger). I think the ideal case would be having 2 legends, one with the chain info and the other with the coordinate info. **TODO**

2020-02-16T07:11:32Z

arviz-devs/arviz

arviz-devs__arviz-1073

c1aeda17ada0992ddb4c89ad4f818dd5d2fd9a62

diff --git a/arviz/plots/backends/matplotlib/distplot.py b/arviz/plots/backends/matplotlib/distplot.py --- a/arviz/plots/backends/matplotlib/distplot.py +++ b/arviz/plots/backends/matplotlib/distplot.py @@ -4,6 +4,7 @@ from . import backend_show from ...kdeplot import plot_kde +from ...plot_utils import matplotlib_kwarg_dealiaser def plot_dist( @@ -49,9 +50,7 @@ def plot_dist( ) elif kind == "kde": - if plot_kwargs is None: - plot_kwargs = {} - + plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") plot_kwargs.setdefault("color", color) legend = label is not None diff --git a/arviz/plots/backends/matplotlib/essplot.py b/arviz/plots/backends/matplotlib/essplot.py --- a/arviz/plots/backends/matplotlib/essplot.py +++ b/arviz/plots/backends/matplotlib/essplot.py @@ -7,6 +7,7 @@ from ...plot_utils import ( make_label, _create_axes_grid, + matplotlib_kwarg_dealiaser, ) @@ -63,8 +64,7 @@ def plot_ess( ess_tail = ess_tail_dataset[var_name].sel(**selection) ax_.plot(xdata, ess_tail, **extra_kwargs) elif rug: - if rug_kwargs is None: - rug_kwargs = {} + rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") if not hasattr(idata, "sample_stats"): raise ValueError("InferenceData object must contain sample_stats for rug plot") if not hasattr(idata.sample_stats, rug_kind): diff --git a/arviz/plots/backends/matplotlib/kdeplot.py b/arviz/plots/backends/matplotlib/kdeplot.py --- a/arviz/plots/backends/matplotlib/kdeplot.py +++ b/arviz/plots/backends/matplotlib/kdeplot.py @@ -4,7 +4,7 @@ import numpy as np from . import backend_show -from ...plot_utils import _scale_fig_size +from ...plot_utils import _scale_fig_size, matplotlib_kwarg_dealiaser def plot_kde( @@ -53,19 +53,15 @@ def plot_kde( figsize, *_, xt_labelsize, linewidth, markersize = _scale_fig_size(figsize, textsize, 1, 1) if values2 is None: - if plot_kwargs is None: - plot_kwargs = {} + plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") plot_kwargs.setdefault("color", "C0") default_color = plot_kwargs.get("color") - if fill_kwargs is None: - fill_kwargs = {} - + fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin") fill_kwargs.setdefault("color", default_color) - if rug_kwargs is None: - rug_kwargs = {} + rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") rug_kwargs.setdefault("marker", "_" if rotated else "|") rug_kwargs.setdefault("linestyle", "None") rug_kwargs.setdefault("color", default_color) @@ -122,13 +118,10 @@ def plot_kde( if legend and label: ax.legend() else: - if contour_kwargs is None: - contour_kwargs = {} + contour_kwargs = matplotlib_kwarg_dealiaser(contour_kwargs, "contour") contour_kwargs.setdefault("colors", "0.5") - if contourf_kwargs is None: - contourf_kwargs = {} - if pcolormesh_kwargs is None: - pcolormesh_kwargs = {} + contourf_kwargs = matplotlib_kwarg_dealiaser(contourf_kwargs, "contour") + pcolormesh_kwargs = matplotlib_kwarg_dealiaser(pcolormesh_kwargs, "pcolormesh") # gridsize = (128, 128) if contour else (256, 256) diff --git a/arviz/plots/backends/matplotlib/mcseplot.py b/arviz/plots/backends/matplotlib/mcseplot.py --- a/arviz/plots/backends/matplotlib/mcseplot.py +++ b/arviz/plots/backends/matplotlib/mcseplot.py @@ -8,6 +8,7 @@ from ...plot_utils import ( make_label, _create_axes_grid, + matplotlib_kwarg_dealiaser, ) @@ -87,8 +88,7 @@ def plot_mcse( **text_kwargs, ) if rug: - if rug_kwargs is None: - rug_kwargs = {} + rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") if not hasattr(idata, "sample_stats"): raise ValueError("InferenceData object must contain sample_stats for rug plot") if not hasattr(idata.sample_stats, rug_kind): diff --git a/arviz/plots/distplot.py b/arviz/plots/distplot.py --- a/arviz/plots/distplot.py +++ b/arviz/plots/distplot.py @@ -1,6 +1,7 @@ # pylint: disable=unexpected-keyword-arg """Plot distribution as histogram or kernel density estimates.""" -from .plot_utils import get_bins, get_plotting_function + +from .plot_utils import get_bins, get_plotting_function, matplotlib_kwarg_dealiaser from ..rcparams import rcParams @@ -148,9 +149,7 @@ def plot_dist( kind = "hist" if values.dtype.kind == "i" else "kde" if kind == "hist": - if hist_kwargs is None: - hist_kwargs = {} - + hist_kwargs = matplotlib_kwarg_dealiaser(hist_kwargs, "hist") hist_kwargs.setdefault("bins", get_bins(values)) hist_kwargs.setdefault("cumulative", cumulative) hist_kwargs.setdefault("color", color) diff --git a/arviz/plots/elpdplot.py b/arviz/plots/elpdplot.py --- a/arviz/plots/elpdplot.py +++ b/arviz/plots/elpdplot.py @@ -5,7 +5,13 @@ from matplotlib.lines import Line2D from ..data import convert_to_inference_data -from .plot_utils import get_coords, format_coords_as_labels, color_from_dim, get_plotting_function +from .plot_utils import ( + get_coords, + format_coords_as_labels, + color_from_dim, + get_plotting_function, + matplotlib_kwarg_dealiaser, +) from ..stats import waic, loo, ELPDData from ..rcparams import rcParams @@ -146,8 +152,8 @@ def plot_elpd( if coords is None: coords = {} - if plot_kwargs is None: - plot_kwargs = {} + plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "scatter") + if backend == "bokeh": plot_kwargs.setdefault("marker", rcParams["plot.bokeh.marker"]) diff --git a/arviz/plots/energyplot.py b/arviz/plots/energyplot.py --- a/arviz/plots/energyplot.py +++ b/arviz/plots/energyplot.py @@ -4,7 +4,7 @@ import numpy as np from ..data import convert_to_dataset -from .plot_utils import _scale_fig_size, get_plotting_function +from .plot_utils import _scale_fig_size, get_plotting_function, matplotlib_kwarg_dealiaser from ..rcparams import rcParams @@ -93,11 +93,9 @@ def plot_energy( """ energy = convert_to_dataset(data, group="sample_stats").energy.values - if fill_kwargs is None: - fill_kwargs = {} - - if plot_kwargs is None: - plot_kwargs = {} + fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin") + types = "hist" if kind in {"hist", "histogram"} else "plot" + plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, types) figsize, _, _, xt_labelsize, linewidth, _ = _scale_fig_size(figsize, textsize, 1, 1) diff --git a/arviz/plots/essplot.py b/arviz/plots/essplot.py --- a/arviz/plots/essplot.py +++ b/arviz/plots/essplot.py @@ -11,6 +11,7 @@ get_coords, filter_plotters_list, get_plotting_function, + matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams from ..utils import _var_names @@ -248,14 +249,15 @@ def plot_ess( (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _markersize) = _scale_fig_size( figsize, textsize, rows, cols ) - _linestyle = kwargs.pop("ls", "-" if kind == "evolution" else "none") + kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot") + _linestyle = "-" if kind == "evolution" else "none" kwargs.setdefault("linestyle", _linestyle) - kwargs.setdefault("linewidth", kwargs.pop("lw", _linewidth)) - kwargs.setdefault("markersize", kwargs.pop("ms", _markersize)) + kwargs.setdefault("linewidth", _linewidth) + kwargs.setdefault("markersize", _markersize) kwargs.setdefault("marker", "o") kwargs.setdefault("zorder", 3) - if extra_kwargs is None: - extra_kwargs = {} + + extra_kwargs = matplotlib_kwarg_dealiaser(extra_kwargs, "plot") if kind == "evolution": extra_kwargs = { **extra_kwargs, @@ -264,28 +266,27 @@ def plot_ess( kwargs.setdefault("label", "bulk") extra_kwargs.setdefault("label", "tail") else: - extra_kwargs.setdefault("linestyle", extra_kwargs.pop("ls", "-")) - extra_kwargs.setdefault("linewidth", extra_kwargs.pop("lw", _linewidth / 2)) + extra_kwargs.setdefault("linestyle", "-") + extra_kwargs.setdefault("linewidth", _linewidth / 2) extra_kwargs.setdefault("color", "k") extra_kwargs.setdefault("alpha", 0.5) kwargs.setdefault("label", kind) - if hline_kwargs is None: - hline_kwargs = {} - hline_kwargs.setdefault("linewidth", hline_kwargs.pop("lw", _linewidth)) - hline_kwargs.setdefault("linestyle", hline_kwargs.pop("ls", "--")) - hline_kwargs.setdefault("color", hline_kwargs.pop("c", "gray")) + + hline_kwargs = matplotlib_kwarg_dealiaser(hline_kwargs, "plot") + hline_kwargs.setdefault("linewidth", _linewidth) + hline_kwargs.setdefault("linestyle", "--") + hline_kwargs.setdefault("color", "gray") hline_kwargs.setdefault("alpha", 0.7) if extra_methods: mean_ess = ess(data, var_names=var_names, method="mean", relative=relative) sd_ess = ess(data, var_names=var_names, method="sd", relative=relative) - if text_kwargs is None: - text_kwargs = {} + text_kwargs = matplotlib_kwarg_dealiaser(text_kwargs, "text") text_x = text_kwargs.pop("x", 1) - text_kwargs.setdefault("fontsize", text_kwargs.pop("size", xt_labelsize * 0.7)) + text_kwargs.setdefault("fontsize", xt_labelsize * 0.7) text_kwargs.setdefault("alpha", extra_kwargs["alpha"]) text_kwargs.setdefault("color", extra_kwargs["color"]) - text_kwargs.setdefault("horizontalalignment", text_kwargs.pop("ha", "right")) - text_va = text_kwargs.pop("verticalalignment", text_kwargs.pop("va", None)) + text_kwargs.setdefault("horizontalalignment", "right") + text_va = text_kwargs.pop("verticalalignment", None) essplot_kwargs = dict( ax=ax, diff --git a/arviz/plots/hpdplot.py b/arviz/plots/hpdplot.py --- a/arviz/plots/hpdplot.py +++ b/arviz/plots/hpdplot.py @@ -4,7 +4,7 @@ from scipy.signal import savgol_filter from ..stats import hpd -from .plot_utils import get_plotting_function +from .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser from ..rcparams import rcParams @@ -64,13 +64,11 @@ def plot_hpd( ------- axes : matplotlib axes or bokeh figures """ - if plot_kwargs is None: - plot_kwargs = {} + plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") plot_kwargs.setdefault("color", color) plot_kwargs.setdefault("alpha", 0) - if fill_kwargs is None: - fill_kwargs = {} + fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin") fill_kwargs.setdefault("color", color) fill_kwargs.setdefault("alpha", 0.5) diff --git a/arviz/plots/jointplot.py b/arviz/plots/jointplot.py --- a/arviz/plots/jointplot.py +++ b/arviz/plots/jointplot.py @@ -1,6 +1,12 @@ """Joint scatter plot of two variables.""" from ..data import convert_to_dataset -from .plot_utils import _scale_fig_size, xarray_var_iter, get_coords, get_plotting_function +from .plot_utils import ( + _scale_fig_size, + xarray_var_iter, + get_coords, + get_plotting_function, + matplotlib_kwarg_dealiaser, +) from ..rcparams import rcParams from ..utils import _var_names @@ -157,8 +163,13 @@ def plot_joint( figsize, ax_labelsize, _, xt_labelsize, linewidth, _ = _scale_fig_size(figsize, textsize) - if joint_kwargs is None: - joint_kwargs = {} + if kind == "kde": + types = "plot" + elif kind == "scatter": + types = "scatter" + else: + types = "hexbin" + joint_kwargs = matplotlib_kwarg_dealiaser(joint_kwargs, types) if marginal_kwargs is None: marginal_kwargs = {} diff --git a/arviz/plots/khatplot.py b/arviz/plots/khatplot.py --- a/arviz/plots/khatplot.py +++ b/arviz/plots/khatplot.py @@ -11,6 +11,7 @@ color_from_dim, format_coords_as_labels, get_plotting_function, + matplotlib_kwarg_dealiaser, ) from ..stats import ELPDData from ..rcparams import rcParams @@ -132,8 +133,7 @@ def plot_khat( >>> az.plot_khat(loo_radon, color=colors) """ - if hlines_kwargs is None: - hlines_kwargs = {} + hlines_kwargs = matplotlib_kwarg_dealiaser(hlines_kwargs, "hlines") hlines_kwargs.setdefault("linestyle", [":", "-.", "--", "-"]) hlines_kwargs.setdefault("alpha", 0.7) hlines_kwargs.setdefault("zorder", -1) @@ -173,6 +173,8 @@ def plot_khat( if markersize is None: markersize = scaled_markersize ** 2 # s in scatter plot mus be markersize square # for dots to have the same size + + kwargs = matplotlib_kwarg_dealiaser(kwargs, "scatter") kwargs.setdefault("s", markersize) kwargs.setdefault("marker", "+") color_mapping = None diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py --- a/arviz/plots/loopitplot.py +++ b/arviz/plots/loopitplot.py @@ -9,6 +9,7 @@ _scale_fig_size, get_plotting_function, _fast_kde, + matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams @@ -137,8 +138,7 @@ def plot_loo_pit( loo_pit = _loo_pit(idata=idata, y=y, y_hat=y_hat, log_weights=log_weights) loo_pit = loo_pit.flatten() if isinstance(loo_pit, np.ndarray) else loo_pit.values.flatten() - if plot_kwargs is None: - plot_kwargs = {} + plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") plot_kwargs["color"] = to_hex(color) plot_kwargs.setdefault("linewidth", linewidth * 1.4) if isinstance(y, str): @@ -155,8 +155,7 @@ def plot_loo_pit( plot_kwargs.setdefault("label", label) plot_kwargs.setdefault("zorder", 5) - if plot_unif_kwargs is None: - plot_unif_kwargs = {} + plot_unif_kwargs = matplotlib_kwarg_dealiaser(plot_unif_kwargs, "plot") light_color = rgb_to_hsv(to_rgb(plot_kwargs.get("color"))) light_color[1] /= 2 # pylint: disable=unsupported-assignment-operation light_color[2] += (1 - light_color[2]) / 2 # pylint: disable=unsupported-assignment-operation diff --git a/arviz/plots/mcseplot.py b/arviz/plots/mcseplot.py --- a/arviz/plots/mcseplot.py +++ b/arviz/plots/mcseplot.py @@ -11,6 +11,7 @@ get_coords, filter_plotters_list, get_plotting_function, + matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams from ..utils import _var_names @@ -134,28 +135,29 @@ def plot_mcse( (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _markersize) = _scale_fig_size( figsize, textsize, rows, cols ) - kwargs.setdefault("linestyle", kwargs.pop("ls", "none")) - kwargs.setdefault("linewidth", kwargs.pop("lw", _linewidth)) - kwargs.setdefault("markersize", kwargs.pop("ms", _markersize)) + kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot") + kwargs.setdefault("linestyle", "none") + kwargs.setdefault("linewidth", _linewidth) + kwargs.setdefault("markersize", _markersize) kwargs.setdefault("marker", "_" if errorbar else "o") kwargs.setdefault("zorder", 3) - if extra_kwargs is None: - extra_kwargs = {} - extra_kwargs.setdefault("linestyle", extra_kwargs.pop("ls", "-")) - extra_kwargs.setdefault("linewidth", extra_kwargs.pop("lw", _linewidth / 2)) + + extra_kwargs = matplotlib_kwarg_dealiaser(extra_kwargs, "plot") + extra_kwargs.setdefault("linestyle", "-") + extra_kwargs.setdefault("linewidth", _linewidth / 2) extra_kwargs.setdefault("color", "k") extra_kwargs.setdefault("alpha", 0.5) if extra_methods: mean_mcse = mcse(data, var_names=var_names, method="mean") sd_mcse = mcse(data, var_names=var_names, method="sd") - if text_kwargs is None: - text_kwargs = {} + + text_kwargs = matplotlib_kwarg_dealiaser(text_kwargs, "text") text_x = text_kwargs.pop("x", 1) - text_kwargs.setdefault("fontsize", text_kwargs.pop("size", xt_labelsize * 0.7)) + text_kwargs.setdefault("fontsize", xt_labelsize * 0.7) text_kwargs.setdefault("alpha", extra_kwargs["alpha"]) text_kwargs.setdefault("color", extra_kwargs["color"]) - text_kwargs.setdefault("horizontalalignment", text_kwargs.pop("ha", "right")) - text_va = text_kwargs.pop("verticalalignment", text_kwargs.pop("va", None)) + text_kwargs.setdefault("horizontalalignment", "right") + text_va = text_kwargs.pop("verticalalignment", None) mcse_kwargs = dict( ax=ax, diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -12,6 +12,7 @@ import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl +import matplotlib.cbook as cbook import xarray as xr @@ -914,3 +915,24 @@ def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): grid /= norm_factor return grid, xmin, xmax, ymin, ymax + + +def matplotlib_kwarg_dealiaser(args, kind, backend="matplotlib"): + """De-aliase the kwargs passed to plots.""" + if args is None: + return {} + matplotlib_kwarg_dealiaser_dict = { + "scatter": mpl.collections.PathCollection, + "plot": mpl.lines.Line2D, + "hist": mpl.patches.Patch, + "hexbin": mpl.collections.PolyCollection, + "hlines": mpl.collections.LineCollection, + "text": mpl.text.Text, + "contour": mpl.contour.ContourSet, + "pcolormesh": mpl.collections.QuadMesh, + } + if backend == "matplotlib": + return cbook.normalize_kwargs( + args, getattr(matplotlib_kwarg_dealiaser_dict[kind], "_alias_map", {}) + ) + return args diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -9,6 +9,7 @@ get_coords, filter_plotters_list, get_plotting_function, + matplotlib_kwarg_dealiaser, ) from ..utils import _var_names from ..rcparams import rcParams @@ -208,6 +209,10 @@ def plot_posterior( (figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _) = _scale_fig_size( figsize, textsize, rows, cols ) + if kind == "hist": + kwargs = matplotlib_kwarg_dealiaser(kwargs, "hist") + else: + kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot") kwargs.setdefault("linewidth", _linewidth) posteriorplot_kwargs = dict( diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -5,7 +5,13 @@ import matplotlib.pyplot as plt -from .plot_utils import get_plotting_function, get_coords, xarray_var_iter, KwargSpec +from .plot_utils import ( + get_plotting_function, + get_coords, + xarray_var_iter, + KwargSpec, + matplotlib_kwarg_dealiaser, +) from ..data import convert_to_dataset, InferenceData, CoordSpec from ..utils import _var_names from ..rcparams import rcParams @@ -216,8 +222,7 @@ def plot_trace( if figsize is None: figsize = (12, len(plotters) * 2) - if trace_kwargs is None: - trace_kwargs = {} + trace_kwargs = matplotlib_kwarg_dealiaser(trace_kwargs, "plot") trace_kwargs.setdefault("alpha", 0.35) if hist_kwargs is None: diff --git a/arviz/plots/violinplot.py b/arviz/plots/violinplot.py --- a/arviz/plots/violinplot.py +++ b/arviz/plots/violinplot.py @@ -6,6 +6,7 @@ filter_plotters_list, default_grid, get_plotting_function, + matplotlib_kwarg_dealiaser, ) from ..utils import _var_names from ..rcparams import rcParams @@ -116,8 +117,7 @@ def plot_violin( list(xarray_var_iter(data, var_names=var_names, combined=True)), "plot_violin" ) - if shade_kwargs is None: - shade_kwargs = {} + shade_kwargs = matplotlib_kwarg_dealiaser(shade_kwargs, "hexbin") rows, cols = default_grid(len(plotters)) @@ -125,8 +125,7 @@ def plot_violin( figsize, textsize, rows, cols ) - if rug_kwargs is None: - rug_kwargs = {} + rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot") if credible_interval is None: credible_interval = rcParams["stats.credible_interval"]

diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py --- a/arviz/tests/base_tests/test_plot_utils.py +++ b/arviz/tests/base_tests/test_plot_utils.py @@ -13,6 +13,7 @@ filter_plotters_list, format_sig_figs, get_plotting_function, + matplotlib_kwarg_dealiaser, ) from ...rcparams import rc_context @@ -200,3 +201,24 @@ def test_bokeh_import(): from arviz.plots.backends.bokeh.distplot import plot_dist assert plot is plot_dist + + [email protected]( + "params", + [ + {"input": ({"dashes": "-",}, "scatter"), "output": "linestyle", }, + { + "input": ({"mfc": "blue", "c": "blue", "line_width": 2}, "plot",), + "output": ("markerfacecolor", "color", "line_width"), + }, + {"input": ({"ec": "blue", "fc": "black"}, "hist"), "output": ("edgecolor", "facecolor")}, + { + "input": ({"edgecolors": "blue", "lw": 3}, "hlines"), + "output": ("edgecolor", "linewidth"), + }, + ], +) +def test_matplotlib_kwarg_dealiaser(params): + dealiased = matplotlib_kwarg_dealiaser(params["input"][0], kind=params["input"][1]) + for returned in dealiased: + assert returned in params["output"]

matplotlib kwarg de-aliasing helper function ## Tell us about it There are many kwarg arguments in matplotlib that accept an alias, which can get quite messy when ArviZ sets default values for them. I propose to pass the kwarg dicts through this helper function and then always work internally with the complete name. ## Thoughts on implementation Use a matplotlib internal function if it exists? Otherwise we should create one (or several, see note). Note: some aliases are not universal like the case of color and c in a scatter plot. References that may (or not) be useful: [Line2D properties](https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D) and [Collection properties](https://matplotlib.org/api/collections_api.html#matplotlib.collections.Collection)

I'll like to work on this one! I found the kwarg normalization in matplotlib [here](https://github.com/matplotlib/matplotlib/blob/85a96fb38b0facd824b52e2ff45899a6b0a071d7/lib/matplotlib/cbook/__init__.py#L1717). This function is then used like this [here](https://github.com/matplotlib/matplotlib/blob/85a96fb38b0facd824b52e2ff45899a6b0a071d7/lib/matplotlib/artist.py#L1077) to set properties. Are we looking for something like this? I think we can create a helper function which calls this `normalize_kwargs` function from matplotlib and will set properties( as is done in case of matplotlib). Yes, something similar. Hey, I was working some examples in the [cookbook](https://github.com/percygautam/arviz-examples/blob/master/de-Aliasing%20helper%20function.ipynb). Can you please confirm this is what we are looking for. Also, Should we create multiple `de-aliaser` dicts or just add all in one ? Even if user add some wrong kwarg, matplotlib can handle the errors. @ahartikainen Should I make different functions for scatter plots and other plots, or use conditionals in one function? Also, how to deal with plot_kwargs of `compareplot` where we are explicitely defining them like `marker_ic` and `marker_dse`?

2020-02-17T10:59:24Z

arviz-devs/arviz

arviz-devs__arviz-1076

0eef3b95eff477541ba599f15687612652074b7e

diff --git a/arviz/plots/backends/matplotlib/traceplot.py b/arviz/plots/backends/matplotlib/traceplot.py --- a/arviz/plots/backends/matplotlib/traceplot.py +++ b/arviz/plots/backends/matplotlib/traceplot.py @@ -1,5 +1,6 @@ """Matplotlib traceplot.""" +import warnings import matplotlib.pyplot as plt from matplotlib.lines import Line2D import numpy as np @@ -48,8 +49,8 @@ def plot_trace( rug : bool If True adds a rugplot. Defaults to False. Ignored for 2D KDE. Only affects continuous variables. - lines : tuple - Tuple of (var_name, {'coord': selection}, [line, positions]) to be overplotted as + lines : tuple or list + list of tuple of (var_name, {'coord': selection}, [line_positions]) to be overplotted as vertical lines on the density and horizontal lines on the trace. combined : bool Flag for combining multiple chains into a single line. If False (default), chains will be @@ -124,6 +125,21 @@ def plot_trace( _, axes = plt.subplots(len(plotters), 2, squeeze=False, figsize=figsize, **backend_kwargs) + # Check the input for lines + if lines is not None: + all_var_names = set(plotter[0] for plotter in plotters) + + invalid_var_names = set() + for line in lines: + if line[0] not in all_var_names: + invalid_var_names.add(line[0]) + if invalid_var_names: + warnings.warn( + "A valid var_name should be provided, found {} expected from {}".format( + invalid_var_names, all_var_names + ) + ) + for idx, (var_name, selection, value) in enumerate(plotters): value = np.atleast_2d(value) @@ -219,6 +235,10 @@ def plot_trace( line_values = [vlines] else: line_values = np.atleast_1d(vlines).ravel() + if not np.issubdtype(line_values.dtype, np.number): + raise ValueError( + "line-positions should be numeric, found {}".format(line_values) + ) axes[idx, 0].vlines(line_values, *ylims[0], colors="black", linewidth=1.5, alpha=0.75) axes[idx, 1].hlines( line_values, *xlims[1], colors="black", linewidth=1.5, alpha=trace_kwargs["alpha"]

diff --git a/arviz/tests/test_plots_matplotlib.py b/arviz/tests/test_plots_matplotlib.py --- a/arviz/tests/test_plots_matplotlib.py +++ b/arviz/tests/test_plots_matplotlib.py @@ -156,6 +156,21 @@ def test_plot_trace_max_subplots_warning(models): assert axes.shape [email protected]("kwargs", [{"var_names": ["mu", "tau"], "lines": [("hey", {}, [1])]}]) +def test_plot_trace_invalid_varname_warning(models, kwargs): + with pytest.warns(UserWarning, match="valid var.+should be provided"): + axes = plot_trace(models.model_1, **kwargs) + assert axes.shape + + [email protected]( + "bad_kwargs", [{"var_names": ["mu", "tau"], "lines": [("mu", {}, ["hey"])]}] +) +def test_plot_trace_bad_lines_value(models, bad_kwargs): + with pytest.raises(ValueError, match="line-positions should be numeric"): + plot_trace(models.model_1, **bad_kwargs) + + @pytest.mark.parametrize("model_fits", [["model_1"], ["model_1", "model_2"]]) @pytest.mark.parametrize( "args_expected", @@ -701,7 +716,6 @@ def test_plot_posterior_point_estimates(models, point_estimate): "kwargs", [{"insample_dev": False}, {"plot_standard_error": False}, {"plot_ic_diff": False}] ) def test_plot_compare(models, kwargs): - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) axes = plot_compare(model_compare, **kwargs)

plot_trace lines is unclear and it may yield unexpected results **Describe the bug** The argument `lines` for the function `plot_trace` can give unexpected results. Moreover, the documentation is a bit nebulous. **To Reproduce** A toy example is defined ```python import pymc3 as pm import arviz as az import numpy as np # fake data mu_real = 0 sigma_real = 1 n_samples = 150 Y = np.random.normal(loc=mu_real, scale=sigma_real, size=n_samples) with pm.Model() as model: mu = pm.Normal('mu', mu=0, sigma=10) sigma = pm.HalfNormal('sigma', sigma=10) likelihood = pm.Normal('likelihood', mu=mu, sigma=sigma, observed=Y) trace = pm.sample() ``` As per [documentation](https://arviz-devs.github.io/arviz/generated/arviz.plot_trace.html#arviz.plot_trace), the argument `lines` accepts a tuple in the form `(var_name, {‘coord’: selection}, [line, positions])`. So, the command ```python az.plot_trace(trace, lines=(('mu', {}, mu_real),)) ``` yields correctly  I can also pass a list of tuples or a list of tuples and lists and it will work fine: ``` az.plot_trace(trace, lines=[('mu', {}, mu_real)]) # list of tuples az.plot_trace(trace, lines=[['mu', {}, mu_real]]) # list of lists az.plot_trace(trace, lines=[['mu', {}, mu_real], ('sigma', {}, sigma_real)]) # list of lists and tuples ``` however, I cannot pass a simple tuple because I will get a `KeyError: 0` ```python az.plot_trace(trace, lines=(['mu', {}, mu_real])) az.plot_trace(trace, lines=(('mu', {}, mu_real))) ``` Also, I can pass a variable or coordinate name that do not exist and Arviz will not complain---but not lines will be plotted (here I would expect a warning) ```python az.plot_trace(trace, lines=[('hey', {}, mu_real)]) az.plot_trace(trace, lines=[('mu', {'hey'}, mu_real)]) ```  The weird behavior happens when I pass a string: ```python az.plot_trace(trace, lines=[('mu', {}, 'hey')]) ```  **Expected behavior** The [documentation](https://arviz-devs.github.io/arviz/generated/arviz.plot_trace.html#arviz.plot_trace) could be improved and the function could check the inputs. In addition to what described above, the placeholder `[line, positions]` in `(var_name, {‘coord’: selection}, [line, positions])` should be something like `[line_positions]` otherwise one may think (like myself :) ) that two values should be inserted (one for `line` and one for `positions`). **Additional context** I am using Win10, fresh conda environment with PyMC3 and Arviz from master. Possibly related https://github.com/pymc-devs/pymc3/issues/3495, https://github.com/pymc-devs/pymc3/issues/3497

Thanks for the feedback. We are on our way to update the interface a bit so I hope we can fix this issue at the same time and the usage would be intuitive. Thank you for your good work 🙌 @ahartikainen @OriolAbril I think the error is caused due to following: https://github.com/arviz-devs/arviz/blob/0774d13979317cf4d22bb995ee298de4804432d9/arviz/plots/backends/matplotlib/traceplot.py#L217-L225 I would check for the list, else raise the ValueError. Please verify. Sounds gook, one option for checking the list is to check the dtype of `line_values` after the atleast_1d to make sure the array contains numeric values. One possibility is to follow this [SO answer](https://stackoverflow.com/questions/29518923/numpy-asarray-how-to-check-up-that-its-result-dtype-is-numeric). Okay, I'd go with that. Also, I'd add the warning for the case of variable or coordinate name that do not exist (as pointed in issue description) > Also, I can pass a variable or coordinate name that do not exist and Arviz will not complain---but > not lines will be plotted (here I would expect a warning) > ``` > az.plot_trace(trace, lines=[('hey', {}, mu_real)]) > az.plot_trace(trace, lines=[('mu', {'hey'}, mu_real)]) > ```

2020-02-17T22:14:31Z

arviz-devs/arviz

arviz-devs__arviz-1084

7ab50b077f524a6ff09e0e9b796afb357c8758cd

diff --git a/arviz/plots/khatplot.py b/arviz/plots/khatplot.py --- a/arviz/plots/khatplot.py +++ b/arviz/plots/khatplot.py @@ -11,6 +11,7 @@ format_coords_as_labels, get_plotting_function, matplotlib_kwarg_dealiaser, + vectorized_to_hex, ) from ..stats import ELPDData from ..rcparams import rcParams @@ -138,6 +139,7 @@ def plot_khat( hlines_kwargs.setdefault("alpha", 0.7) hlines_kwargs.setdefault("zorder", -1) hlines_kwargs.setdefault("color", "C1") + hlines_kwargs["color"] = vectorized_to_hex(hlines_kwargs["color"]) if coords is None: coords = {} @@ -200,6 +202,7 @@ def plot_khat( khats = khats if isinstance(khats, np.ndarray) else khats.values.flatten() alphas = 0.5 + 0.2 * (khats > 0.5) + 0.3 * (khats > 1) rgba_c[:, 3] = alphas + rgba_c = vectorized_to_hex(rgba_c) plot_khat_kwargs = dict( hover_label=hover_label, diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -4,6 +4,7 @@ from itertools import product, tee import importlib from scipy.stats import mode +from matplotlib.colors import to_hex import packaging import numpy as np @@ -468,7 +469,7 @@ def color_from_dim(dataarray, dim_name): ---------- dataarray : xarray.DataArray dim_name : str - dimension whose coordinates will be used as color code. + dimension whose coordinates will be used as color code. Returns ------- @@ -490,6 +491,28 @@ def color_from_dim(dataarray, dim_name): return colors, color_mapping +def vectorized_to_hex(c_values, keep_alpha=False): + """Convert a color (including vector of colors) to hex. + + Parameters + ---------- + c: Matplotlib color + + keep_alpha: boolean + to select if alpha values should be kept in the final hex values. + + Returns + ------- + rgba_hex : vector of hex values + """ + try: + hex_color = to_hex(c_values, keep_alpha) + + except ValueError: + hex_color = [to_hex(color, keep_alpha) for color in c_values] + return hex_color + + def format_coords_as_labels(dataarray, skip_dims=None): """Format 1d or multi-d dataarray coords as strings.

diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py --- a/arviz/tests/base_tests/test_plot_utils.py +++ b/arviz/tests/base_tests/test_plot_utils.py @@ -15,6 +15,7 @@ matplotlib_kwarg_dealiaser, xarray_to_ndarray, xarray_var_iter, + vectorized_to_hex, ) from ...rcparams import rc_context from ...numeric_utils import get_bins @@ -229,3 +230,17 @@ def test_matplotlib_kwarg_dealiaser(params): dealiased = matplotlib_kwarg_dealiaser(params["input"][0], kind=params["input"][1]) for returned in dealiased: assert returned in params["output"] + + [email protected]("c_values", ["#0000ff", "blue", [0, 0, 1]]) +def test_vectorized_to_hex_scalar(c_values): + output = vectorized_to_hex(c_values) + assert output == "#0000ff" + + [email protected]( + "c_values", [["blue", "blue"], ["blue", "#0000ff"], np.array([[0, 0, 1], [0, 0, 1]])] +) +def test_vectorized_to_hex_array(c_values): + output = vectorized_to_hex(c_values) + assert np.all([item == "#0000ff" for item in output])

Bokeh, transform all colors to hex Input all colors to bokeh functions as hex. We can use matplotlib helper functions to transform special syntax color="C0" etc

@OriolAbril can I try fixing it? @mrinalnilotpal Definitely! can anybody tell me which files I should look at in order to solve the issue? I think you would need to create a function that transforms certain colors (rgb, 'C{i}' syntax etc to hex) These are for bokeh plots matplotlib have some functions for color conversion. https://matplotlib.org/api/colors_api.html#functions and the "CN" colors can be accessed from `matplotlib.rcParams['axes.prop_cycle']` Is this issue still to be resolved? @mrinalnilotpal @animesh-007 @sahajsk21 It is still pending. Just to make sure it is clear what this PR entails, I'll try to extend the description. The goal is to have some easy way to convert all kinds of colors used by matplotlib (such as `C0, C1` to refer to default colors in prop_cycle, named colors, single letter color abbreviations, rgb...) to hex in order to use them in bokeh. Then, modify the code where necessary to make both backends plot the same colors (which is currently not the case, e.g. [matplotlib's ppc](https://arviz-devs.github.io/arviz/examples/matplotlib/mpl_plot_ppc.html) vs [bokeh's ppc](https://arviz-devs.github.io/arviz/examples/bokeh/bokeh_plot_ppc.html) It looks like [`to_hex`](https://matplotlib.org/api/_as_gen/matplotlib.colors.to_hex.html#matplotlib.colors.to_hex) is exactly what we are looking for. I think a way to tackle this would be to convert all color references to hex before passing them to the backend files. Then both backends should plot the same colors without much hassle. Thanks @OriolAbril I'll be working to solve this issue @OriolAbril I think this issue has been inactive for a while. Could I work on it? @sahajsk21 do you still plan to work on this? @amukh18 @OriolAbril I have been working on issue so he can take up the issue.Thanks > I have been working on issue so he **can't** take up the issue.Thanks Could there be a typo so the message is this? @sahajsk21 There is no rush, work at your own pace, this is why we ask :). Sorry if my previous wording sounded harsh, it wasn't the intention. Also, please let us know if you were to need any help @OriolAbril No it wasn't a typo. Don't worry, I didn't take the comment in the negative sense. Actually, I have been going through the code and making myself familiar first before working on the issue. That is why it is taking so long. But I think @amukh18 can still take up this issue. > @amukh18 @OriolAbril I have been working on another issue so he can take up the issue. Thanks @sahajsk21 @OriolAbril Thank you. I'll begin working on it right away. @OriolAbril I have a question. If I were to add the to_hex function to plot_khat as follows: ``` if hlines_kwargs is None: hlines_kwargs = {} hlines_kwargs.setdefault("linestyle", [":", "-.", "--", "-"]) hlines_kwargs.setdefault("alpha", 0.7) hlines_kwargs.setdefault("zorder", -1) hlines_kwargs.setdefault("color", to_hex("C1")) if coords is None: coords = {} if color is None: color = "C0" color = to_hex(color) ``` Is this supposed to return any errors? I think this should work for this case Hmm, pytest returned some errors. I will see what the unit tests on the pipeline say on committing.

2020-02-21T17:56:22Z

arviz-devs/arviz

arviz-devs__arviz-1096

b159698ad1c6aaeba57183e07a3abe49533b5237

diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py --- a/arviz/plots/densityplot.py +++ b/arviz/plots/densityplot.py @@ -196,7 +196,7 @@ def plot_density( label = make_label(var_name, selection) if label not in all_labels: all_labels.append(label) - length_plotters = max(length_plotters) + length_plotters = len(all_labels) max_plots = rcParams["plot.max_subplots"] max_plots = length_plotters if max_plots is None else max_plots if length_plotters > max_plots:

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -90,6 +90,14 @@ def test_plot_density_discrete(discrete_model): assert axes.shape[0] == 1 +def test_plot_density_no_subset(): + """Test plot_density works when variables are not subset of one another (#1093).""" + model_ab = from_dict({"a": np.random.normal(size=200), "b": np.random.normal(size=200),}) + model_bc = from_dict({"b": np.random.normal(size=200), "c": np.random.normal(size=200),}) + axes = plot_density([model_ab, model_bc]) + assert axes.shape[0] == 3 + + def test_plot_density_bad_kwargs(models): obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] with pytest.raises(ValueError): diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -112,6 +112,14 @@ def test_plot_density_discrete(discrete_model): assert axes.shape[0] == 2 +def test_plot_density_no_subset(): + """Test plot_density works when variables are not subset of one another (#1093).""" + model_ab = from_dict({"a": np.random.normal(size=200), "b": np.random.normal(size=200),}) + model_bc = from_dict({"b": np.random.normal(size=200), "c": np.random.normal(size=200),}) + axes = plot_density([model_ab, model_bc]) + assert axes.shape[0] == 3 + + def test_plot_density_bad_kwargs(models): obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] with pytest.raises(ValueError):

`plot_density` does not work for models with different variables **Describe the bug** When passing data of several models to `plot_density`, it throws an error if both models contain parameters that the other model does not have. As long the the variables in the one model are a subset of the the variables of the other model it works. I think that the problem lies in how `plot_density` determines the number of plots to create. It calculates the number of parameters in each model and then uses the maximum: ``` 192 length_plotters = [] 193 for plotters in to_plot: 194 length_plotters.append(len(plotters)) 195 for var_name, selection, _ in plotters: 196 label = make_label(var_name, selection) 197 if label not in all_labels: 198 all_labels.append(label) 199 length_plotters = max(length_plotters) ``` That does not account for the situation where the union of parameters over all models is larger than the set parameters in each single model. **To Reproduce** This simple example should demonstrate what I mean: ```python import numpy as np import xarray as xr import arviz n_draws = 1000 model_ab = xr.Dataset({ "a": ("draw", np.random.normal(size=n_draws)), "b": ("draw", np.random.normal(size=n_draws)), }) model_b = xr.Dataset({ "b": ("draw", np.random.normal(size=n_draws)), }) model_bc = xr.Dataset({ "c": ("draw", np.random.normal(size=n_draws)), "b": ("draw", np.random.normal(size=n_draws)), }) # Works arviz.plot_density([model_ab, model_b], data_labels=["ab", "b"]); # Does not work arviz.plot_density([model_ab, model_bc], data_labels=["ab", "bc"]); ``` **Expected behavior** In the second case, the code should create 3 subplots, for parameters a, b, and c. While the plots for a and c would contain only one density, the plot for b would contain two densities. **Additional context** arviz Version: 0.6.1

Thanks for reporting! Also feel free to reach out if you'd like to make a PR to fix it an need some guidance

2020-02-29T15:16:00Z

arviz-devs/arviz

arviz-devs__arviz-1098

ec33b4cc7d4a6d5ba95a87a43ef226a49c2cb287

diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -1,7 +1,7 @@ """PyMC3-specific conversion code.""" import logging import warnings -from typing import Dict, List, Any, Optional, Iterable, Union, TYPE_CHECKING, Tuple +from typing import Dict, List, Tuple, Any, Optional, Iterable, Union, TYPE_CHECKING from types import ModuleType import numpy as np @@ -149,18 +149,21 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: self.coords = coords self.dims = dims - self.observations = self.find_observations() + self.observations, self.multi_observations = self.find_observations() - def find_observations(self) -> Optional[Dict[str, Var]]: + def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]: """If there are observations available, return them as a dictionary.""" - has_observations = False - if self.model is not None: - if any((hasattr(obs, "observations") for obs in self.model.observed_RVs)): - has_observations = True - if has_observations: - assert self.model is not None - return {obs.name: obs.observations for obs in self.model.observed_RVs} - return None + if self.model is None: + return (None, None) + observations = {} + multi_observations = {} + for obs in self.model.observed_RVs: + if hasattr(obs, "observations"): + observations[obs.name] = obs.observations + elif hasattr(obs, "data"): + for key, val in obs.data.items(): + multi_observations[key] = val.eval() if hasattr(val, "eval") else val + return observations, multi_observations def split_trace(self) -> Tuple[Union[None, MultiTrace], Union[None, MultiTrace]]: """Split MultiTrace object into posterior and warmup. @@ -361,7 +364,7 @@ def priors_to_xarray(self): ) return priors_dict - @requires("observations") + @requires(["observations", "multi_observations"]) @requires("model") def observed_data_to_xarray(self): """Convert observed data to xarray.""" @@ -372,7 +375,7 @@ def observed_data_to_xarray(self): else: dims = self.dims observed_data = {} - for name, vals in self.observations.items(): + for name, vals in {**self.observations, **self.multi_observations}.items(): if hasattr(vals, "get_value"): vals = vals.get_value() vals = utils.one_de(vals)

diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -227,7 +227,7 @@ def test_multiple_observed_rv(self, log_likelihood): "posterior": ["x"], "observed_data": ["y1", "y2"], "log_likelihood": ["y1", "y2"], - "sample_stats": ["diverging", "lp"], + "sample_stats": ["diverging", "lp", "~log_likelihood"], } if not log_likelihood: test_dict.pop("log_likelihood") @@ -237,7 +237,6 @@ def test_multiple_observed_rv(self, log_likelihood): fails = check_multiple_attrs(test_dict, inference_data) assert not fails - assert not hasattr(inference_data.sample_stats, "log_likelihood") @pytest.mark.skipif( version_info < (3, 6), reason="Requires updated PyMC3, which needs Python 3.6" @@ -250,11 +249,48 @@ def test_multiple_observed_rv_without_observations(self): ) trace = pm.sample(100, chains=2) inference_data = from_pymc3(trace=trace) - assert inference_data - assert not hasattr(inference_data, "observed_data") - assert hasattr(inference_data, "posterior") - assert hasattr(inference_data, "sample_stats") - assert hasattr(inference_data, "log_likelihood") + test_dict = { + "posterior": ["mu"], + "sample_stats": ["lp"], + "log_likelihood": ["x"], + "observed_data": ["value", "~x"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + assert inference_data.observed_data.value.dtype.kind == "f" + + def test_multiobservedrv_to_observed_data(self): + # fake regression data, with weights (W) + np.random.seed(2019) + N = 100 + X = np.random.uniform(size=N) + W = 1 + np.random.poisson(size=N) + a, b = 5, 17 + Y = a + np.random.normal(b * X) + + with pm.Model(): + a = pm.Normal("a", 0, 10) + b = pm.Normal("b", 0, 10) + mu = a + b * X + sigma = pm.HalfNormal("sigma", 1) + + def weighted_normal(y, w): + return w * pm.Normal.dist(mu=mu, sd=sigma).logp(y) + + y_logp = pm.DensityDist( # pylint: disable=unused-variable + "y_logp", weighted_normal, observed={"y": Y, "w": W} + ) + trace = pm.sample(20, tune=20) + idata = from_pymc3(trace) + test_dict = { + "posterior": ["a", "b", "sigma"], + "sample_stats": ["lp"], + "log_likelihood": ["y_logp"], + "observed_data": ["y", "w"], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails + assert idata.observed_data.y.dtype.kind == "f" def test_single_observation(self): with pm.Model():

Attribute error when observed has multiple dimensions: MultiObservedRV Here's a Weibull survival model in pymc3: ``` def weibull_lccdf(alpha, beta, x): return -(x / beta)**alpha class Weibull_like(pm.Continuous): def __init__(self, alpha, lambd, *args, **kwargs): self.alpha = tt.as_tensor_variable(alpha) self.lambd = tt.as_tensor_variable(lambd) super().__init__(*args, **kwargs) def logp(self, failure, value): alpha = self.alpha beta = 1/tt.exp(self.lambd) no_censoring = failure.astype('bool') beta_nocens = beta[no_censoring] beta_censor = beta[~no_censoring] uncensored = pm.Weibull.dist(alpha, beta_nocens).logp(value[no_censoring]).sum() censored = weibull_lccdf(alpha, beta_censor, value[~no_censoring]).sum() return uncensored + censored ``` and the setting up the model ``` with pm.Model() as weibull: beta0 = pm.Normal('β_0', 0, 100) beta1 = pm.Normal('β_1', 0, 100) alpha = pm.Gamma('α', 1.0, 1/0.001) lam_ = pm.Deterministic('λ', beta0 + beta1*(treatment - treatment.mean())) like = Weibull_like('survival', alpha=alpha, lambd=lam_, observed={'failure':failure, 'value':t}) trace_weibull = pm.sample(tune=1000, draws=3000, chains=2) ``` Running any of plots gives an Attribute error: ``` av.plot_trace(trace_EVM) ``` ``` /anaconda3/lib/python3.6/site-packages/arviz/data/io_pymc3.py in <dictcomp>(.0) 107 model = self.trace._straces[0].model # pylint: disable=protected-access 108 --> 109 observations = {obs.name: obs.observations for obs in model.observed_RVs} 110 if self.dims is None: 111 dims = {} AttributeError: 'MultiObservedRV' object has no attribute 'observations' ``` Poking around a bit, it looks like `MultiObservedRV`, doesn't create an observations parameter but instead saves in self.data: https://github.com/pymc-devs/pymc3/blob/master/pymc3/model.py#L1369 Suggestion on how to fix this? Is it as simple as just patching the PyMC3Converter class where it build the `observations` dict? https://github.com/arviz-devs/arviz/blob/master/arviz/data/io_pymc3.py#L110 I'm assuming just doing this will break some of the plots?

Will it create correct InferenceData object? Darn, I'd never even seen `MultiObservedRV`. I think patching there will work, and will probably break a few plots that assume there is only 1 observed variable. Tests will continue to pass, since nothing checks for this. Maybe a solution is to add a survival model ([like this](https://docs.pymc.io/notebooks/survival_analysis.html), but with your likelihood) to the model zoo, and include plot(s) from it in the documentation. Is the model you're working with public, or could it be? Ok cool. I’ll make the changes and do a PR. And maybe add a test case or two. Happy to add it to the model zoo. Right now I’m running it on a publicly available dataset as a POC so happy to share. On Wed, Oct 31, 2018 at 11:46 AM Colin <[email protected]> wrote: > Darn, I'd never even seen MultiObservedRV. > > I think patching there will work, and will probably break a few plots that > assume there is only 1 observed variable. Tests will continue to pass, > since nothing checks for this. > > Maybe a solution is to add a survival model (like this > <https://docs.pymc.io/notebooks/survival_analysis.html>, but with your > likelihood) to the model zoo, and include plot(s) from it in the > documentation. Is the model you're working with public, or could it be? > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > <https://github.com/arviz-devs/arviz/issues/384#issuecomment-434736067>, > or mute the thread > <https://github.com/notifications/unsubscribe-auth/ANeLhZjJvsZ7LuVU4rRx8HipzanuamdUks5uqcXhgaJpZM4YEUUJ> > . > I just ran into the same problem. Hit the same issue when using `pymc3.DensityDist` for making a weighted model. FWIW, here is the reproducible example I made for this. ``` import numpy as np import pymc3 as pm import arviz as az # fake regression data, with weights (W) np.random.seed(2019) N = 100 X = np.random.uniform(size=N) W = 1 + np.random.poisson(size=N) a, b = 5, 17 Y = a + np.random.normal(b*X) with pm.Model(): a = pm.Normal('a', 0, 10) b = pm.Normal('b', 0, 10) mu = a + b*X sigma = pm.HalfNormal('sigma', 1) def weighted_normal(y, w): return w*pm.Normal.dist(mu=mu, sd=sigma).logp(y) y_logp = pm.DensityDist('y_logp', weighted_normal, observed={'y': Y, 'w': W}) trace = pm.sample(2000, tune=2000, random_seed=2019) traz = az.from_pymc3(trace) ``` which gives the error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-50-3aba64ad8aca> in <module> ----> 1 traz = az.from_pymc3(trace) ~/miniconda3/envs/pymc36/lib/python3.7/site-packages/arviz/data/io_pymc3.py in from_pymc3(trace, prior, posterior_predictive, coords, dims) 150 posterior_predictive=posterior_predictive, 151 coords=coords, --> 152 dims=dims, 153 ).to_inference_data() ~/miniconda3/envs/pymc36/lib/python3.7/site-packages/arviz/data/io_pymc3.py in to_inference_data(self) 138 "posterior_predictive": self.posterior_predictive_to_xarray(), 139 "prior": self.prior_to_xarray(), --> 140 "observed_data": self.observed_data_to_xarray(), 141 } 142 ) ~/miniconda3/envs/pymc36/lib/python3.7/site-packages/arviz/data/base.py in wrapped(cls, *args, **kwargs) 23 if getattr(cls, prop) is None: 24 return None ---> 25 return func(cls, *args, **kwargs) 26 27 return wrapped ~/miniconda3/envs/pymc36/lib/python3.7/site-packages/arviz/data/io_pymc3.py in observed_data_to_xarray(self) 108 model = self.trace._straces[0].model # pylint: disable=protected-access 109 --> 110 observations = {obs.name: obs.observations for obs in model.observed_RVs} 111 if self.dims is None: 112 dims = {} ~/miniconda3/envs/pymc36/lib/python3.7/site-packages/arviz/data/io_pymc3.py in <dictcomp>(.0) 108 model = self.trace._straces[0].model # pylint: disable=protected-access 109 --> 110 observations = {obs.name: obs.observations for obs in model.observed_RVs} 111 if self.dims is None: 112 dims = {} AttributeError: 'MultiObservedRV' object has no attribute 'observations' ``` --- ## Environment Info PyMC3 3.6 ArviZ 0.3.3

2020-03-01T18:45:44Z

arviz-devs/arviz

arviz-devs__arviz-1117

9909aab2e4a1a4ad897c31c3dac2487037d0c3ad

diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -12,7 +12,7 @@ from scipy.optimize import minimize import xarray as xr -from ..plots.plot_utils import _fast_kde, get_bins +from ..plots.plot_utils import _fast_kde, get_bins, get_coords from ..data import convert_to_inference_data, convert_to_dataset, InferenceData, CoordSpec, DimSpec from .diagnostics import _multichain_statistics, _mc_error, ess from .stats_utils import ( @@ -305,7 +305,18 @@ def _ic_matrix(ics, ic_i): return rows, cols, ic_i_val -def hpd(ary, credible_interval=None, circular=False, multimodal=False, skipna=False): +def hpd( + ary, + credible_interval=None, + circular=False, + multimodal=False, + skipna=False, + group="posterior", + var_names=None, + coords=None, + max_modes=10, + **kwargs +): """ Calculate highest posterior density (HPD) of array for given credible_interval. @@ -313,8 +324,10 @@ def hpd(ary, credible_interval=None, circular=False, multimodal=False, skipna=Fa Parameters ---------- - ary : Numpy array - An array containing posterior samples + ary : obj + object containing posterior samples. + Any object that can be converted to an az.InferenceData object. + Refer to documentation of az.convert_to_dataset for details. credible_interval : float, optional Credible interval to compute. Defaults to 0.94. circular : bool, optional @@ -326,10 +339,22 @@ def hpd(ary, credible_interval=None, circular=False, multimodal=False, skipna=Fa modes are well separated. skipna : bool If true ignores nan values when computing the hpd interval. Defaults to false. + group : str, optional + Specifies which InferenceData group should be used to calculate hpd. + Defaults to 'posterior' + var_names : list, optional + Names of variables to include in the hpd report + coords: mapping, optional + Specifies the subset over to calculate hpd. + max_modes: int, optional + Specifies the maximume number of modes for multimodal case. + kwargs : dict, optional + Additional keywords passed to `wrap_xarray_ufunc`. + See the docstring of :obj:`wrap_xarray_ufunc method </.stats_utils.wrap_xarray_ufunc>`. Returns ------- - np.ndarray + np.ndarray or xarray.Dataset, depending upon input lower(s) and upper(s) values of the interval(s). Examples @@ -342,6 +367,34 @@ def hpd(ary, credible_interval=None, circular=False, multimodal=False, skipna=Fa ...: import numpy as np ...: data = np.random.normal(size=2000) ...: az.hpd(data, credible_interval=.68) + + Calculate the hpd of a dataset: + + .. ipython:: + + In [1]: import arviz as az + ...: data = az.load_arviz_data('centered_eight') + ...: az.hpd(data) + + We can also calculate the hpd of some of the variables of dataset: + + .. ipython:: + + In [1]: az.hpd(data, var_names=["mu", "theta"]) + + If we want to calculate the hpd over specified dimension of dataset, + we can pass `input_core_dims` by kwargs: + + .. ipython:: + + In [1]: az.hpd(data, input_core_dims = [["chain"]]) + + We can also calculate the hpd over a particular selection over all groups: + + .. ipython:: + + In [1]: az.hpd(data, coords={"chain":[0, 1, 3]}, input_core_dims = [["draw"]]) + """ if credible_interval is None: credible_interval = rcParams["stats.credible_interval"] @@ -349,84 +402,113 @@ def hpd(ary, credible_interval=None, circular=False, multimodal=False, skipna=Fa if not 1 >= credible_interval > 0: raise ValueError("The value of credible_interval should be in the interval (0, 1]") - if ary.ndim > 1: - hpd_array = np.array( - [ - hpd( - row, - credible_interval=credible_interval, - circular=circular, - multimodal=multimodal, - ) - for row in ary.T - ] - ) - return hpd_array + func_kwargs = { + "credible_interval": credible_interval, + "skipna": skipna, + "out_shape": (max_modes, 2,) if multimodal else (2,), + } + kwargs.setdefault("output_core_dims", [["hpd", "mode"] if multimodal else ["hpd"]]) + if not multimodal: + func_kwargs["circular"] = circular + else: + func_kwargs["max_modes"] = max_modes - if multimodal: - if skipna: - ary = ary[~np.isnan(ary)] + func = _hpd_multimodal if multimodal else _hpd - if ary.dtype.kind == "f": - density, lower, upper = _fast_kde(ary) - range_x = upper - lower - dx = range_x / len(density) - bins = np.linspace(lower, upper, len(density)) - else: - bins = get_bins(ary) - _, density, _ = histogram(ary, bins=bins) - dx = np.diff(bins)[0] + isarray = isinstance(ary, np.ndarray) + if isarray and ary.ndim <= 1: + func_kwargs.pop("out_shape") + hpd_data = func(ary, **func_kwargs) # pylint: disable=unexpected-keyword-arg + return hpd_data[~np.isnan(hpd_data).all(axis=1), :] if multimodal else hpd_data - density *= dx + if isarray and ary.ndim == 2: + kwargs.setdefault("input_core_dims", [["chain"]]) - idx = np.argsort(-density) - intervals = bins[idx][density[idx].cumsum() <= credible_interval] - intervals.sort() + ary = convert_to_dataset(ary, group=group) + if coords is not None: + ary = get_coords(ary, coords) + var_names = _var_names(var_names, ary) + ary = ary[var_names] if var_names else ary + + hpd_data = _wrap_xarray_ufunc(func, ary, func_kwargs=func_kwargs, **kwargs) + hpd_data = hpd_data.dropna("mode", how="all") if multimodal else hpd_data + return hpd_data.x.values if isarray else hpd_data + + +def _hpd(ary, credible_interval, circular, skipna): + """Compute hpd over the flattened array.""" + ary = ary.flatten() + if skipna: + nans = np.isnan(ary) + if not nans.all(): + ary = ary[~nans] + n = len(ary) - intervals_splitted = np.split(intervals, np.where(np.diff(intervals) >= dx * 1.1)[0] + 1) + if circular: + mean = st.circmean(ary, high=np.pi, low=-np.pi) + ary = ary - mean + ary = np.arctan2(np.sin(ary), np.cos(ary)) - hpd_intervals = [] - for interval in intervals_splitted: - if interval.size == 0: - hpd_intervals.append((bins[0], bins[0])) - else: - hpd_intervals.append((interval[0], interval[-1])) + ary = np.sort(ary) + interval_idx_inc = int(np.floor(credible_interval * n)) + n_intervals = n - interval_idx_inc + interval_width = ary[interval_idx_inc:] - ary[:n_intervals] - hpd_intervals = np.array(hpd_intervals) + if len(interval_width) == 0: + raise ValueError("Too few elements for interval calculation. ") - else: - if skipna: - nans = np.isnan(ary) - if not nans.all(): - ary = ary[~nans] - n = len(ary) + min_idx = np.argmin(interval_width) + hdi_min = ary[min_idx] + hdi_max = ary[min_idx + interval_idx_inc] - if circular: - mean = st.circmean(ary, high=np.pi, low=-np.pi) - ary = ary - mean - ary = np.arctan2(np.sin(ary), np.cos(ary)) + if circular: + hdi_min = hdi_min + mean + hdi_max = hdi_max + mean + hdi_min = np.arctan2(np.sin(hdi_min), np.cos(hdi_min)) + hdi_max = np.arctan2(np.sin(hdi_max), np.cos(hdi_max)) - ary = np.sort(ary) - interval_idx_inc = int(np.floor(credible_interval * n)) - n_intervals = n - interval_idx_inc - interval_width = ary[interval_idx_inc:] - ary[:n_intervals] + hpd_intervals = np.array([hdi_min, hdi_max]) - if len(interval_width) == 0: - raise ValueError("Too few elements for interval calculation. ") + return hpd_intervals - min_idx = np.argmin(interval_width) - hdi_min = ary[min_idx] - hdi_max = ary[min_idx + interval_idx_inc] - if circular: - hdi_min = hdi_min + mean - hdi_max = hdi_max + mean - hdi_min = np.arctan2(np.sin(hdi_min), np.cos(hdi_min)) - hdi_max = np.arctan2(np.sin(hdi_max), np.cos(hdi_max)) +def _hpd_multimodal(ary, credible_interval, skipna, max_modes): + """Compute hpd if the distribution is multimodal.""" + ary = ary.flatten() + if skipna: + ary = ary[~np.isnan(ary)] - hpd_intervals = np.array([hdi_min, hdi_max]) + if ary.dtype.kind == "f": + density, lower, upper = _fast_kde(ary) + range_x = upper - lower + dx = range_x / len(density) + bins = np.linspace(lower, upper, len(density)) + else: + bins = get_bins(ary) + _, density, _ = histogram(ary, bins=bins) + dx = np.diff(bins)[0] - return hpd_intervals + density *= dx + + idx = np.argsort(-density) + intervals = bins[idx][density[idx].cumsum() <= credible_interval] + intervals.sort() + + intervals_splitted = np.split(intervals, np.where(np.diff(intervals) >= dx * 1.1)[0] + 1) + + hpd_intervals = np.full((max_modes, 2,), np.nan,) + for i, interval in enumerate(intervals_splitted): + if i == max_modes: + warnings.warn( + "found more modes than {0}, returning only the first {0} modes", max_modes + ) + break + if interval.size == 0: + hpd_intervals[i] = np.asarray([bins[0], bins[0]]) + else: + hpd_intervals[i] = np.asarray([interval[0], interval[-1]]) + + return np.array(hpd_intervals) def loo(data, pointwise=False, reff=None, scale=None):

diff --git a/arviz/tests/base_tests/test_stats.py b/arviz/tests/base_tests/test_stats.py --- a/arviz/tests/base_tests/test_stats.py +++ b/arviz/tests/base_tests/test_stats.py @@ -45,6 +45,52 @@ def test_hpd(): assert_array_almost_equal(interval, [-1.88, 1.88], 2) +def test_hpd_2darray(): + normal_sample = np.random.randn(12000, 5) + result = hpd(normal_sample) + assert result.shape == (5, 2,) + + +def test_hpd_multidimension(): + normal_sample = np.random.randn(12000, 10, 3) + result = hpd(normal_sample) + assert result.shape == (3, 2,) + + +def test_hpd_idata(centered_eight): + data = centered_eight.posterior + result = hpd(data) + assert isinstance(result, Dataset) + assert result.dims == {"school": 8, "hpd": 2} + + result = hpd(data, input_core_dims=[["chain"]]) + assert isinstance(result, Dataset) + assert result.dims == {"draw": 500, "hpd": 2, "school": 8} + + +def test_hpd_idata_varnames(centered_eight): + data = centered_eight.posterior + result = hpd(data, var_names=["mu", "theta"]) + assert isinstance(result, Dataset) + assert result.dims == {"hpd": 2, "school": 8} + assert list(result.data_vars.keys()) == ["mu", "theta"] + + +def test_hpd_idata_group(centered_eight): + result_posterior = hpd(centered_eight, group="posterior", var_names="mu") + result_prior = hpd(centered_eight, group="prior", var_names="mu") + assert result_prior.dims == {"hpd": 2} + range_posterior = result_posterior.mu.values[1] - result_posterior.mu.values[0] + range_prior = result_prior.mu.values[1] - result_prior.mu.values[0] + assert range_posterior < range_prior + + +def test_hpd_coords(centered_eight): + data = centered_eight.posterior + result = hpd(data, coords={"chain": [0, 1, 3]}, input_core_dims=[["draw"]]) + assert_array_equal(result.coords["chain"], [0, 1, 3]) + + def test_hpd_multimodal(): normal_sample = np.concatenate( (np.random.normal(-4, 1, 2500000), np.random.normal(2, 0.5, 2500000))

Unexpected behavior when using `pm.hpd` with multidimensional (ndim>2)posterior arrays **Describe the bug** I am using `hpd` on multi-dimensional posteriors with larger than 2 dimensions. One example of where this can come up is if I use a 2D array to represent the coefficients for the interaction between levels of 2 different predictors. The output trace of this 2D array of coefficients from PyMC3 will now be 3D, with the 0th dimension representing the MCMC samples. I find that with a 3D trace/array, `hpd` treats dimension 1 as the MCMC dimension (instead of dimension 0, the actual MCMC dimension) **To Reproduce** ``` #12000 MCMC samples from a 10x3 dimensional distribution a = np.random.normal(size=(12000, 10, 3)) pm.hpd(a).shape ``` **Output: (3, 12000, 2)** **Should instead be: (10, 3, 2)** This happens because the assumption in `hpd` is that `ndim==2` if `ndim>1`: https://github.com/arviz-devs/arviz/blob/master/arviz/stats/stats.py#L346 `for row in ary.T` works properly for arrays that have 2 dimensions, but for anything larger than that, it infers the wrong MCMC dimension. **Suggestion:** Will it make sense to have an `axis` parameter to specify the axis of the MCMC samples, much like say `np.mean` etc?

Possible solution: First dimension = chains Second dimension = draws 3rd --> = parameter dimensions. You need to add one dimension to you data. Or use pymc3 trace object with `vars` info? Ah yes [I had to do that](https://github.com/aloctavodia/BAP/blob/master/extras/multinomial_ppcs.ipynb) the other day. My solution was to slice the array you give to `az.hpd`: `az.hpd(a[:, :, 0]).shape` should give you (10, 2) -- I'm using `az` instead of `pm` here as PyMC is based on ArviZ for diagnostics. So, if the `3`in the shape corresponds to categories for instance, you do `az.hpd(a[:, :, i])` for each of them and you should get a (10, 3, 2) by concatenation and transposition 🎉 That being said, implementing an `axis` paremeter could indeed be interesting -- if easily doable -- but I'm no expert here. @AlexAndorra Yeah, I used the concatenation and transposition idea that you outlined for my use-case, but wondering if its good to have some sort of general structure in arviz that clears up the confusion that might be caused by multiple array dimensions. Glad it worked out! As for adding a general structure to ArviZ, I find it a good idea but I'll defer to the core devs -- in particular, I don't know how widespread the use case is, and how painful it would be to add this functionnality to ArviZ. I think the most coherent approach here would be to use `make_ufunc` and `wrap_xarray_ufunc`, similarly to what is done with `ess` or `rhat` I'll like to work on this.

2020-03-14T12:10:22Z

arviz-devs/arviz

arviz-devs__arviz-1125

d1e1a2a0db994da72a4b23fb3f7a8237d7e3be4d

diff --git a/arviz/data/io_dict.py b/arviz/data/io_dict.py --- a/arviz/data/io_dict.py +++ b/arviz/data/io_dict.py @@ -16,6 +16,7 @@ def __init__( *, posterior=None, posterior_predictive=None, + predictions=None, sample_stats=None, log_likelihood=None, prior=None, @@ -23,11 +24,13 @@ def __init__( sample_stats_prior=None, observed_data=None, constant_data=None, + predictions_constant_data=None, coords=None, dims=None ): self.posterior = posterior self.posterior_predictive = posterior_predictive + self.predictions = predictions self.sample_stats = sample_stats self.log_likelihood = log_likelihood self.prior = prior @@ -35,6 +38,7 @@ def __init__( self.sample_stats_prior = sample_stats_prior self.observed_data = observed_data self.constant_data = constant_data + self.predictions_constant_data = predictions_constant_data self.coords = coords self.dims = dims @@ -89,6 +93,15 @@ def posterior_predictive_to_xarray(self): return dict_to_dataset(data, library=None, coords=self.coords, dims=self.dims) + @requires("predictions") + def predictions_to_xarray(self): + """Convert predictions to xarray.""" + data = self.predictions + if not isinstance(data, dict): + raise TypeError("DictConverter.predictions is not a dictionary") + + return dict_to_dataset(data, library=None, coords=self.coords, dims=self.dims) + @requires("prior") def prior_to_xarray(self): """Convert prior samples to xarray.""" @@ -116,45 +129,41 @@ def prior_predictive_to_xarray(self): return dict_to_dataset(data, library=None, coords=self.coords, dims=self.dims) - @requires("observed_data") - def observed_data_to_xarray(self): - """Convert observed_data to xarray.""" - data = self.observed_data + def data_to_xarray(self, dct, group): + """Convert data to xarray.""" + data = dct if not isinstance(data, dict): - raise TypeError("DictConverter.observed_data is not a dictionary") + raise TypeError("DictConverter.{} is not a dictionary".format(group)) if self.dims is None: dims = {} else: dims = self.dims - observed_data = dict() + new_data = dict() for key, vals in data.items(): vals = utils.one_de(vals) val_dims = dims.get(key) val_dims, coords = generate_dims_coords( vals.shape, key, dims=val_dims, coords=self.coords ) - observed_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=observed_data, attrs=make_attrs(library=None)) + new_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) + return xr.Dataset(data_vars=new_data, attrs=make_attrs(library=None)) + + @requires("observed_data") + def observed_data_to_xarray(self): + """Convert observed_data to xarray.""" + return self.data_to_xarray(self.observed_data, group="observed_data") @requires("constant_data") def constant_data_to_xarray(self): """Convert constant_data to xarray.""" - data = self.constant_data - if not isinstance(data, dict): - raise TypeError("DictConverter.constant_data is not a dictionary") - if self.dims is None: - dims = {} - else: - dims = self.dims - constant_data = dict() - for key, vals in data.items(): - vals = utils.one_de(vals) - val_dims = dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - constant_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=constant_data, attrs=make_attrs(library=None)) + return self.data_to_xarray(self.constant_data, group="constant_data") + + @requires("predictions_constant_data") + def predictions_constant_data_to_xarray(self): + """Convert predictions_constant_data to xarray.""" + return self.data_to_xarray( + self.predictions_constant_data, group="predictions_constant_data" + ) def to_inference_data(self): """Convert all available data to an InferenceData object. @@ -168,11 +177,13 @@ def to_inference_data(self): "sample_stats": self.sample_stats_to_xarray(), "log_likelihood": self.log_likelihood_to_xarray(), "posterior_predictive": self.posterior_predictive_to_xarray(), + "predictions": self.predictions_to_xarray(), "prior": self.prior_to_xarray(), "sample_stats_prior": self.sample_stats_prior_to_xarray(), "prior_predictive": self.prior_predictive_to_xarray(), "observed_data": self.observed_data_to_xarray(), "constant_data": self.constant_data_to_xarray(), + "predictions_constant_data": self.predictions_constant_data_to_xarray(), } ) @@ -182,6 +193,7 @@ def from_dict( posterior=None, *, posterior_predictive=None, + predictions=None, sample_stats=None, log_likelihood=None, prior=None, @@ -189,6 +201,7 @@ def from_dict( sample_stats_prior=None, observed_data=None, constant_data=None, + predictions_constant_data=None, coords=None, dims=None ): @@ -198,6 +211,7 @@ def from_dict( ---------- posterior : dict posterior_predictive : dict + predictions: dict sample_stats : dict log_likelihood : dict For stats functions, log likelihood data should be stored here. @@ -205,6 +219,7 @@ def from_dict( prior_predictive : dict observed_data : dict constant_data : dict + predictions_constant_data: dict coords : dict[str, iterable] A dictionary containing the values that are used as index. The key is the name of the dimension, the values are the index values. @@ -218,6 +233,7 @@ def from_dict( return DictConverter( posterior=posterior, posterior_predictive=posterior_predictive, + predictions=predictions, sample_stats=sample_stats, log_likelihood=log_likelihood, prior=prior, @@ -225,6 +241,7 @@ def from_dict( sample_stats_prior=sample_stats_prior, observed_data=observed_data, constant_data=constant_data, + predictions_constant_data=predictions_constant_data, coords=coords, dims=dims, ).to_inference_data() diff --git a/arviz/data/io_numpyro.py b/arviz/data/io_numpyro.py --- a/arviz/data/io_numpyro.py +++ b/arviz/data/io_numpyro.py @@ -20,7 +20,18 @@ class NumPyroConverter: ndraws = None # type: int def __init__( - self, *, posterior=None, prior=None, posterior_predictive=None, coords=None, dims=None + self, + *, + posterior=None, + prior=None, + posterior_predictive=None, + predictions=None, + constant_data=None, + predictions_constant_data=None, + coords=None, + dims=None, + pred_dims=None, + num_chains=1 ): """Convert NumPyro data into an InferenceData object. @@ -32,10 +43,20 @@ def __init__( Prior samples from a NumPyro model posterior_predictive : dict Posterior predictive samples for the posterior + predictions: dict + Out of sample predictions + constant_data: dict + Dictionary containing constant data variables mapped to their values. + predictions_constant_data: dict + Constant data used for out-of-sample predictions. coords : dict[str] -> list[str] Map of dimensions to coordinates dims : dict[str] -> list[str] Map variable names to their coordinates + pred_dims: dict + Dims for predictions data. Map variable names to their coordinates. + num_chains: int + Number of chains used for sampling. Ignored if posterior is present. """ import jax import numpyro @@ -43,10 +64,17 @@ def __init__( self.posterior = posterior self.prior = jax.device_get(prior) self.posterior_predictive = jax.device_get(posterior_predictive) + self.predictions = predictions + self.constant_data = constant_data + self.predictions_constant_data = predictions_constant_data self.coords = coords self.dims = dims + self.pred_dims = pred_dims self.numpyro = numpyro + def arbitrary_element(dct): + return next(iter(dct.values())) + if posterior is not None: samples = jax.device_get(self.posterior.get_samples(group_by_chain=True)) if not isinstance(samples, dict): @@ -65,7 +93,22 @@ def __init__( self._args = self.posterior._args # pylint: disable=protected-access self._kwargs = self.posterior._kwargs # pylint: disable=protected-access else: - self.nchains = self.ndraws = 0 + self.nchains = num_chains + get_from = None + if predictions is not None: + get_from = predictions + elif posterior_predictive is not None: + get_from = posterior_predictive + elif prior is not None: + get_from = prior + if get_from is None and constant_data is None and predictions_constant_data is None: + raise ValueError( + "When constructing InferenceData must have at least" + " one of posterior, prior, posterior_predictive or predictions." + ) + if get_from is not None: + aelem = arbitrary_element(get_from) + self.ndraws = aelem.shape[0] // self.nchains observations = {} if self.model is not None: @@ -120,11 +163,10 @@ def log_likelihood_to_xarray(self): data[obs_name] = np.reshape(log_like.copy(), shape) return dict_to_dataset(data, library=self.numpyro, dims=self.dims, coords=self.coords) - @requires("posterior_predictive") - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" + def translate_posterior_predictive_dict_to_xarray(self, dct, dims): + """Convert posterior_predictive or prediction samples to xarray.""" data = {} - for k, ary in self.posterior_predictive.items(): + for k, ary in dct.items(): shape = ary.shape if shape[0] == self.nchains and shape[1] == self.ndraws: data[k] = ary @@ -136,7 +178,19 @@ def posterior_predictive_to_xarray(self): "posterior predictive shape not compatible with number of chains and draws. " "This can mean that some draws or even whole chains are not represented." ) - return dict_to_dataset(data, library=self.numpyro, coords=self.coords, dims=self.dims) + return dict_to_dataset(data, library=self.numpyro, coords=self.coords, dims=dims) + + @requires("posterior_predictive") + def posterior_predictive_to_xarray(self): + """Convert posterior_predictive samples to xarray.""" + return self.translate_posterior_predictive_dict_to_xarray( + self.posterior_predictive, self.dims + ) + + @requires("predictions") + def predictions_to_xarray(self): + """Convert predictions to xarray.""" + return self.translate_posterior_predictive_dict_to_xarray(self.predictions, self.pred_dims) def priors_to_xarray(self): """Convert prior samples (and if possible prior predictive too) to xarray.""" @@ -184,6 +238,32 @@ def observed_data_to_xarray(self): observed_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) return xr.Dataset(data_vars=observed_data, attrs=make_attrs(library=self.numpyro)) + def convert_constant_data_to_xarray(self, dct, dims): + """Convert constant_data or predictions_constant_data to xarray.""" + if dims is None: + dims = {} + constant_data = {} + for name, vals in dct.items(): + vals = utils.one_de(vals) + val_dims = dims.get(name) + val_dims, coords = generate_dims_coords( + vals.shape, name, dims=val_dims, coords=self.coords + ) + # filter coords based on the dims + coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} + constant_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) + return xr.Dataset(data_vars=constant_data, attrs=make_attrs(library=self.numpyro)) + + @requires("constant_data") + def constant_data_to_xarray(self): + """Convert constant_data to xarray.""" + return self.convert_constant_data_to_xarray(self.constant_data, self.dims) + + @requires("predictions_constant_data") + def predictions_constant_data_to_xarray(self): + """Convert predictions_constant_data to xarray.""" + return self.convert_constant_data_to_xarray(self.predictions_constant_data, self.pred_dims) + def to_inference_data(self): """Convert all available data to an InferenceData object. @@ -197,13 +277,28 @@ def to_inference_data(self): "sample_stats": self.sample_stats_to_xarray(), "log_likelihood": self.log_likelihood_to_xarray(), "posterior_predictive": self.posterior_predictive_to_xarray(), + "predictions": self.predictions_to_xarray(), **self.priors_to_xarray(), "observed_data": self.observed_data_to_xarray(), + "constant_data": self.constant_data_to_xarray(), + "predictions_constant_data": self.predictions_constant_data_to_xarray(), } ) -def from_numpyro(posterior=None, *, prior=None, posterior_predictive=None, coords=None, dims=None): +def from_numpyro( + posterior=None, + *, + prior=None, + posterior_predictive=None, + predictions=None, + constant_data=None, + predictions_constant_data=None, + coords=None, + dims=None, + pred_dims=None, + num_chains=1 +): """Convert NumPyro data into an InferenceData object. Parameters @@ -214,15 +309,30 @@ def from_numpyro(posterior=None, *, prior=None, posterior_predictive=None, coord Prior samples from a NumPyro model posterior_predictive : dict Posterior predictive samples for the posterior + predictions: dict + Out of sample predictions + constant_data: dict + Dictionary containing constant data variables mapped to their values. + predictions_constant_data: dict + Constant data used for out-of-sample predictions. coords : dict[str] -> list[str] Map of dimensions to coordinates dims : dict[str] -> list[str] Map variable names to their coordinates + pred_dims: dict + Dims for predictions data. Map variable names to their coordinates. + num_chains: int + Number of chains used for sampling. Ignored if posterior is present. """ return NumPyroConverter( posterior=posterior, prior=prior, posterior_predictive=posterior_predictive, + predictions=predictions, + constant_data=constant_data, + predictions_constant_data=predictions_constant_data, coords=coords, dims=dims, + pred_dims=pred_dims, + num_chains=num_chains, ).to_inference_data() diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -120,8 +120,8 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: if get_from is None: # pylint: disable=line-too-long raise ValueError( - """When constructing InferenceData must have at least - one of trace, prior, posterior_predictive or predictions.""" + "When constructing InferenceData must have at least" + " one of trace, prior, posterior_predictive or predictions." ) aelem = arbitrary_element(get_from) diff --git a/arviz/data/io_pyro.py b/arviz/data/io_pyro.py --- a/arviz/data/io_pyro.py +++ b/arviz/data/io_pyro.py @@ -92,8 +92,8 @@ def arbitrary_element(dct): get_from = prior if get_from is None and constant_data is None and predictions_constant_data is None: raise ValueError( - """When constructing InferenceData must have at least - one of posterior, prior, posterior_predictive or predictions.""" + "When constructing InferenceData must have at least" + " one of posterior, prior, posterior_predictive or predictions." ) if get_from is not None: aelem = arbitrary_element(get_from)

diff --git a/arviz/tests/external_tests/test_data_numpyro.py b/arviz/tests/external_tests/test_data_numpyro.py --- a/arviz/tests/external_tests/test_data_numpyro.py +++ b/arviz/tests/external_tests/test_data_numpyro.py @@ -27,7 +27,25 @@ class Data: return Data - def get_inference_data(self, data, eight_schools_params): + @pytest.fixture(scope="class") + def predictions_params(self): + """Predictions data for eight schools.""" + return { + "J": 8, + "sigma": np.array([5.0, 7.0, 12.0, 4.0, 6.0, 10.0, 3.0, 9.0]), + } + + @pytest.fixture(scope="class") + def predictions_data(self, data, predictions_params): + """Generate predictions for predictions_params""" + posterior_samples = data.obj.get_samples() + model = data.obj.sampler.model + predictions = Predictive(model, posterior_samples)( + PRNGKey(2), predictions_params["J"], predictions_params["sigma"] + ) + return predictions + + def get_inference_data(self, data, eight_schools_params, predictions_data, predictions_params): posterior_samples = data.obj.get_samples() model = data.obj.sampler.model posterior_predictive = Predictive(model, posterior_samples)( @@ -36,21 +54,30 @@ def get_inference_data(self, data, eight_schools_params): prior = Predictive(model, num_samples=500)( PRNGKey(2), eight_schools_params["J"], eight_schools_params["sigma"] ) + predictions = predictions_data return from_numpyro( posterior=data.obj, prior=prior, posterior_predictive=posterior_predictive, - coords={"school": np.arange(eight_schools_params["J"])}, - dims={"theta": ["school"], "eta": ["school"]}, + predictions=predictions, + coords={ + "school": np.arange(eight_schools_params["J"]), + "school_pred": np.arange(predictions_params["J"]), + }, + dims={"theta": ["school"], "eta": ["school"], "obs": ["school"]}, + pred_dims={"theta": ["school_pred"], "eta": ["school_pred"], "obs": ["school_pred"]}, ) - def test_inference_data(self, data, eight_schools_params): - inference_data = self.get_inference_data(data, eight_schools_params) + def test_inference_data(self, data, eight_schools_params, predictions_data, predictions_params): + inference_data = self.get_inference_data( + data, eight_schools_params, predictions_data, predictions_params + ) test_dict = { "posterior": ["mu", "tau", "eta"], "sample_stats": ["diverging", "tree_size", "depth"], "log_likelihood": ["obs"], "posterior_predictive": ["obs"], + "predictions": ["obs"], "prior": ["mu", "tau", "eta"], "prior_predictive": ["obs"], "observed_data": ["obs"], @@ -58,6 +85,72 @@ def test_inference_data(self, data, eight_schools_params): fails = check_multiple_attrs(test_dict, inference_data) assert not fails + # test dims + dims = inference_data.posterior_predictive.dims["school"] + pred_dims = inference_data.predictions.dims["school_pred"] + assert dims == 8 + assert pred_dims == 8 + + def test_inference_data_no_posterior( + self, data, eight_schools_params, predictions_data, predictions_params + ): + posterior_samples = data.obj.get_samples() + model = data.obj.sampler.model + posterior_predictive = Predictive(model, posterior_samples)( + PRNGKey(1), eight_schools_params["J"], eight_schools_params["sigma"] + ) + prior = Predictive(model, num_samples=500)( + PRNGKey(2), eight_schools_params["J"], eight_schools_params["sigma"] + ) + predictions = predictions_data + constant_data = {"J": 8, "sigma": eight_schools_params["sigma"]} + predictions_constant_data = predictions_params + # only prior + inference_data = from_numpyro(prior=prior) + test_dict = {"prior": ["mu", "tau", "eta"]} + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails, "only prior: {}".format(fails) + # only posterior_predictive + inference_data = from_numpyro(posterior_predictive=posterior_predictive) + test_dict = {"posterior_predictive": ["obs"]} + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails, "only posterior_predictive: {}".format(fails) + # only predictions + inference_data = from_numpyro(predictions=predictions) + test_dict = {"predictions": ["obs"]} + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails, "only predictions: {}".format(fails) + # only constant_data + inference_data = from_numpyro(constant_data=constant_data) + test_dict = {"constant_data": ["J", "sigma"]} + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails, "only constant_data: {}".format(fails) + # only predictions_constant_data + inference_data = from_numpyro(predictions_constant_data=predictions_constant_data) + test_dict = {"predictions_constant_data": ["J", "sigma"]} + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails, "only predictions_constant_data: {}".format(fails) + # prior and posterior_predictive + idata = from_numpyro( + prior=prior, + posterior_predictive=posterior_predictive, + coords={"school": np.arange(eight_schools_params["J"])}, + dims={"theta": ["school"], "eta": ["school"]}, + ) + test_dict = {"posterior_predictive": ["obs"], "prior": ["mu", "tau", "eta", "obs"]} + fails = check_multiple_attrs(test_dict, idata) + assert not fails, "prior and posterior_predictive: {}".format(fails) + + def test_inference_data_only_posterior(self, data): + idata = from_numpyro(data.obj) + test_dict = { + "posterior": ["mu", "tau", "eta"], + "sample_stats": ["diverging"], + "log_likelihood": ["obs"], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails + def test_multiple_observed_rv(self): import numpyro import numpyro.distributions as dist @@ -89,3 +182,48 @@ def model_example_multiple_obs(y1=None, y2=None): # print(waic_results.waic, waic_results.waic_se) assert not fails assert not hasattr(inference_data.sample_stats, "log_likelihood") + + def test_inference_data_constant_data(self): + import numpyro + import numpyro.distributions as dist + from numpyro.infer import MCMC, NUTS + + x1 = 10 + x2 = 12 + y1 = np.random.randn(10) + + def model_constant_data(x, y1=None): + _x = numpyro.sample("x", dist.Normal(1, 3)) + numpyro.sample("y1", dist.Normal(x * _x, 1), obs=y1) + + nuts_kernel = NUTS(model_constant_data) + mcmc = MCMC(nuts_kernel, num_samples=10, num_warmup=2) + mcmc.run(PRNGKey(0), x=x1, y1=y1) + posterior = mcmc.get_samples() + posterior_predictive = Predictive(model_constant_data, posterior)(PRNGKey(1), x1) + predictions = Predictive(model_constant_data, posterior)(PRNGKey(2), x2) + inference_data = from_numpyro( + mcmc, + posterior_predictive=posterior_predictive, + predictions=predictions, + constant_data={"x1": x1}, + predictions_constant_data={"x2": x2}, + ) + test_dict = { + "posterior": ["x"], + "posterior_predictive": ["y1"], + "sample_stats": ["diverging"], + "log_likelihood": ["y1"], + "predictions": ["y1"], + "observed_data": ["y1"], + "constant_data": ["x1"], + "predictions_constant_data": ["x2"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + + def test_inference_data_num_chains(self, predictions_data, chains): + predictions = predictions_data + inference_data = from_numpyro(predictions=predictions, num_chains=chains) + nchains = inference_data.predictions.dims["chain"] + assert nchains == chains

New predictions group! ## Tell us about it Two new groups were recently added to the `InferenceData` schema: [`predictions`](https://arviz-devs.github.io/arviz/schema/schema.html#predictions) and [`predictions_constant_data`](https://arviz-devs.github.io/arviz/schema/schema.html#predictions-constant-data). #983 added them in `from_pymc3`, however it is still pending for the other libraries. Current status: * [x] from_pymc3 * [x] from_pystan * [x] from_pyro * [x] from_cmdstan * [x] from_cmdstanpy * [x] (from_emcee) * [ ] from_numpyro * [ ] from_dict ## Thoughts on implementation ### PyStan @ahartikainen what do you think would be better? Mimicking `posterior_predictive` or `log_likelihood` and adding the dict option to rename variables on creation?

I think following `posterior_predictive` should be the correct For future, we need to think about how do we handle post-run generated_quantities. Can I work on this? Yes! #1032 added predictions and predictions constant data to `from_pystan` (we forgot to link here) but there are still converters missing it. @OriolAbril which converters are you referring to? The ```predictions_to_xarray``` and ```predictions_constant_data_to_xarray``` are already present Other converters such as `from_pyro`, `from_cmdstan` and other inference libraries allowing out-of- sample posterior predictive sampling. Should the ```predictions``` implementation in ```from_pyro``` also follow the ```posterior_predictive``` implementation? Also, it doesn't seem to have ```constant_data``` so ```predictions_constant_data``` and ```constant_data``` both are to be added? I don't really know the details of pyro well enough, I don't know if it is even possible or if this data is stored in pyro. A good start would be to replicate the model and workflow in the [examples](https://arviz-devs.github.io/arviz/schema/schema.html#examples) used to illustrate the schema specification. There is a version in PyStan and in PyMC3 implementing the same model and populating all the groups in InferenceData (with the exception of sample_stats_prior in PyMC3) `from_cmdstan` and `from_cmdstanpy` are implemented in #1064 Hi, I was trying to implement [this](https://arviz-devs.github.io/arviz/schema/PyMC3_schema_example.html) example in Pyro. I have come up with [this](https://github.com/nitishp25/arviz-playground/blob/master/arviz-pyro-example.ipynb) so far but as you can see I have incorrect dimensions in the InferenceData. Can you please help me understand what's the problem? The dimension looks ok (the extra dims are 1, the data contained there looks correct). Maybe it is an issue with a missing squeeze? Also, it looks like you have no log_likelihood group, are you using latest pyro release and arviz master? > The dimension looks ok (the extra dims are 1, the data contained there looks correct). Maybe it is an issue with a missing squeeze? If you compare it with the [PyMC3 example](https://arviz-devs.github.io/arviz/schema/PyMC3_schema_example.html) then the ```prior``` and ```log_likelihood``` have different dimensions right? I don't understand how the parameters have entered the ```prior``` dimensions. Does this require a squeeze? > Also, it looks like you have no log_likelihood group, are you using latest pyro release and arviz master? I had not updated arviz but now I have and you can [review again](https://github.com/nitishp25/arviz-playground/blob/master/arviz-pyro-example.ipynb). In the case of log_likelihood it looks like an error of dimension handling in io_pyro. In the case of the prior, all extra dimensions (b0_dim_o, c1_dim_0,...) have length 1 which is why I believe a squeeze may solve the "problem". The data is the same for both a shape `(2,)` array `[3, 4]` and a shape `(2,1)` array `[[3], [4]]` I was reading the code from my mobile on the train, so double check and it looks like log likelihood to xarray uses dims instead of self.dims, should be an easy fix. I am a little bit puzzled by the extra dims in prior, a squeeze (before adding the chain dim) should solve the issue, but they probably shold not be there to begin with > I was reading the code from my mobile on the train, so double check and it looks like log likelihood to xarray uses dims instead of self.dims, should be an easy fix. Got it. > I am a little bit puzzled by the extra dims in prior, a squeeze (before adding the chain dim) should solve the issue, but they probably shold not be there to begin with Does it have anything to do with my implementation? I don't have much experience with Pyro. I'll still keep looking for something in ```io_pyro```. Is posterior squeezing the input in io_pyro? In Pyro 1D arrays are actually 2D with last dimension being 1. --> column vector > Is posterior squeezing the input in io_pyro? There seems to be no squeeze for posterior. > In Pyro 1D arrays are actually 2D with last dimension being 1. --> column vector Yes, I just checked the shape of ```prior``` that is passed to ```from_pyro``` and it is (400, 1). Calling ```expand_dims``` on this makes its shape (1, 400, 1). I think this is causing the extra dim right? Hi, I am now looking to get predictions on new data but what I get is the data has shape of ```posterior_predictive``` even though the new data has different shape than the one passed to get ```posterior```. I've even tried to remove ```pyro.plate``` or use ```for loop``` instead and also used ```guide``` but the results were same. It seems like everytime you need new predictions you generate a new trace in Pyro? But there must be someway to get the right dims on new data with same trace. Here's the [example](https://github.com/nitishp25/arviz-playground/blob/master/arviz-pyro-example.ipynb) @OriolAbril @ahartikainen. @nitishp25 I don't know how to fix this, as I said above I don't even know if it is possible to get out of sample posterior predictive samples (what we shortened to predictions) in Pyro. If you want to keep working on this maybe [their forum](https://forum.pyro.ai/) is good place to get some help. Side comment: I was looking at `posterior_predictive` and `prior_predictive` groups and it looks like Pyro is only repeating over and over the observed data. I can't explain why though, it could either be because of an error in the notebook or because of a bug in Pyro. I have asked [these questions](https://forum.pyro.ai/t/out-of-sample-predictions-in-pyro/1620) on their forum. I'll wait for the reply. Thanks to the Pyro forum I was able to figure out how to make out-of-sample predictions. Here's [the notebook](https://github.com/nitishp25/arviz-playground/blob/master/arviz-pyro-example.ipynb). I'm thinking of implementing this data similar to PyMC3 by creating a ```from_pyro_predictions``` API. And for ```constant_data``` I'm thinking of adding a ```constant_data``` argument to ```from_pyro``` and ```predictions_constant_data``` argument to ```from_pyro_predictions```. What do you think @OriolAbril @ahartikainen? I am not sure about following pymc3 or pystan approach. PyMC3 needs a different function, because the information stored in the model is needed for the conversion, and to obtain predictions, the model changes, so a single function would not be able to access both the old and new instances of the model. It is probably better to follow pystan converter, because then both options become possible; users can merge the current inference data object with the new inference data containing only predictions (pymc3 like workflow) or generate the inference data with everything at once. In this case it would then be important to test that `from_pyro` works with only `predictions` and `predictions_constant_data` arguments. Yes that makes more sense. Also note that currently there seems to be no way to store constant data directly in Pyro, there exists [```pyro.determinictics```](http://docs.pyro.ai/en/stable/primitives.html#pyro.deterministic) but it samples the constant data with the rest of the data. So, the only way for the user is to manually pass the dict of `constant_data` and `predictions_constant_data` containing variable names to data values and we basically just use `dict_to_dataset`. So is it worth it to add these groups right now? I'd say it is useful to include the group because it let's users keep track of how where the predictions generated. For example, in the model you are working with, if you only get the inference data without the constant data groups you are missing quite some information as you would not have the times used to generate the posterior predictive samples. Why it (group input) can not be a dictionary that user provides? > Why it (group input) can not be a dictionary that user provides? I _think_ this was the idea? @nitishp25 > Why it (group input) can not be a dictionary that user provides? It can be. I was just trying to find an easier way from the user's perspective like if Pyro had an option like `pymc3.data` or `pym3.deterministics` then all the user would have to do would be to declare the constant data in the model and ArviZ would convert these automatically to inference data (like in PyMC3) without having to pass an argument separately for them. But since this is not yet possible in Pyro, a dictionary can definitely be the desired input type. I was just looking for a better way and I thought if waiting for such a solution is worth it or not :)

2020-03-26T13:46:28Z

arviz-devs/arviz

arviz-devs__arviz-1142

3352a8adfc42bd5ba2aa90efbde28df18f1eb74d

diff --git a/arviz/__init__.py b/arviz/__init__.py --- a/arviz/__init__.py +++ b/arviz/__init__.py @@ -25,8 +25,8 @@ from .rcparams import rcParams, rc_context from .data import * -from .plots import * from .stats import * +from .plots import * from .utils import Numba, interactive_backend from .plots import backends from .wrappers import * diff --git a/arviz/numeric_utils.py b/arviz/numeric_utils.py new file mode 100644 --- /dev/null +++ b/arviz/numeric_utils.py @@ -0,0 +1,215 @@ +"""Numerical utility functions for ArviZ.""" +import warnings +import numpy as np +from scipy.signal import convolve, convolve2d +from scipy.signal.windows import gaussian +from scipy.sparse import coo_matrix + +from .stats.stats_utils import histogram +from .utils import _stack, _dot, _cov + + +def _fast_kde(x, cumulative=False, bw=4.5, xmin=None, xmax=None): + """Fast Fourier transform-based Gaussian kernel density estimate (KDE). + + The code was adapted from https://github.com/mfouesneau/faststats + + Parameters + ---------- + x : Numpy array or list + cumulative : bool + If true, estimate the cdf instead of the pdf + bw : float + Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the + smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule + of thumb (the default rule used by SciPy). + xmin : float + Manually set lower limit. + xmax : float + Manually set upper limit. + + Returns + ------- + density: A gridded 1D KDE of the input points (x) + xmin: minimum value of x + xmax: maximum value of x + """ + x = np.asarray(x, dtype=float) + x = x[np.isfinite(x)] + if x.size == 0: + warnings.warn("kde plot failed, you may want to check your data") + return np.array([np.nan]), np.nan, np.nan + + len_x = len(x) + n_points = 200 if (xmin or xmax) is None else 500 + + if xmin is None: + xmin = np.min(x) + if xmax is None: + xmax = np.max(x) + + assert np.min(x) >= xmin + assert np.max(x) <= xmax + + log_len_x = np.log(len_x) * bw + + n_bins = min(int(len_x ** (1 / 3) * log_len_x * 2), n_points) + if n_bins < 2: + warnings.warn("kde plot failed, you may want to check your data") + return np.array([np.nan]), np.nan, np.nan + + # hist, bin_edges = np.histogram(x, bins=n_bins, range=(xmin, xmax)) + # grid = hist / (hist.sum() * np.diff(bin_edges)) + + _, grid, _ = histogram(x, n_bins, range_hist=(xmin, xmax)) + + scotts_factor = len_x ** (-0.2) + kern_nx = int(scotts_factor * 2 * np.pi * log_len_x) + kernel = gaussian(kern_nx, scotts_factor * log_len_x) + + npad = min(n_bins, 2 * kern_nx) + grid = np.concatenate([grid[npad:0:-1], grid, grid[n_bins : n_bins - npad : -1]]) + density = convolve(grid, kernel, mode="same", method="direct")[npad : npad + n_bins] + norm_factor = (2 * np.pi * log_len_x ** 2 * scotts_factor ** 2) ** 0.5 + + density /= norm_factor + + if cumulative: + density = density.cumsum() / density.sum() + + return density, xmin, xmax + + +def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): + """ + 2D fft-based Gaussian kernel density estimate (KDE). + + The code was adapted from https://github.com/mfouesneau/faststats + + Parameters + ---------- + x : Numpy array or list + y : Numpy array or list + gridsize : tuple + Number of points used to discretize data. Use powers of 2 for fft optimization + circular: bool + If True, use circular boundaries. Defaults to False + Returns + ------- + grid: A gridded 2D KDE of the input points (x, y) + xmin: minimum value of x + xmax: maximum value of x + ymin: minimum value of y + ymax: maximum value of y + """ + x = np.asarray(x, dtype=float) + x = x[np.isfinite(x)] + y = np.asarray(y, dtype=float) + y = y[np.isfinite(y)] + + xmin, xmax = x.min(), x.max() + ymin, ymax = y.min(), y.max() + + len_x = len(x) + weights = np.ones(len_x) + n_x, n_y = gridsize + + d_x = (xmax - xmin) / (n_x - 1) + d_y = (ymax - ymin) / (n_y - 1) + + xyi = _stack(x, y).T + xyi -= [xmin, ymin] + xyi /= [d_x, d_y] + xyi = np.floor(xyi, xyi).T + + scotts_factor = len_x ** (-1 / 6) + cov = _cov(xyi) + std_devs = np.diag(cov ** 0.5) + kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) + + inv_cov = np.linalg.inv(cov * scotts_factor ** 2) + + x_x = np.arange(kern_nx) - kern_nx / 2 + y_y = np.arange(kern_ny) - kern_ny / 2 + x_x, y_y = np.meshgrid(x_x, y_y) + + kernel = _stack(x_x.flatten(), y_y.flatten()) + kernel = _dot(inv_cov, kernel) * kernel + kernel = np.exp(-kernel.sum(axis=0) / 2) + kernel = kernel.reshape((int(kern_ny), int(kern_nx))) + + boundary = "wrap" if circular else "symm" + + grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() + grid = convolve2d(grid, kernel, mode="same", boundary=boundary) + + norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor ** 2) + norm_factor = len_x * d_x * d_y * norm_factor ** 0.5 + + grid /= norm_factor + + return grid, xmin, xmax, ymin, ymax + + +def get_bins(values): + """ + Automatically compute the number of bins for discrete variables. + + Parameters + ---------- + values = numpy array + values + + Returns + ------- + array with the bins + + Notes + ----- + Computes the width of the bins by taking the maximun of the Sturges and the Freedman-Diaconis + estimators. Acording to numpy `np.histogram` this provides good all around performance. + + The Sturges is a very simplistic estimator based on the assumption of normality of the data. + This estimator has poor performance for non-normal data, which becomes especially obvious for + large data sets. The estimate depends only on size of the data. + + The Freedman-Diaconis rule uses interquartile range (IQR) to estimate the binwidth. + It is considered a robusts version of the Scott rule as the IQR is less affected by outliers + than the standard deviation. However, the IQR depends on fewer points than the standard + deviation, so it is less accurate, especially for long tailed distributions. + """ + x_min = values.min().astype(int) + x_max = values.max().astype(int) + + # Sturges histogram bin estimator + bins_sturges = (x_max - x_min) / (np.log2(values.size) + 1) + + # The Freedman-Diaconis histogram bin estimator. + iqr = np.subtract(*np.percentile(values, [75, 25])) # pylint: disable=assignment-from-no-return + bins_fd = 2 * iqr * values.size ** (-1 / 3) + + width = round(np.max([1, bins_sturges, bins_fd])).astype(int) + + return np.arange(x_min, x_max + width + 1, width) + + +def _sturges_formula(dataset, mult=1): + """Use Sturges' formula to determine number of bins. + + See https://en.wikipedia.org/wiki/Histogram#Sturges'_formula + or https://doi.org/10.1080%2F01621459.1926.10502161 + + Parameters + ---------- + dataset: xarray.DataSet + Must have the `draw` dimension + + mult: float + Used to scale the number of bins up or down. Default is 1 for Sturges' formula. + + Returns + ------- + int + Number of bins to use + """ + return int(np.ceil(mult * np.log2(dataset.draw.size)) + 1) diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -20,7 +20,7 @@ from .rankplot import plot_rank from .traceplot import plot_trace from .violinplot import plot_violin -from .plot_utils import _fast_kde, _fast_kde_2d +from ..numeric_utils import _fast_kde_2d __all__ = [ @@ -35,7 +35,6 @@ "plot_hpd", "plot_joint", "plot_kde", - "_fast_kde", "_fast_kde_2d", "plot_khat", "plot_loo_pit", diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py --- a/arviz/plots/backends/bokeh/densityplot.py +++ b/arviz/plots/backends/bokeh/densityplot.py @@ -10,11 +10,9 @@ make_label, _create_axes_grid, calculate_point_estimate, - _fast_kde, - get_bins, ) from ....stats import hpd -from ....stats.stats_utils import histogram +from ....numeric_utils import _fast_kde, histogram, get_bins def plot_density( diff --git a/arviz/plots/backends/bokeh/distplot.py b/arviz/plots/backends/bokeh/distplot.py --- a/arviz/plots/backends/bokeh/distplot.py +++ b/arviz/plots/backends/bokeh/distplot.py @@ -6,7 +6,7 @@ from . import backend_kwarg_defaults from .. import show_layout from ...kdeplot import plot_kde -from ...plot_utils import get_bins +from ....numeric_utils import get_bins def plot_dist( diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -12,11 +12,11 @@ from . import backend_kwarg_defaults from .. import show_layout -from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins, _fast_kde +from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label from ....rcparams import rcParams from ....stats import hpd from ....stats.diagnostics import _ess, _rhat -from ....stats.stats_utils import histogram +from ....numeric_utils import _fast_kde, histogram, get_bins from ....utils import conditional_jit diff --git a/arviz/plots/backends/bokeh/posteriorplot.py b/arviz/plots/backends/bokeh/posteriorplot.py --- a/arviz/plots/backends/bokeh/posteriorplot.py +++ b/arviz/plots/backends/bokeh/posteriorplot.py @@ -14,9 +14,9 @@ format_sig_figs, round_num, calculate_point_estimate, - get_bins, ) from ....stats import hpd +from ....numeric_utils import get_bins def plot_posterior( diff --git a/arviz/plots/backends/bokeh/ppcplot.py b/arviz/plots/backends/bokeh/ppcplot.py --- a/arviz/plots/backends/bokeh/ppcplot.py +++ b/arviz/plots/backends/bokeh/ppcplot.py @@ -4,11 +4,8 @@ from . import backend_kwarg_defaults from .. import show_layout from ...kdeplot import plot_kde, _fast_kde -from ...plot_utils import ( - _create_axes_grid, - get_bins, -) -from ....stats.stats_utils import histogram +from ...plot_utils import _create_axes_grid +from ....numeric_utils import histogram, get_bins def plot_ppc( diff --git a/arviz/plots/backends/bokeh/violinplot.py b/arviz/plots/backends/bokeh/violinplot.py --- a/arviz/plots/backends/bokeh/violinplot.py +++ b/arviz/plots/backends/bokeh/violinplot.py @@ -4,10 +4,9 @@ from . import backend_kwarg_defaults from .. import show_layout -from ...kdeplot import _fast_kde -from ...plot_utils import get_bins, make_label, _create_axes_grid +from ....numeric_utils import _fast_kde, histogram, get_bins +from ...plot_utils import make_label, _create_axes_grid from ....stats import hpd -from ....stats.stats_utils import histogram def plot_violin( diff --git a/arviz/plots/backends/matplotlib/densityplot.py b/arviz/plots/backends/matplotlib/densityplot.py --- a/arviz/plots/backends/matplotlib/densityplot.py +++ b/arviz/plots/backends/matplotlib/densityplot.py @@ -8,9 +8,8 @@ make_label, _create_axes_grid, calculate_point_estimate, - _fast_kde, - get_bins, ) +from ....numeric_utils import _fast_kde, get_bins def plot_density( diff --git a/arviz/plots/backends/matplotlib/distplot.py b/arviz/plots/backends/matplotlib/distplot.py --- a/arviz/plots/backends/matplotlib/distplot.py +++ b/arviz/plots/backends/matplotlib/distplot.py @@ -5,7 +5,7 @@ from . import backend_show from ...kdeplot import plot_kde from ...plot_utils import matplotlib_kwarg_dealiaser -from ...plot_utils import get_bins +from ....numeric_utils import get_bins def plot_dist( diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -9,8 +9,8 @@ from . import backend_kwarg_defaults, backend_show from ....stats import hpd from ....stats.diagnostics import _ess, _rhat -from ....stats.stats_utils import histogram -from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label, get_bins, _fast_kde +from ....numeric_utils import _fast_kde, histogram, get_bins +from ...plot_utils import _scale_fig_size, xarray_var_iter, make_label from ....utils import conditional_jit diff --git a/arviz/plots/backends/matplotlib/loopitplot.py b/arviz/plots/backends/matplotlib/loopitplot.py --- a/arviz/plots/backends/matplotlib/loopitplot.py +++ b/arviz/plots/backends/matplotlib/loopitplot.py @@ -3,7 +3,7 @@ import numpy as np from . import backend_kwarg_defaults, backend_show -from ...plot_utils import _fast_kde +from ....numeric_utils import _fast_kde from ...hpdplot import plot_hpd diff --git a/arviz/plots/backends/matplotlib/posteriorplot.py b/arviz/plots/backends/matplotlib/posteriorplot.py --- a/arviz/plots/backends/matplotlib/posteriorplot.py +++ b/arviz/plots/backends/matplotlib/posteriorplot.py @@ -13,8 +13,8 @@ format_sig_figs, round_num, calculate_point_estimate, - get_bins, ) +from ....numeric_utils import get_bins def plot_posterior( diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -8,10 +8,8 @@ from ...plot_utils import ( make_label, _create_axes_grid, - get_bins, - _fast_kde, ) -from ....stats.stats_utils import histogram +from ....numeric_utils import _fast_kde, histogram, get_bins def plot_ppc( diff --git a/arviz/plots/backends/matplotlib/traceplot.py b/arviz/plots/backends/matplotlib/traceplot.py --- a/arviz/plots/backends/matplotlib/traceplot.py +++ b/arviz/plots/backends/matplotlib/traceplot.py @@ -9,7 +9,8 @@ from . import backend_kwarg_defaults, backend_show from ...distplot import plot_dist from ...rankplot import plot_rank -from ...plot_utils import _scale_fig_size, get_bins, make_label, format_coords_as_labels +from ...plot_utils import _scale_fig_size, make_label, format_coords_as_labels +from ....numeric_utils import get_bins def plot_trace( diff --git a/arviz/plots/backends/matplotlib/violinplot.py b/arviz/plots/backends/matplotlib/violinplot.py --- a/arviz/plots/backends/matplotlib/violinplot.py +++ b/arviz/plots/backends/matplotlib/violinplot.py @@ -4,8 +4,8 @@ from . import backend_show from ....stats import hpd -from ....stats.stats_utils import histogram -from ...plot_utils import get_bins, make_label, _create_axes_grid, _fast_kde +from ....numeric_utils import _fast_kde, histogram, get_bins +from ...plot_utils import make_label, _create_axes_grid def plot_violin( diff --git a/arviz/plots/distplot.py b/arviz/plots/distplot.py --- a/arviz/plots/distplot.py +++ b/arviz/plots/distplot.py @@ -2,7 +2,8 @@ """Plot distribution as histogram or kernel density estimates.""" import xarray as xr -from .plot_utils import get_bins, get_plotting_function, matplotlib_kwarg_dealiaser +from .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser +from ..numeric_utils import get_bins from ..data import InferenceData from ..rcparams import rcParams diff --git a/arviz/plots/elpdplot.py b/arviz/plots/elpdplot.py --- a/arviz/plots/elpdplot.py +++ b/arviz/plots/elpdplot.py @@ -6,7 +6,6 @@ from ..data import convert_to_inference_data from .plot_utils import ( - get_coords, format_coords_as_labels, color_from_dim, get_plotting_function, @@ -14,6 +13,7 @@ ) from ..stats import waic, loo, ELPDData from ..rcparams import rcParams +from ..utils import get_coords def plot_elpd( diff --git a/arviz/plots/essplot.py b/arviz/plots/essplot.py --- a/arviz/plots/essplot.py +++ b/arviz/plots/essplot.py @@ -8,13 +8,12 @@ xarray_var_iter, _scale_fig_size, default_grid, - get_coords, filter_plotters_list, get_plotting_function, matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams -from ..utils import _var_names +from ..utils import _var_names, get_coords def plot_ess( diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -1,7 +1,7 @@ """Forest plot.""" from ..data import convert_to_dataset -from .plot_utils import get_coords, get_plotting_function -from ..utils import _var_names +from .plot_utils import get_plotting_function +from ..utils import _var_names, get_coords from ..rcparams import rcParams diff --git a/arviz/plots/jointplot.py b/arviz/plots/jointplot.py --- a/arviz/plots/jointplot.py +++ b/arviz/plots/jointplot.py @@ -5,12 +5,11 @@ from .plot_utils import ( _scale_fig_size, xarray_var_iter, - get_coords, get_plotting_function, matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams -from ..utils import _var_names +from ..utils import _var_names, get_coords def plot_joint( diff --git a/arviz/plots/kdeplot.py b/arviz/plots/kdeplot.py --- a/arviz/plots/kdeplot.py +++ b/arviz/plots/kdeplot.py @@ -3,7 +3,8 @@ import xarray as xr from ..data import InferenceData -from .plot_utils import get_plotting_function, _fast_kde, _fast_kde_2d +from ..numeric_utils import _fast_kde, _fast_kde_2d +from .plot_utils import get_plotting_function from ..rcparams import rcParams diff --git a/arviz/plots/khatplot.py b/arviz/plots/khatplot.py --- a/arviz/plots/khatplot.py +++ b/arviz/plots/khatplot.py @@ -7,7 +7,6 @@ from .plot_utils import ( _scale_fig_size, - get_coords, color_from_dim, format_coords_as_labels, get_plotting_function, @@ -15,6 +14,7 @@ ) from ..stats import ELPDData from ..rcparams import rcParams +from ..utils import get_coords def plot_khat( diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py --- a/arviz/plots/loopitplot.py +++ b/arviz/plots/loopitplot.py @@ -5,10 +5,10 @@ from xarray import DataArray from ..stats import loo_pit as _loo_pit +from ..numeric_utils import _fast_kde from .plot_utils import ( _scale_fig_size, get_plotting_function, - _fast_kde, matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams diff --git a/arviz/plots/mcseplot.py b/arviz/plots/mcseplot.py --- a/arviz/plots/mcseplot.py +++ b/arviz/plots/mcseplot.py @@ -8,13 +8,12 @@ xarray_var_iter, _scale_fig_size, default_grid, - get_coords, filter_plotters_list, get_plotting_function, matplotlib_kwarg_dealiaser, ) from ..rcparams import rcParams -from ..utils import _var_names +from ..utils import _var_names, get_coords def plot_mcse( diff --git a/arviz/plots/pairplot.py b/arviz/plots/pairplot.py --- a/arviz/plots/pairplot.py +++ b/arviz/plots/pairplot.py @@ -3,9 +3,9 @@ import numpy as np from ..data import convert_to_dataset, convert_to_inference_data -from .plot_utils import xarray_to_ndarray, get_coords, get_plotting_function +from .plot_utils import xarray_to_ndarray, get_plotting_function from ..rcparams import rcParams -from ..utils import _var_names +from ..utils import _var_names, get_coords def plot_pair( diff --git a/arviz/plots/parallelplot.py b/arviz/plots/parallelplot.py --- a/arviz/plots/parallelplot.py +++ b/arviz/plots/parallelplot.py @@ -3,9 +3,9 @@ from scipy.stats.mstats import rankdata from ..data import convert_to_dataset -from .plot_utils import _scale_fig_size, xarray_to_ndarray, get_coords, get_plotting_function +from .plot_utils import _scale_fig_size, xarray_to_ndarray, get_plotting_function from ..rcparams import rcParams -from ..utils import _var_names, _numba_var +from ..utils import _var_names, _numba_var, get_coords from ..stats.stats_utils import stats_variance_2d as svar diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -3,9 +3,6 @@ from typing import Dict, Any from itertools import product, tee import importlib -from scipy.signal import convolve, convolve2d -from scipy.sparse import coo_matrix -from scipy.signal.windows import gaussian from scipy.stats import mode import packaging @@ -15,8 +12,7 @@ import matplotlib.cbook as cbook import xarray as xr - -from ..utils import conditional_jit, _stack +from ..numeric_utils import _fast_kde from ..rcparams import rcParams KwargSpec = Dict[str, Any] @@ -99,70 +95,6 @@ def _scale_fig_size(figsize, textsize, rows=1, cols=1): return (width, height), ax_labelsize, titlesize, xt_labelsize, linewidth, markersize -def get_bins(values): - """ - Automatically compute the number of bins for discrete variables. - - Parameters - ---------- - values = numpy array - values - - Returns - ------- - array with the bins - - Notes - ----- - Computes the width of the bins by taking the maximun of the Sturges and the Freedman-Diaconis - estimators. Acording to numpy `np.histogram` this provides good all around performance. - - The Sturges is a very simplistic estimator based on the assumption of normality of the data. - This estimator has poor performance for non-normal data, which becomes especially obvious for - large data sets. The estimate depends only on size of the data. - - The Freedman-Diaconis rule uses interquartile range (IQR) to estimate the binwidth. - It is considered a robusts version of the Scott rule as the IQR is less affected by outliers - than the standard deviation. However, the IQR depends on fewer points than the standard - deviation, so it is less accurate, especially for long tailed distributions. - """ - x_min = values.min().astype(int) - x_max = values.max().astype(int) - - # Sturges histogram bin estimator - bins_sturges = (x_max - x_min) / (np.log2(values.size) + 1) - - # The Freedman-Diaconis histogram bin estimator. - iqr = np.subtract(*np.percentile(values, [75, 25])) # pylint: disable=assignment-from-no-return - bins_fd = 2 * iqr * values.size ** (-1 / 3) - - width = round(np.max([1, bins_sturges, bins_fd])).astype(int) - - return np.arange(x_min, x_max + width + 1, width) - - -def _sturges_formula(dataset, mult=1): - """Use Sturges' formula to determine number of bins. - - See https://en.wikipedia.org/wiki/Histogram#Sturges'_formula - or https://doi.org/10.1080%2F01621459.1926.10502161 - - Parameters - ---------- - dataset: xarray.DataSet - Must have the `draw` dimension - - mult: float - Used to scale the number of bins up or down. Default is 1 for Sturges' formula. - - Returns - ------- - int - Number of bins to use - """ - return int(np.ceil(mult * np.log2(dataset.draw.size)) + 1) - - def default_grid(n_items, max_cols=4, min_cols=3): # noqa: D202 """Make a grid for subplots. @@ -529,51 +461,6 @@ def xarray_to_ndarray(data, *, var_names=None, combined=True): return unpacked_var_names, unpacked_data -def get_coords(data, coords): - """Subselects xarray DataSet or DataArray object to provided coords. Raises exception if fails. - - Raises - ------ - ValueError - If coords name are not available in data - - KeyError - If coords dims are not available in data - - Returns - ------- - data: xarray - xarray.DataSet or xarray.DataArray object, same type as input - """ - if not isinstance(data, (list, tuple)): - try: - return data.sel(**coords) - - except ValueError: - invalid_coords = set(coords.keys()) - set(data.coords.keys()) - raise ValueError("Coords {} are invalid coordinate keys".format(invalid_coords)) - - except KeyError as err: - raise KeyError( - ( - "Coords should follow mapping format {{coord_name:[dim1, dim2]}}. " - "Check that coords structure is correct and" - " dimensions are valid. {}" - ).format(err) - ) - if not isinstance(coords, (list, tuple)): - coords = [coords] * len(data) - data_subset = [] - for idx, (datum, coords_dict) in enumerate(zip(data, coords)): - try: - data_subset.append(get_coords(datum, coords_dict)) - except ValueError as err: - raise ValueError("Error in data[{}]: {}".format(idx, err)) - except KeyError as err: - raise KeyError("Error in data[{}]: {}".format(idx, err)) - return data_subset - - def color_from_dim(dataarray, dim_name): """Return colors and color mapping of a DataArray using coord values as color code. @@ -744,179 +631,6 @@ def calculate_point_estimate(point_estimate, values, bw=4.5): return point_value -def _fast_kde(x, cumulative=False, bw=4.5, xmin=None, xmax=None): - """Fast Fourier transform-based Gaussian kernel density estimate (KDE). - - The code was adapted from https://github.com/mfouesneau/faststats - - Parameters - ---------- - x : Numpy array or list - cumulative : bool - If true, estimate the cdf instead of the pdf - bw : float - Bandwidth scaling factor for the KDE. Should be larger than 0. The higher this number the - smoother the KDE will be. Defaults to 4.5 which is essentially the same as the Scott's rule - of thumb (the default rule used by SciPy). - xmin : float - Manually set lower limit. - xmax : float - Manually set upper limit. - - Returns - ------- - density: A gridded 1D KDE of the input points (x) - xmin: minimum value of x - xmax: maximum value of x - """ - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - if x.size == 0: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - len_x = len(x) - n_points = 200 if (xmin or xmax) is None else 500 - - if xmin is None: - xmin = np.min(x) - if xmax is None: - xmax = np.max(x) - - assert np.min(x) >= xmin - assert np.max(x) <= xmax - - log_len_x = np.log(len_x) * bw - - n_bins = min(int(len_x ** (1 / 3) * log_len_x * 2), n_points) - if n_bins < 2: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - hist, bin_edges = np.histogram(x, bins=n_bins, range=(xmin, xmax)) - grid = hist / (hist.sum() * np.diff(bin_edges)) - - # _, grid, _ = histogram(x, n_bins, range_hist=(xmin, xmax)) - - scotts_factor = len_x ** (-0.2) - kern_nx = int(scotts_factor * 2 * np.pi * log_len_x) - kernel = gaussian(kern_nx, scotts_factor * log_len_x) - - npad = min(n_bins, 2 * kern_nx) - grid = np.concatenate([grid[npad:0:-1], grid, grid[n_bins : n_bins - npad : -1]]) - density = convolve(grid, kernel, mode="same", method="direct")[npad : npad + n_bins] - norm_factor = (2 * np.pi * log_len_x ** 2 * scotts_factor ** 2) ** 0.5 - - density /= norm_factor - - if cumulative: - density = density.cumsum() / density.sum() - - return density, xmin, xmax - - -def _cov_1d(x): - x = x - x.mean(axis=0) - ddof = x.shape[0] - 1 - return np.dot(x.T, x.conj()) / ddof - - -def _cov(data): - if data.ndim == 1: - return _cov_1d(data) - elif data.ndim == 2: - x = data.astype(float) - avg, _ = np.average(x, axis=1, weights=None, returned=True) - ddof = x.shape[1] - 1 - if ddof <= 0: - warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) - ddof = 0.0 - x -= avg[:, None] - prod = _dot(x, x.T.conj()) - prod *= np.true_divide(1, ddof) - prod = prod.squeeze() - prod += 1e-6 * np.eye(prod.shape[0]) - return prod - else: - raise ValueError("{} dimension arrays are not supported".format(data.ndim)) - - -@conditional_jit(cache=True) -def _dot(x, y): - return np.dot(x, y) - - -def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): - """ - 2D fft-based Gaussian kernel density estimate (KDE). - - The code was adapted from https://github.com/mfouesneau/faststats - - Parameters - ---------- - x : Numpy array or list - y : Numpy array or list - gridsize : tuple - Number of points used to discretize data. Use powers of 2 for fft optimization - circular: bool - If True, use circular boundaries. Defaults to False - Returns - ------- - grid: A gridded 2D KDE of the input points (x, y) - xmin: minimum value of x - xmax: maximum value of x - ymin: minimum value of y - ymax: maximum value of y - """ - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - y = np.asarray(y, dtype=float) - y = y[np.isfinite(y)] - - xmin, xmax = x.min(), x.max() - ymin, ymax = y.min(), y.max() - - len_x = len(x) - weights = np.ones(len_x) - n_x, n_y = gridsize - - d_x = (xmax - xmin) / (n_x - 1) - d_y = (ymax - ymin) / (n_y - 1) - - xyi = _stack(x, y).T - xyi -= [xmin, ymin] - xyi /= [d_x, d_y] - xyi = np.floor(xyi, xyi).T - - scotts_factor = len_x ** (-1 / 6) - cov = _cov(xyi) - std_devs = np.diag(cov ** 0.5) - kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) - - inv_cov = np.linalg.inv(cov * scotts_factor ** 2) - - x_x = np.arange(kern_nx) - kern_nx / 2 - y_y = np.arange(kern_ny) - kern_ny / 2 - x_x, y_y = np.meshgrid(x_x, y_y) - - kernel = _stack(x_x.flatten(), y_y.flatten()) - kernel = _dot(inv_cov, kernel) * kernel - kernel = np.exp(-kernel.sum(axis=0) / 2) - kernel = kernel.reshape((int(kern_ny), int(kern_nx))) - - boundary = "wrap" if circular else "symm" - - grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() - grid = convolve2d(grid, kernel, mode="same", boundary=boundary) - - norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor ** 2) - norm_factor = len_x * d_x * d_y * norm_factor ** 0.5 - - grid /= norm_factor - - return grid, xmin, xmax, ymin, ymax - - def matplotlib_kwarg_dealiaser(args, kind, backend="matplotlib"): """De-aliase the kwargs passed to plots.""" if args is None: diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -6,12 +6,11 @@ xarray_var_iter, _scale_fig_size, default_grid, - get_coords, filter_plotters_list, get_plotting_function, matplotlib_kwarg_dealiaser, ) -from ..utils import _var_names +from ..utils import _var_names, get_coords from ..rcparams import rcParams diff --git a/arviz/plots/rankplot.py b/arviz/plots/rankplot.py --- a/arviz/plots/rankplot.py +++ b/arviz/plots/rankplot.py @@ -8,11 +8,11 @@ xarray_var_iter, default_grid, filter_plotters_list, - _sturges_formula, get_plotting_function, ) from ..rcparams import rcParams from ..utils import _var_names +from ..numeric_utils import _sturges_formula def plot_rank( diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -7,13 +7,12 @@ from .plot_utils import ( get_plotting_function, - get_coords, xarray_var_iter, KwargSpec, matplotlib_kwarg_dealiaser, ) from ..data import convert_to_dataset, InferenceData, CoordSpec -from ..utils import _var_names +from ..utils import _var_names, get_coords from ..rcparams import rcParams diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -12,7 +12,6 @@ from scipy.optimize import minimize import xarray as xr -from ..plots.plot_utils import _fast_kde, get_bins, get_coords from ..data import convert_to_inference_data, convert_to_dataset, InferenceData, CoordSpec, DimSpec from .diagnostics import _multichain_statistics, _mc_error, ess from .stats_utils import ( @@ -21,11 +20,11 @@ logsumexp as _logsumexp, ELPDData, stats_variance_2d as svar, - histogram, _circular_standard_deviation, get_log_likelihood as _get_log_likelihood, ) -from ..utils import _var_names, Numba, _numba_var +from ..numeric_utils import _fast_kde, histogram, get_bins +from ..utils import _var_names, Numba, _numba_var, get_coords from ..rcparams import rcParams _log = logging.getLogger(__name__) diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -319,6 +319,37 @@ def expand_dims(x): return x.reshape(shape[:0] + (1,) + shape[0:]) +@conditional_jit(cache=True) +def _dot(x, y): + return np.dot(x, y) + + +def _cov_1d(x): + x = x - x.mean(axis=0) + ddof = x.shape[0] - 1 + return np.dot(x.T, x.conj()) / ddof + + +def _cov(data): + if data.ndim == 1: + return _cov_1d(data) + elif data.ndim == 2: + x = data.astype(float) + avg, _ = np.average(x, axis=1, weights=None, returned=True) + ddof = x.shape[1] - 1 + if ddof <= 0: + warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2) + ddof = 0.0 + x -= avg[:, None] + prod = _dot(x, x.T.conj()) + prod *= np.true_divide(1, ddof) + prod = prod.squeeze() + prod += 1e-6 * np.eye(prod.shape[0]) + return prod + else: + raise ValueError("{} dimension arrays are not supported".format(data.ndim)) + + @conditional_jit def full(shape, x, dtype=None): """Jitting numpy full.""" @@ -498,3 +529,48 @@ def flatten_inference_data_to_dict( data_dict[var_name_dim] = var_values[loc] return data_dict + + +def get_coords(data, coords): + """Subselects xarray DataSet or DataArray object to provided coords. Raises exception if fails. + + Raises + ------ + ValueError + If coords name are not available in data + + KeyError + If coords dims are not available in data + + Returns + ------- + data: xarray + xarray.DataSet or xarray.DataArray object, same type as input + """ + if not isinstance(data, (list, tuple)): + try: + return data.sel(**coords) + + except ValueError: + invalid_coords = set(coords.keys()) - set(data.coords.keys()) + raise ValueError("Coords {} are invalid coordinate keys".format(invalid_coords)) + + except KeyError as err: + raise KeyError( + ( + "Coords should follow mapping format {{coord_name:[dim1, dim2]}}. " + "Check that coords structure is correct and" + " dimensions are valid. {}" + ).format(err) + ) + if not isinstance(coords, (list, tuple)): + coords = [coords] * len(data) + data_subset = [] + for idx, (datum, coords_dict) in enumerate(zip(data, coords)): + try: + data_subset.append(get_coords(datum, coords_dict)) + except ValueError as err: + raise ValueError("Error in data[{}]: {}".format(idx, err)) + except KeyError as err: + raise KeyError("Error in data[{}]: {}".format(idx, err)) + return data_subset diff --git a/benchmarks/__init__.py b/asv_benchmarks/benchmarks/__init__.py similarity index 100% rename from benchmarks/__init__.py rename to asv_benchmarks/benchmarks/__init__.py diff --git a/asv_benchmarks/benchmarks/benchmarks.py b/asv_benchmarks/benchmarks/benchmarks.py new file mode 100644 --- /dev/null +++ b/asv_benchmarks/benchmarks/benchmarks.py @@ -0,0 +1,105 @@ +# Write the benchmarking functions here. +# See "Writing benchmarks" in the airspeed velocity docs for more information. +# https://asv.readthedocs.io/en/stable/ +import numpy as np +from scipy.stats import circstd +import arviz as az +from arviz.stats.stats_utils import _circular_standard_deviation, stats_variance_2d +from arviz.numeric_utils import _fast_kde, _fast_kde_2d, histogram + + +class Hist: + params = (True, False) + param_names = ("Numba",) + + def setup(self, numba_flag): + self.data = np.random.rand(10000, 1000) + if numba_flag: + az.Numba.enable_numba() + else: + az.Numba.disable_numba() + + def time_histogram(self, numba_flag): + histogram(self.data, bins=100) + + +class Variance: + params = (True, False) + param_names = ("Numba",) + + def setup(self, numba_flag): + self.data = np.random.rand(10000, 1000) + if numba_flag: + az.Numba.enable_numba() + else: + az.Numba.disable_numba() + + def time_variance_2d(self, numba_flag): + stats_variance_2d(self.data) + + +class CircStd: + params = (True, False) + param_names = ("Numba",) + + def setup(self, numba_flag): + self.data = np.random.randn(10000, 1000) + if numba_flag: + self.circstd = _circular_standard_deviation + else: + self.circstd = circstd + + def time_circ_std(self, numba_flag): + self.circstd(self.data) + + +class Fast_Kde_1d: + params = [(True, False), (10 ** 5, 10 ** 6, 10 ** 7)] + param_names = ("Numba", "n") + + def setup(self, numba_flag, n): + self.x = np.random.randn(n // 10, 10) + if numba_flag: + az.Numba.enable_numba() + else: + az.Numba.disable_numba() + + def time_fast_kde_normal(self, numba_flag, n): + _fast_kde(self.x) + + +class Fast_KDE_2d: + params = [(True, False), ((100, 10 ** 4), (10 ** 4, 100), (1000, 1000))] + param_names = ("Numba", "shape") + + def setup(self, numba_flag, shape): + self.x = np.random.randn(*shape) + self.y = np.random.randn(*shape) + if numba_flag: + az.Numba.enable_numba() + else: + az.Numba.disable_numba() + + def time_fast_kde_2d(self, numba_flag, shape): + _fast_kde_2d(self.x, self.y) + + +class Atleast_Nd: + params = ("az.utils", "numpy") + param_names = ("source",) + + def setup(self, source): + self.data = np.random.randn(100000) + self.x = np.random.randn(100000).tolist() + if source == "az.utils": + self.atleast_2d = az.utils.two_de + self.atleast_1d = az.utils.one_de + else: + self.atleast_2d = np.atleast_2d + self.atleast_1d = np.atleast_1d + + def time_atleast_2d_array(self, source): + self.atleast_2d(self.data) + + def time_atleast_1d(self, source): + self.atleast_1d(self.x) diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py deleted file mode 100644 --- a/benchmarks/benchmarks.py +++ /dev/null @@ -1,401 +0,0 @@ -# Write the benchmarking functions here. -# See "Writing benchmarks" in the airspeed velocity docs for more information. -# https://asv.readthedocs.io/en/stable/ -import numpy as np -from numpy import newaxis -from scipy.stats import circstd -from scipy.sparse import coo_matrix -import scipy.signal as ss -import warnings - - -class Hist: - def time_histogram(self): - try: - data = np.random.rand(10000, 1000) - import numba - - @numba.njit(cache=True) - def _hist(data): - return np.histogram(data, bins=100) - - return _hist(data) - except ImportError: - data = np.random.rand(10000, 1000) - return np.histogram(data, bins=100) - - -class Variance: - def time_variance(self): - try: - data = np.random.randn(10000, 10000) - import numba - - @numba.njit(cache=True) - def stats_variance_1d(data, ddof=0): - a, b = 0, 0 - for i in data: - a = a + i - b = b + i * i - var = b / (len(data)) - ((a / (len(data))) ** 2) - var = var * (len(data) / (len(data) - ddof)) - return var - - def stats_variance_2d(data, ddof=0, axis=1): - a, b = data.shape - if axis == 1: - var = np.zeros(a) - for i in range(a): - var[i] = stats_variance_1d(data[i], ddof=ddof) - else: - var = np.zeros(b) - for i in range(b): - var[i] = stats_variance_1d(data[:, i], ddof=ddof) - return var - - return stats_variance_2d(data) - except ImportError: - data = np.random.randn(10000, 10000) - return np.var(data, axis=1) - - -class CircStd: - def time_circ_std(self): - try: - data = np.random.randn(10000, 1000) - import numba - - def _circfunc(samples, high, low): - samples = np.asarray(samples) - if samples.size == 0: - return np.nan, np.nan - return samples, _angle(samples, low, high, np.pi) - - @numba.vectorize(nopython=True) - def _angle(samples, low, high, pi=np.pi): - ang = (samples - low) * 2.0 * pi / (high - low) - return ang - - def _circular_standard_deviation(samples, high=2 * np.pi, low=0, axis=None): - pi = np.pi - samples, ang = _circfunc(samples, high, low) - S = np.sin(ang).mean(axis=axis) - C = np.cos(ang).mean(axis=axis) - R = np.hypot(S, C) - return ((high - low) / 2.0 / pi) * np.sqrt(-2 * np.log(R)) - - return _circular_standard_deviation(data) - except ImportError: - data = np.random.randn(10000, 1000) - return circstd(data) - - -class Fast_Kde_1d: - def time_fast_kde(self): - - try: - x = np.random.randn(10000, 100) - import numba - - def _fast_kde(x, cumulative=False, bw=4.5, xmin=None, xmax=None): - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - if x.size == 0: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - len_x = len(x) - n_points = 200 if (xmin or xmax) is None else 500 - - if xmin is None: - xmin = np.min(x) - if xmax is None: - xmax = np.max(x) - - assert np.min(x) >= xmin - assert np.max(x) <= xmax - - log_len_x = np.log(len_x) * bw - - n_bins = min(int(len_x ** (1 / 3) * log_len_x * 2), n_points) - if n_bins < 2: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - d_x = (xmax - xmin) / (n_bins - 1) - grid = _histogram(x, n_bins, range_hist=(xmin, xmax)) - - scotts_factor = len_x ** (-0.2) - kern_nx = int(scotts_factor * 2 * np.pi * log_len_x) - kernel = ss.gaussian(kern_nx, scotts_factor * log_len_x) - - npad = min(n_bins, 2 * kern_nx) - grid = np.concatenate([grid[npad:0:-1], grid, grid[n_bins : n_bins - npad : -1]]) - density = ss.convolve(grid, kernel, mode="same", method="direct")[ - npad : npad + n_bins - ] - norm_factor = len_x * d_x * (2 * np.pi * log_len_x ** 2 * scotts_factor ** 2) ** 0.5 - - density /= norm_factor - - if cumulative: - density = density.cumsum() / density.sum() - - return density, xmin, xmax - - @numba.njit(cache=True) - def _histogram(x, n_bins, range_hist=None): - grid, _ = np.histogram(x, bins=n_bins, range=range_hist) - return grid - - return _fast_kde(x) - - except ImportError: - - x = np.random.randn(10000, 100) - - def _fast_kde(x, cumulative=False, bw=4.5, xmin=None, xmax=None): - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - if x.size == 0: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - len_x = len(x) - n_points = 200 if (xmin or xmax) is None else 500 - - if xmin is None: - xmin = np.min(x) - if xmax is None: - xmax = np.max(x) - - assert np.min(x) >= xmin - assert np.max(x) <= xmax - - log_len_x = np.log(len_x) * bw - - n_bins = min(int(len_x ** (1 / 3) * log_len_x * 2), n_points) - if n_bins < 2: - warnings.warn("kde plot failed, you may want to check your data") - return np.array([np.nan]), np.nan, np.nan - - d_x = (xmax - xmin) / (n_bins - 1) - grid = _histogram(x, n_bins, range_hist=(xmin, xmax)) - - scotts_factor = len_x ** (-0.2) - kern_nx = int(scotts_factor * 2 * np.pi * log_len_x) - kernel = ss.gaussian(kern_nx, scotts_factor * log_len_x) - - npad = min(n_bins, 2 * kern_nx) - grid = np.concatenate([grid[npad:0:-1], grid, grid[n_bins : n_bins - npad : -1]]) - density = ss.convolve(grid, kernel, mode="same", method="direct")[ - npad : npad + n_bins - ] - norm_factor = len_x * d_x * (2 * np.pi * log_len_x ** 2 * scotts_factor ** 2) ** 0.5 - - density /= norm_factor - - if cumulative: - density = density.cumsum() / density.sum() - - return density, xmin, xmax - - def _histogram(x, n_bins, range_hist=None): - grid, _ = np.histogram(x, bins=n_bins, range=range_hist) - return grid - - return _fast_kde(x) - - -class Fast_KDE_2d: - def time_fast_kde_2d(self): - try: - x = np.random.randn(10000, 100) - y = np.random.randn(10000, 100) - import numba - - def _cov_1d(x): - x = x - x.mean(axis=0) - fact = x.shape[0] - 1 - by_hand = np.dot(x.T, x.conj()) / fact - return np.array(by_hand) - - def _cov(data): - if data.ndim == 1: - return _cov_1d(data) - elif data.ndim == 2: - x = data.astype(float) - avg, _ = np.average(x, axis=1, weights=None, returned=True) - fact = x.shape[1] - 1 - if fact <= 0: - warnings.warn( - "Degrees of freedom <= 0 for slice", RuntimeWarning, stacklevel=2 - ) - fact = 0.0 - x -= avg[:, None] - x_t = x.T - c_c = _dot(x, x_t.conj()) - c_c *= np.true_divide(1, fact) - return c_c.squeeze() - else: - raise ValueError("{} dimension arrays are not supported".format(data.ndimn)) - - @numba.njit(cache=True) - def _dot(x, y): - return np.dot(x, y) - - def _stack(x, y): - return np.vstack((x, y)) - - def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - y = np.asarray(y, dtype=float) - y = y[np.isfinite(y)] - - xmin, xmax = x.min(), x.max() - ymin, ymax = y.min(), y.max() - - len_x = len(x) - weights = np.ones(len_x) - n_x, n_y = gridsize - - d_x = (xmax - xmin) / (n_x - 1) - d_y = (ymax - ymin) / (n_y - 1) - - xyi = _stack(x, y).T - xyi -= [xmin, ymin] - xyi /= [d_x, d_y] - xyi = np.floor(xyi, xyi).T - - scotts_factor = len_x ** (-1 / 6) - cov = _cov(xyi) - std_devs = np.diag(cov ** 0.5) - kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) - - inv_cov = np.linalg.inv(cov * scotts_factor ** 2) - - x_x = np.arange(kern_nx) - kern_nx / 2 - y_y = np.arange(kern_ny) - kern_ny / 2 - x_x, y_y = np.meshgrid(x_x, y_y) - - kernel = _stack(x_x.flatten(), y_y.flatten()) - kernel = _dot(inv_cov, kernel) * kernel - kernel = np.exp(-kernel.sum(axis=0) / 2) - kernel = kernel.reshape((int(kern_ny), int(kern_nx))) - - boundary = "wrap" if circular else "symm" - - grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() - grid = ss.convolve2d(grid, kernel, mode="same", boundary=boundary) - - norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor ** 2) - norm_factor = len_x * d_x * d_y * norm_factor ** 0.5 - - grid /= norm_factor - - return grid, xmin, xmax, ymin, ymax - - return _fast_kde_2d(x, y) - - except ImportError: - x = np.random.randn(10000, 100) - y = np.random.randn(10000, 100) - - def _cov(data): - return np.cov(data) - - def _stack(x, y): - return np.vstack((x, y)) - - def _fast_kde_2d(x, y, gridsize=(128, 128), circular=False): - x = np.asarray(x, dtype=float) - x = x[np.isfinite(x)] - y = np.asarray(y, dtype=float) - y = y[np.isfinite(y)] - - xmin, xmax = x.min(), x.max() - ymin, ymax = y.min(), y.max() - - len_x = len(x) - weights = np.ones(len_x) - n_x, n_y = gridsize - - d_x = (xmax - xmin) / (n_x - 1) - d_y = (ymax - ymin) / (n_y - 1) - - xyi = _stack(x, y).T - xyi -= [xmin, ymin] - xyi /= [d_x, d_y] - xyi = np.floor(xyi, xyi).T - - scotts_factor = len_x ** (-1 / 6) - cov = _cov(xyi) - std_devs = np.diag(cov ** 0.5) - kern_nx, kern_ny = np.round(scotts_factor * 2 * np.pi * std_devs) - - inv_cov = np.linalg.inv(cov * scotts_factor ** 2) - - x_x = np.arange(kern_nx) - kern_nx / 2 - y_y = np.arange(kern_ny) - kern_ny / 2 - x_x, y_y = np.meshgrid(x_x, y_y) - - kernel = _stack(x_x.flatten(), y_y.flatten()) - kernel = np.dot(inv_cov, kernel) * kernel - kernel = np.exp(-kernel.sum(axis=0) / 2) - kernel = kernel.reshape((int(kern_ny), int(kern_nx))) - - boundary = "wrap" if circular else "symm" - - grid = coo_matrix((weights, xyi), shape=(n_x, n_y)).toarray() - grid = ss.convolve2d(grid, kernel, mode="same", boundary=boundary) - - norm_factor = np.linalg.det(2 * np.pi * cov * scotts_factor ** 2) - norm_factor = len_x * d_x * d_y * norm_factor ** 0.5 - - grid /= norm_factor - - return grid, xmin, xmax, ymin, ymax - - return _fast_kde_2d(x, y) - - -class Data: - def time_2d_custom(self): - data = np.random.randn(100000) - - def two_de(data): - if not isinstance(data, np.ndarray): - return np.atleast_2d(data) - if data.ndim == 0: - result = data.reshape(1, 1) - elif data.ndim == 1: - result = data[newaxis, :] - else: - result = data - return result - - return two_de(data) - - def time_numpy_2d(self): - data = np.random.randn(100000) - return np.atleast_2d(data) - - def time_1d_custom(self): - x = np.random.randn(100000).tolist() - - def one_de(x): - """Jitting numpy atleast_1d.""" - if not isinstance(x, np.ndarray): - return np.atleast_1d(x) - if x.ndim == 0: - result = x.reshape(1) - else: - result = x - return result - - return one_de(x) - - def time_numpy_1d(self): - x = np.random.randn(100000).tolist() - return np.atleast_1d(x)

diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py --- a/arviz/tests/base_tests/test_plot_utils.py +++ b/arviz/tests/base_tests/test_plot_utils.py @@ -10,8 +10,6 @@ from ...plots.plot_utils import ( filter_plotters_list, format_sig_figs, - get_bins, - get_coords, get_plotting_function, make_2d, matplotlib_kwarg_dealiaser, @@ -19,6 +17,8 @@ xarray_var_iter, ) from ...rcparams import rc_context +from ...numeric_utils import get_bins +from ...utils import get_coords @pytest.mark.parametrize( diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -41,7 +41,8 @@ plot_loo_pit, plot_mcse, ) -from ...plots.plot_utils import _fast_kde, _cov +from ...utils import _cov +from ...numeric_utils import _fast_kde rcParams["data.load"] = "eager"

A better way to benchmark the speed tests The benchmark tests which are in use now can be improved much more. Importing arviz into the benchmark tests throws the following error:  The workaround right now is to include only the jitted methods in `benchmarks.py`. It is indicative of the extent of the speedup but isn't very accurate.

@Ban-zee Is this still an open issue? Yes

2020-04-07T15:24:41Z

arviz-devs/arviz

arviz-devs__arviz-1148

09af86e8cdf8fe35f5eb3d81b36df3bfe95f6386

diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -17,6 +17,7 @@ from .parallelplot import plot_parallel from .posteriorplot import plot_posterior from .ppcplot import plot_ppc +from .distcomparisonplot import plot_dist_comparison from .rankplot import plot_rank from .traceplot import plot_trace from .violinplot import plot_violin @@ -44,6 +45,7 @@ "plot_parallel", "plot_posterior", "plot_ppc", + "plot_dist_comparison", "plot_rank", "plot_trace", "plot_violin", diff --git a/arviz/plots/backends/bokeh/distcomparisonplot.py b/arviz/plots/backends/bokeh/distcomparisonplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/backends/bokeh/distcomparisonplot.py @@ -0,0 +1,21 @@ +"""Bokeh Density Comparison plot.""" + + +def plot_dist_comparison( + ax, + nvars, + ngroups, + figsize, + dc_plotters, + legend, + groups, + prior_kwargs, + posterior_kwargs, + observed_kwargs, + backend_kwargs, + show, +): + """Bokeh Density Comparison plot.""" + raise NotImplementedError( + "The bokeh backend is still under development. Use matplotlib bakend." + ) diff --git a/arviz/plots/backends/matplotlib/distcomparisonplot.py b/arviz/plots/backends/matplotlib/distcomparisonplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/backends/matplotlib/distcomparisonplot.py @@ -0,0 +1,67 @@ +"""Matplotlib Density Comparison plot.""" +import matplotlib.pyplot as plt +import numpy as np + +from . import backend_show +from ...distplot import plot_dist +from ...plot_utils import make_label +from . import backend_kwarg_defaults + + +def plot_dist_comparison( + ax, + nvars, + ngroups, + figsize, + dc_plotters, + legend, + groups, + prior_kwargs, + posterior_kwargs, + observed_kwargs, + backend_kwargs, + show, +): + """Matplotlib Density Comparison plot.""" + backend_kwargs = {**backend_kwarg_defaults(), **backend_kwargs} + if ax is None: + axes = np.empty((nvars, ngroups + 1), dtype=object) + fig = plt.figure(**backend_kwargs, figsize=figsize) + gs = fig.add_gridspec(ncols=ngroups, nrows=nvars * 2) + for i in range(nvars): + for j in range(ngroups): + axes[i, j] = fig.add_subplot(gs[2 * i, j]) + axes[i, -1] = fig.add_subplot(gs[2 * i + 1, :]) + + else: + axes = ax + if ax.shape != (nvars, ngroups + 1): + raise ValueError( + "Found {} shape of axes, which is not equal to data shape {}.".format( + axes.shape, (nvars, ngroups + 1) + ) + ) + + for idx, plotter in enumerate(dc_plotters): + group = groups[idx] + kwargs = ( + prior_kwargs + if group.startswith("prior") + else posterior_kwargs + if group.startswith("posterior") + else observed_kwargs + ) + for idx2, (var, selection, data,) in enumerate(plotter): + label = make_label(var, selection) + label = f"{group} {label}" + plot_dist( + data, label=label if legend else None, ax=axes[idx2, idx], **kwargs, + ) + plot_dist( + data, label=label if legend else None, ax=axes[idx2, -1], **kwargs, + ) + + if backend_show(show): + plt.show() + + return axes diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -1,4 +1,4 @@ -"""Matplotib Posterior predictive plot.""" +"""Matplotlib Posterior predictive plot.""" import platform import logging from matplotlib import animation, get_backend diff --git a/arviz/plots/distcomparisonplot.py b/arviz/plots/distcomparisonplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/distcomparisonplot.py @@ -0,0 +1,197 @@ +"""Density Comparison plot.""" + +from .plot_utils import ( + xarray_var_iter, + _scale_fig_size, + get_plotting_function, + vectorized_to_hex, +) +from ..utils import _var_names, get_coords +from ..rcparams import rcParams + + +def plot_dist_comparison( + data, + kind="latent", + figsize=None, + textsize=None, + var_names=None, + coords=None, + transform=None, + legend=True, + ax=None, + prior_kwargs=None, + posterior_kwargs=None, + observed_kwargs=None, + backend=None, + backend_kwargs=None, + show=None, +): + """Plot to compare fitted and unfitted distributions. + + The resulting plots will show the compared distributions both on + separate axes (particularly useful when one of them is substantially tighter + than another), and plotted together, so three plots per distribution + + Parameters + ---------- + data : az.InferenceData object + InferenceData object containing the posterior/prior data. + kind : str + kind of plot to display {"latent", "observed"}, defaults to 'latent'. + "latent" includes {"prior", "posterior"} and "observed" includes + {"observed_data", "prior_predictive", "posterior_predictive"} + figsize : tuple + Figure size. If None it will be defined automatically. + textsize: float + Text size scaling factor for labels, titles and lines. If None it will be + autoscaled based on figsize. + var_names : str, list, list of lists + if str, plot the variable. if list, plot all the variables in list + of all groups. if list of lists, plot the vars of groups in respective lists. + coords : dict + Dictionary mapping dimensions to selected coordinates to be plotted. + Dimensions without a mapping specified will include all coordinates for + that dimension. + transform : callable + Function to transform data (defaults to None i.e. the identity function) + legend : bool + Add legend to figure. By default True. + ax: axes, optional + Matplotlib axes: The ax argument should have shape (nvars, 3), where the + last column is for the combined before/after plots and columns 0 and 1 are + for the before and after plots, respectively. + prior_kwargs : dicts, optional + Additional keywords passed to `arviz.plot_dist` for prior/predictive groups. + posterior_kwargs : dicts, optional + Additional keywords passed to `arviz.plot_dist` for posterior/predictive groups. + observed_kwargs : dicts, optional + Additional keywords passed to `arviz.plot_dist` for observed_data group. + backend: str, optional + Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + backend_kwargs: bool, optional + These are kwargs specific to the backend being used. For additional documentation + check the plotting method of the backend. + show : bool, optional + Call backend show function. + + Returns + ------- + axes : a numpy 2d array of matplotlib axes. Returned object will have shape (nvars, 3), + where the last column is the combined plot and the first columns are the single plots. + + Examples + -------- + Plot the prior/posterior plot for specified vars and coords. + + .. plot:: + :context: close-figs + + >>> import arviz as az + >>> data = az.load_arviz_data('radon') + >>> az.plot_dist_comparison(data, var_names=["defs"], coords={"team" : ["Italy"]}) + + """ + all_groups = ["prior", "posterior"] + + if kind == "observed": + all_groups = ["observed_data", "prior_predictive", "posterior_predictive"] + + if coords is None: + coords = {} + + if prior_kwargs is None: + prior_kwargs = {} + + if posterior_kwargs is None: + posterior_kwargs = {} + + if observed_kwargs is None: + observed_kwargs = {} + + if backend_kwargs is None: + backend_kwargs = {} + + datasets = [] + groups = [] + for group in all_groups: + try: + datasets.append(getattr(data, group)) + groups.append(group) + except: # pylint: disable=bare-except + pass + + if var_names is None: + var_names = list(datasets[0].data_vars) + + if isinstance(var_names, str): + var_names = [var_names] + + if isinstance(var_names[0], str): + var_names = [var_names for _ in datasets] + + var_names = [_var_names(vars, dataset) for vars, dataset in zip(var_names, datasets)] + + if transform is not None: + datasets = [transform(dataset) for dataset in datasets] + + datasets = get_coords(datasets, coords) + len_plots = rcParams["plot.max_subplots"] // (len(groups) + 1) + len_plots = len_plots if len_plots else 1 + dc_plotters = [ + list(xarray_var_iter(data, var_names=var, combined=True))[:len_plots] + for data, var in zip(datasets, var_names) + ] + + nvars = len(dc_plotters[0]) + ngroups = len(groups) + + (figsize, _, _, _, linewidth, _) = _scale_fig_size(figsize, textsize, 2 * nvars, ngroups) + + posterior_kwargs.setdefault("plot_kwargs", dict()) + posterior_kwargs["plot_kwargs"]["color"] = vectorized_to_hex( + posterior_kwargs["plot_kwargs"].get("color", "C0") + ) + posterior_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) + posterior_kwargs.setdefault("hist_kwargs", dict()) + posterior_kwargs["hist_kwargs"].setdefault("alpha", 0.5) + + prior_kwargs.setdefault("plot_kwargs", dict()) + prior_kwargs["plot_kwargs"]["color"] = vectorized_to_hex( + prior_kwargs["plot_kwargs"].get("color", "C1") + ) + prior_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) + prior_kwargs.setdefault("hist_kwargs", dict()) + prior_kwargs["hist_kwargs"].setdefault("alpha", 0.5) + + observed_kwargs.setdefault("plot_kwargs", dict()) + observed_kwargs["plot_kwargs"]["color"] = vectorized_to_hex( + observed_kwargs["plot_kwargs"].get("color", "C2") + ) + observed_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) + observed_kwargs.setdefault("hist_kwargs", dict()) + observed_kwargs["hist_kwargs"].setdefault("alpha", 0.5) + + distcomparisonplot_kwargs = dict( + ax=ax, + nvars=nvars, + ngroups=ngroups, + figsize=figsize, + dc_plotters=dc_plotters, + legend=legend, + groups=groups, + prior_kwargs=prior_kwargs, + posterior_kwargs=posterior_kwargs, + observed_kwargs=observed_kwargs, + backend_kwargs=backend_kwargs, + show=show, + ) + + if backend is None: + backend = rcParams["plot.backend"] + backend = backend.lower() + + # TODO: Add backend kwargs + plot = get_plotting_function("plot_dist_comparison", "distcomparisonplot", backend) + axes = plot(**distcomparisonplot_kwargs) + return axes

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -35,6 +35,7 @@ plot_parallel, plot_posterior, plot_ppc, + plot_dist_comparison, plot_rank, plot_trace, plot_violin, @@ -956,3 +957,8 @@ def test_plot_posterior_point_estimates(models, point_estimate): def test_plot_rank(models, kwargs): axes = plot_rank(models.model_1, backend="bokeh", show=False, **kwargs) assert axes.shape + + +def test_plot_dist_comparison_warn(models): + with pytest.raises(NotImplementedError, match="The bokeh backend.+Use matplotlib bakend."): + plot_dist_comparison(models.model_1, backend="bokeh") diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -29,6 +29,7 @@ plot_parallel, plot_pair, plot_joint, + plot_dist_comparison, plot_ppc, plot_violin, plot_compare, @@ -1194,3 +1195,35 @@ def test_plot_mcse_no_divergences(models): idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) with pytest.raises(ValueError, match="not contain diverging"): plot_mcse(idata, rug=True) + + [email protected]( + "kwargs", + [ + {}, + {"var_names": ["theta"]}, + {"var_names": ["theta"], "coords": {"theta_dim_0": [0, 1]}}, + {"var_names": ["eta"], "posterior_kwargs": {"rug": True, "rug_kwargs": {"color": "r"}}}, + {"var_names": ["mu"], "prior_kwargs": {"fill_kwargs": {"alpha": 0.5}}}, + { + "var_names": ["tau"], + "prior_kwargs": {"plot_kwargs": {"color": "r"}}, + "posterior_kwargs": {"plot_kwargs": {"color": "b"}}, + }, + {"var_names": ["y"], "kind": "observed"}, + ], +) +def test_plot_dist_comparison(models, kwargs): + idata = models.model_1 + ax = plot_dist_comparison(idata, **kwargs) + assert np.all(ax) + + +def test_plot_dist_comparison_different_vars(): + data = from_dict( + posterior={"x": np.random.randn(4, 100, 30),}, prior={"x_hat": np.random.randn(4, 100, 30)}, + ) + with pytest.raises(KeyError): + plot_dist_comparison(data, var_names="x") + ax = plot_dist_comparison(data, var_names=[["x_hat"], ["x"]]) + assert np.all(ax)

Prior-Posterior Plot Worked on an example with @raulpl to display how the prior changes after running inference. Here is an example with code! ```python rugby = az.load_arviz_data('rugby') prior_def = rugby.prior.defs.sel(team='Italy') post_def = rugby.posterior.defs.sel(team='Italy') fig = plt.figure(figsize=(10, 7)) ax1 = plt.subplot2grid((2, 2), (0, 0), fig=fig) ax2 = plt.subplot2grid((2, 2), (0, 1), fig=fig) ax3 = plt.subplot2grid((2, 2), (1, 0), colspan=2, fig=fig) az.plot_kde(prior_def, ax=ax1) ax1.set_title("Prior") az.plot_kde(post_def, ax=ax2) ax2.set_title("Posterior") az.plot_kde(prior_def, ax=ax3) az.plot_kde(post_def.values, ax=ax3) ax3.set_title("Both"); ``` 

Note this also might be used for something like prior/posterior predictive/observed. Here we see that the Cauchy prior for number of goals scored might be inappropriate!  I'll like to work on this. Feel free to ping me if you want any help! I think the scales and the layout will be the hardest thing... This is to keep my progress in check ( https://github.com/percygautam/arviz-examples/blob/master/Prior_Posterior_plot.ipynb ). Please feel free to review along the way.

2020-04-11T18:57:21Z

arviz-devs/arviz

arviz-devs__arviz-1171

4afbfaeae688d55a1afe32f28bc9845bbc45c596

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -25,7 +25,7 @@ "predictions_constant_data", ] -WARMUP_TAG = "_warmup_" +WARMUP_TAG = "warmup_" SUPPORTED_GROUPS_WARMUP = [ "{}posterior".format(WARMUP_TAG), diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -9,6 +9,7 @@ from .. import utils from .inference_data import InferenceData, concat from .base import requires, dict_to_dataset, generate_dims_coords, make_attrs, CoordSpec, DimSpec +from ..rcparams import rcParams if TYPE_CHECKING: import pymc3 as pm @@ -60,7 +61,8 @@ def __init__( predictions=None, coords: Optional[Coords] = None, dims: Optional[Dims] = None, - model=None + model=None, + save_warmup: Optional[bool] = None, ): import pymc3 import theano @@ -70,6 +72,7 @@ def __init__( self.pymc3 = pymc3 self.theano = theano + self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup self.trace = trace # this permits us to get the model from command-line argument or from with model: @@ -78,26 +81,34 @@ def __init__( except TypeError: self.model = None + if self.model is None: + warnings.warn( + "Using `from_pymc3` without the model will be deprecated in a future release. " + "Not using the model will return less accurate and less useful results. " + "Make sure you use the model argument or call from_pymc3 within a model context.", + PendingDeprecationWarning, + ) + # This next line is brittle and may not work forever, but is a secret # way to access the model from the trace. + self.attrs = None if trace is not None: if self.model is None: self.model = list(self.trace._straces.values())[ # pylint: disable=protected-access 0 ].model self.nchains = trace.nchains if hasattr(trace, "nchains") else 1 - self.ndraws = len(trace) + if hasattr(trace.report, "n_tune"): + self.ndraws = trace.report.n_draws + self.attrs = { + "sampling_time": trace.report.t_sampling, + "tuning_steps": trace.report.n_tune, + } + else: + self.nchains = len(trace) else: self.nchains = self.ndraws = 0 - if self.model is None: - warnings.warn( - "Using `from_pymc3` without the model will be deprecated in a future release. " - "Not using the model will return less accurate and less useful results. " - "Make sure you use the model argument or call from_pymc3 within a model context.", - PendingDeprecationWarning, - ) - self.prior = prior self.posterior_predictive = posterior_predictive self.log_likelihood = log_likelihood @@ -151,7 +162,7 @@ def log_likelihood_vals_point(self, point, var, log_like_fun): @requires("trace") @requires("model") - def _extract_log_likelihood(self): + def _extract_log_likelihood(self, trace): """Compute log likelihood of each observation.""" # If we have predictions, then we have a thinned trace which does not # support extracting a log likelihood. @@ -165,7 +176,7 @@ def _extract_log_likelihood(self): ] try: log_likelihood_dict = self.pymc3.sampling._DefaultTrace( # pylint: disable=protected-access - len(self.trace.chains) + len(trace.chains) ) except AttributeError: raise AttributeError( @@ -173,10 +184,10 @@ def _extract_log_likelihood(self): "`pip install pymc3>=3.8` or `conda install -c conda-forge pymc3>=3.8`." ) for var, log_like_fun in cached: - for chain in self.trace.chains: + for chain in trace.chains: log_like_chain = [ self.log_likelihood_vals_point(point, var, log_like_fun) - for point in self.trace.points([chain]) + for point in trace.points([chain]) ] log_likelihood_dict.insert(var.name, np.stack(log_like_chain), chain) return log_likelihood_dict.trace_dict @@ -184,13 +195,31 @@ def _extract_log_likelihood(self): @requires("trace") def posterior_to_xarray(self): """Convert the posterior to an xarray dataset.""" - var_names = self.pymc3.util.get_default_varnames( # pylint: disable=no-member + var_names = self.pymc3.util.get_default_varnames( self.trace.varnames, include_transformed=False ) data = {} + data_warmup = {} for var_name in var_names: - data[var_name] = np.array(self.trace.get_values(var_name, combine=False, squeeze=False)) - return dict_to_dataset(data, library=self.pymc3, coords=self.coords, dims=self.dims) + if self.save_warmup: + data_warmup[var_name] = np.array( + self.trace[: -self.ndraws].get_values(var_name, combine=False, squeeze=False) + ) + data[var_name] = np.array( + self.trace[-self.ndraws :].get_values(var_name, combine=False, squeeze=False) + ) + return ( + dict_to_dataset( + data, library=self.pymc3, coords=self.coords, dims=self.dims, attrs=self.attrs + ), + dict_to_dataset( + data_warmup, + library=self.pymc3, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + ), + ) @requires("trace") def sample_stats_to_xarray(self): @@ -198,11 +227,25 @@ def sample_stats_to_xarray(self): data = {} rename_key = {"model_logp": "lp"} data = {} + data_warmup = {} for stat in self.trace.stat_names: name = rename_key.get(stat, stat) - data[name] = np.array(self.trace.get_sampler_stats(stat, combine=False)) - - return dict_to_dataset(data, library=self.pymc3, dims=None, coords=self.coords) + if name == "tune": + continue + if self.save_warmup: + data_warmup[name] = np.array( + self.trace[: -self.ndraws].get_sampler_stats(stat, combine=False) + ) + data[name] = np.array(self.trace[-self.ndraws :].get_sampler_stats(stat, combine=False)) + + return ( + dict_to_dataset( + data, library=self.pymc3, dims=None, coords=self.coords, attrs=self.attrs + ), + dict_to_dataset( + data_warmup, library=self.pymc3, dims=None, coords=self.coords, attrs=self.attrs + ), + ) @requires("trace") @requires("model") @@ -211,14 +254,20 @@ def log_likelihood_to_xarray(self): if self.predictions or not self.log_likelihood: return None try: - data = self._extract_log_likelihood() + data = self._extract_log_likelihood(self.trace[-self.ndraws :]) except TypeError: warnings.warn( """Could not compute log_likelihood, it will be omitted. Check your model object or set log_likelihood=False""" ) return None - return dict_to_dataset(data, library=self.pymc3, dims=self.dims, coords=self.coords) + data_warmup = {} + if self.save_warmup: + data_warmup = self._extract_log_likelihood(self.trace[: -self.ndraws]) + return ( + dict_to_dataset(data, library=self.pymc3, dims=self.dims, coords=self.coords), + dict_to_dataset(data_warmup, library=self.pymc3, dims=self.dims, coords=self.coords), + ) def translate_posterior_predictive_dict_to_xarray(self, dct) -> xr.Dataset: """Take Dict of variables to numpy ndarrays (samples) and translate into dataset.""" @@ -375,7 +424,7 @@ def to_inference_data(self): id_dict["predictions_constant_data"] = self.constant_data_to_xarray() else: id_dict["constant_data"] = self.constant_data_to_xarray() - return InferenceData(**id_dict) + return InferenceData(save_warmup=self.save_warmup, **id_dict) def from_pymc3( @@ -386,7 +435,8 @@ def from_pymc3( log_likelihood: Union[bool, Iterable[str]] = True, coords: Optional[CoordSpec] = None, dims: Optional[DimSpec] = None, - model: Optional[Model] = None + model: Optional[Model] = None, + save_warmup: Optional[bool] = None, ) -> InferenceData: """Convert pymc3 data into an InferenceData object. @@ -413,6 +463,9 @@ def from_pymc3( model : pymc3.Model, optional Model used to generate ``trace``. It is not necessary to pass ``model`` if in ``with`` context. + save_warmup : bool, optional + Save warmup iterations InferenceData object. If not defined, use default + defined by the rcParams. Returns ------- @@ -426,6 +479,7 @@ def from_pymc3( coords=coords, dims=dims, model=model, + save_warmup=save_warmup, ).to_inference_data() diff --git a/arviz/plots/backends/__init__.py b/arviz/plots/backends/__init__.py --- a/arviz/plots/backends/__init__.py +++ b/arviz/plots/backends/__init__.py @@ -33,8 +33,8 @@ def to_cds( "posterior_groups_warmup"} - posterior_groups: posterior, posterior_predictive, sample_stats - prior_groups: prior, prior_predictive, sample_stats_prior - - posterior_groups_warmup: _warmup_posterior, _warmup_posterior_predictive, - _warmup_sample_stats + - posterior_groups_warmup: warmup_posterior, warmup_posterior_predictive, + warmup_sample_stats ignore_groups : str or list of str, optional Ignore specific groups from CDS. dimension : str, or list of str, optional diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -406,8 +406,8 @@ def flatten_inference_data_to_dict( {"posterior_groups", "prior_groups", "posterior_groups_warmup"} - posterior_groups: posterior, posterior_predictive, sample_stats - prior_groups: prior, prior_predictive, sample_stats_prior - - posterior_groups_warmup: _warmup_posterior, _warmup_posterior_predictive, - _warmup_sample_stats + - posterior_groups_warmup: warmup_posterior, warmup_posterior_predictive, + warmup_sample_stats ignore_groups : str or list of str, optional Ignore specific groups from CDS. dimension : str, or list of str, optional @@ -455,7 +455,7 @@ def flatten_inference_data_to_dict( elif groups.lower() == "prior_groups": groups = ["prior", "prior_predictive", "sample_stats_prior"] elif groups.lower() == "posterior_groups_warmup": - groups = ["_warmup_posterior", "_warmup_posterior_predictive", "_warmup_sample_stats"] + groups = ["warmup_posterior", "warmup_posterior_predictive", "warmup_sample_stats"] else: raise TypeError( (

diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -397,3 +397,35 @@ def test_no_model_deprecation(self): } fails = check_multiple_attrs(test_dict, inference_data) assert not fails + + @pytest.mark.parametrize("save_warmup", [False, True]) + def test_save_warmup(self, save_warmup): + with pm.Model(): + pm.Uniform("u1") + pm.Normal("n1") + trace = pm.sample( + tune=100, + draws=200, + chains=2, + cores=1, + step=pm.Metropolis(), + discard_tuned_samples=False, + ) + assert isinstance(trace, pm.backends.base.MultiTrace) + idata = from_pymc3(trace, save_warmup=save_warmup) + prefix = "" if save_warmup else "~" + test_dict = { + "posterior": ["u1", "n1"], + "sample_stats": ["~tune", "accept"], + f"{prefix}warmup_posterior": ["u1", "n1"], + f"{prefix}warmup_sample_stats": ["~tune"], + "~warmup_log_likelihood": [], + "~log_likelihood": [], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails + assert idata.posterior.dims["chain"] == 2 + assert idata.posterior.dims["draw"] == 200 + if save_warmup: + assert idata.warmup_posterior.dims["chain"] == 2 + assert idata.warmup_posterior.dims["draw"] == 100 diff --git a/arviz/tests/external_tests/test_data_pystan.py b/arviz/tests/external_tests/test_data_pystan.py --- a/arviz/tests/external_tests/test_data_pystan.py +++ b/arviz/tests/external_tests/test_data_pystan.py @@ -179,7 +179,7 @@ def test_inference_data(self, data, eight_schools_params): } if pystan_version() == 2: test_dict.update( - {"_warmup_posterior": ["theta"], "_warmup_sample_stats": ["diverging", "lp"]} + {"warmup_posterior": ["theta"], "warmup_sample_stats": ["diverging", "lp"]} ) fails = check_multiple_attrs(test_dict, inference_data4) assert not fails

Automatically handle warmup draws and sampling metadata from_pymc3 ## Tell us about it The `trace.report` in PyMC3 now has attributes `n_tune`, `n_draws` and `t_sampling` (see https://github.com/pymc-devs/pymc3/pull/3827). I have this snippet to store those metadata with the trace: ``` idat = arviz.from_pymc3(trace) if hasattr(trace.report, 'n_tune'): idat.posterior.attrs['n_tune'] = trace.report.n_tune idat.posterior.attrs['n_draws'] = trace.report.n_draws idat.posterior.attrs['t_sampling'] = trace.report.t_sampling ``` Using `n_draws`, one can now slice a trace that contains warmup iterations: ``` trace_warmup = trace[:-trace.report.n_draws] # <-- may result in len(trace_warmup) == 0 trace_posterior = trace[-trace.report.n_draws:] ``` ## Thoughts on implementation I suppose both snippets should find their place in the [PyMC3Converter](https://github.com/arviz-devs/arviz/blob/68406c4f86d6e560e948c1dd37c6c4314cc895dd/arviz/data/io_pymc3.py#L43), but I'm not sure how saving the warmup should be done.

2020-05-04T15:43:45Z

arviz-devs/arviz

arviz-devs__arviz-1190

4db86744249636f92897134b053f2f286ef86919

diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -7,7 +7,7 @@ from .energyplot import plot_energy from .essplot import plot_ess from .forestplot import plot_forest -from .hpdplot import plot_hpd +from .hdiplot import plot_hdi, plot_hpd from .jointplot import plot_joint from .kdeplot import plot_kde from .khatplot import plot_khat @@ -32,6 +32,7 @@ "plot_energy", "plot_ess", "plot_forest", + "plot_hdi", "plot_hpd", "plot_joint", "plot_kde", diff --git a/arviz/plots/backends/bokeh/__init__.py b/arviz/plots/backends/bokeh/__init__.py --- a/arviz/plots/backends/bokeh/__init__.py +++ b/arviz/plots/backends/bokeh/__init__.py @@ -36,7 +36,7 @@ def backend_kwarg_defaults(*args, **kwargs): from .energyplot import plot_energy from .essplot import plot_ess from .forestplot import plot_forest -from .hpdplot import plot_hpd +from .hdiplot import plot_hdi from .jointplot import plot_joint from .kdeplot import plot_kde from .khatplot import plot_khat diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -417,7 +417,7 @@ def forestplot(self, hdi_prob, quartiles, linewidth, markersize, ax, rope): x=values[mid], y=y, size=markersize * 0.75, fill_color=color, ) _title = Title() - _title.text = "{:.1%} hdi Interval".format(hdi_prob) + _title.text = "{:.1%} hdi".format(hdi_prob) ax.title = _title return ax diff --git a/arviz/plots/backends/bokeh/hpdplot.py b/arviz/plots/backends/bokeh/hdiplot.py similarity index 92% rename from arviz/plots/backends/bokeh/hpdplot.py rename to arviz/plots/backends/bokeh/hdiplot.py --- a/arviz/plots/backends/bokeh/hpdplot.py +++ b/arviz/plots/backends/bokeh/hdiplot.py @@ -1,4 +1,4 @@ -"""Bokeh hpdplot.""" +"""Bokeh hdiplot.""" from itertools import cycle import bokeh.plotting as bkp @@ -9,8 +9,8 @@ from .. import show_layout -def plot_hpd(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): - """Bokeh hpd plot.""" +def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): + """Bokeh hdi plot.""" if backend_kwargs is None: backend_kwargs = {} diff --git a/arviz/plots/backends/bokeh/loopitplot.py b/arviz/plots/backends/bokeh/loopitplot.py --- a/arviz/plots/backends/bokeh/loopitplot.py +++ b/arviz/plots/backends/bokeh/loopitplot.py @@ -4,7 +4,7 @@ from . import backend_kwarg_defaults from .. import show_layout -from ...hpdplot import plot_hpd +from ...hdiplot import plot_hdi from ...kdeplot import _fast_kde @@ -19,10 +19,10 @@ def plot_loo_pit( p025, fill_kwargs, ecdf_fill, - use_hpd, + use_hdi, x_vals, unif_densities, - hpd_kwargs, + hdi_kwargs, n_unif, unif, plot_unif_kwargs, @@ -114,15 +114,15 @@ def plot_loo_pit( line_width=plot_unif_kwargs.get("linewidth", 1.0), ) else: - if use_hpd: - plot_hpd( + if use_hdi: + plot_hdi( x_vals, unif_densities, backend="bokeh", ax=ax, backend_kwargs={}, show=False, - **hpd_kwargs + **hdi_kwargs ) else: for idx in range(n_unif): diff --git a/arviz/plots/backends/bokeh/posteriorplot.py b/arviz/plots/backends/bokeh/posteriorplot.py --- a/arviz/plots/backends/bokeh/posteriorplot.py +++ b/arviz/plots/backends/bokeh/posteriorplot.py @@ -195,24 +195,24 @@ def display_point_estimate(max_data): ax.text(x=[point_value], y=[max_data * 0.8], text=[point_text], text_align="center") - def display_hpd(max_data): + def display_hdi(max_data): # np.ndarray with 2 entries, min and max # pylint: disable=line-too-long hdi_probs = hdi(values, hdi_prob=hdi_prob, multimodal=multimodal) # type: np.ndarray - for hpdi in np.atleast_2d(hdi_probs): + for hdi_i in np.atleast_2d(hdi_probs): ax.line( - hpdi, + hdi_i, (max_data * 0.02, max_data * 0.02), line_width=linewidth * 2, line_color="black", ) ax.text( - x=list(hpdi) + [(hpdi[0] + hpdi[1]) / 2], + x=list(hdi_i) + [(hdi_i[0] + hdi_i[1]) / 2], y=[max_data * 0.07, max_data * 0.07, max_data * 0.3], - text=list(map(str, map(lambda x: round_num(x, round_to), hpdi))) - + [format_as_percent(hdi_prob) + " HPD"], + text=list(map(str, map(lambda x: round_num(x, round_to), hdi_i))) + + [format_as_percent(hdi_prob) + " HDI"], text_align="center", ) @@ -254,7 +254,7 @@ def format_axes(): format_axes() max_data = hist.max() if hdi_prob != "hide": - display_hpd(max_data) + display_hdi(max_data) display_point_estimate(max_data) display_ref_val(max_data) display_rope(max_data) diff --git a/arviz/plots/backends/matplotlib/__init__.py b/arviz/plots/backends/matplotlib/__init__.py --- a/arviz/plots/backends/matplotlib/__init__.py +++ b/arviz/plots/backends/matplotlib/__init__.py @@ -31,7 +31,7 @@ def backend_show(show): from .energyplot import plot_energy from .essplot import plot_ess from .forestplot import plot_forest -from .hpdplot import plot_hpd +from .hdiplot import plot_hdi from .jointplot import plot_joint from .kdeplot import plot_kde from .khatplot import plot_khat diff --git a/arviz/plots/backends/matplotlib/densityplot.py b/arviz/plots/backends/matplotlib/densityplot.py --- a/arviz/plots/backends/matplotlib/densityplot.py +++ b/arviz/plots/backends/matplotlib/densityplot.py @@ -122,7 +122,7 @@ def _d_helper( markersize : float Size of markers hdi_prob : float - hdi intervals. Defaults to 0.94 + Probability for the highest density interval. Defaults to 0.94 point_estimate : Optional[str] Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. Defaults to 'auto' i.e. it falls back to default set in rcParams. diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -338,7 +338,7 @@ def forestplot( color=color, ) ax.tick_params(labelsize=xt_labelsize) - ax.set_title("{:.1%} hdi Interval".format(hdi_prob), fontsize=titlesize, wrap=True) + ax.set_title("{:.1%} hdi".format(hdi_prob), fontsize=titlesize, wrap=True) if rope is None or isinstance(rope, dict): return elif len(rope) == 2: diff --git a/arviz/plots/backends/matplotlib/hpdplot.py b/arviz/plots/backends/matplotlib/hdiplot.py similarity index 79% rename from arviz/plots/backends/matplotlib/hpdplot.py rename to arviz/plots/backends/matplotlib/hdiplot.py --- a/arviz/plots/backends/matplotlib/hpdplot.py +++ b/arviz/plots/backends/matplotlib/hdiplot.py @@ -1,16 +1,16 @@ -"""Matplotlib hpdplot.""" +"""Matplotlib hdiplot.""" import warnings import matplotlib.pyplot as plt from . import backend_show -def plot_hpd(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): - """Matplotlib hpd plot.""" +def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): + """Matplotlib hdi plot.""" if backend_kwargs is not None: warnings.warn( ( - "Argument backend_kwargs has not effect in matplotlib.plot_hpd" + "Argument backend_kwargs has not effect in matplotlib.plot_hdi" "Supplied value won't be used" ) ) diff --git a/arviz/plots/backends/matplotlib/loopitplot.py b/arviz/plots/backends/matplotlib/loopitplot.py --- a/arviz/plots/backends/matplotlib/loopitplot.py +++ b/arviz/plots/backends/matplotlib/loopitplot.py @@ -4,7 +4,7 @@ from . import backend_kwarg_defaults, backend_show from ....numeric_utils import _fast_kde -from ...hpdplot import plot_hpd +from ...hdiplot import plot_hdi def plot_loo_pit( @@ -18,10 +18,10 @@ def plot_loo_pit( p025, fill_kwargs, ecdf_fill, - use_hpd, + use_hdi, x_vals, unif_densities, - hpd_kwargs, + hdi_kwargs, n_unif, unif, plot_unif_kwargs, @@ -54,8 +54,8 @@ def plot_loo_pit( else: ax.plot(unif_ecdf, p975 - unif_ecdf, unif_ecdf, p025 - unif_ecdf, **plot_unif_kwargs) else: - if use_hpd: - plot_hpd(x_vals, unif_densities, **hpd_kwargs) + if use_hdi: + plot_hdi(x_vals, unif_densities, **hdi_kwargs) else: for idx in range(n_unif): unif_density, _, _ = _fast_kde(unif[idx, :], xmin=0, xmax=1) @@ -64,7 +64,7 @@ def plot_loo_pit( ax.tick_params(labelsize=xt_labelsize) if legend: - if not (use_hpd or (ecdf and ecdf_fill)): + if not (use_hdi or (ecdf and ecdf_fill)): label = "{:.3g}% credible interval".format(credible_interval) if ecdf else "Uniform" ax.plot([], label=label, **plot_unif_kwargs) ax.legend() diff --git a/arviz/plots/backends/matplotlib/posteriorplot.py b/arviz/plots/backends/matplotlib/posteriorplot.py --- a/arviz/plots/backends/matplotlib/posteriorplot.py +++ b/arviz/plots/backends/matplotlib/posteriorplot.py @@ -195,37 +195,37 @@ def display_point_estimate(): horizontalalignment="center", ) - def display_hpd(): + def display_hdi(): # np.ndarray with 2 entries, min and max # pylint: disable=line-too-long hdi_probs = hdi(values, hdi_prob=hdi_prob, multimodal=multimodal) # type: np.ndarray - for hpdi in np.atleast_2d(hdi_probs): + for hdi_i in np.atleast_2d(hdi_probs): ax.plot( - hpdi, + hdi_i, (plot_height * 0.02, plot_height * 0.02), lw=linewidth * 2, color="k", solid_capstyle="butt", ) ax.text( - hpdi[0], + hdi_i[0], plot_height * 0.07, - round_num(hpdi[0], round_to), + round_num(hdi_i[0], round_to), size=ax_labelsize, horizontalalignment="center", ) ax.text( - hpdi[1], + hdi_i[1], plot_height * 0.07, - round_num(hpdi[1], round_to), + round_num(hdi_i[1], round_to), size=ax_labelsize, horizontalalignment="center", ) ax.text( - (hpdi[0] + hpdi[1]) / 2, + (hdi_i[0] + hdi_i[1]) / 2, plot_height * 0.3, - format_as_percent(hdi_prob) + " HPD", + format_as_percent(hdi_prob) + " HDI", size=ax_labelsize, horizontalalignment="center", ) @@ -270,7 +270,7 @@ def format_axes(): format_axes() if hdi_prob != "hide": - display_hpd() + display_hdi() display_point_estimate() display_ref_val() display_rope() diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py --- a/arviz/plots/densityplot.py +++ b/arviz/plots/densityplot.py @@ -40,8 +40,8 @@ def plot_density( ): """Generate KDE plots for continuous variables and histograms for discrete ones. - Plots are truncated at their 100*(1-alpha)% hpd intervals. Plots are grouped per variable - and colors assigned to models. + Plots are truncated at their 100*(1-alpha)% highest density intervals. Plots are grouped per + variable and colors assigned to models. Parameters ---------- @@ -63,7 +63,8 @@ def plot_density( transform : callable Function to transform data (defaults to None i.e. the identity function) hdi_prob : float - hpd interval. Should be in the interval (0, 1]. Defaults to 0.94. + Probability for the highest density interval. Should be in the interval (0, 1]. + Defaults to 0.94. point_estimate : Optional[str] Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None. Defaults to 'auto' i.e. it falls back to default set in rcParams. @@ -75,8 +76,8 @@ def plot_density( outline : bool Use a line to draw KDEs and histograms. Default to True hdi_markers : str - A valid `matplotlib.markers` like 'v', used to indicate the limits of the hpd interval. - Defaults to empty string (no marker). + A valid `matplotlib.markers` like 'v', used to indicate the limits of the highest density + interval. Defaults to empty string (no marker). shade : Optional[float] Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1 (opaque). Defaults to 0. @@ -132,7 +133,7 @@ def plot_density( >>> az.plot_density([centered, non_centered], var_names=["mu"], group="prior") - Specify hpd interval + Specify highest density interval .. plot:: :context: close-figs diff --git a/arviz/plots/hpdplot.py b/arviz/plots/hdiplot.py similarity index 82% rename from arviz/plots/hpdplot.py rename to arviz/plots/hdiplot.py --- a/arviz/plots/hpdplot.py +++ b/arviz/plots/hdiplot.py @@ -1,4 +1,6 @@ -"""Plot hpd intervals for regression data.""" +"""Plot highest density intervals for regression data.""" +import warnings + import numpy as np from scipy.interpolate import griddata from scipy.signal import savgol_filter @@ -9,7 +11,7 @@ from ..utils import credible_interval_warning -def plot_hpd( +def plot_hdi( x, y, hdi_prob=None, @@ -33,13 +35,13 @@ def plot_hpd( x : array-like Values to plot y : array-like - values from which to compute the hpd. Assumed shape (chain, draw, \*shape). + values from which to compute the hdi. Assumed shape (chain, draw, \*shape). hdi_prob : float, optional - HDI interval to plot. Defaults to 0.94. + Probability for the highest density interval. Defaults to 0.94. color : str - Color used for the limits of the HPD interval and fill. Should be a valid matplotlib color + Color used for the limits of the hdi and fill. Should be a valid matplotlib color circular : bool, optional - Whether to compute the hpd taking into account `x` is a circular variable + Whether to compute the hdi taking into account `x` is a circular variable (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). smooth : boolean If True the result will be smoothed by first computing a linear interpolation of the data @@ -51,7 +53,7 @@ def plot_hpd( fill_kwargs : dict Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill). plot_kwargs : dict - Keywords passed to HPD limits + Keywords passed to hdi limits ax: axes, optional Matplotlib axes or bokeh figures. backend: str, optional @@ -109,14 +111,14 @@ def plot_hpd( smooth_kwargs.setdefault("polyorder", 2) x_data = np.linspace(x.min(), x.max(), 200) x_data[0] = (x_data[0] + x_data[1]) / 2 - hpd_interp = griddata(x, hdi_, x_data) - y_data = savgol_filter(hpd_interp, axis=0, **smooth_kwargs) + hdi_interp = griddata(x, hdi_, x_data) + y_data = savgol_filter(hdi_interp, axis=0, **smooth_kwargs) else: idx = np.argsort(x) x_data = x[idx] y_data = hdi_[idx] - hpdplot_kwargs = dict( + hdiplot_kwargs = dict( ax=ax, x_data=x_data, y_data=y_data, @@ -131,6 +133,11 @@ def plot_hpd( backend = backend.lower() # TODO: Add backend kwargs - plot = get_plotting_function("plot_hpd", "hpdplot", backend) - ax = plot(**hpdplot_kwargs) + plot = get_plotting_function("plot_hdi", "hdiplot", backend) + ax = plot(**hdiplot_kwargs) return ax + + +def plot_hpd(*args, **kwargs): # noqa: D103 + warnings.warn("plot_hdi has been deprecated, please use plot_hdi", DeprecationWarning) + return plot_hdi(*args, **kwargs) diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py --- a/arviz/plots/loopitplot.py +++ b/arviz/plots/loopitplot.py @@ -22,7 +22,7 @@ def plot_loo_pit( ecdf=False, ecdf_fill=True, n_unif=100, - use_hpd=False, + use_hdi=False, credible_interval=None, figsize=None, textsize=None, @@ -31,7 +31,7 @@ def plot_loo_pit( ax=None, plot_kwargs=None, plot_unif_kwargs=None, - hpd_kwargs=None, + hdi_kwargs=None, fill_kwargs=None, backend=None, backend_kwargs=None, @@ -64,10 +64,10 @@ def plot_loo_pit( border lines. n_unif : int, optional Number of datasets to simulate and overlay from the uniform distribution. - use_hpd : bool, optional - Use plot_hpd to fill between hpd values instead of overlaying the uniform distributions. + use_hdi : bool, optional + Use plot_hdi to fill between hdi values instead of overlaying the uniform distributions. credible_interval : float, optional - Credible interval of the hpd or of the ECDF theoretical credible interval + Credible interval of the hdi or of the ECDF theoretical credible interval figsize : figure size tuple, optional If None, size is (8 + numvars, 8 + numvars) textsize: int, optional @@ -85,8 +85,8 @@ def plot_loo_pit( plot_unif_kwargs : dict, optional Additional keywords passed to ax.plot for overlaid uniform distributions or for beta credible interval lines if ``ecdf=True`` - hpd_kwargs : dict, optional - Additional keywords passed to az.plot_hpd + hdi_kwargs : dict, optional + Additional keywords passed to az.plot_hdi fill_kwargs : dict, optional Additional kwargs passed to ax.fill_between backend: str, optional @@ -119,7 +119,7 @@ def plot_loo_pit( >>> idata = az.load_arviz_data("centered_eight") >>> az.plot_loo_pit(idata=idata, y="obs") - Fill the area containing the 94% credible interval of the difference between uniform + Fill the area containing the 94% highest density interval of the difference between uniform variables empirical CDF and the real uniform CDF. A LOO-PIT ECDF clearly outside of these theoretical boundaries indicates that the observations and the posterior predictive samples do not follow the same distribution. @@ -130,8 +130,8 @@ def plot_loo_pit( >>> az.plot_loo_pit(idata=idata, y="obs", ecdf=True) """ - if ecdf and use_hpd: - raise ValueError("use_hpd is incompatible with ecdf plot") + if ecdf and use_hdi: + raise ValueError("use_hdi is incompatible with ecdf plot") (figsize, _, _, xt_labelsize, linewidth, _) = _scale_fig_size(figsize, textsize, 1, 1) @@ -210,14 +210,14 @@ def plot_loo_pit( unif = np.random.uniform(size=(n_unif, loo_pit.size)) x_vals = np.linspace(0, 1, len(loo_pit_kde)) - if use_hpd: - if hpd_kwargs is None: - hpd_kwargs = {} - hpd_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) - hpd_fill_kwargs = hpd_kwargs.pop("fill_kwargs", {}) - hpd_fill_kwargs.setdefault("label", "Uniform HPD") - hpd_kwargs["fill_kwargs"] = hpd_fill_kwargs - hpd_kwargs["credible_interval"] = credible_interval + if use_hdi: + if hdi_kwargs is None: + hdi_kwargs = {} + hdi_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) + hdi_fill_kwargs = hdi_kwargs.pop("fill_kwargs", {}) + hdi_fill_kwargs.setdefault("label", "Uniform hdi") + hdi_kwargs["fill_kwargs"] = hdi_fill_kwargs + hdi_kwargs["credible_interval"] = credible_interval unif_densities = np.empty((n_unif, len(loo_pit_kde))) @@ -232,10 +232,10 @@ def plot_loo_pit( p025=p025, fill_kwargs=fill_kwargs, ecdf_fill=ecdf_fill, - use_hpd=use_hpd, + use_hdi=use_hdi, x_vals=x_vals, unif_densities=unif_densities, - hpd_kwargs=hpd_kwargs, + hdi_kwargs=hdi_kwargs, n_unif=n_unif, unif=unif, plot_unif_kwargs=plot_unif_kwargs, @@ -255,12 +255,12 @@ def plot_loo_pit( if backend == "bokeh": if ( - loo_pit_kwargs["hpd_kwargs"] is not None - and "fill_kwargs" in loo_pit_kwargs["hpd_kwargs"] - and loo_pit_kwargs["hpd_kwargs"]["fill_kwargs"] is not None - and "label" in loo_pit_kwargs["hpd_kwargs"]["fill_kwargs"] + loo_pit_kwargs["hdi_kwargs"] is not None + and "fill_kwargs" in loo_pit_kwargs["hdi_kwargs"] + and loo_pit_kwargs["hdi_kwargs"]["fill_kwargs"] is not None + and "label" in loo_pit_kwargs["hdi_kwargs"]["fill_kwargs"] ): - loo_pit_kwargs["hpd_kwargs"]["fill_kwargs"].pop("label") + loo_pit_kwargs["hdi_kwargs"]["fill_kwargs"].pop("label") loo_pit_kwargs.pop("legend") loo_pit_kwargs.pop("xt_labelsize") loo_pit_kwargs.pop("credible_interval") diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -64,8 +64,8 @@ def plot_posterior( Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. hdi_prob: float, optional - Plots highest posterior density interval for chosen percentage of density. - Use 'hide' to hide the HPD interval. Defaults to 0.94. + Plots highest density interval for chosen percentage of density. + Use 'hide' to hide the highest density interval. Defaults to 0.94. multimodal: bool If true (default) it may compute more than one credible interval if the distribution is multimodal and the modes are well separated. @@ -184,7 +184,7 @@ def plot_posterior( >>> az.plot_posterior(data, var_names=['mu'], kind='hist') - Change size of HPD interval + Change size of highest density interval .. plot:: :context: close-figs diff --git a/arviz/plots/violinplot.py b/arviz/plots/violinplot.py --- a/arviz/plots/violinplot.py +++ b/arviz/plots/violinplot.py @@ -72,8 +72,8 @@ def plot_violin( figsize: tuple Figure size. If None it will be defined automatically. textsize: int - Text size of the point_estimates, axis ticks, and HPD. If None it will be autoscaled - based on figsize. + Text size of the point_estimates, axis ticks, and highest density interval. If None it will + be autoscaled based on figsize. sharex: bool Defaults to True, violinplots share a common x-axis scale. sharey: bool diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -387,7 +387,7 @@ def hdi( skipna: bool If true ignores nan values when computing the hdi interval. Defaults to false. group: str, optional - Specifies which InferenceData group should be used to calculate hpd. + Specifies which InferenceData group should be used to calculate hdi. Defaults to 'posterior' var_names: list, optional Names of variables to include in the hdi report. Prefix the variables by `~` diff --git a/examples/bokeh/bokeh_plot_hpd.py b/examples/bokeh/bokeh_plot_hdi.py similarity index 84% rename from examples/bokeh/bokeh_plot_hpd.py rename to examples/bokeh/bokeh_plot_hdi.py --- a/examples/bokeh/bokeh_plot_hpd.py +++ b/examples/bokeh/bokeh_plot_hdi.py @@ -1,5 +1,5 @@ """ -Plot HPD +Plot HDI ======== _thumb: .8, .8 @@ -13,7 +13,7 @@ y_data_rep = np.random.normal(y_data, 0.5, (200, 100)) x_data_sorted = np.sort(x_data) -ax = az.plot_hpd(x_data, y_data_rep, color="red", backend="bokeh", show=False) +ax = az.plot_hdi(x_data, y_data_rep, color="red", backend="bokeh", show=False) ax.line(x_data_sorted, 2 + x_data_sorted * 0.5, line_color="black", line_width=3) if az.rcParams["plot.bokeh.show"]: diff --git a/examples/matplotlib/mpl_plot_hpd.py b/examples/matplotlib/mpl_plot_hdi.py similarity index 80% rename from examples/matplotlib/mpl_plot_hpd.py rename to examples/matplotlib/mpl_plot_hdi.py --- a/examples/matplotlib/mpl_plot_hpd.py +++ b/examples/matplotlib/mpl_plot_hdi.py @@ -1,5 +1,5 @@ """ -Plot HPD +Plot HDI ======== _thumb: .8, .8 @@ -14,6 +14,6 @@ y_data = 2 + x_data * 0.5 y_data_rep = np.random.normal(y_data, 0.5, (200, 100)) plt.plot(x_data, y_data, "C6") -az.plot_hpd(x_data, y_data_rep, color="k", plot_kwargs={"ls": "--"}) +az.plot_hdi(x_data, y_data_rep, color="k", plot_kwargs={"ls": "--"}) plt.show()

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -25,7 +25,7 @@ plot_energy, plot_ess, plot_forest, - plot_hpd, + plot_hdi, plot_joint, plot_kde, plot_khat, @@ -488,8 +488,8 @@ def test_plot_forest_bad(models, model_fits): {"smooth": False}, ], ) -def test_plot_hpd(models, data, kwargs): - axis = plot_hpd( +def test_plot_hdi(models, data, kwargs): + axis = plot_hdi( data["y"], models.model_1.posterior["theta"], backend="bokeh", show=False, **kwargs ) assert axis @@ -602,9 +602,9 @@ def test_plot_khat_bad_input(models): [ {}, {"n_unif": 50}, - {"use_hpd": True, "color": "gray"}, - {"use_hpd": True, "credible_interval": 0.68, "plot_kwargs": {"alpha": 0.9}}, - {"use_hpd": True, "hpd_kwargs": {"smooth": False}}, + {"use_hdi": True, "color": "gray"}, + {"use_hdi": True, "credible_interval": 0.68, "plot_kwargs": {"alpha": 0.9}}, + {"use_hdi": True, "hdi_kwargs": {"smooth": False}}, {"ecdf": True}, {"ecdf": True, "ecdf_fill": False, "plot_unif_kwargs": {"line_dash": "--"}}, {"ecdf": True, "credible_interval": 0.97, "fill_kwargs": {"color": "red"}}, @@ -616,10 +616,10 @@ def test_plot_loo_pit(models, kwargs): def test_plot_loo_pit_incompatible_args(models): - """Test error when both ecdf and use_hpd are True.""" + """Test error when both ecdf and use_hdi are True.""" with pytest.raises(ValueError, match="incompatible"): plot_loo_pit( - idata=models.model_1, y="y", ecdf=True, use_hpd=True, backend="bokeh", show=False + idata=models.model_1, y="y", ecdf=True, use_hdi=True, backend="bokeh", show=False ) diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -34,7 +34,7 @@ plot_compare, plot_kde, plot_khat, - plot_hpd, + plot_hdi, plot_dist, plot_rank, plot_elpd, @@ -805,8 +805,8 @@ def test_plot_compare_no_ic(models): {"smooth": False}, ], ) -def test_plot_hpd(models, data, kwargs): - plot_hpd(data["y"], models.model_1.posterior["theta"], **kwargs) +def test_plot_hdi(models, data, kwargs): + plot_hdi(data["y"], models.model_1.posterior["theta"], **kwargs) @pytest.mark.parametrize("limits", [(-10.0, 10.0), (-5, 5), (None, None)]) @@ -1076,9 +1076,9 @@ def test_plot_ess_no_divergences(models): [ {}, {"n_unif": 50, "legend": False}, - {"use_hpd": True, "color": "gray"}, - {"use_hpd": True, "credible_interval": 0.68, "plot_kwargs": {"ls": "--"}}, - {"use_hpd": True, "hpd_kwargs": {"smooth": False}}, + {"use_hdi": True, "color": "gray"}, + {"use_hdi": True, "credible_interval": 0.68, "plot_kwargs": {"ls": "--"}}, + {"use_hdi": True, "hdi_kwargs": {"smooth": False}}, {"ecdf": True}, {"ecdf": True, "ecdf_fill": False, "plot_unif_kwargs": {"ls": "--"}}, {"ecdf": True, "credible_interval": 0.97, "fill_kwargs": {"hatch": "/"}}, @@ -1090,9 +1090,9 @@ def test_plot_loo_pit(models, kwargs): def test_plot_loo_pit_incompatible_args(models): - """Test error when both ecdf and use_hpd are True.""" + """Test error when both ecdf and use_hdi are True.""" with pytest.raises(ValueError, match="incompatible"): - plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hpd=True) + plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hdi=True) @pytest.mark.parametrize(

Change `plot_hpd` to `plot_hdi` (with a deprecation warning) I think we should also change this to `plot_hdi` (with a deprecation warning). It is OK with me if we do it in another PR, but before the next release. _Originally posted by @aloctavodia in https://github.com/arviz-devs/arviz/pull/1176_

2020-05-18T13:34:36Z

arviz-devs/arviz

arviz-devs__arviz-1198

b062cbcc494f96b2ab350ada196e6a1882500315

diff --git a/arviz/plots/backends/matplotlib/densityplot.py b/arviz/plots/backends/matplotlib/densityplot.py --- a/arviz/plots/backends/matplotlib/densityplot.py +++ b/arviz/plots/backends/matplotlib/densityplot.py @@ -74,8 +74,8 @@ def plot_density( if n_data > 1: for m_idx, label in enumerate(data_labels): - ax[0].plot([], label=label, c=colors[m_idx], markersize=markersize) - ax[0].legend(fontsize=xt_labelsize) + ax.item(0).plot([], label=label, c=colors[m_idx], markersize=markersize) + ax.item(0).legend(fontsize=xt_labelsize) if backend_show(show): plt.show()

diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -101,12 +101,16 @@ def fig_ax(): {"hdi_markers": ["v"]}, {"shade": 1}, {"transform": lambda x: x + 1}, + {"ax": plt.subplots(6, 3)[1]}, ], ) def test_plot_density_float(models, kwargs): obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] axes = plot_density(obj, **kwargs) - assert axes.shape[0] >= 18 + if "ax" in kwargs: + assert axes.shape == (6, 3) + else: + assert axes.shape[0] >= 18 def test_plot_density_discrete(discrete_model):

Passing axes to plot_density fails with several datasets **Describe the bug** I would like to pass existing axes to plot_density with the `ax` keyword. This works fine if I plot data from a single dataset. But I get an error when I try to plot several datasets at once (I think the error arises when it tries to produce a legend). Plotting several datasets without providing axes also works fine. **To Reproduce** ```python import arviz import matplotlib.pyplot as plt test_data = arviz.load_arviz_data('centered_eight') # This works fig, ax = plt.subplots(3, 3) ax1 = arviz.plot_density(data=[test_data.posterior], var_names=['mu', 'theta'], ax=ax); # This works as well ax2 = arviz.plot_density(data=[test_data.prior, test_data.posterior], var_names=['mu', 'theta']); # This does not work fig3, ax3 = plt.subplots(3, 3) arviz.plot_density(data=[test_data.prior, test_data.posterior], var_names=['mu', 'theta'], ax=ax3); ``` This is the error I get: ```python --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-133-7feead268671> in <module> 11 # This does not work 12 fig3, ax3 = plt.subplots(3, 3) ---> 13 arviz.plot_density(data=[test_data.prior, test_data.posterior], var_names=['mu', 'theta'], ax=ax3); ~/miniconda3/envs/sunode/lib/python3.7/site-packages/arviz/plots/densityplot.py in plot_density(data, group, data_labels, var_names, transform, credible_interval, point_estimate, colors, outline, hpd_markers, shade, bw, figsize, textsize, ax, backend, backend_kwargs, show) 263 # TODO: Add backend kwargs 264 plot = get_plotting_function("plot_density", "densityplot", backend) --> 265 ax = plot(**plot_density_kwargs) 266 return ax ~/miniconda3/envs/sunode/lib/python3.7/site-packages/arviz/plots/backends/matplotlib/densityplot.py in plot_density(ax, all_labels, to_plot, colors, bw, figsize, length_plotters, rows, cols, titlesize, xt_labelsize, linewidth, markersize, credible_interval, point_estimate, hpd_markers, outline, shade, n_data, data_labels, backend_kwargs, show) 75 if n_data > 1: 76 for m_idx, label in enumerate(data_labels): ---> 77 ax[0].plot([], label=label, c=colors[m_idx], markersize=markersize) 78 ax[0].legend(fontsize=xt_labelsize) 79 AttributeError: 'numpy.ndarray' object has no attribute 'plot' ``` **Additional context** ArviZ version: 0.7.0 matplotlib version: 3.2.1

Thanks for reporting the issue. I will take a look at it.

2020-05-20T12:57:57Z

arviz-devs/arviz

arviz-devs__arviz-1201

29b35cee5fd117004a499386a45459ac57c47a95

diff --git a/arviz/data/base.py b/arviz/data/base.py --- a/arviz/data/base.py +++ b/arviz/data/base.py @@ -17,6 +17,7 @@ import json # type: ignore from .. import __version__, utils +from ..rcparams import rcParams CoordSpec = Dict[str, List[Any]] DimSpec = Dict[str, List[str]] @@ -49,7 +50,13 @@ def wrapped(cls, *args, **kwargs): def generate_dims_coords( - shape, var_name, dims=None, coords=None, default_dims=None, skip_event_dims=None + shape, + var_name, + dims=None, + coords=None, + default_dims=None, + index_origin=None, + skip_event_dims=None, ): """Generate default dimensions and coordinates for a variable. @@ -70,6 +77,9 @@ def generate_dims_coords( when manipulating Monte Carlo traces, the ``default_dims`` would be ``["chain" , "draw"]`` which ArviZ uses as its own names for dimensions of MCMC traces. + index_origin : int, optional + Starting value of integer coordinate values. Defaults to the value in rcParam + ``data.index_origin``. skip_event_dims : bool, default False Returns @@ -79,6 +89,8 @@ def generate_dims_coords( dict[str] -> list[str] Default coords """ + if index_origin is None: + index_origin = rcParams["data.index_origin"] if default_dims is None: default_dims = [] if dims is None: @@ -127,19 +139,30 @@ def generate_dims_coords( dims[idx] = dim_name dim_name = dims[idx] if dim_name not in coords: - coords[dim_name] = utils.arange(dim_len) + coords[dim_name] = np.arange(index_origin, dim_len + index_origin) coords = {key: coord for key, coord in coords.items() if any(key == dim for dim in dims)} return dims, coords -def numpy_to_data_array(ary, *, var_name="data", coords=None, dims=None, skip_event_dims=None): +def numpy_to_data_array( + ary, + *, + var_name="data", + coords=None, + dims=None, + default_dims=None, + index_origin=None, + skip_event_dims=None, +): """Convert a numpy array to an xarray.DataArray. - The first two dimensions will be (chain, draw), and any remaining + By default, the first two dimensions will be (chain, draw), and any remaining dimensions will be "shape". - If the numpy array is 1d, this dimension is interpreted as draw - If the numpy array is 2d, it is interpreted as (chain, draw) - If the numpy array is 3 or more dimensions, the last dimensions are kept as shapes. + * If the numpy array is 1d, this dimension is interpreted as draw + * If the numpy array is 2d, it is interpreted as (chain, draw) + * If the numpy array is 3 or more dimensions, the last dimensions are kept as shapes. + + To modify this behaviour, use ``default_dims``. Parameters ---------- @@ -154,6 +177,11 @@ def numpy_to_data_array(ary, *, var_name="data", coords=None, dims=None, skip_ev is the name of the dimension, the values are the index values. dims : List(str) A list of coordinate names for the variable + default_dims : list of str, optional + Passed to :py:func:`generate_dims_coords`. Defaults to ``["chain", "draw"]``, and + an empty list is accepted + index_origin : int, optional + Passed to :py:func:`generate_dims_coords` skip_event_dims : bool Returns @@ -162,37 +190,43 @@ def numpy_to_data_array(ary, *, var_name="data", coords=None, dims=None, skip_ev Will have the same data as passed, but with coordinates and dimensions """ # manage and transform copies - default_dims = ["chain", "draw"] - ary = utils.two_de(ary) - n_chains, n_samples, *shape = ary.shape - if n_chains > n_samples: - warnings.warn( - "More chains ({n_chains}) than draws ({n_samples}). " - "Passed array should have shape (chains, draws, *shape)".format( - n_chains=n_chains, n_samples=n_samples - ), - UserWarning, - ) + if default_dims is None: + default_dims = ["chain", "draw"] + if "chain" in default_dims and "draw" in default_dims: + ary = utils.two_de(ary) + n_chains, n_samples, *_ = ary.shape + if n_chains > n_samples: + warnings.warn( + "More chains ({n_chains}) than draws ({n_samples}). " + "Passed array should have shape (chains, draws, *shape)".format( + n_chains=n_chains, n_samples=n_samples + ), + UserWarning, + ) + else: + ary = utils.one_de(ary) dims, coords = generate_dims_coords( - shape, + ary.shape[len(default_dims) :], var_name, dims=dims, coords=coords, default_dims=default_dims, + index_origin=index_origin, skip_event_dims=skip_event_dims, ) # reversed order for default dims: 'chain', 'draw' - if "draw" not in dims: + if "draw" not in dims and "draw" in default_dims: dims = ["draw"] + dims - if "chain" not in dims: + if "chain" not in dims and "chain" in default_dims: dims = ["chain"] + dims - if "chain" not in coords: - coords["chain"] = utils.arange(n_chains) - if "draw" not in coords: - coords["draw"] = utils.arange(n_samples) + index_origin = rcParams["data.index_origin"] + if "chain" not in coords and "chain" in default_dims: + coords["chain"] = np.arange(index_origin, n_chains + index_origin) + if "draw" not in coords and "draw" in default_dims: + coords["draw"] = np.arange(index_origin, n_samples + index_origin) # filter coords based on the dims coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in dims} @@ -200,7 +234,15 @@ def numpy_to_data_array(ary, *, var_name="data", coords=None, dims=None, skip_ev def dict_to_dataset( - data, *, attrs=None, library=None, coords=None, dims=None, skip_event_dims=None + data, + *, + attrs=None, + library=None, + coords=None, + dims=None, + default_dims=None, + index_origin=None, + skip_event_dims=None, ): """Convert a dictionary of numpy arrays to an xarray.Dataset. @@ -217,6 +259,10 @@ def dict_to_dataset( dims : dict[str] -> list[str] Dimensions of each variable. The keys are variable names, values are lists of coordinates. + default_dims : list of str, optional + Passed to :py:func:`numpy_to_data_array` + index_origin : int, optional + Passed to :py:func:`numpy_to_data_array` skip_event_dims : bool If True, cut extra dims whenever present to match the shape of the data. Necessary for PPLs which have the same name in both observed data and log @@ -238,7 +284,13 @@ def dict_to_dataset( data_vars = {} for key, values in data.items(): data_vars[key] = numpy_to_data_array( - values, var_name=key, coords=coords, dims=dims.get(key), skip_event_dims=skip_event_dims + values, + var_name=key, + coords=coords, + dims=dims.get(key), + default_dims=default_dims, + index_origin=index_origin, + skip_event_dims=skip_event_dims, ) return xr.Dataset(data_vars=data_vars, attrs=make_attrs(attrs=attrs, library=library)) @@ -312,7 +364,7 @@ def wrapped(self, *args, **kwargs): return None if _inplace else out description_default = """{method_name} method is extended from xarray.Dataset methods. - + {description}For more info see :meth:`xarray:xarray.Dataset.{method_name}` """.format( description=description, method_name=func.__name__ # pylint: disable=no-member diff --git a/arviz/data/io_cmdstan.py b/arviz/data/io_cmdstan.py --- a/arviz/data/io_cmdstan.py +++ b/arviz/data/io_cmdstan.py @@ -7,11 +7,10 @@ from typing import Dict, List, Optional, Union import numpy as np -import xarray as xr from .. import utils from ..rcparams import rcParams -from .base import CoordSpec, DimSpec, dict_to_dataset, generate_dims_coords, requires +from .base import CoordSpec, DimSpec, dict_to_dataset, requires from .inference_data import InferenceData _log = logging.getLogger(__name__) @@ -51,6 +50,7 @@ def __init__( predictions_constant_data=None, predictions_constant_data_var=None, log_likelihood=None, + index_origin=None, coords=None, dims=None, disable_glob=False, @@ -80,6 +80,7 @@ def __init__( self.attrs_prior = None self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup + self.index_origin = index_origin if dtypes is None: self.dtypes = {} @@ -200,8 +201,20 @@ def posterior_to_xarray(self): data = _unpack_ndarrays(self.posterior[0], valid_cols, self.dtypes) data_warmup = _unpack_ndarrays(self.posterior[1], valid_cols, self.dtypes) return ( - dict_to_dataset(data, coords=self.coords, dims=self.dims, attrs=self.attrs), - dict_to_dataset(data_warmup, coords=self.coords, dims=self.dims, attrs=self.attrs), + dict_to_dataset( + data, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, + ), ) @requires("posterior") @@ -231,12 +244,14 @@ def sample_stats_to_xarray(self): coords=self.coords, dims=self.dims, attrs={item: key for key, item in rename_dict.items()}, + index_origin=self.index_origin, ), dict_to_dataset( data_warmup, coords=self.coords, dims=self.dims, attrs={item: key for key, item in rename_dict.items()}, + index_origin=self.index_origin, ), ) @@ -284,8 +299,20 @@ def posterior_predictive_to_xarray(self): attrs = None return ( - dict_to_dataset(data, coords=self.coords, dims=self.dims, attrs=attrs), - dict_to_dataset(data_warmup, coords=self.coords, dims=self.dims, attrs=attrs), + dict_to_dataset( + data, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + ), ) @requires("posterior") @@ -330,8 +357,20 @@ def predictions_to_xarray(self): attrs = None return ( - dict_to_dataset(data, coords=self.coords, dims=self.dims, attrs=attrs), - dict_to_dataset(data_warmup, coords=self.coords, dims=self.dims, attrs=attrs), + dict_to_dataset( + data, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + ), ) @requires("posterior") @@ -377,10 +416,20 @@ def log_likelihood_to_xarray(self): attrs = None return ( dict_to_dataset( - data, coords=self.coords, dims=self.dims, attrs=attrs, skip_event_dims=True + data, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + skip_event_dims=True, ), dict_to_dataset( - data_warmup, coords=self.coords, dims=self.dims, attrs=attrs, skip_event_dims=True + data_warmup, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + skip_event_dims=True, ), ) @@ -410,9 +459,19 @@ def prior_to_xarray(self): data = _unpack_ndarrays(self.prior[0], valid_cols, self.dtypes) data_warmup = _unpack_ndarrays(self.prior[1], valid_cols, self.dtypes) return ( - dict_to_dataset(data, coords=self.coords, dims=self.dims, attrs=self.attrs_prior), dict_to_dataset( - data_warmup, coords=self.coords, dims=self.dims, attrs=self.attrs_prior + data, + coords=self.coords, + dims=self.dims, + attrs=self.attrs_prior, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + coords=self.coords, + dims=self.dims, + attrs=self.attrs_prior, + index_origin=self.index_origin, ), ) @@ -443,12 +502,14 @@ def sample_stats_prior_to_xarray(self): coords=self.coords, dims=self.dims, attrs={item: key for key, item in rename_dict.items()}, + index_origin=self.index_origin, ), dict_to_dataset( data_warmup, coords=self.coords, dims=self.dims, attrs={item: key for key, item in rename_dict.items()}, + index_origin=self.index_origin, ), ) @@ -491,8 +552,20 @@ def prior_predictive_to_xarray(self): data_warmup = _unpack_ndarrays(self.prior[1], columns, self.dtypes) attrs = None return ( - dict_to_dataset(data, coords=self.coords, dims=self.dims, attrs=attrs), - dict_to_dataset(data_warmup, coords=self.coords, dims=self.dims, attrs=attrs), + dict_to_dataset( + data, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + coords=self.coords, + dims=self.dims, + attrs=attrs, + index_origin=self.index_origin, + ), ) @requires("observed_data") @@ -506,13 +579,14 @@ def observed_data_to_xarray(self): for key, vals in observed_data_raw.items(): if variables is not None and key not in variables: continue - vals = utils.one_de(vals) - val_dims = self.dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - observed_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=observed_data) + observed_data[key] = utils.one_de(vals) + return dict_to_dataset( + observed_data, + coords=self.coords, + dims=self.dims, + default_dims=[], + index_origin=self.index_origin, + ) @requires("constant_data") def constant_data_to_xarray(self): @@ -525,13 +599,14 @@ def constant_data_to_xarray(self): for key, vals in constant_data_raw.items(): if variables is not None and key not in variables: continue - vals = utils.one_de(vals) - val_dims = self.dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - constant_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=constant_data) + constant_data[key] = utils.one_de(vals) + return dict_to_dataset( + constant_data, + coords=self.coords, + dims=self.dims, + default_dims=[], + index_origin=self.index_origin, + ) @requires("predictions_constant_data") def predictions_constant_data_to_xarray(self): @@ -545,12 +620,14 @@ def predictions_constant_data_to_xarray(self): if variables is not None and key not in variables: continue vals = utils.one_de(vals) - val_dims = self.dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - predictions_constant_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=predictions_constant_data) + predictions_constant_data[key] = utils.one_de(vals) + return dict_to_dataset( + predictions_constant_data, + coords=self.coords, + dims=self.dims, + default_dims=[], + index_origin=self.index_origin, + ) def to_inference_data(self): """Convert all available data to an InferenceData object. @@ -821,6 +898,7 @@ def from_cmdstan( predictions_constant_data: Optional[str] = None, predictions_constant_data_var: Optional[Union[str, List[str]]] = None, log_likelihood: Optional[Union[str, List[str]]] = None, + index_origin: Optional[int] = None, coords: Optional[CoordSpec] = None, dims: Optional[DimSpec] = None, disable_glob: Optional[bool] = False, @@ -862,6 +940,9 @@ def from_cmdstan( If not defined, all data variables are imported. log_likelihood : str or list of str, optional Pointwise log_likelihood for the data. + index_origin : int, optional + Starting value of integer coordinate values. Defaults to the value in rcParam + ``data.index_origin``. coords : dict of {str: array_like}, optional A dictionary containing the values that are used as index. The key is the name of the dimension, the values are the index values. @@ -893,6 +974,7 @@ def from_cmdstan( predictions_constant_data=predictions_constant_data, predictions_constant_data_var=predictions_constant_data_var, log_likelihood=log_likelihood, + index_origin=index_origin, coords=coords, dims=dims, disable_glob=disable_glob, diff --git a/arviz/data/io_cmdstanpy.py b/arviz/data/io_cmdstanpy.py --- a/arviz/data/io_cmdstanpy.py +++ b/arviz/data/io_cmdstanpy.py @@ -5,11 +5,9 @@ from copy import deepcopy import numpy as np -import xarray as xr -from .. import utils from ..rcparams import rcParams -from .base import dict_to_dataset, generate_dims_coords, make_attrs, requires +from .base import dict_to_dataset, make_attrs, requires from .inference_data import InferenceData _log = logging.getLogger(__name__) @@ -32,6 +30,7 @@ def __init__( constant_data=None, predictions_constant_data=None, log_likelihood=None, + index_origin=None, coords=None, dims=None, save_warmup=None, @@ -45,6 +44,7 @@ def __init__( self.constant_data = constant_data self.predictions_constant_data = predictions_constant_data self.log_likelihood = log_likelihood + self.index_origin = index_origin self.coords = coords self.dims = dims @@ -133,9 +133,19 @@ def stats_to_xarray(self, fit): if data_warmup: data_warmup[name] = data_warmup.pop(item).astype(dtypes.get(item, float)) return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=self.coords, dims=self.dims), dict_to_dataset( - data_warmup, library=self.cmdstanpy, coords=self.coords, dims=self.dims + data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ), ) @@ -171,9 +181,19 @@ def predictive_to_xarray(self, names, fit): ) return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=self.coords, dims=self.dims), dict_to_dataset( - data_warmup, library=self.cmdstanpy, coords=self.coords, dims=self.dims + data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ), ) @@ -200,9 +220,19 @@ def predictions_to_xarray(self): ) return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=self.coords, dims=self.dims), dict_to_dataset( - data_warmup, library=self.cmdstanpy, coords=self.coords, dims=self.dims + data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ), ) @@ -233,6 +263,7 @@ def log_likelihood_to_xarray(self): library=self.cmdstanpy, coords=self.coords, dims=self.dims, + index_origin=self.index_origin, skip_event_dims=True, ), dict_to_dataset( @@ -240,6 +271,7 @@ def log_likelihood_to_xarray(self): library=self.cmdstanpy, coords=self.coords, dims=self.dims, + index_origin=self.index_origin, skip_event_dims=True, ), ) @@ -275,51 +307,57 @@ def prior_to_xarray(self): ) return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=self.coords, dims=self.dims), dict_to_dataset( - data_warmup, library=self.cmdstanpy, coords=self.coords, dims=self.dims + data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ), ) @requires("observed_data") def observed_data_to_xarray(self): """Convert observed data to xarray.""" - observed_data = {} - for key, vals in self.observed_data.items(): - vals = utils.one_de(vals) - val_dims = self.dims.get(key) if self.dims is not None else None - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - observed_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=observed_data, attrs=make_attrs(library=self.cmdstanpy)) + return dict_to_dataset( + self.observed_data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + default_dims=[], + index_origin=self.index_origin, + ) @requires("constant_data") def constant_data_to_xarray(self): """Convert constant data to xarray.""" - constant_data = {} - for key, vals in self.constant_data.items(): - vals = utils.one_de(vals) - val_dims = self.dims.get(key) if self.dims is not None else None - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - constant_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=constant_data, attrs=make_attrs(library=self.cmdstanpy)) + return dict_to_dataset( + self.constant_data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + default_dims=[], + index_origin=self.index_origin, + ) @requires("predictions_constant_data") def predictions_constant_data_to_xarray(self): """Convert constant data to xarray.""" - predictions_constant_data = {} - for key, vals in self.predictions_constant_data.items(): - vals = utils.one_de(vals) - val_dims = self.dims.get(key) if self.dims is not None else None - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - predictions_constant_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset( - data_vars=predictions_constant_data, attrs=make_attrs(library=self.cmdstanpy) + return dict_to_dataset( + self.predictions_constant_data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + attrs=make_attrs(library=self.cmdstanpy), + default_dims=[], + index_origin=self.index_origin, ) def to_inference_data(self): @@ -614,6 +652,7 @@ def from_cmdstanpy( constant_data=None, predictions_constant_data=None, log_likelihood=None, + index_origin=None, coords=None, dims=None, save_warmup=None, @@ -643,6 +682,9 @@ def from_cmdstanpy( Constant data for predictions used in the sampling. log_likelihood : str, list of str Pointwise log_likelihood for the data. + index_origin : int, optional + Starting value of integer coordinate values. Defaults to the value in rcParam + ``data.index_origin``. coords : dict of str or dict of iterable A dictionary containing the values that are used as index. The key is the name of the dimension, the values are the index values. @@ -666,6 +708,7 @@ def from_cmdstanpy( constant_data=constant_data, predictions_constant_data=predictions_constant_data, log_likelihood=log_likelihood, + index_origin=index_origin, coords=coords, dims=dims, save_warmup=save_warmup, diff --git a/arviz/data/io_dict.py b/arviz/data/io_dict.py --- a/arviz/data/io_dict.py +++ b/arviz/data/io_dict.py @@ -1,11 +1,9 @@ """Dictionary specific conversion code.""" import warnings +from typing import Optional -import xarray as xr - -from .. import utils from ..rcparams import rcParams -from .base import dict_to_dataset, generate_dims_coords, make_attrs, requires +from .base import dict_to_dataset, requires from .inference_data import WARMUP_TAG, InferenceData @@ -33,6 +31,7 @@ def __init__( warmup_log_likelihood=None, warmup_sample_stats=None, save_warmup=None, + index_origin=None, coords=None, dims=None, pred_dims=None, @@ -63,6 +62,8 @@ def __init__( if coords is None else {**coords, **pred_coords} ) + self.index_origin = index_origin + self.coords = coords self.dims = dims self.pred_dims = dims if pred_dims is None else pred_dims self.attrs = {} if attrs is None else attrs @@ -92,10 +93,20 @@ def posterior_to_xarray(self): return ( dict_to_dataset( - data, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ), dict_to_dataset( - data_warmup, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data_warmup, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ), ) @@ -119,10 +130,20 @@ def sample_stats_to_xarray(self): return ( dict_to_dataset( - data, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ), dict_to_dataset( - data_warmup, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data_warmup, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ), ) @@ -143,6 +164,7 @@ def log_likelihood_to_xarray(self): coords=self.coords, dims=self.dims, attrs=self.attrs, + index_origin=self.index_origin, skip_event_dims=True, ), dict_to_dataset( @@ -151,6 +173,7 @@ def log_likelihood_to_xarray(self): coords=self.coords, dims=self.dims, attrs=self.attrs, + index_origin=self.index_origin, skip_event_dims=True, ), ) @@ -167,10 +190,20 @@ def posterior_predictive_to_xarray(self): return ( dict_to_dataset( - data, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ), dict_to_dataset( - data_warmup, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data_warmup, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ), ) @@ -186,10 +219,20 @@ def predictions_to_xarray(self): return ( dict_to_dataset( - data, library=None, coords=self.coords, dims=self.pred_dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.pred_dims, + attrs=self.attrs, + index_origin=self.index_origin, ), dict_to_dataset( - data_warmup, library=None, coords=self.coords, dims=self.pred_dims, attrs=self.attrs + data_warmup, + library=None, + coords=self.coords, + dims=self.pred_dims, + attrs=self.attrs, + index_origin=self.index_origin, ), ) @@ -201,7 +244,12 @@ def prior_to_xarray(self): raise TypeError("DictConverter.prior is not a dictionary") return dict_to_dataset( - data, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ) @requires("sample_stats_prior") @@ -212,7 +260,12 @@ def sample_stats_prior_to_xarray(self): raise TypeError("DictConverter.sample_stats_prior is not a dictionary") return dict_to_dataset( - data, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ) @requires("prior_predictive") @@ -223,25 +276,29 @@ def prior_predictive_to_xarray(self): raise TypeError("DictConverter.prior_predictive is not a dictionary") return dict_to_dataset( - data, library=None, coords=self.coords, dims=self.dims, attrs=self.attrs + data, + library=None, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + index_origin=self.index_origin, ) - def data_to_xarray(self, dct, group, dims=None): + def data_to_xarray(self, data, group, dims=None): """Convert data to xarray.""" - data = dct if not isinstance(data, dict): raise TypeError("DictConverter.{} is not a dictionary".format(group)) if dims is None: dims = {} if self.dims is None else self.dims - new_data = dict() - for key, vals in data.items(): - vals = utils.one_de(vals) - val_dims = dims.get(key) - val_dims, coords = generate_dims_coords( - vals.shape, key, dims=val_dims, coords=self.coords - ) - new_data[key] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=new_data, attrs=make_attrs(attrs=self.attrs, library=None)) + return dict_to_dataset( + data, + library=None, + coords=self.coords, + dims=self.dims, + default_dims=[], + attrs=self.attrs, + index_origin=self.index_origin, + ) @requires("observed_data") def observed_data_to_xarray(self): @@ -304,6 +361,7 @@ def from_dict( warmup_log_likelihood=None, warmup_sample_stats=None, save_warmup=None, + index_origin: Optional[int] = None, coords=None, dims=None, pred_dims=None, @@ -336,6 +394,7 @@ def from_dict( save_warmup : bool Save warmup iterations InferenceData object. If not defined, use default defined by the rcParams. + index_origin : int, optional coords : dict[str, iterable] A dictionary containing the values that are used as index. The key is the name of the dimension, the values are the index values. @@ -370,6 +429,7 @@ def from_dict( warmup_log_likelihood=warmup_log_likelihood, warmup_sample_stats=warmup_sample_stats, save_warmup=save_warmup, + index_origin=index_origin, coords=coords, dims=dims, pred_dims=pred_dims, diff --git a/arviz/data/io_emcee.py b/arviz/data/io_emcee.py --- a/arviz/data/io_emcee.py +++ b/arviz/data/io_emcee.py @@ -85,6 +85,7 @@ def _verify_names(sampler, var_names, arg_names, slices): return var_names, arg_names, slices +# pylint: disable=too-many-instance-attributes class EmceeConverter: """Encapsulate emcee specific logic.""" @@ -97,6 +98,7 @@ def __init__( arg_groups=None, blob_names=None, blob_groups=None, + index_origin=None, coords=None, dims=None, ): @@ -108,6 +110,7 @@ def __init__( self.arg_groups = arg_groups self.blob_names = blob_names self.blob_groups = blob_groups + self.index_origin = index_origin self.coords = coords self.dims = dims import emcee @@ -124,7 +127,13 @@ def posterior_to_xarray(self): if hasattr(self.sampler, "get_chain") else self.sampler.chain[(..., idx)] ) - return dict_to_dataset(data, library=self.emcee, coords=self.coords, dims=self.dims) + return dict_to_dataset( + data, + library=self.emcee, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ) def args_to_xarray(self): """Convert emcee args to observed and constant_data xarray Datasets.""" @@ -157,7 +166,11 @@ def args_to_xarray(self): ) arg_dims = dims.get(arg_name) arg_dims, coords = generate_dims_coords( - arg_array.shape, arg_name, dims=arg_dims, coords=self.coords + arg_array.shape, + arg_name, + dims=arg_dims, + coords=self.coords, + index_origin=self.index_origin, ) # filter coords based on the dims coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in arg_dims} @@ -227,7 +240,11 @@ def blobs_to_dict(self): ) for key, values in blob_dict.items(): blob_dict[key] = dict_to_dataset( - values, library=self.emcee, coords=self.coords, dims=self.dims + values, + library=self.emcee, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ) return blob_dict @@ -248,6 +265,7 @@ def from_emcee( arg_groups=None, blob_names=None, blob_groups=None, + index_origin=None, coords=None, dims=None, ): @@ -461,6 +479,7 @@ def from_emcee( arg_groups=arg_groups, blob_names=blob_names, blob_groups=blob_groups, + index_origin=index_origin, coords=coords, dims=dims, ).to_inference_data() diff --git a/arviz/data/io_numpyro.py b/arviz/data/io_numpyro.py --- a/arviz/data/io_numpyro.py +++ b/arviz/data/io_numpyro.py @@ -3,10 +3,9 @@ from typing import Callable, Optional import numpy as np -import xarray as xr from .. import utils -from .base import dict_to_dataset, generate_dims_coords, make_attrs, requires +from .base import dict_to_dataset, requires from .inference_data import InferenceData _log = logging.getLogger(__name__) @@ -30,6 +29,7 @@ def __init__( predictions=None, constant_data=None, predictions_constant_data=None, + index_origin=None, coords=None, dims=None, pred_dims=None, @@ -51,6 +51,7 @@ def __init__( Dictionary containing constant data variables mapped to their values. predictions_constant_data: dict Constant data used for out-of-sample predictions. + index_origin : int, optinal coords : dict[str] -> list[str] Map of dimensions to coordinates dims : dict[str] -> list[str] @@ -69,6 +70,7 @@ def __init__( self.predictions = predictions self.constant_data = constant_data self.predictions_constant_data = predictions_constant_data + self.index_origin = index_origin self.coords = coords self.dims = dims self.pred_dims = pred_dims @@ -127,7 +129,13 @@ def arbitrary_element(dct): def posterior_to_xarray(self): """Convert the posterior to an xarray dataset.""" data = self._samples - return dict_to_dataset(data, library=self.numpyro, coords=self.coords, dims=self.dims) + return dict_to_dataset( + data, + library=self.numpyro, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ) @requires("posterior") def sample_stats_to_xarray(self): @@ -147,7 +155,13 @@ def sample_stats_to_xarray(self): data[name] = value if stat == "num_steps": data["tree_depth"] = np.log2(value).astype(int) + 1 - return dict_to_dataset(data, library=self.numpyro, dims=None, coords=self.coords) + return dict_to_dataset( + data, + library=self.numpyro, + dims=None, + coords=self.coords, + index_origin=self.index_origin, + ) @requires("posterior") @requires("model") @@ -163,7 +177,12 @@ def log_likelihood_to_xarray(self): shape = (self.nchains, self.ndraws) + log_like.shape[1:] data[obs_name] = np.reshape(log_like.copy(), shape) return dict_to_dataset( - data, library=self.numpyro, dims=self.dims, coords=self.coords, skip_event_dims=True + data, + library=self.numpyro, + dims=self.dims, + coords=self.coords, + index_origin=self.index_origin, + skip_event_dims=True, ) def translate_posterior_predictive_dict_to_xarray(self, dct, dims): @@ -181,7 +200,13 @@ def translate_posterior_predictive_dict_to_xarray(self, dct, dims): "posterior predictive shape not compatible with number of chains and draws. " "This can mean that some draws or even whole chains are not represented." ) - return dict_to_dataset(data, library=self.numpyro, coords=self.coords, dims=dims) + return dict_to_dataset( + data, + library=self.numpyro, + coords=self.coords, + dims=dims, + index_origin=self.index_origin, + ) @requires("posterior_predictive") def posterior_predictive_to_xarray(self): @@ -217,6 +242,7 @@ def priors_to_xarray(self): library=self.numpyro, coords=self.coords, dims=self.dims, + index_origin=self.index_origin, ) ) return priors_dict @@ -225,47 +251,38 @@ def priors_to_xarray(self): @requires("model") def observed_data_to_xarray(self): """Convert observed data to xarray.""" - if self.dims is None: - dims = {} - else: - dims = self.dims - observed_data = {} - for name, vals in self.observations.items(): - vals = utils.one_de(vals) - val_dims = dims.get(name) - val_dims, coords = generate_dims_coords( - vals.shape, name, dims=val_dims, coords=self.coords - ) - # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} - observed_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=observed_data, attrs=make_attrs(library=self.numpyro)) - - def convert_constant_data_to_xarray(self, dct, dims): - """Convert constant_data or predictions_constant_data to xarray.""" - if dims is None: - dims = {} - constant_data = {} - for name, vals in dct.items(): - vals = utils.one_de(vals) - val_dims = dims.get(name) - val_dims, coords = generate_dims_coords( - vals.shape, name, dims=val_dims, coords=self.coords - ) - # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} - constant_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=constant_data, attrs=make_attrs(library=self.numpyro)) + return dict_to_dataset( + self.observations, + library=self.numpyro, + dims=self.dims, + coords=self.coords, + default_dims=[], + index_origin=self.index_origin, + ) @requires("constant_data") def constant_data_to_xarray(self): """Convert constant_data to xarray.""" - return self.convert_constant_data_to_xarray(self.constant_data, self.dims) + return dict_to_dataset( + self.constant_data, + library=self.numpyro, + dims=self.dims, + coords=self.coords, + default_dims=[], + index_origin=self.index_origin, + ) @requires("predictions_constant_data") def predictions_constant_data_to_xarray(self): """Convert predictions_constant_data to xarray.""" - return self.convert_constant_data_to_xarray(self.predictions_constant_data, self.pred_dims) + return dict_to_dataset( + self.predictions_constant_data, + library=self.numpyro, + dims=self.pred_dims, + coords=self.coords, + default_dims=[], + index_origin=self.index_origin, + ) def to_inference_data(self): """Convert all available data to an InferenceData object. @@ -297,6 +314,7 @@ def from_numpyro( predictions=None, constant_data=None, predictions_constant_data=None, + index_origin=None, coords=None, dims=None, pred_dims=None, @@ -321,6 +339,7 @@ def from_numpyro( Dictionary containing constant data variables mapped to their values. predictions_constant_data: dict Constant data used for out-of-sample predictions. + index_origin : int, optional coords : dict[str] -> list[str] Map of dimensions to coordinates dims : dict[str] -> list[str] @@ -337,6 +356,7 @@ def from_numpyro( predictions=predictions, constant_data=constant_data, predictions_constant_data=predictions_constant_data, + index_origin=index_origin, coords=coords, dims=dims, pred_dims=pred_dims, diff --git a/arviz/labels.py b/arviz/labels.py new file mode 100644 --- /dev/null +++ b/arviz/labels.py @@ -0,0 +1,210 @@ +# pylint: disable=unused-argument +"""Utilities to generate labels from xarray objects.""" +from typing import Union + +__all__ = [ + "mix_labellers", + "BaseLabeller", + "DimCoordLabeller", + "DimIdxLabeller", + "MapLabeller", + "NoRepeatLabeller", + "NoModelLabeller", +] + + +def mix_labellers(labellers, class_name="MixtureLabeller"): + """Combine Labeller classes dynamically. + + The Labeller class aims to split plot labeling in ArviZ into atomic tasks to maximize + extensibility, and the few classes provided are designed with small deviations + from the base class, in many cases only one method is modified by the child class. + It is to be expected then to want to use multiple classes "at once". + + This functions helps combine classes dynamically. + + Parameters + ---------- + labellers : iterable of types + Iterable of Labeller types to combine + class_name : str, optional + The name of the generated class + + Returns + ------- + type + Mixture class object. *It is not initialized* + + Examples + -------- + Combine the :class:`~arviz.labels.DimCoordLabeller` with the + :class:`~arviz.labels.MapLabeller` to generate labels in the style of the + ``DimCoordLabeller`` but using the mappings defined by ``MapLabeller``. + Note that this works even though both modify the same methods because + ``MapLabeller`` implements the mapping and then calls `super().method`. + + .. ipython:: + + In [1]: from arviz.labels import mix_labellers, DimCoordLabeller, MapLabeller + ...: l1 = DimCoordLabeller() + ...: sel = {"dim1": "a", "dim2": "top"} + ...: print(f"Output of DimCoordLabeller alone > {l1.sel_to_str(sel, sel)}") + ...: l2 = MapLabeller(dim_map={"dim1": "$d_1$", "dim2": r"$d_2$"}) + ...: print(f"Output of MapLabeller alone > {l2.sel_to_str(sel, sel)}") + ...: l3 = mix_labellers( + ...: (MapLabeller, DimCoordLabeller) + ...: )(dim_map={"dim1": "$d_1$", "dim2": r"$d_2$"}) + ...: print(f"Output of mixture labeller > {l3.sel_to_str(sel, sel)}") + + We can see how the mappings are taken into account as well as the dim+coord style. However, + he order in the ``labellers`` arg iterator is important! See for yourself: + + .. ipython:: python + + l4 = mix_labellers( + (DimCoordLabeller, MapLabeller) + )(dim_map={"dim1": "$d_1$", "dim2": r"$d_2$"}) + print(f"Output of inverted mixture labeller > {l4.sel_to_str(sel, sel)}") + + """ + return type(class_name, labellers, {}) + + +class BaseLabeller: + """WIP.""" + + def dim_coord_to_str(self, dim, coord_val, coord_idx): + """WIP.""" + return f"{coord_val}" + + def sel_to_str(self, sel: dict, isel: dict): + """WIP.""" + if not sel: + return "" + return ", ".join( + [ + self.dim_coord_to_str(dim, v, i) + for (dim, v), (_, i) in zip(sel.items(), isel.items()) + ] + ) + + def var_name_to_str(self, var_name: Union[str, None]): + """WIP.""" + return var_name + + def var_pp_to_str(self, var_name, pp_var_name): + """WIP.""" + var_name_str = self.var_name_to_str(var_name) + pp_var_name_str = self.var_name_to_str(pp_var_name) + return f"{var_name_str} / {pp_var_name_str}" + + def model_name_to_str(self, model_name): + """WIP.""" + return model_name + + def make_label_vert(self, var_name: Union[str, None], sel: dict, isel: dict): + """WIP.""" + var_name_str = self.var_name_to_str(var_name) + sel_str = self.sel_to_str(sel, isel) + if not sel_str: + return var_name_str + if var_name_str is None: + return sel_str + return f"{var_name_str}\n{sel_str}" + + def make_label_flat(self, var_name: str, sel: dict, isel: dict): + """WIP.""" + var_name_str = self.var_name_to_str(var_name) + sel_str = self.sel_to_str(sel, isel) + if not sel_str: + return var_name_str + if var_name is None: + return sel_str + return f"{var_name_str}[{sel_str}]" + + def make_pp_label(self, var_name, pp_var_name, sel, isel): + """WIP.""" + names = self.var_pp_to_str(var_name, pp_var_name) + return self.make_label_vert(names, sel, isel) + + def make_model_label(self, model_name, label): + """WIP.""" + model_name_str = self.model_name_to_str(model_name) + if model_name_str is None: + return label + return f"{model_name}: {label}" + + +class DimCoordLabeller(BaseLabeller): + """WIP.""" + + def dim_coord_to_str(self, dim, coord_val, coord_idx): + """WIP.""" + return f"{dim}: {coord_val}" + + +class IdxLabeller(BaseLabeller): + """WIP.""" + + def dim_coord_to_str(self, dim, coord_val, coord_idx): + """WIP.""" + return f"{coord_idx}" + + +class DimIdxLabeller(BaseLabeller): + """WIP.""" + + def dim_coord_to_str(self, dim, coord_val, coord_idx): + """WIP.""" + return f"{dim}#{coord_idx}" + + +class MapLabeller(BaseLabeller): + """WIP.""" + + def __init__(self, var_name_map=None, dim_map=None, coord_map=None, model_name_map=None): + """WIP.""" + self.var_name_map = {} if var_name_map is None else var_name_map + self.dim_map = {} if dim_map is None else dim_map + self.coord_map = {} if coord_map is None else coord_map + self.model_name_map = {} if model_name_map is None else model_name_map + + def dim_coord_to_str(self, dim, coord_val, coord_idx): + """WIP.""" + dim_str = self.dim_map.get(dim, dim) + coord_str = self.coord_map.get(dim, {}).get(coord_val, coord_val) + return super().dim_coord_to_str(dim_str, coord_str, coord_idx) + + def var_name_to_str(self, var_name): + """WIP.""" + var_name_str = self.var_name_map.get(var_name, var_name) + return super().var_name_to_str(var_name_str) + + def model_name_to_str(self, model_name): + """WIP.""" + model_name_str = self.var_name_map.get(model_name, model_name) + return super().model_name_to_str(model_name_str) + + +class NoRepeatLabeller(BaseLabeller): + """WIP.""" + + def __init__(self): + """WIP.""" + self.current_var = None + + def var_name_to_str(self, var_name): + """WIP.""" + current_var = getattr(self, "current_var", None) + if var_name == current_var: + return "" + self.current_var = var_name + return var_name + + +class NoModelLabeller(BaseLabeller): + """WIP.""" + + def make_model_label(self, model_name, label): + """WIP.""" + return label diff --git a/arviz/plots/autocorrplot.py b/arviz/plots/autocorrplot.py --- a/arviz/plots/autocorrplot.py +++ b/arviz/plots/autocorrplot.py @@ -1,8 +1,10 @@ """Autocorrelation plot of data.""" from ..data import convert_to_dataset +from ..labels import BaseLabeller +from ..sel_utils import xarray_var_iter from ..rcparams import rcParams from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function def plot_autocorr( @@ -14,6 +16,7 @@ def plot_autocorr( grid=None, figsize=None, textsize=None, + labeller=None, ax=None, backend=None, backend_config=None, @@ -52,6 +55,9 @@ def plot_autocorr( textsize: float Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. ax: numpy array-like of matplotlib axes or bokeh figures, optional A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it). @@ -110,6 +116,9 @@ def plot_autocorr( if max_lag is None: max_lag = min(100, data["draw"].shape[0]) + if labeller is None: + labeller = BaseLabeller() + plotters = filter_plotters_list( list(xarray_var_iter(data, var_names, combined)), "plot_autocorr" ) @@ -124,6 +133,7 @@ def plot_autocorr( cols=cols, combined=combined, textsize=textsize, + labeller=labeller, backend_kwargs=backend_kwargs, show=show, ) diff --git a/arviz/plots/backends/bokeh/autocorrplot.py b/arviz/plots/backends/bokeh/autocorrplot.py --- a/arviz/plots/backends/bokeh/autocorrplot.py +++ b/arviz/plots/backends/bokeh/autocorrplot.py @@ -5,7 +5,7 @@ from ....stats import autocorr -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from .. import show_layout from . import backend_kwarg_defaults, create_axes_grid @@ -19,6 +19,7 @@ def plot_autocorr( cols, combined, textsize, + labeller, backend_config, backend_kwargs, show, @@ -27,7 +28,7 @@ def plot_autocorr( if backend_config is None: backend_config = {} - len_y = plotters[0][2].size + len_y = plotters[0][-1].size backend_config.setdefault("bounds_x_range", (0, len_y)) backend_config = { @@ -69,7 +70,7 @@ def plot_autocorr( start=-1, end=1, bounds=backend_config["bounds_y_range"], min_interval=0.1 ) - for (var_name, selection, x), ax in zip( + for (var_name, selection, isel, x), ax in zip( plotters, (item for item in axes.flatten() if item is not None) ): x_prime = x @@ -89,7 +90,7 @@ def plot_autocorr( ) title = Title() - title.text = make_label(var_name, selection) + title.text = labeller.make_label_vert(var_name, selection, isel) ax.title = title ax.x_range = data_range_x ax.y_range = data_range_y diff --git a/arviz/plots/backends/bokeh/bpvplot.py b/arviz/plots/backends/bokeh/bpvplot.py --- a/arviz/plots/backends/bokeh/bpvplot.py +++ b/arviz/plots/backends/bokeh/bpvplot.py @@ -36,6 +36,7 @@ def plot_bpv( color, figsize, textsize, + labeller, plot_ref_kwargs, backend_kwargs, show, @@ -83,8 +84,8 @@ def plot_bpv( ) for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)): - var_name, _, obs_vals = obs_plotters[i] - pp_var_name, _, pp_vals = pp_plotters[i] + var_name, sel, isel, obs_vals = obs_plotters[i] + pp_var_name, _, _, pp_vals = pp_plotters[i] obs_vals = obs_vals.flatten() pp_vals = pp_vals.reshape(total_pp_samples, -1) @@ -175,12 +176,8 @@ def plot_bpv( obs_vals.mean(), 0, fill_color=color, line_color="black", size=markersize ) - if var_name != pp_var_name: - xlabel = "{} / {}".format(var_name, pp_var_name) - else: - xlabel = var_name _title = Title() - _title.text = xlabel + _title.text = labeller.make_pp_label(var_name, pp_var_name, sel, isel) ax_i.title = _title size = str(int(ax_labelsize)) ax_i.title.text_font_size = f"{size}pt" diff --git a/arviz/plots/backends/bokeh/compareplot.py b/arviz/plots/backends/bokeh/compareplot.py --- a/arviz/plots/backends/bokeh/compareplot.py +++ b/arviz/plots/backends/bokeh/compareplot.py @@ -44,9 +44,6 @@ def plot_compare( yticks_pos = list(yticks_pos) if plot_ic_diff: - yticks_labels[0] = comp_df.index[0] - yticks_labels[2::2] = comp_df.index[1:] - ax.yaxis.ticker = yticks_pos ax.yaxis.major_label_overrides = { dtype(key): value @@ -77,7 +74,6 @@ def plot_compare( ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_dse", "grey")) else: - yticks_labels = comp_df.index ax.yaxis.ticker = yticks_pos[::2] ax.yaxis.major_label_overrides = { key: value for key, value in zip(yticks_pos[::2], yticks_labels) diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py --- a/arviz/plots/backends/bokeh/densityplot.py +++ b/arviz/plots/backends/bokeh/densityplot.py @@ -8,7 +8,7 @@ from ....stats import hdi from ....stats.density_utils import get_bins, histogram, kde -from ...plot_utils import _scale_fig_size, calculate_point_estimate, make_label, vectorized_to_hex +from ...plot_utils import _scale_fig_size, calculate_point_estimate, vectorized_to_hex from .. import show_layout from . import backend_kwarg_defaults, create_axes_grid @@ -25,6 +25,7 @@ def plot_density( rows, cols, textsize, + labeller, hdi_prob, point_estimate, hdi_markers, @@ -78,8 +79,8 @@ def plot_density( legend_items = defaultdict(list) for m_idx, plotters in enumerate(to_plot): - for var_name, selection, values in plotters: - label = make_label(var_name, selection) + for var_name, selection, isel, values in plotters: + label = labeller.make_label_vert(var_name, selection, isel) if data_labels: data_label = data_labels[m_idx] diff --git a/arviz/plots/backends/bokeh/distcomparisonplot.py b/arviz/plots/backends/bokeh/distcomparisonplot.py --- a/arviz/plots/backends/bokeh/distcomparisonplot.py +++ b/arviz/plots/backends/bokeh/distcomparisonplot.py @@ -10,6 +10,7 @@ def plot_dist_comparison( legend, groups, textsize, + labeller, prior_kwargs, posterior_kwargs, observed_kwargs, diff --git a/arviz/plots/backends/bokeh/essplot.py b/arviz/plots/backends/bokeh/essplot.py --- a/arviz/plots/backends/bokeh/essplot.py +++ b/arviz/plots/backends/bokeh/essplot.py @@ -5,9 +5,9 @@ from bokeh.models.annotations import Legend, Title from scipy.stats import rankdata -from ...plot_utils import _scale_fig_size, make_label from .. import show_layout from . import backend_kwarg_defaults, create_axes_grid +from ...plot_utils import _scale_fig_size def plot_ess( @@ -31,6 +31,7 @@ def plot_ess( n_samples, relative, min_ess, + labeller, ylabel, rug, rug_kind, @@ -61,7 +62,7 @@ def plot_ess( else: ax = np.atleast_2d(ax) - for (var_name, selection, x), ax_ in zip( + for (var_name, selection, isel, x), ax_ in zip( plotters, (item for item in ax.flatten() if item is not None) ): bulk_points = ax_.circle(np.asarray(xdata), np.asarray(x), size=6) @@ -153,7 +154,7 @@ def plot_ess( ax_.legend.click_policy = "hide" title = Title() - title.text = make_label(var_name, selection) + title.text = labeller.make_label_vert(var_name, selection, isel) ax_.title = title ax_.xaxis.axis_label = "Total number of draws" if kind == "evolution" else "Quantile" diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -10,12 +10,13 @@ from bokeh.models.annotations import Title from bokeh.models.tickers import FixedTicker +from ....sel_utils import xarray_var_iter from ....rcparams import rcParams from ....stats import hdi from ....stats.density_utils import get_bins, histogram, kde from ....stats.diagnostics import _ess, _rhat from ....utils import conditional_jit -from ...plot_utils import _scale_fig_size, make_label, xarray_var_iter +from ...plot_utils import _scale_fig_size from .. import show_layout from . import backend_kwarg_defaults @@ -49,6 +50,8 @@ def plot_forest( ridgeplot_truncate, ridgeplot_quantiles, textsize, + legend, + labeller, ess, r_hat, backend_config, @@ -57,7 +60,12 @@ def plot_forest( ): """Bokeh forest plot.""" plot_handler = PlotHandler( - datasets, var_names=var_names, model_names=model_names, combined=combined, colors=colors + datasets, + var_names=var_names, + model_names=model_names, + combined=combined, + colors=colors, + labeller=labeller, ) if figsize is None: @@ -195,7 +203,7 @@ class PlotHandler: # pylint: disable=inconsistent-return-statements - def __init__(self, datasets, var_names, model_names, combined, colors): + def __init__(self, datasets, var_names, model_names, combined, colors, labeller): self.data = datasets if model_names is None: @@ -233,6 +241,7 @@ def __init__(self, datasets, var_names, model_names, combined, colors): colors = [colors for _ in self.data] self.colors = list(reversed(colors)) # y-values are upside down + self.labeller = labeller self.plotters = self.make_plotters() @@ -247,6 +256,7 @@ def make_plotters(self): model_names=self.model_names, combined=self.combined, colors=self.colors, + labeller=self.labeller, ) y = plotters[var_name].y_max() return plotters @@ -540,13 +550,14 @@ def y_max(self): class VarHandler: """Handle individual variable logic.""" - def __init__(self, var_name, data, y_start, model_names, combined, colors): + def __init__(self, var_name, data, y_start, model_names, combined, colors, labeller): self.var_name = var_name self.data = data self.y_start = y_start self.model_names = model_names self.combined = combined self.colors = colors + self.labeller = labeller self.model_color = dict(zip(self.model_names, self.colors)) max_chains = max(datum.chain.max().values for datum in data) self.chain_offset = len(data) * 0.45 / max(1, max_chains) @@ -573,7 +584,7 @@ def iterator(self): reverse_selections=True, ) datum_list = list(datum_iter) - for _, selection, values in datum_list: + for _, selection, isel, values in datum_list: selection_list.append(selection) if not selection: var_name = self.var_name @@ -581,7 +592,7 @@ def iterator(self): var_name = self.var_name + ":" else: var_name = "" - label = make_label(var_name, selection, position="beside") + label = self.labeller.make_label_flat(var_name, selection, isel) if label not in label_dict: label_dict[label] = OrderedDict() if name not in label_dict[label]: diff --git a/arviz/plots/backends/bokeh/jointplot.py b/arviz/plots/backends/bokeh/jointplot.py --- a/arviz/plots/backends/bokeh/jointplot.py +++ b/arviz/plots/backends/bokeh/jointplot.py @@ -4,9 +4,10 @@ from ...distplot import plot_dist from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, make_label from .. import show_layout from . import backend_kwarg_defaults +from ...plot_utils import _scale_fig_size +from ....sel_utils import make_label def plot_joint( @@ -80,8 +81,8 @@ def plot_joint( axjoin.yaxis.axis_label = y_var_name # Flatten data - x = plotters[0][2].flatten() - y = plotters[1][2].flatten() + x = plotters[0][-1].flatten() + y = plotters[1][-1].flatten() if kind == "scatter": axjoin.circle(x, y, **joint_kwargs) diff --git a/arviz/plots/backends/bokeh/loopitplot.py b/arviz/plots/backends/bokeh/loopitplot.py --- a/arviz/plots/backends/bokeh/loopitplot.py +++ b/arviz/plots/backends/bokeh/loopitplot.py @@ -34,6 +34,7 @@ def plot_loo_pit( y, color, textsize, + labeller, hdi_prob, plot_kwargs, backend_kwargs, @@ -63,15 +64,21 @@ def plot_loo_pit( plot_kwargs.setdefault("color", to_hex(color)) plot_kwargs.setdefault("linewidth", linewidth * 1.4) if isinstance(y, str): - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y elif isinstance(y, DataArray) and y.name is not None: - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y.name) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y.name elif isinstance(y_hat, str): - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y_hat) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y_hat elif isinstance(y_hat, DataArray) and y_hat.name is not None: - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y_hat.name) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y_hat.name else: label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = "" + xlabel = labeller.var_name_to_str(xlabel) plot_kwargs.setdefault("legend_label", label) @@ -204,6 +211,7 @@ def plot_loo_pit( ) # Sets xlim(0, 1) + ax.xaxis.axis_label = xlabel ax.line(0, 0) ax.line(1, 0) show_layout(ax, show) diff --git a/arviz/plots/backends/bokeh/mcseplot.py b/arviz/plots/backends/bokeh/mcseplot.py --- a/arviz/plots/backends/bokeh/mcseplot.py +++ b/arviz/plots/backends/bokeh/mcseplot.py @@ -5,7 +5,7 @@ from scipy.stats import rankdata from ....stats.stats_utils import quantile as _quantile -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from .. import show_layout from . import backend_kwarg_defaults, create_axes_grid @@ -26,6 +26,7 @@ def plot_mcse( mean_mcse, sd_mcse, textsize, + labeller, text_kwargs, # pylint: disable=unused-argument rug_kwargs, extra_kwargs, @@ -61,7 +62,7 @@ def plot_mcse( else: ax = np.atleast_2d(ax) - for (var_name, selection, x), ax_ in zip( + for (var_name, selection, isel, x), ax_ in zip( plotters, (item for item in ax.flatten() if item is not None) ): if errorbar or rug: @@ -164,7 +165,7 @@ def plot_mcse( ax_.add_glyph(cds_rug, glyph) title = Title() - title.text = make_label(var_name, selection) + title.text = labeller.make_label_vert(var_name, selection, isel) ax_.title = title ax_.xaxis.axis_label = "Quantile" diff --git a/arviz/plots/backends/bokeh/posteriorplot.py b/arviz/plots/backends/bokeh/posteriorplot.py --- a/arviz/plots/backends/bokeh/posteriorplot.py +++ b/arviz/plots/backends/bokeh/posteriorplot.py @@ -12,7 +12,6 @@ _scale_fig_size, calculate_point_estimate, format_sig_figs, - make_label, round_num, ) from .. import show_layout @@ -38,6 +37,7 @@ def plot_posterior( textsize, ref_val, rope, + labeller, kwargs, backend_kwargs, show, @@ -66,7 +66,7 @@ def plot_posterior( else: ax = np.atleast_2d(ax) idx = 0 - for (var_name, selection, x), ax_ in zip( + for (var_name, selection, isel, x), ax_ in zip( plotters, (item for item in ax.flatten() if item is not None) ): _plot_posterior_op( @@ -92,7 +92,7 @@ def plot_posterior( ) idx += 1 _title = Title() - _title.text = make_label(var_name, selection) + _title.text = labeller.make_label_vert(var_name, selection, isel) ax_.title = _title show_layout(ax, show) diff --git a/arviz/plots/backends/bokeh/ppcplot.py b/arviz/plots/backends/bokeh/ppcplot.py --- a/arviz/plots/backends/bokeh/ppcplot.py +++ b/arviz/plots/backends/bokeh/ppcplot.py @@ -29,6 +29,7 @@ def plot_ppc( jitter, total_pp_samples, legend, # pylint: disable=unused-argument + labeller, group, # pylint: disable=unused-argument animation_kwargs, # pylint: disable=unused-argument num_pp_samples, @@ -82,8 +83,8 @@ def plot_ppc( raise ValueError("jitter must be >=0.") for i, ax_i in enumerate((item for item in axes.flatten() if item is not None)): - var_name, _, obs_vals = obs_plotters[i] - pp_var_name, _, pp_vals = pp_plotters[i] + var_name, sel, isel, obs_vals = obs_plotters[i] + pp_var_name, _, _, pp_vals = pp_plotters[i] dtype = predictive_dataset[pp_var_name].dtype.kind # flatten non-specified dimensions @@ -271,11 +272,7 @@ def plot_ppc( ax_i.yaxis.minor_tick_line_color = None ax_i.yaxis.major_label_text_font_size = "0pt" - if var_name != pp_var_name: - xlabel = "{} / {}".format(var_name, pp_var_name) - else: - xlabel = var_name - ax_i.xaxis.axis_label = xlabel + ax_i.xaxis.axis_label = labeller.make_pp_label(var_name, pp_var_name, sel, isel) show_layout(axes, show) diff --git a/arviz/plots/backends/bokeh/rankplot.py b/arviz/plots/backends/bokeh/rankplot.py --- a/arviz/plots/backends/bokeh/rankplot.py +++ b/arviz/plots/backends/bokeh/rankplot.py @@ -6,7 +6,7 @@ from bokeh.models.tickers import FixedTicker from ....stats.density_utils import histogram -from ...plot_utils import _scale_fig_size, make_label, compute_ranks +from ...plot_utils import _scale_fig_size, compute_ranks from .. import show_layout from . import backend_kwarg_defaults, create_axes_grid @@ -23,6 +23,7 @@ def plot_rank( colors, ref_line, labels, + labeller, ref_line_kwargs, bar_kwargs, vlines_kwargs, @@ -71,7 +72,7 @@ def plot_rank( else: axes = np.atleast_2d(axes) - for ax, (var_name, selection, var_data) in zip( + for ax, (var_name, selection, isel, var_data) in zip( (item for item in axes.flatten() if item is not None), plotters ): ranks = compute_ranks(var_data) @@ -138,7 +139,7 @@ def plot_rank( ax.yaxis.major_label_text_font_size = "0pt" _title = Title() - _title.text = make_label(var_name, selection) + _title.text = labeller.make_label_vert(var_name, selection, isel) ax.title = _title show_layout(axes, show) diff --git a/arviz/plots/backends/bokeh/traceplot.py b/arviz/plots/backends/bokeh/traceplot.py --- a/arviz/plots/backends/bokeh/traceplot.py +++ b/arviz/plots/backends/bokeh/traceplot.py @@ -10,10 +10,11 @@ from bokeh.models.annotations import Title from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size, make_label, xarray_var_iter +from ...plot_utils import _scale_fig_size from ...rankplot import plot_rank from .. import show_layout from . import backend_kwarg_defaults, dealiase_sel_kwargs +from ....sel_utils import xarray_var_iter def plot_trace( @@ -31,6 +32,7 @@ def plot_trace( combined, chain_prop, legend, + labeller, plot_kwargs, fill_kwargs, rug_kwargs, @@ -167,7 +169,7 @@ def plot_trace( cds_var_groups = {} draw_name = "draw" - for var_name, selection, value in list( + for var_name, selection, isel, value in list( xarray_var_iter(data, var_names=var_names, combined=True) ): if selection: @@ -204,7 +206,7 @@ def plot_trace( cds_data = {chain_idx: ColumnDataSource(cds) for chain_idx, cds in cds_data.items()} - for idx, (var_name, selection, value) in enumerate(plotters): + for idx, (var_name, selection, isel, value) in enumerate(plotters): value = np.atleast_2d(value) if len(value.shape) == 2: @@ -269,7 +271,7 @@ def plot_trace( for col in (0, 1): _title = Title() - _title.text = make_label(var_name, selection) + _title.text = labeller.make_label_vert(var_name, selection, isel) axes[idx, col].title = _title axes[idx, col].y_range = DataRange1d( bounds=backend_config["bounds_y_range"], min_interval=0.1 diff --git a/arviz/plots/backends/bokeh/violinplot.py b/arviz/plots/backends/bokeh/violinplot.py --- a/arviz/plots/backends/bokeh/violinplot.py +++ b/arviz/plots/backends/bokeh/violinplot.py @@ -4,7 +4,7 @@ from ....stats import hdi from ....stats.density_utils import get_bins, histogram, kde -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from .. import show_layout from . import backend_kwarg_defaults, create_axes_grid @@ -23,6 +23,7 @@ def plot_violin( rug_kwargs, bw, textsize, + labeller, circular, hdi_prob, quartiles, @@ -58,7 +59,7 @@ def plot_violin( else: ax = np.atleast_2d(ax) - for (var_name, selection, x), ax_ in zip( + for (var_name, selection, isel, x), ax_ in zip( plotters, (item for item in ax.flatten() if item is not None) ): val = x.flatten() @@ -90,7 +91,7 @@ def plot_violin( _title = Title() _title.align = "center" - _title.text = make_label(var_name, selection) + _title.text = labeller.make_label_vert(var_name, selection, isel) ax_.title = _title ax_.xaxis.major_tick_line_color = None ax_.xaxis.minor_tick_line_color = None diff --git a/arviz/plots/backends/matplotlib/autocorrplot.py b/arviz/plots/backends/matplotlib/autocorrplot.py --- a/arviz/plots/backends/matplotlib/autocorrplot.py +++ b/arviz/plots/backends/matplotlib/autocorrplot.py @@ -3,7 +3,7 @@ import numpy as np from ....stats import autocorr -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, create_axes_grid @@ -16,6 +16,7 @@ def plot_autocorr( cols, combined, textsize, + labeller, backend_kwargs, show, ): @@ -45,7 +46,7 @@ def plot_autocorr( backend_kwargs=backend_kwargs, ) - for (var_name, selection, x), ax in zip(plotters, np.ravel(axes)): + for (var_name, selection, isel, x), ax in zip(plotters, np.ravel(axes)): x_prime = x if combined: x_prime = x.flatten() @@ -55,7 +56,9 @@ def plot_autocorr( ax.fill_between([0, max_lag], -c_i, c_i, color="0.75") ax.vlines(x=np.arange(0, max_lag), ymin=0, ymax=y[0:max_lag], lw=linewidth) - ax.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True) + ax.set_title( + labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True + ) ax.tick_params(labelsize=xt_labelsize) if np.asarray(axes).size > 0: diff --git a/arviz/plots/backends/matplotlib/bpvplot.py b/arviz/plots/backends/matplotlib/bpvplot.py --- a/arviz/plots/backends/matplotlib/bpvplot.py +++ b/arviz/plots/backends/matplotlib/bpvplot.py @@ -9,7 +9,6 @@ from ...plot_utils import ( _scale_fig_size, is_valid_quantile, - make_label, sample_reference_distribution, ) from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser @@ -34,6 +33,7 @@ def plot_bpv( color, figsize, textsize, + labeller, plot_ref_kwargs, backend_kwargs, show, @@ -82,8 +82,8 @@ def plot_bpv( ) for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]): - var_name, selection, obs_vals = obs_plotters[i] - pp_var_name, _, pp_vals = pp_plotters[i] + var_name, selection, isel, obs_vals = obs_plotters[i] + pp_var_name, _, _, pp_vals = pp_plotters[i] obs_vals = obs_vals.flatten() pp_vals = pp_vals.reshape(total_pp_samples, -1) @@ -167,11 +167,9 @@ def plot_bpv( obs_vals.mean(), 0, "o", color=color, markeredgecolor="k", markersize=markersize ) - if var_name != pp_var_name: - xlabel = "{} / {}".format(var_name, pp_var_name) - else: - xlabel = var_name - ax_i.set_title(make_label(xlabel, selection), fontsize=ax_labelsize) + ax_i.set_title( + labeller.make_pp_label(var_name, pp_var_name, selection, isel), fontsize=ax_labelsize + ) if backend_show(show): plt.show() diff --git a/arviz/plots/backends/matplotlib/compareplot.py b/arviz/plots/backends/matplotlib/compareplot.py --- a/arviz/plots/backends/matplotlib/compareplot.py +++ b/arviz/plots/backends/matplotlib/compareplot.py @@ -42,8 +42,6 @@ def plot_compare( _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) if plot_ic_diff: - yticks_labels[0] = comp_df.index[0] - yticks_labels[2::2] = comp_df.index[1:] ax.set_yticks(yticks_pos) ax.errorbar( x=comp_df[information_criterion].iloc[1:], @@ -56,7 +54,6 @@ def plot_compare( ) else: - yticks_labels = comp_df.index ax.set_yticks(yticks_pos[::2]) if plot_standard_error: diff --git a/arviz/plots/backends/matplotlib/densityplot.py b/arviz/plots/backends/matplotlib/densityplot.py --- a/arviz/plots/backends/matplotlib/densityplot.py +++ b/arviz/plots/backends/matplotlib/densityplot.py @@ -6,7 +6,7 @@ from ....stats import hdi from ....stats.density_utils import get_bins, kde -from ...plot_utils import _scale_fig_size, calculate_point_estimate, make_label +from ...plot_utils import _scale_fig_size, calculate_point_estimate from . import backend_kwarg_defaults, backend_show, create_axes_grid @@ -22,6 +22,7 @@ def plot_density( rows, cols, textsize, + labeller, hdi_prob, point_estimate, hdi_markers, @@ -68,8 +69,8 @@ def plot_density( axis_map = {label: ax_ for label, ax_ in zip(all_labels, np.ravel(ax))} for m_idx, plotters in enumerate(to_plot): - for var_name, selection, values in plotters: - label = make_label(var_name, selection) + for var_name, selection, isel, values in plotters: + label = labeller.make_label_vert(var_name, selection, isel) _d_helper( values.flatten(), label, diff --git a/arviz/plots/backends/matplotlib/distcomparisonplot.py b/arviz/plots/backends/matplotlib/distcomparisonplot.py --- a/arviz/plots/backends/matplotlib/distcomparisonplot.py +++ b/arviz/plots/backends/matplotlib/distcomparisonplot.py @@ -3,7 +3,7 @@ import numpy as np from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show @@ -16,6 +16,7 @@ def plot_dist_comparison( legend, groups, textsize, + labeller, prior_kwargs, posterior_kwargs, observed_kwargs, @@ -93,12 +94,12 @@ def plot_dist_comparison( else observed_kwargs ) for idx2, ( - var, - selection, + var_name, + sel, + isel, data, ) in enumerate(plotter): - label = make_label(var, selection) - label = f"{group} {label}" + label = f"{group}" plot_dist( data, label=label if legend else None, @@ -111,6 +112,8 @@ def plot_dist_comparison( ax=axes[idx2, -1], **kwargs, ) + if idx == 0: + axes[idx2, -1].set_xlabel(labeller.make_label_vert(var_name, sel, isel)) if backend_show(show): plt.show() diff --git a/arviz/plots/backends/matplotlib/essplot.py b/arviz/plots/backends/matplotlib/essplot.py --- a/arviz/plots/backends/matplotlib/essplot.py +++ b/arviz/plots/backends/matplotlib/essplot.py @@ -3,7 +3,7 @@ import numpy as np from scipy.stats import rankdata -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser @@ -28,6 +28,7 @@ def plot_ess( n_samples, relative, min_ess, + labeller, ylabel, rug, rug_kind, @@ -95,7 +96,7 @@ def plot_ess( backend_kwargs=backend_kwargs, ) - for (var_name, selection, x), ax_ in zip(plotters, np.ravel(ax)): + for (var_name, selection, isel, x), ax_ in zip(plotters, np.ravel(ax)): ax_.plot(xdata, x, **kwargs) if kind == "evolution": ess_tail = ess_tail_dataset[var_name].sel(**selection) @@ -143,7 +144,9 @@ def plot_ess( ax_.axhline(400 / n_samples if relative else min_ess, **hline_kwargs) - ax_.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True) + ax_.set_title( + labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True + ) ax_.tick_params(labelsize=xt_labelsize) ax_.set_xlabel( "Total number of draws" if kind == "evolution" else "Quantile", fontsize=ax_labelsize diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -5,12 +5,14 @@ import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import to_rgba +from matplotlib.lines import Line2D from ....stats import hdi from ....stats.density_utils import get_bins, histogram, kde from ....stats.diagnostics import _ess, _rhat +from ....sel_utils import xarray_var_iter from ....utils import conditional_jit -from ...plot_utils import _scale_fig_size, make_label, xarray_var_iter +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show @@ -43,6 +45,8 @@ def plot_forest( ridgeplot_truncate, ridgeplot_quantiles, textsize, + legend, + labeller, ess, r_hat, backend_kwargs, @@ -51,7 +55,12 @@ def plot_forest( ): """Matplotlib forest plot.""" plot_handler = PlotHandler( - datasets, var_names=var_names, model_names=model_names, combined=combined, colors=colors + datasets, + var_names=var_names, + model_names=model_names, + combined=combined, + colors=colors, + labeller=labeller, ) if figsize is None: @@ -152,6 +161,8 @@ def plot_forest( if kind == "ridgeplot": # space at the top y_max += ridgeplot_overlap axes[0].set_ylim(-all_plotters[0].group_offset, y_max) + if legend: + plot_handler.legend(ax=axes[0]) if backend_show(show): plt.show() @@ -164,14 +175,14 @@ class PlotHandler: # pylint: disable=inconsistent-return-statements - def __init__(self, datasets, var_names, model_names, combined, colors): + def __init__(self, datasets, var_names, model_names, combined, colors, labeller): self.data = datasets if model_names is None: if len(self.data) > 1: model_names = ["Model {}".format(idx) for idx, _ in enumerate(self.data)] else: - model_names = [""] + model_names = [None] elif len(model_names) != len(self.data): raise ValueError("The number of model names does not match the number of models") @@ -192,11 +203,13 @@ def __init__(self, datasets, var_names, model_names, combined, colors): self.combined = combined if colors == "cycle": + # TODO: Use matplotlib prop cycle instead colors = ["C{}".format(idx) for idx, _ in enumerate(self.data)] elif isinstance(colors, str): colors = [colors for _ in self.data] self.colors = list(reversed(colors)) # y-values are upside down + self.labeller = labeller self.plotters = self.make_plotters() @@ -211,6 +224,7 @@ def make_plotters(self): model_names=self.model_names, combined=self.combined, colors=self.colors, + labeller=self.labeller, ) y = plotters[var_name].y_max() return plotters @@ -230,6 +244,11 @@ def label_idxs(): return label_idxs() + def legend(self, ax): + """Add legend with colorcoded model info.""" + handles = [Line2D([], [], color=c) for c in self.colors] + ax.legend(handles=handles, labels=self.model_names) + def display_multiple_ropes(self, rope, ax, y, linewidth, var_name, selection): """Display ROPE when more than one interval is provided.""" for sel in rope.get(var_name, []): @@ -479,16 +498,18 @@ def y_max(self): return max(p.y_max() for p in self.plotters.values()) +# pylint: disable=too-many-instance-attributes class VarHandler: """Handle individual variable logic.""" - def __init__(self, var_name, data, y_start, model_names, combined, colors): + def __init__(self, var_name, data, y_start, model_names, combined, colors, labeller): self.var_name = var_name self.data = data self.y_start = y_start self.model_names = model_names self.combined = combined self.colors = colors + self.labeller = labeller self.model_color = dict(zip(self.model_names, self.colors)) max_chains = max(datum.chain.max().values for datum in data) self.chain_offset = len(data) * 0.45 / max(1, max_chains) @@ -515,15 +536,13 @@ def iterator(self): reverse_selections=True, ) datum_list = list(datum_iter) - for _, selection, values in datum_list: + for _, selection, isel, values in datum_list: selection_list.append(selection) - if not selection: + if not selection or not len(selection_list) % len(datum_list): var_name = self.var_name - elif not len(selection_list) % len(datum_list): - var_name = self.var_name + ":" else: var_name = "" - label = make_label(var_name, selection, position="beside") + label = self.labeller.make_label_flat(var_name, selection, isel) if label not in label_dict: label_dict[label] = OrderedDict() if name not in label_dict[label]: @@ -533,10 +552,7 @@ def iterator(self): y = self.y_start for idx, (label, model_data) in enumerate(label_dict.items()): for model_name, value_list in model_data.items(): - if model_name: - row_label = "{}: {}".format(model_name, label) - else: - row_label = label + row_label = self.labeller.make_model_label(model_name, label) for values in value_list: yield y, row_label, label, selection_list[idx], values, self.model_color[ model_name diff --git a/arviz/plots/backends/matplotlib/jointplot.py b/arviz/plots/backends/matplotlib/jointplot.py --- a/arviz/plots/backends/matplotlib/jointplot.py +++ b/arviz/plots/backends/matplotlib/jointplot.py @@ -4,8 +4,9 @@ from ...distplot import plot_dist from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, matplotlib_kwarg_dealiaser +from ....sel_utils import make_label def plot_joint( @@ -77,8 +78,8 @@ def plot_joint( axjoin.tick_params(labelsize=xt_labelsize) # Flatten data - x = plotters[0][2].flatten() - y = plotters[1][2].flatten() + x = plotters[0][-1].flatten() + y = plotters[1][-1].flatten() if kind == "scatter": axjoin.scatter(x, y, **joint_kwargs) diff --git a/arviz/plots/backends/matplotlib/loopitplot.py b/arviz/plots/backends/matplotlib/loopitplot.py --- a/arviz/plots/backends/matplotlib/loopitplot.py +++ b/arviz/plots/backends/matplotlib/loopitplot.py @@ -29,6 +29,7 @@ def plot_loo_pit( plot_unif_kwargs, loo_pit_kde, legend, + labeller, y_hat, y, color, @@ -58,15 +59,21 @@ def plot_loo_pit( plot_kwargs["color"] = to_hex(color) plot_kwargs.setdefault("linewidth", linewidth * 1.4) if isinstance(y, str): - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y elif isinstance(y, DataArray) and y.name is not None: - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y.name) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y.name elif isinstance(y_hat, str): - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y_hat) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y_hat elif isinstance(y_hat, DataArray) and y_hat.name is not None: - label = ("{} LOO-PIT ECDF" if ecdf else "{} LOO-PIT").format(y_hat.name) + label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = y_hat.name else: label = "LOO-PIT ECDF" if ecdf else "LOO-PIT" + xlabel = "" + xlabel = labeller.var_name_to_str(y) plot_kwargs.setdefault("label", label) plot_kwargs.setdefault("zorder", 5) @@ -126,6 +133,7 @@ def plot_loo_pit( ax.plot(x_vals, loo_pit_kde, **plot_kwargs) ax.set_xlim(0, 1) ax.set_ylim(0, None) + ax.set_xlabel(xlabel) ax.tick_params(labelsize=xt_labelsize) if legend: if not (use_hdi or (ecdf and ecdf_fill)): diff --git a/arviz/plots/backends/matplotlib/mcseplot.py b/arviz/plots/backends/matplotlib/mcseplot.py --- a/arviz/plots/backends/matplotlib/mcseplot.py +++ b/arviz/plots/backends/matplotlib/mcseplot.py @@ -4,7 +4,7 @@ from scipy.stats import rankdata from ....stats.stats_utils import quantile as _quantile -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser @@ -24,6 +24,7 @@ def plot_mcse( mean_mcse, sd_mcse, textsize, + labeller, text_kwargs, rug_kwargs, extra_kwargs, @@ -78,7 +79,7 @@ def plot_mcse( backend_kwargs=backend_kwargs, ) - for (var_name, selection, x), ax_ in zip(plotters, np.ravel(ax)): + for (var_name, selection, isel, x), ax_ in zip(plotters, np.ravel(ax)): if errorbar or rug: values = data[var_name].sel(**selection).values.flatten() if errorbar: @@ -132,7 +133,9 @@ def plot_mcse( ax_.plot(rug_x, rug_y, **rug_kwargs) ax_.axhline(y_min, color="k", linewidth=_linewidth, alpha=0.7) - ax_.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True) + ax_.set_title( + labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True + ) ax_.tick_params(labelsize=xt_labelsize) ax_.set_xlabel("Quantile", fontsize=ax_labelsize, wrap=True) ax_.set_ylabel( diff --git a/arviz/plots/backends/matplotlib/posteriorplot.py b/arviz/plots/backends/matplotlib/posteriorplot.py --- a/arviz/plots/backends/matplotlib/posteriorplot.py +++ b/arviz/plots/backends/matplotlib/posteriorplot.py @@ -11,7 +11,6 @@ _scale_fig_size, calculate_point_estimate, format_sig_figs, - make_label, round_num, ) from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser @@ -36,6 +35,7 @@ def plot_posterior( textsize, ref_val, rope, + labeller, kwargs, backend_kwargs, show, @@ -69,7 +69,7 @@ def plot_posterior( backend_kwargs=backend_kwargs, ) idx = 0 - for (var_name, selection, x), ax_ in zip(plotters, np.ravel(ax)): + for (var_name, selection, isel, x), ax_ in zip(plotters, np.ravel(ax)): _plot_posterior_op( idx, x.flatten(), @@ -92,7 +92,9 @@ def plot_posterior( **kwargs, ) idx += 1 - ax_.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True) + ax_.set_title( + labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize, wrap=True + ) if backend_show(show): plt.show() diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -8,7 +8,7 @@ from ....stats.density_utils import get_bins, histogram, kde from ...kdeplot import plot_kde -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, create_axes_grid _log = logging.getLogger(__name__) @@ -34,6 +34,7 @@ def plot_ppc( jitter, total_pp_samples, legend, + labeller, group, animation_kwargs, num_pp_samples, @@ -111,8 +112,8 @@ def plot_ppc( raise ValueError("All axes must be on the same figure for animation to work") for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]): - var_name, selection, obs_vals = obs_plotters[i] - pp_var_name, _, pp_vals = pp_plotters[i] + var_name, selection, isel, obs_vals = obs_plotters[i] + pp_var_name, _, _, pp_vals = pp_plotters[i] dtype = predictive_dataset[pp_var_name].dtype.kind # flatten non-specified dimensions @@ -342,11 +343,9 @@ def plot_ppc( ax_i.set_yticks([]) - if var_name != pp_var_name: - xlabel = "{} / {}".format(var_name, pp_var_name) - else: - xlabel = var_name - ax_i.set_xlabel(make_label(xlabel, selection), fontsize=ax_labelsize) + ax_i.set_xlabel( + labeller.make_pp_label(var_name, pp_var_name, selection, isel), fontsize=ax_labelsize + ) if legend: if i == 0: diff --git a/arviz/plots/backends/matplotlib/rankplot.py b/arviz/plots/backends/matplotlib/rankplot.py --- a/arviz/plots/backends/matplotlib/rankplot.py +++ b/arviz/plots/backends/matplotlib/rankplot.py @@ -3,7 +3,7 @@ import numpy as np from ....stats.density_utils import histogram -from ...plot_utils import _scale_fig_size, make_label, compute_ranks +from ...plot_utils import _scale_fig_size, compute_ranks from . import backend_kwarg_defaults, backend_show, create_axes_grid @@ -19,6 +19,7 @@ def plot_rank( colors, ref_line, labels, + labeller, ref_line_kwargs, bar_kwargs, vlines_kwargs, @@ -64,7 +65,7 @@ def plot_rank( backend_kwargs=backend_kwargs, ) - for ax, (var_name, selection, var_data) in zip(np.ravel(axes), plotters): + for ax, (var_name, selection, isel, var_data) in zip(np.ravel(axes), plotters): ranks = compute_ranks(var_data) bin_ary = np.histogram_bin_edges(ranks, bins=bins, range=(0, ranks.size)) all_counts = np.empty((len(ranks), len(bin_ary) - 1)) @@ -107,7 +108,7 @@ def plot_rank( ax.set_xlabel("Rank (all chains)", fontsize=ax_labelsize) ax.set_yticks(y_ticks) ax.set_yticklabels(np.arange(len(y_ticks))) - ax.set_title(make_label(var_name, selection), fontsize=titlesize) + ax.set_title(labeller.make_label_vert(var_name, selection, isel), fontsize=titlesize) else: ax.set_yticks([]) diff --git a/arviz/plots/backends/matplotlib/traceplot.py b/arviz/plots/backends/matplotlib/traceplot.py --- a/arviz/plots/backends/matplotlib/traceplot.py +++ b/arviz/plots/backends/matplotlib/traceplot.py @@ -10,7 +10,7 @@ from ....stats.density_utils import get_bins from ...distplot import plot_dist -from ...plot_utils import _scale_fig_size, format_coords_as_labels, make_label +from ...plot_utils import _scale_fig_size, format_coords_as_labels from ...rankplot import plot_rank from . import backend_kwarg_defaults, backend_show, dealiase_sel_kwargs, matplotlib_kwarg_dealiaser @@ -30,6 +30,7 @@ def plot_trace( combined, chain_prop, legend, + labeller, plot_kwargs, fill_kwargs, rug_kwargs, @@ -221,7 +222,7 @@ def plot_trace( spec = gridspec.GridSpec(ncols=2, nrows=len(plotters), figure=fig) # pylint: disable=too-many-nested-blocks - for idx, (var_name, selection, value) in enumerate(plotters): + for idx, (var_name, selection, isel, value) in enumerate(plotters): for idy in range(2): value = np.atleast_2d(value) @@ -325,7 +326,10 @@ def plot_trace( if circular: y = 0.13 if selection else 0.12 ax.set_title( - make_label(var_name, selection), fontsize=titlesize, wrap=True, y=textsize * y + labeller.make_label_vert(var_name, selection, isel), + fontsize=titlesize, + wrap=True, + y=textsize * y, ) ax.tick_params(labelsize=xt_labelsize) diff --git a/arviz/plots/backends/matplotlib/violinplot.py b/arviz/plots/backends/matplotlib/violinplot.py --- a/arviz/plots/backends/matplotlib/violinplot.py +++ b/arviz/plots/backends/matplotlib/violinplot.py @@ -4,7 +4,7 @@ from ....stats import hdi from ....stats.density_utils import get_bins, histogram, kde -from ...plot_utils import _scale_fig_size, make_label +from ...plot_utils import _scale_fig_size from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser @@ -22,6 +22,7 @@ def plot_violin( rug_kwargs, bw, textsize, + labeller, circular, hdi_prob, quartiles, @@ -64,7 +65,7 @@ def plot_violin( ax = np.atleast_1d(ax) current_col = 0 - for (var_name, selection, x), ax_ in zip(plotters, ax.flatten()): + for (var_name, selection, isel, x), ax_ in zip(plotters, ax.flatten()): val = x.flatten() if val[0].dtype.kind == "i": dens = cat_hist(val, rug, shade, ax_, **shade_kwargs) @@ -83,7 +84,7 @@ def plot_violin( ax_.plot([0, 0], hdi_probs, lw=linewidth, color="k", solid_capstyle="round") ax_.plot(0, per[-1], "wo", ms=linewidth * 1.5) - ax_.set_title(make_label(var_name, selection), fontsize=ax_labelsize) + ax_.set_title(labeller.make_label_vert(var_name, selection, isel), fontsize=ax_labelsize) ax_.set_xticks([]) ax_.tick_params(labelsize=xt_labelsize) ax_.grid(None, axis="x") diff --git a/arviz/plots/bpvplot.py b/arviz/plots/bpvplot.py --- a/arviz/plots/bpvplot.py +++ b/arviz/plots/bpvplot.py @@ -1,9 +1,11 @@ """Bayesian p-value Posterior/Prior predictive plot.""" import numpy as np +from ..labels import BaseLabeller from ..rcparams import rcParams from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function +from ..sel_utils import xarray_var_iter def plot_bpv( @@ -20,6 +22,7 @@ def plot_bpv( grid=None, figsize=None, textsize=None, + labeller=None, data_pairs=None, var_names=None, filter_vars=None, @@ -93,6 +96,9 @@ def plot_bpv( For example, `data_pairs = {'y' : 'y_hat'}` If None, it will assume that the observed data and the posterior/prior predictive data have the same variable name. + labeller : labeller instance, optional + Class providing the method `make_pp_label` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. var_names : list of variable names Variables to be plotted, if `None` all variable are plotted. Prefix the variables by `~` when you want to exclude them from the plot. @@ -186,6 +192,9 @@ def plot_bpv( if data_pairs is None: data_pairs = {} + if labeller is None: + labeller = BaseLabeller() + if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() @@ -260,6 +269,7 @@ def plot_bpv( color=color, figsize=figsize, textsize=textsize, + labeller=labeller, plot_ref_kwargs=plot_ref_kwargs, backend_kwargs=backend_kwargs, show=show, diff --git a/arviz/plots/compareplot.py b/arviz/plots/compareplot.py --- a/arviz/plots/compareplot.py +++ b/arviz/plots/compareplot.py @@ -1,6 +1,7 @@ """Summary plot for model comparison.""" import numpy as np +from ..labels import BaseLabeller from ..rcparams import rcParams from .plot_utils import get_plotting_function @@ -13,6 +14,7 @@ def plot_compare( order_by_rank=True, figsize=None, textsize=None, + labeller=None, plot_kwargs=None, ax=None, backend=None, @@ -50,6 +52,9 @@ def plot_compare( textsize: float Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. + labeller : labeller instance, optional + Class providing the method `model_name_to_str` to generate the labels in the plot. + Read the :ref:`label_guide` for more details and usage examples. plot_kwargs : dict, optional Optional arguments for plot elements. Currently accepts 'color_ic', 'marker_ic', 'color_insample_dev', 'marker_insample_dev', 'color_dse', @@ -92,10 +97,19 @@ def plot_compare( if plot_kwargs is None: plot_kwargs = {} + if labeller is None: + labeller = BaseLabeller() + yticks_pos, step = np.linspace(0, -1, (comp_df.shape[0] * 2) - 1, retstep=True) yticks_pos[1::2] = yticks_pos[1::2] + step / 2 + labels = [labeller.model_name_to_str(model_name) for model_name in comp_df.index] - yticks_labels = [""] * len(yticks_pos) + if plot_ic_diff: + yticks_labels = [""] * len(yticks_pos) + yticks_labels[0] = labels[0] + yticks_labels[2::2] = labels[1:] + else: + yticks_labels = labels _information_criterion = ["loo", "waic"] column_index = [c.lower() for c in comp_df.columns] diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py --- a/arviz/plots/densityplot.py +++ b/arviz/plots/densityplot.py @@ -2,9 +2,13 @@ import warnings from ..data import convert_to_dataset +from ..labels import BaseLabeller +from ..sel_utils import ( + xarray_var_iter, +) from ..rcparams import rcParams from ..utils import _var_names -from .plot_utils import default_grid, get_plotting_function, make_label, xarray_var_iter +from .plot_utils import default_grid, get_plotting_function # pylint:disable-msg=too-many-function-args @@ -25,6 +29,7 @@ def plot_density( grid=None, figsize=None, textsize=None, + labeller=None, ax=None, backend=None, backend_kwargs=None, @@ -91,6 +96,9 @@ def plot_density( textsize: Optional[float] Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. ax: numpy array-like of matplotlib axes or bokeh figures, optional A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it). @@ -168,6 +176,9 @@ def plot_density( if transform is not None: datasets = [transform(dataset) for dataset in datasets] + if labeller is None: + labeller = BaseLabeller() + var_names = _var_names(var_names, datasets) n_data = len(datasets) @@ -193,8 +204,8 @@ def plot_density( length_plotters = [] for plotters in to_plot: length_plotters.append(len(plotters)) - for var_name, selection, _ in plotters: - label = make_label(var_name, selection) + for var_name, selection, isel, _ in plotters: + label = labeller.make_label_vert(var_name, selection, isel) if label not in all_labels: all_labels.append(label) length_plotters = len(all_labels) @@ -211,8 +222,8 @@ def plot_density( to_plot = [ [ (var_name, selection, values) - for var_name, selection, values in plotters - if make_label(var_name, selection) in all_labels + for var_name, selection, isel, values in plotters + if labeller.make_label_vert(var_name, selection, isel) in all_labels ] for plotters in to_plot ] @@ -237,6 +248,7 @@ def plot_density( rows=rows, cols=cols, textsize=textsize, + labeller=labeller, hdi_prob=hdi_prob, point_estimate=point_estimate, hdi_markers=hdi_markers, diff --git a/arviz/plots/distcomparisonplot.py b/arviz/plots/distcomparisonplot.py --- a/arviz/plots/distcomparisonplot.py +++ b/arviz/plots/distcomparisonplot.py @@ -1,7 +1,9 @@ """Density Comparison plot.""" +from ..labels import BaseLabeller from ..rcparams import rcParams from ..utils import _var_names, get_coords -from .plot_utils import get_plotting_function, xarray_var_iter +from .plot_utils import get_plotting_function +from ..sel_utils import xarray_var_iter def plot_dist_comparison( @@ -13,6 +15,7 @@ def plot_dist_comparison( coords=None, transform=None, legend=True, + labeller=None, ax=None, prior_kwargs=None, posterior_kwargs=None, @@ -51,6 +54,9 @@ def plot_dist_comparison( Function to transform data (defaults to None i.e. the identity function) legend : bool Add legend to figure. By default True. + labeller : labeller instance, optional + Class providing the method `make_pp_label` to generate the labels in the plot. + Read the :ref:`label_guide` for more details and usage examples. ax: axes, optional Matplotlib axes: The ax argument should have shape (nvars, 3), where the last column is for the combined before/after plots and columns 0 and 1 are @@ -94,6 +100,9 @@ def plot_dist_comparison( if coords is None: coords = {} + if labeller is None: + labeller = BaseLabeller() + datasets = [] groups = [] for group in all_groups: @@ -137,6 +146,7 @@ def plot_dist_comparison( legend=legend, groups=groups, textsize=textsize, + labeller=labeller, prior_kwargs=prior_kwargs, posterior_kwargs=posterior_kwargs, observed_kwargs=observed_kwargs, diff --git a/arviz/plots/essplot.py b/arviz/plots/essplot.py --- a/arviz/plots/essplot.py +++ b/arviz/plots/essplot.py @@ -3,10 +3,12 @@ import xarray as xr from ..data import convert_to_dataset +from ..labels import BaseLabeller from ..rcparams import rcParams +from ..sel_utils import xarray_var_iter from ..stats import ess from ..utils import _var_names, get_coords -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function def plot_ess( @@ -24,6 +26,7 @@ def plot_ess( n_points=20, extra_methods=False, min_ess=400, + labeller=None, ax=None, extra_kwargs=None, text_kwargs=None, @@ -74,6 +77,9 @@ def plot_ess( Plot mean and sd ESS as horizontal lines. Not taken into account in evolution kind min_ess: int Minimum number of ESS desired. + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. ax: numpy array-like of matplotlib axes or bokeh figures, optional A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it). @@ -173,6 +179,8 @@ def plot_ess( coords = {} if "chain" in coords or "draw" in coords: raise ValueError("chain and draw are invalid coordinates for this kind of plot") + if labeller is None: + labeller = BaseLabeller() extra_methods = False if kind == "evolution" else extra_methods data = get_coords(convert_to_dataset(idata, group="posterior"), coords) @@ -273,6 +281,7 @@ def plot_ess( n_samples=n_samples, relative=relative, min_ess=min_ess, + labeller=labeller, ylabel=ylabel, rug=rug, rug_kind=rug_kind, diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -1,5 +1,6 @@ """Forest plot.""" from ..data import convert_to_dataset +from ..labels import BaseLabeller, NoModelLabeller from ..rcparams import rcParams from ..utils import _var_names, get_coords from .plot_utils import get_plotting_function @@ -23,6 +24,8 @@ def plot_forest( textsize=None, linewidth=None, markersize=None, + legend=True, + labeller=None, ridgeplot_alpha=None, ridgeplot_overlap=2, ridgeplot_kind="auto", @@ -88,6 +91,12 @@ def plot_forest( Line width throughout. If None it will be autoscaled based on figsize. markersize: int Markersize throughout. If None it will be autoscaled based on figsize. + legend : bool, optional + Show a legend with the color encoded model information. + Defaults to true if there are multiple models + labeller : labeller instance, optional + Class providing the method `make_model_label` to generate the labels in the plot. + Read the :ref:`label_guide` for more details and usage examples. ridgeplot_alpha: float Transparency for ridgeplot fill. If 0, border is colored by model, otherwise a black outline is used. @@ -182,10 +191,15 @@ def plot_forest( """ if not isinstance(data, (list, tuple)): data = [data] + if len(data) == 1: + legend = False if coords is None: coords = {} + if labeller is None: + labeller = NoModelLabeller() if legend else BaseLabeller() + datasets = [convert_to_dataset(datum) for datum in reversed(data)] if transform is not None: datasets = [transform(dataset) for dataset in datasets] @@ -233,6 +247,8 @@ def plot_forest( ridgeplot_truncate=ridgeplot_truncate, ridgeplot_quantiles=ridgeplot_quantiles, textsize=textsize, + legend=legend, + labeller=labeller, ess=ess, r_hat=r_hat, backend_kwargs=backend_kwargs, diff --git a/arviz/plots/jointplot.py b/arviz/plots/jointplot.py --- a/arviz/plots/jointplot.py +++ b/arviz/plots/jointplot.py @@ -2,9 +2,10 @@ import warnings from ..data import convert_to_dataset +from ..sel_utils import xarray_var_iter from ..rcparams import rcParams from ..utils import _var_names, get_coords -from .plot_utils import get_plotting_function, xarray_var_iter +from .plot_utils import get_plotting_function def plot_joint( diff --git a/arviz/plots/khatplot.py b/arviz/plots/khatplot.py --- a/arviz/plots/khatplot.py +++ b/arviz/plots/khatplot.py @@ -143,7 +143,7 @@ def plot_khat( References ---------- - ..[1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J., + .. [1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J., 2019. Pareto Smoothed Importance Sampling. arXiv:1507.02646 [stat]. """ diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py --- a/arviz/plots/loopitplot.py +++ b/arviz/plots/loopitplot.py @@ -2,6 +2,7 @@ import numpy as np import scipy.stats as stats +from ..labels import BaseLabeller from ..rcparams import rcParams from ..stats import loo_pit as _loo_pit from ..stats.density_utils import kde @@ -20,6 +21,7 @@ def plot_loo_pit( hdi_prob=None, figsize=None, textsize=None, + labeller=None, color="C0", legend=True, ax=None, @@ -66,6 +68,9 @@ def plot_loo_pit( If None, size is (8 + numvars, 8 + numvars) textsize: int, optional Text size for labels. If None it will be autoscaled based on figsize. + labeller : labeller instance, optional + Class providing the method `make_pp_label` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. color : str or array_like, optional Color of the LOO-PIT estimated pdf plot. If ``plot_unif_kwargs`` has no "color" key, an slightly lighter color than this argument will be used for the uniform kde lines. @@ -127,6 +132,9 @@ def plot_loo_pit( if ecdf and use_hdi: raise ValueError("use_hdi is incompatible with ecdf plot") + if labeller is None: + labeller = BaseLabeller() + loo_pit = _loo_pit(idata=idata, y=y, y_hat=y_hat, log_weights=log_weights) loo_pit = loo_pit.flatten() if isinstance(loo_pit, np.ndarray) else loo_pit.values.flatten() @@ -184,6 +192,7 @@ def plot_loo_pit( plot_unif_kwargs=plot_unif_kwargs, loo_pit_kde=loo_pit_kde, textsize=textsize, + labeller=labeller, color=color, legend=legend, y_hat=y_hat, diff --git a/arviz/plots/mcseplot.py b/arviz/plots/mcseplot.py --- a/arviz/plots/mcseplot.py +++ b/arviz/plots/mcseplot.py @@ -3,10 +3,12 @@ import xarray as xr from ..data import convert_to_dataset -from ..rcparams import rcParams +from ..labels import BaseLabeller +from ..sel_utils import xarray_var_iter from ..stats import mcse +from ..rcparams import rcParams from ..utils import _var_names, get_coords -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function def plot_mcse( @@ -22,6 +24,7 @@ def plot_mcse( rug=False, rug_kind="diverging", n_points=20, + labeller=None, ax=None, rug_kwargs=None, extra_kwargs=None, @@ -68,6 +71,9 @@ def plot_mcse( n_points: int Number of points for which to plot their quantile/local ess or number of subsets in the evolution plot. + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. ax: numpy array-like of matplotlib axes or bokeh figures, optional A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it). @@ -119,6 +125,9 @@ def plot_mcse( if "chain" in coords or "draw" in coords: raise ValueError("chain and draw are invalid coordinates for this kind of plot") + if labeller is None: + labeller = BaseLabeller() + data = get_coords(convert_to_dataset(idata, group="posterior"), coords) var_names = _var_names(var_names, data, filter_vars) @@ -154,6 +163,7 @@ def plot_mcse( mean_mcse=mean_mcse, sd_mcse=sd_mcse, textsize=textsize, + labeller=labeller, text_kwargs=text_kwargs, rug_kwargs=rug_kwargs, extra_kwargs=extra_kwargs, diff --git a/arviz/plots/pairplot.py b/arviz/plots/pairplot.py --- a/arviz/plots/pairplot.py +++ b/arviz/plots/pairplot.py @@ -5,14 +5,11 @@ import numpy as np from ..data import convert_to_dataset +from ..labels import BaseLabeller +from ..sel_utils import xarray_to_ndarray, xarray_var_iter +from .plot_utils import get_plotting_function from ..rcparams import rcParams from ..utils import _var_names, get_coords -from .plot_utils import ( - get_plotting_function, - xarray_to_ndarray, - xarray_var_iter, - make_label, -) def plot_pair( @@ -31,6 +28,7 @@ def plot_pair( fill_last=False, divergences=False, colorbar=False, + labeller=None, ax=None, divergences_kwargs=None, scatter_kwargs=None, @@ -91,6 +89,9 @@ def plot_pair( colorbar: bool If True a colorbar will be included as part of the plot (Defaults to False). Only works when kind=hexbin + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot. + Read the :ref:`label_guide` for more details and usage examples. ax: axes, optional Matplotlib axes or bokeh figures. divergences_kwargs: dicts, optional @@ -191,13 +192,18 @@ def plot_pair( if coords is None: coords = {} + if labeller is None: + labeller = BaseLabeller() + # Get posterior draws and combine chains dataset = convert_to_dataset(data, group=group) var_names = _var_names(var_names, dataset, filter_vars) plotters = list( xarray_var_iter(get_coords(dataset, coords), var_names=var_names, combined=True) ) - flat_var_names = [make_label(var_name, selection) for var_name, selection, _ in plotters] + flat_var_names = [ + labeller.make_label_vert(var_name, sel, isel) for var_name, sel, isel, _ in plotters + ] divergent_data = None diverging_mask = None diff --git a/arviz/plots/parallelplot.py b/arviz/plots/parallelplot.py --- a/arviz/plots/parallelplot.py +++ b/arviz/plots/parallelplot.py @@ -3,10 +3,12 @@ from scipy.stats import rankdata from ..data import convert_to_dataset +from ..labels import BaseLabeller +from ..sel_utils import xarray_to_ndarray from ..rcparams import rcParams from ..stats.stats_utils import stats_variance_2d as svar from ..utils import _numba_var, _var_names, get_coords -from .plot_utils import get_plotting_function, xarray_to_ndarray +from .plot_utils import get_plotting_function def plot_parallel( @@ -20,6 +22,7 @@ def plot_parallel( colornd="k", colord="C1", shadend=0.025, + labeller=None, ax=None, norm_method=None, backend=None, @@ -62,6 +65,9 @@ def plot_parallel( shadend: float Alpha blending value for non-divergent points, between 0 (invisible) and 1 (opaque). Defaults to .025 + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot. + Read the :ref:`label_guide` for more details and usage examples. ax: axes, optional Matplotlib axes or bokeh figures. norm_method: str @@ -104,6 +110,9 @@ def plot_parallel( if coords is None: coords = {} + if labeller is None: + labeller = BaseLabeller() + # Get diverging draws and combine chains divergent_data = convert_to_dataset(data, group="sample_stats") _, diverging_mask = xarray_to_ndarray(divergent_data, var_names=("diverging",), combined=True) @@ -113,7 +122,10 @@ def plot_parallel( posterior_data = convert_to_dataset(data, group="posterior") var_names = _var_names(var_names, posterior_data, filter_vars) var_names, _posterior = xarray_to_ndarray( - get_coords(posterior_data, coords), var_names=var_names, combined=True + get_coords(posterior_data, coords), + var_names=var_names, + combined=True, + label_fun=labeller.make_label_vert, ) if len(var_names) < 2: raise ValueError("Number of variables to be plotted must be 2 or greater.") diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -1,13 +1,11 @@ """Utilities for plotting.""" import importlib import warnings -from itertools import product, tee from typing import Any, Dict import matplotlib as mpl import numpy as np import packaging -import xarray as xr from matplotlib.colors import to_hex from scipy.stats import mode, rankdata from scipy.interpolate import CubicSpline @@ -143,51 +141,6 @@ def in_bounds(val): return rows, cols -def selection_to_string(selection): - """Convert dictionary of coordinates to a string for labels. - - Parameters - ---------- - selection : dict[Any] -> Any - - Returns - ------- - str - key1: value1, key2: value2, ... - """ - return ", ".join(["{}".format(v) for _, v in selection.items()]) - - -def make_label(var_name, selection, position="below"): - """Consistent labelling for plots. - - Parameters - ---------- - var_name : str - Name of the variable - - selection : dict[Any] -> Any - Coordinates of the variable - position : str - Whether to position the coordinates' label "below" (default) or "beside" - the name of the variable - - Returns - ------- - label - A text representation of the label - """ - if selection: - sel = selection_to_string(selection) - if position == "below": - sep = "\n" - elif position == "beside": - sep = " " - else: - sep = sel = "" - return "{}{}{}".format(var_name, sep, sel) - - def format_sig_figs(value, default=None): """Get a default number of significant figures. @@ -222,179 +175,6 @@ def round_num(n, round_to): return "{n:.{sig_figs}g}".format(n=n, sig_figs=sig_figs) -def purge_duplicates(list_in): - """Remove duplicates from list while preserving order. - - Parameters - ---------- - list_in: Iterable - - Returns - ------- - list - List of first occurrences in order - """ - # Algorithm taken from Stack Overflow, - # https://stackoverflow.com/questions/480214. Content by Georgy - # Skorobogatov (https://stackoverflow.com/users/7851470/georgy) and - # Markus Jarderot - # (https://stackoverflow.com/users/22364/markus-jarderot), licensed - # under CC-BY-SA 4.0. - # https://creativecommons.org/licenses/by-sa/4.0/. - - seen = set() - seen_add = seen.add - return [x for x in list_in if not (x in seen or seen_add(x))] - - -def _dims(data, var_name, skip_dims): - return [dim for dim in data[var_name].dims if dim not in skip_dims] - - -def _zip_dims(new_dims, vals): - return [{k: v for k, v in zip(new_dims, prod)} for prod in product(*vals)] - - -def xarray_sel_iter(data, var_names=None, combined=False, skip_dims=None, reverse_selections=False): - """Convert xarray data to an iterator over variable names and selections. - - Iterates over each var_name and all of its coordinates, returning the variable - names and selections that allow properly obtain the data from ``data`` as desired. - - Parameters - ---------- - data : xarray.Dataset - Posterior data in an xarray - - var_names : iterator of strings (optional) - Should be a subset of data.data_vars. Defaults to all of them. - - combined : bool - Whether to combine chains or leave them separate - - skip_dims : set - dimensions to not iterate over - - reverse_selections : bool - Whether to reverse selections before iterating. - - Returns - ------- - Iterator of (var_name: str, selection: dict(str, any)) - The string is the variable name, the dictionary are coordinate names to values,. - To get the values of the variable at these coordinates, do - ``data[var_name].sel(**selection)``. - """ - if skip_dims is None: - skip_dims = set() - - if combined: - skip_dims = skip_dims.union({"chain", "draw"}) - else: - skip_dims.add("draw") - - if var_names is None: - if isinstance(data, xr.Dataset): - var_names = list(data.data_vars) - elif isinstance(data, xr.DataArray): - var_names = [data.name] - data = {data.name: data} - - for var_name in var_names: - if var_name in data: - new_dims = _dims(data, var_name, skip_dims) - vals = [purge_duplicates(data[var_name][dim].values) for dim in new_dims] - dims = _zip_dims(new_dims, vals) - if reverse_selections: - dims = reversed(dims) - - for selection in dims: - yield var_name, selection - - -def xarray_var_iter(data, var_names=None, combined=False, skip_dims=None, reverse_selections=False): - """Convert xarray data to an iterator over vectors. - - Iterates over each var_name and all of its coordinates, returning the 1d - data. - - Parameters - ---------- - data : xarray.Dataset - Posterior data in an xarray - - var_names : iterator of strings (optional) - Should be a subset of data.data_vars. Defaults to all of them. - - combined : bool - Whether to combine chains or leave them separate - - skip_dims : set - dimensions to not iterate over - - reverse_selections : bool - Whether to reverse selections before iterating. - - Returns - ------- - Iterator of (str, dict(str, any), np.array) - The string is the variable name, the dictionary are coordinate names to values, - and the array are the values of the variable at those coordinates. - """ - data_to_sel = data - if var_names is None and isinstance(data, xr.DataArray): - data_to_sel = {data.name: data} - - for var_name, selection in xarray_sel_iter( - data, - var_names=var_names, - combined=combined, - skip_dims=skip_dims, - reverse_selections=reverse_selections, - ): - yield var_name, selection, data_to_sel[var_name].sel(**selection).values - - -def xarray_to_ndarray(data, *, var_names=None, combined=True): - """Take xarray data and unpacks into variables and data into list and numpy array respectively. - - Assumes that chain and draw are in coordinates - - Parameters - ---------- - data: xarray.DataSet - Data in an xarray from an InferenceData object. Examples include posterior or sample_stats - - var_names: iter - Should be a subset of data.data_vars not including chain and draws. Defaults to all of them - - combined: bool - Whether to combine chain into one array - - Returns - ------- - var_names: list - List of variable names - data: np.array - Data values - """ - data_to_sel = data - if var_names is None and isinstance(data, xr.DataArray): - data_to_sel = {data.name: data} - - iterator1, iterator2 = tee(xarray_sel_iter(data, var_names=var_names, combined=combined)) - vars_and_sel = list(iterator1) - unpacked_var_names = [make_label(var_name, selection) for var_name, selection in vars_and_sel] - - # Merge chains and variables, check dtype to be compatible with divergences data - data0 = data_to_sel[vars_and_sel[0][0]].sel(**vars_and_sel[0][1]) - unpacked_data = np.empty((len(unpacked_var_names), data0.size), dtype=data0.dtype) - for idx, (var_name, selection) in enumerate(iterator2): - unpacked_data[idx] = data_to_sel[var_name].sel(**selection).values.flatten() - - return unpacked_var_names, unpacked_data - - def color_from_dim(dataarray, dim_name): """Return colors and color mapping of a DataArray using coord values as color code. diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -1,8 +1,10 @@ """Plot posterior densities.""" from ..data import convert_to_dataset -from ..rcparams import rcParams +from ..labels import BaseLabeller +from ..sel_utils import xarray_var_iter from ..utils import _var_names, get_coords -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from ..rcparams import rcParams +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function def plot_posterior( @@ -26,6 +28,7 @@ def plot_posterior( bw="default", circular=False, bins=None, + labeller=None, ax=None, backend=None, backend_kwargs=None, @@ -99,6 +102,9 @@ def plot_posterior( Controls the number of bins, accepts the same keywords `matplotlib.hist()` does. Only works if `kind == hist`. If None (default) it will use `auto` for continuous variables and `range(xmin, xmax + 1)` for discrete variables. + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. ax: numpy array-like of matplotlib axes or bokeh figures, optional A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it). @@ -209,6 +215,9 @@ def plot_posterior( if coords is None: coords = {} + if labeller is None: + labeller = BaseLabeller() + if hdi_prob is None: hdi_prob = rcParams["stats.hdi_prob"] elif hdi_prob not in (None, "hide"): @@ -246,6 +255,7 @@ def plot_posterior( textsize=textsize, ref_val=ref_val, rope=rope, + labeller=labeller, kwargs=kwargs, backend_kwargs=backend_kwargs, show=show, diff --git a/arviz/plots/ppcplot.py b/arviz/plots/ppcplot.py --- a/arviz/plots/ppcplot.py +++ b/arviz/plots/ppcplot.py @@ -4,9 +4,11 @@ import numpy as np +from ..labels import BaseLabeller +from ..sel_utils import xarray_var_iter from ..rcparams import rcParams from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function _log = logging.getLogger(__name__) @@ -33,6 +35,7 @@ def plot_ppc( animated=False, animation_kwargs=None, legend=True, + labeller=None, ax=None, backend=None, backend_kwargs=None, @@ -123,6 +126,9 @@ def plot_ppc( Keywords passed to `animation.FuncAnimation`. Ignored with matploblib backend. legend : bool Add legend to figure. By default True. + labeller : labeller instance, optional + Class providing the method `make_pp_label` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. ax: numpy array-like of matplotlib axes or bokeh figures, optional A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it). @@ -235,6 +241,9 @@ def plot_ppc( if coords is None: coords = {} + if labeller is None: + labeller = BaseLabeller() + if random_seed is not None: np.random.seed(random_seed) @@ -306,6 +315,7 @@ def plot_ppc( observed=observed, total_pp_samples=total_pp_samples, legend=legend, + labeller=labeller, group=group, animation_kwargs=animation_kwargs, num_pp_samples=num_pp_samples, diff --git a/arviz/plots/rankplot.py b/arviz/plots/rankplot.py --- a/arviz/plots/rankplot.py +++ b/arviz/plots/rankplot.py @@ -4,10 +4,12 @@ import matplotlib.pyplot as plt from ..data import convert_to_dataset +from ..labels import BaseLabeller +from ..sel_utils import xarray_var_iter from ..rcparams import rcParams from ..stats.density_utils import _sturges_formula from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function def plot_rank( @@ -21,6 +23,7 @@ def plot_rank( colors="cycle", ref_line=True, labels=True, + labeller=None, grid=None, figsize=None, ax=None, @@ -78,6 +81,9 @@ def plot_rank( Whether to include a dashed line showing where a uniform distribution would lie labels: bool whether to plot or not the x and y labels, defaults to True + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. grid : tuple Number of rows and columns. Defaults to None, the rows and columns are automatically inferred. @@ -163,6 +169,9 @@ def plot_rank( if bins is None: bins = _sturges_formula(posterior_data, mult=2) + if labeller is None: + labeller = BaseLabeller() + rows, cols = default_grid(length_plotters, grid=grid) chains = len(posterior_data.chain) @@ -188,6 +197,7 @@ def plot_rank( colors=colors, ref_line=ref_line, labels=labels, + labeller=labeller, ref_line_kwargs=ref_line_kwargs, bar_kwargs=bar_kwargs, vlines_kwargs=vlines_kwargs, diff --git a/arviz/plots/separationplot.py b/arviz/plots/separationplot.py --- a/arviz/plots/separationplot.py +++ b/arviz/plots/separationplot.py @@ -77,10 +77,9 @@ def plot_separation( References ---------- - * Greenhill, B. *et al.*, The Separation Plot: A New Visual Method - for Evaluating the Fit of Binary Models, *American Journal of - Political Science, (2011) see - https://doi.org/10.1111/j.1540-5907.2011.00525.x + .. [1] Greenhill, B. *et al.*, The Separation Plot: A New Visual Method + for Evaluating the Fit of Binary Models, *American Journal of + Political Science*, (2011) see https://doi.org/10.1111/j.1540-5907.2011.00525.x Examples -------- diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -3,9 +3,11 @@ from typing import Any, Callable, List, Mapping, Optional, Tuple, Union from ..data import CoordSpec, InferenceData, convert_to_dataset +from ..labels import BaseLabeller from ..rcparams import rcParams +from ..sel_utils import xarray_var_iter from ..utils import _var_names, get_coords -from .plot_utils import KwargSpec, get_plotting_function, xarray_var_iter +from .plot_utils import KwargSpec, get_plotting_function def plot_trace( @@ -32,6 +34,7 @@ def plot_trace( hist_kwargs: Optional[KwargSpec] = None, trace_kwargs: Optional[KwargSpec] = None, rank_kwargs: Optional[KwargSpec] = None, + labeller=None, axes=None, backend: Optional[str] = None, backend_config: Optional[KwargSpec] = None, @@ -92,6 +95,9 @@ def plot_trace( Extra keyword arguments passed to `arviz.plot_dist`. Only affects continuous variables. trace_kwargs: dict, optional Extra keyword arguments passed to `plt.plot` + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. rank_kwargs : dict, optional Extra keyword arguments passed to `arviz.plot_rank` axes: axes, optional @@ -169,6 +175,9 @@ def plot_trace( if coords is None: coords = {} + if labeller is None: + labeller = BaseLabeller() + if divergences: divergence_data = get_coords( divergence_data, {k: v for k, v in coords.items() if k in ("chain", "draw")} @@ -226,6 +235,7 @@ def plot_trace( combined=combined, chain_prop=chain_prop, legend=legend, + labeller=labeller, # Generated kwargs divergence_data=divergence_data, # skip_dims=skip_dims, diff --git a/arviz/plots/violinplot.py b/arviz/plots/violinplot.py --- a/arviz/plots/violinplot.py +++ b/arviz/plots/violinplot.py @@ -1,8 +1,10 @@ """Plot posterior traces as violin plot.""" from ..data import convert_to_dataset -from ..rcparams import rcParams +from ..labels import BaseLabeller +from ..sel_utils import xarray_var_iter from ..utils import _var_names -from .plot_utils import default_grid, filter_plotters_list, get_plotting_function, xarray_var_iter +from ..rcparams import rcParams +from .plot_utils import default_grid, filter_plotters_list, get_plotting_function def plot_violin( @@ -21,6 +23,7 @@ def plot_violin( grid=None, figsize=None, textsize=None, + labeller=None, ax=None, shade_kwargs=None, rug_kwargs=None, @@ -77,6 +80,9 @@ def plot_violin( textsize: int Text size of the point_estimates, axis ticks, and highest density interval. If None it will be autoscaled based on figsize. + labeller : labeller instance, optional + Class providing the method `make_label_vert` to generate the labels in the plot titles. + Read the :ref:`label_guide` for more details and usage examples. sharex: bool Defaults to True, violinplots share a common x-axis scale. sharey: bool @@ -120,6 +126,9 @@ def plot_violin( >>> az.plot_violin(data, var_names="tau", transform=np.log) """ + if labeller is None: + labeller = BaseLabeller() + data = convert_to_dataset(data, group="posterior") if transform is not None: data = transform(data) @@ -151,6 +160,7 @@ def plot_violin( rug_kwargs=rug_kwargs, bw=bw, textsize=textsize, + labeller=labeller, circular=circular, hdi_prob=hdi_prob, quartiles=quartiles, diff --git a/arviz/sel_utils.py b/arviz/sel_utils.py new file mode 100644 --- /dev/null +++ b/arviz/sel_utils.py @@ -0,0 +1,234 @@ +"""Utilities for selecting and iterating on xarray objects.""" +from itertools import product, tee + +import numpy as np +import xarray as xr + +from .labels import BaseLabeller + +__all__ = ["xarray_sel_iter", "xarray_var_iter", "xarray_to_ndarray"] + + +def selection_to_string(selection): + """Convert dictionary of coordinates to a string for labels. + + Parameters + ---------- + selection : dict[Any] -> Any + + Returns + ------- + str + key1: value1, key2: value2, ... + """ + return ", ".join(["{}".format(v) for _, v in selection.items()]) + + +def make_label(var_name, selection, position="below"): + """Consistent labelling for plots. + + Parameters + ---------- + var_name : str + Name of the variable + + selection : dict[Any] -> Any + Coordinates of the variable + position : str + Whether to position the coordinates' label "below" (default) or "beside" + the name of the variable + + Returns + ------- + label + A text representation of the label + """ + if selection: + sel = selection_to_string(selection) + if position == "below": + base = "{}\n{}" + elif position == "beside": + base = "{}[{}]" + else: + sel = "" + base = "{}{}" + return base.format(var_name, sel) + + +def purge_duplicates(list_in): + """Remove duplicates from list while preserving order. + + Parameters + ---------- + list_in: Iterable + + Returns + ------- + list + List of first occurrences in order + """ + # Algorithm taken from Stack Overflow, + # https://stackoverflow.com/questions/480214. Content by Georgy + # Skorobogatov (https://stackoverflow.com/users/7851470/georgy) and + # Markus Jarderot + # (https://stackoverflow.com/users/22364/markus-jarderot), licensed + # under CC-BY-SA 4.0. + # https://creativecommons.org/licenses/by-sa/4.0/. + + seen = set() + seen_add = seen.add + return [x for x in list_in if not (x in seen or seen_add(x))] + + +def _dims(data, var_name, skip_dims): + return [dim for dim in data[var_name].dims if dim not in skip_dims] + + +def _zip_dims(new_dims, vals): + return [{k: v for k, v in zip(new_dims, prod)} for prod in product(*vals)] + + +def xarray_sel_iter(data, var_names=None, combined=False, skip_dims=None, reverse_selections=False): + """Convert xarray data to an iterator over variable names and selections. + + Iterates over each var_name and all of its coordinates, returning the variable + names and selections that allow properly obtain the data from ``data`` as desired. + + Parameters + ---------- + data : xarray.Dataset + Posterior data in an xarray + + var_names : iterator of strings (optional) + Should be a subset of data.data_vars. Defaults to all of them. + + combined : bool + Whether to combine chains or leave them separate + + skip_dims : set + dimensions to not iterate over + + reverse_selections : bool + Whether to reverse selections before iterating. + + Returns + ------- + Iterator of (var_name: str, selection: dict(str, any)) + The string is the variable name, the dictionary are coordinate names to values,. + To get the values of the variable at these coordinates, do + ``data[var_name].sel(**selection)``. + """ + if skip_dims is None: + skip_dims = set() + + if combined: + skip_dims = skip_dims.union({"chain", "draw"}) + else: + skip_dims.add("draw") + + if var_names is None: + if isinstance(data, xr.Dataset): + var_names = list(data.data_vars) + elif isinstance(data, xr.DataArray): + var_names = [data.name] + data = {data.name: data} + + for var_name in var_names: + if var_name in data: + new_dims = _dims(data, var_name, skip_dims) + vals = [purge_duplicates(data[var_name][dim].values) for dim in new_dims] + dims = _zip_dims(new_dims, vals) + idims = _zip_dims(new_dims, [range(len(v)) for v in vals]) + if reverse_selections: + dims = reversed(dims) + idims = reversed(idims) + + for selection, iselection in zip(dims, idims): + yield var_name, selection, iselection + + +def xarray_var_iter(data, var_names=None, combined=False, skip_dims=None, reverse_selections=False): + """Convert xarray data to an iterator over vectors. + + Iterates over each var_name and all of its coordinates, returning the 1d + data. + + Parameters + ---------- + data : xarray.Dataset + Posterior data in an xarray + + var_names : iterator of strings (optional) + Should be a subset of data.data_vars. Defaults to all of them. + + combined : bool + Whether to combine chains or leave them separate + + skip_dims : set + dimensions to not iterate over + + reverse_selections : bool + Whether to reverse selections before iterating. + + Returns + ------- + Iterator of (str, dict(str, any), np.array) + The string is the variable name, the dictionary are coordinate names to values, + and the array are the values of the variable at those coordinates. + """ + data_to_sel = data + if var_names is None and isinstance(data, xr.DataArray): + data_to_sel = {data.name: data} + + for var_name, selection, iselection in xarray_sel_iter( + data, + var_names=var_names, + combined=combined, + skip_dims=skip_dims, + reverse_selections=reverse_selections, + ): + yield var_name, selection, iselection, data_to_sel[var_name].sel(**selection).values + + +def xarray_to_ndarray(data, *, var_names=None, combined=True, label_fun=None): + """Take xarray data and unpacks into variables and data into list and numpy array respectively. + + Assumes that chain and draw are in coordinates + + Parameters + ---------- + data: xarray.DataSet + Data in an xarray from an InferenceData object. Examples include posterior or sample_stats + + var_names: iter + Should be a subset of data.data_vars not including chain and draws. Defaults to all of them + + combined: bool + Whether to combine chain into one array + + Returns + ------- + var_names: list + List of variable names + data: np.array + Data values + """ + if label_fun is None: + label_fun = BaseLabeller().make_label_vert + data_to_sel = data + if var_names is None and isinstance(data, xr.DataArray): + data_to_sel = {data.name: data} + + iterator1, iterator2 = tee(xarray_sel_iter(data, var_names=var_names, combined=combined)) + vars_and_sel = list(iterator1) + unpacked_var_names = [ + label_fun(var_name, selection, isel) for var_name, selection, isel in vars_and_sel + ] + + # Merge chains and variables, check dtype to be compatible with divergences data + data0 = data_to_sel[vars_and_sel[0][0]].sel(**vars_and_sel[0][1]) + unpacked_data = np.empty((len(unpacked_var_names), data0.size), dtype=data0.dtype) + for idx, (var_name, selection, _) in enumerate(iterator2): + unpacked_data[idx] = data_to_sel[var_name].sel(**selection).values.flatten() + + return unpacked_var_names, unpacked_data diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -1,9 +1,8 @@ # pylint: disable=too-many-lines """Statistical functions in ArviZ.""" import warnings -from collections import OrderedDict from copy import deepcopy -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, List, Optional, Tuple, Union import numpy as np import pandas as pd @@ -12,7 +11,7 @@ from scipy.optimize import minimize from arviz import _log -from ..data import CoordSpec, DimSpec, InferenceData, convert_to_dataset, convert_to_inference_data +from ..data import InferenceData, convert_to_dataset, convert_to_inference_data from ..rcparams import rcParams from ..utils import Numba, _numba_var, _var_names, get_coords from .density_utils import get_bins as _get_bins @@ -25,6 +24,8 @@ from .stats_utils import make_ufunc as _make_ufunc from .stats_utils import stats_variance_2d as svar from .stats_utils import wrap_xarray_ufunc as _wrap_xarray_ufunc +from ..sel_utils import xarray_var_iter +from ..labels import BaseLabeller if TYPE_CHECKING: from typing_extensions import Literal @@ -974,11 +975,11 @@ def summary( stat_funcs=None, extend=True, hdi_prob=None, - order: "Literal['C', 'F']" = "C", - index_origin=None, skipna=False, - coords: Optional[CoordSpec] = None, - dims: Optional[DimSpec] = None, + labeller=None, + coords=None, + index_origin=None, + order=None, ) -> Union[pd.DataFrame, xr.Dataset]: """Create a data frame with summary statistics. @@ -996,6 +997,8 @@ def summary( interpret var_names as substrings of the real variables names. If "regex", interpret var_names as regular expressions on the real variables names. A la `pandas.filter`. + coords: Dict[str, List[Any]], optional + Coordinate subset for which to calculate the summary. group: str Select a group for summary. Defaults to "posterior", "prior" or first group in that order, depending what groups exists. @@ -1023,18 +1026,19 @@ def summary( hdi_prob: float, optional Highest density interval to compute. Defaults to 0.94. This is only meaningful when ``stat_funcs`` is None. - order: {"C", "F"} - If fmt is "wide", use either C or F unpacking order. Defaults to C. - index_origin: int - If fmt is "wide, select n-based indexing for multivariate parameters. - Defaults to rcParam data.index.origin, which is 0. skipna: bool If true ignores nan values when computing the summary statistics, it does not affect the behaviour of the functions passed to ``stat_funcs``. Defaults to false. - coords: Dict[str, List[Any]], optional - Coordinates specification to be used if the ``fmt`` is ``'xarray'``. - dims: Dict[str, List[str]], optional - Dimensions specification for the variables to be used if the ``fmt`` is ``'xarray'``. + labeller : labeller instance, optional + Class providing the method `make_label_flat` to generate the labels in the plot titles. + For more details on ``labeller`` usage see :ref:`label_guide` + credible_interval: float, optional + deprecated: Please see hdi_prob + order + deprecated: order is now ignored. + index_origin + deprecated: index_origin is now ignored, modify the coordinate values to change the + value used in summary. Returns ------- @@ -1096,13 +1100,18 @@ def summary( """ _log.cache = [] - extra_args = {} # type: Dict[str, Any] - if coords is not None: - extra_args["coords"] = coords - if dims is not None: - extra_args["dims"] = dims - if index_origin is None: + if coords is None: + coords = {} + + if index_origin is not None: + warnings.warn( + "index_origin has been deprecated. summary now shows coordinate values, " + "to change the label shown, modify the coordinate values before calling sumary", + DeprecationWarning, + ) index_origin = rcParams["data.index_origin"] + if labeller is None: + labeller = BaseLabeller() if hdi_prob is None: hdi_prob = rcParams["stats.hdi_prob"] else: @@ -1125,22 +1134,24 @@ def summary( raise TypeError(f"InferenceData does not contain group: {group}") dataset = data[group] else: - dataset = convert_to_dataset(data, group="posterior", **extra_args) + dataset = convert_to_dataset(data, group="posterior") var_names = _var_names(var_names, dataset, filter_vars) dataset = dataset if var_names is None else dataset[var_names] + dataset = get_coords(dataset, coords) fmt_group = ("wide", "long", "xarray") if not isinstance(fmt, str) or (fmt.lower() not in fmt_group): raise TypeError(f"Invalid format: '{fmt}'. Formatting options are: {fmt_group}") - unpack_order_group = ("C", "F") - if not isinstance(order, str) or (order.upper() not in unpack_order_group): - raise TypeError(f"Invalid order: '{order}'. Unpacking options are: {unpack_order_group}") - kind_group = ("all", "stats", "diagnostics") if not isinstance(kind, str) or kind not in kind_group: raise TypeError(f"Invalid kind: '{kind}'. Kind options are: {kind_group}") + if order is not None: + warnings.warn( + "order has been deprecated. summary now shows coordinate values.", DeprecationWarning + ) + alpha = 1 - hdi_prob extra_metrics = [] @@ -1267,28 +1278,18 @@ def summary( joined = ( xr.concat(metrics, dim="metric").assign_coords(metric=metric_names).reset_coords(drop=True) ) + n_metrics = len(metric_names) + n_vars = np.sum([joined[var].size // n_metrics for var in joined.data_vars]) if fmt.lower() == "wide": - dfs = [] - for var_name, values in joined.data_vars.items(): - if len(values.shape[1:]): - index_metric = list(values.metric.values) - data_dict = OrderedDict() - for idx in np.ndindex(values.shape[1:] if order == "C" else values.shape[1:][::-1]): - if order == "F": - idx = tuple(idx[::-1]) - ser = pd.Series(values[(Ellipsis, *idx)].values, index=index_metric) - key_index = ",".join(map(str, (i + index_origin for i in idx))) - key = f"{var_name}[{key_index}]" - data_dict[key] = ser - df = pd.DataFrame.from_dict(data_dict, orient="index") - df = df.loc[list(data_dict.keys())] - else: - df = values.to_dataframe() - df.index = list(df.index) - df = df.T - dfs.append(df) - summary_df = pd.concat(dfs, sort=False) + summary_df = pd.DataFrame(np.full((n_vars, n_metrics), np.nan), columns=metric_names) + indexs = [] + for i, (var_name, sel, isel, values) in enumerate( + xarray_var_iter(joined, skip_dims={"metric"}) + ): + summary_df.iloc[i] = values + indexs.append(labeller.make_label_flat(var_name, sel, isel)) + summary_df.index = indexs elif fmt.lower() == "long": df = joined.to_dataframe().reset_index().set_index("metric") df.index = list(df.index) diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -406,7 +406,7 @@ def _cov_1d(x): return np.dot(x.T, x.conj()) / ddof -@conditional_jit(cache=True) +# @conditional_jit(cache=True) def _cov(data): if data.ndim == 1: return _cov_1d(data) diff --git a/doc/source/conf.py b/doc/source/conf.py --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -82,7 +82,8 @@ # # MyST related params -jupyter_execute_notebooks = "off" +jupyter_execute_notebooks = "auto" +execution_excludepatterns = ['*.ipynb'] myst_heading_anchors = 3 panels_add_bootstrap_css = False

diff --git a/arviz/tests/base_tests/test_data_zarr.py b/arviz/tests/base_tests/test_data_zarr.py --- a/arviz/tests/base_tests/test_data_zarr.py +++ b/arviz/tests/base_tests/test_data_zarr.py @@ -1,5 +1,4 @@ # pylint: disable=redefined-outer-name -import importlib import os import shutil from collections.abc import MutableMapping @@ -16,14 +15,10 @@ draws, eight_schools_params, running_on_ci, + importorskip, ) -pytestmark = pytest.mark.skipif( # pylint: disable=invalid-name - importlib.util.find_spec("zarr") is None and not running_on_ci(), - reason="test requires zarr which is not installed", -) - -import zarr # pylint: disable=wrong-import-position, wrong-import-order +zarr = importorskip("zarr") # pylint: disable=invalid-name class TestDataZarr: diff --git a/arviz/tests/base_tests/test_diagnostics.py b/arviz/tests/base_tests/test_diagnostics.py --- a/arviz/tests/base_tests/test_diagnostics.py +++ b/arviz/tests/base_tests/test_diagnostics.py @@ -8,9 +8,9 @@ from numpy.testing import assert_almost_equal from ...data import from_cmdstan, load_arviz_data -from ...plots.plot_utils import xarray_var_iter from ...rcparams import rc_context, rcParams from ...stats import bfmi, ess, mcse, rhat +from ...sel_utils import xarray_var_iter from ...stats.diagnostics import ( _ess, _ess_quantile, @@ -148,7 +148,7 @@ def test_deterministic(self): "mcse_quantile30": lambda x: mcse(x, method="quantile", prob=0.3), } results = {} - for key, coord_dict, vals in xarray_var_iter(posterior.posterior, combined=True): + for key, coord_dict, _, vals in xarray_var_iter(posterior.posterior, combined=True): if coord_dict: key = key + ".{}".format(list(coord_dict.values())[0] + 1) results[key] = {func_name: func(vals) for func_name, func in funcs.items()} diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py --- a/arviz/tests/base_tests/test_plot_utils.py +++ b/arviz/tests/base_tests/test_plot_utils.py @@ -14,10 +14,10 @@ make_2d, set_bokeh_circular_ticks_labels, vectorized_to_hex, - xarray_to_ndarray, - xarray_var_iter, compute_ranks, ) +from ...sel_utils import xarray_to_ndarray, xarray_sel_iter + from ...rcparams import rc_context from ...stats.density_utils import get_bins from ...utils import get_coords @@ -91,7 +91,7 @@ def test_dataset_to_numpy_combined(sample_dataset): assert (data[var_names.index("tau")] == tau.reshape(1, 6)).all() -def test_xarray_var_iter_ordering(): +def test_xarray_sel_iter_ordering(): """Assert that coordinate names stay the provided order""" coords = list("dcba") data = from_dict( # pylint: disable=no-member @@ -100,21 +100,21 @@ def test_xarray_var_iter_ordering(): dims={"x": ["in_order"]}, ).posterior - coord_names = [sel["in_order"] for _, sel, _ in xarray_var_iter(data)] + coord_names = [sel["in_order"] for _, sel, _ in xarray_sel_iter(data)] assert coord_names == coords -def test_xarray_var_iter_ordering_combined(sample_dataset): # pylint: disable=invalid-name +def test_xarray_sel_iter_ordering_combined(sample_dataset): # pylint: disable=invalid-name """Assert that varname order stays consistent when chains are combined""" _, _, data = sample_dataset - var_names = [var for (var, _, _) in xarray_var_iter(data, var_names=None, combined=True)] + var_names = [var for (var, _, _) in xarray_sel_iter(data, var_names=None, combined=True)] assert set(var_names) == {"mu", "tau"} -def test_xarray_var_iter_ordering_uncombined(sample_dataset): # pylint: disable=invalid-name +def test_xarray_sel_iter_ordering_uncombined(sample_dataset): # pylint: disable=invalid-name """Assert that varname order stays consistent when chains are not combined""" _, _, data = sample_dataset - var_names = [(var, selection) for (var, selection, _) in xarray_var_iter(data, var_names=None)] + var_names = [(var, selection) for (var, selection, _) in xarray_sel_iter(data, var_names=None)] assert len(var_names) == 4 for var_name in var_names: @@ -126,13 +126,13 @@ def test_xarray_var_iter_ordering_uncombined(sample_dataset): # pylint: disable ] -def test_xarray_var_data_array(sample_dataset): # pylint: disable=invalid-name +def test_xarray_sel_data_array(sample_dataset): # pylint: disable=invalid-name """Assert that varname order stays consistent when chains are combined Touches code that is hard to reach. """ _, _, data = sample_dataset - var_names = [var for (var, _, _) in xarray_var_iter(data.mu, var_names=None, combined=True)] + var_names = [var for (var, _, _) in xarray_sel_iter(data.mu, var_names=None, combined=True)] assert set(var_names) == {"mu"} diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -1312,42 +1312,6 @@ def test_plot_loo_pit_incompatible_args(models): plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hdi=True) [email protected]( - "args", - [ - {"y": "str"}, - {"y": "DataArray", "y_hat": "str"}, - {"y": "ndarray", "y_hat": "str"}, - {"y": "ndarray", "y_hat": "DataArray"}, - {"y": "ndarray", "y_hat": "ndarray"}, - ], -) -def test_plot_loo_pit_label(models, args): - assert_name = args["y"] != "ndarray" or args.get("y_hat") != "ndarray" - - if args["y"] == "str": - y = "y" - elif args["y"] == "DataArray": - y = models.model_1.observed_data.y - elif args["y"] == "ndarray": - y = models.model_1.observed_data.y.values - - if args.get("y_hat") == "str": - y_hat = "y" - elif args.get("y_hat") == "DataArray": - y_hat = models.model_1.posterior_predictive.y.stack(sample=("chain", "draw")) - elif args.get("y_hat") == "ndarray": - y_hat = models.model_1.posterior_predictive.y.stack(sample=("chain", "draw")).values - else: - y_hat = None - - ax = plot_loo_pit(idata=models.model_1, y=y, y_hat=y_hat) - if assert_name: - assert "y" in ax.get_legend_handles_labels()[1][0] - else: - assert "y" not in ax.get_legend_handles_labels()[1][0] - - @pytest.mark.parametrize( "kwargs", [ diff --git a/arviz/tests/base_tests/test_stats.py b/arviz/tests/base_tests/test_stats.py --- a/arviz/tests/base_tests/test_stats.py +++ b/arviz/tests/base_tests/test_stats.py @@ -261,40 +261,24 @@ def test_summary_fmt(centered_eight, fmt): assert summary(centered_eight, fmt=fmt) is not None [email protected]("order", ["C", "F"]) -def test_summary_unpack_order(order): - data = from_dict({"a": np.random.randn(4, 100, 4, 5, 3)}) - az_summary = summary(data, order=order, fmt="wide") +def test_summary_labels(): + coords1 = list("abcd") + coords2 = np.arange(1, 6) + data = from_dict( + {"a": np.random.randn(4, 100, 4, 5)}, + coords={"dim1": coords1, "dim2": coords2}, + dims={"a": ["dim1", "dim2"]}, + ) + az_summary = summary(data, fmt="wide") assert az_summary is not None - if order != "F": - first_index = 4 - second_index = 5 - third_index = 3 - else: - first_index = 3 - second_index = 5 - third_index = 4 column_order = [] - for idx1 in range(first_index): - for idx2 in range(second_index): - for idx3 in range(third_index): - if order != "F": - column_order.append("a[{},{},{}]".format(idx1, idx2, idx3)) - else: - column_order.append("a[{},{},{}]".format(idx3, idx2, idx1)) + for coord1 in coords1: + for coord2 in coords2: + column_order.append("a[{}, {}]".format(coord1, coord2)) for col1, col2 in zip(list(az_summary.index), column_order): assert col1 == col2 [email protected]("origin", [0, 1, 2, 3]) -def test_summary_index_origin(origin): - data = from_dict({"a": np.random.randn(2, 50, 10)}) - az_summary = summary(data, index_origin=origin, fmt="wide") - assert az_summary is not None - for i, col in enumerate(list(az_summary.index)): - assert col == "a[{}]".format(i + origin) - - @pytest.mark.parametrize( "stat_funcs", [[np.var], {"var": np.var, "var2": lambda x: np.var(x) ** 2}] ) @@ -306,12 +290,12 @@ def test_summary_stat_func(centered_eight, stat_funcs): def test_summary_nan(centered_eight): centered_eight = deepcopy(centered_eight) - centered_eight.posterior.theta[:, :, 0] = np.nan + centered_eight.posterior["theta"].loc[{"school": "Deerfield"}] = np.nan summary_xarray = summary(centered_eight) assert summary_xarray is not None - assert summary_xarray.loc["theta[0]"].isnull().all() + assert summary_xarray.loc["theta[Deerfield]"].isnull().all() assert ( - summary_xarray.loc[[ix for ix in summary_xarray.index if ix != "theta[0]"]] + summary_xarray.loc[[ix for ix in summary_xarray.index if ix != "theta[Deerfield]"]] .notnull() .all() .all() @@ -320,9 +304,9 @@ def test_summary_nan(centered_eight): def test_summary_skip_nan(centered_eight): centered_eight = deepcopy(centered_eight) - centered_eight.posterior.theta[:, :10, 1] = np.nan + centered_eight.posterior["theta"].loc[{"draw": slice(10), "school": "Deerfield"}] = np.nan summary_xarray = summary(centered_eight) - theta_1 = summary_xarray.loc["theta[1]"].isnull() + theta_1 = summary_xarray.loc["theta[Deerfield]"].isnull() assert summary_xarray is not None assert ~theta_1[:4].all() assert theta_1[4:].all() @@ -334,16 +318,14 @@ def test_summary_bad_fmt(centered_eight, fmt): summary(centered_eight, fmt=fmt) [email protected]("kind", [1, "bad_kind"]) -def test_summary_bad_kind(centered_eight, kind): - with pytest.raises(TypeError, match="Invalid kind"): - summary(centered_eight, kind=kind) +def test_summary_order_deprecation(centered_eight): + with pytest.warns(DeprecationWarning, match="order"): + summary(centered_eight, order="C") [email protected]("order", [1, "bad_order"]) -def test_summary_bad_unpack_order(centered_eight, order): - with pytest.raises(TypeError, match="Invalid order"): - summary(centered_eight, order=order) +def test_summary_index_origin_deprecation(centered_eight): + with pytest.warns(DeprecationWarning, match="index_origin"): + summary(centered_eight, index_origin=1) @pytest.mark.parametrize("scale", ["log", "negative_log", "deviance"])

arviz.summary() indexes parameters from 0, instead of taking the index described in coordinates **Describe the bug** Stan indexes from 1. When calling arviz.summary(), the parameters are indexed from 0, regardless of the coordinates specified in the arviz.from_pystan() call. **To Reproduce** ``` X = np.random.rand(100,9) y = (X*0.1).sum(axis=1) + np.random.normal(0,1,100) reg_data = {'X': X, 'y': y, 'N': X.shape[0], 'K': X.shape[1]} reg_code = """ data { int<lower=0> N; // number of samples int<lower=0> K; // number of predictors vector[N] y; // response variable matrix[N, K] X; // predictor matrix } parameters { real alpha; vector[K] beta; // coefficients for predictors real<lower=0> sigma; } transformed parameters { vector[N] mu; mu = alpha + X * beta; } model { y ~ normal(mu, sigma); } generated quantities { vector[N] log_lik; vector[N] y_hat; for (i in 1:N) { log_lik[i] = normal_lpdf(y[i] | mu[i], sigma); y_hat[i] = normal_rng(mu[i], sigma); } } """ # fit the model sm = ps.StanModel(model_code=reg_code) fit = sm.sampling(data=reg_data, iter=5000, chains=4) # convert pystan to arviz fit_az = az.from_pystan(posterior=fit, posterior_predictive="y_hat", observed_data=["y"], log_likelihood={"y": "log_lik"}, coords={"chain": np.arange(4), "draw": np.arange(2500), "var": np.arange(1,X.shape[1]+1)}, dims={'alpha': ["chain","draw"], 'beta': ["chain","draw", "var"], 'sigma': ["chain","draw"]}) # plot_forest() correctly inherits the labels from the fit_az object axes = az.plot_forest( fit_az, kind="ridgeplot", var_names=["alpha", "beta", "sigma"], combined=True, figsize=(9, 4), hdi_prob=0.95 ) plt.show() # but summary just indexes the betas from 0, even though stan calls them beta[1], beta[2]...beta[9] az.summary(fit_az, var_names=["alpha", "beta", "sigma"]) ``` **Expected behavior** az.summary() should number the parameters 1-9, as is specified in the coords, in the same way that plot_forest does. **Additional context** I think this is particular to pystan, because other packages may just index from 0 like python does. integrate index_origin rcParam ## Tell us about it In some cases, users may prefer to visualize the results using 1 indexed values instead of python's 0 indexed ones. Part of #792. ## Thoughts on implementation The `summary` function already has an `index_origin` argument, but does not use the rcParam value as default. Moreover, the labels used for plots should also be updated to allow 1 indexed visualization (they all call `make_label` and [`selection_to_string`](https://github.com/arviz-devs/arviz/blob/f4f68d98e4a9a16470b66992f7428ce9b3cfb549/arviz/plots/plot_utils.py#L275)). #981 is also related to this.

@OriolAbril Can I work on this issue? @Shashankjain12 Definitely! I'm not sure if `make_label` can be fixed currently. And summary has been fixed. The selection function still needs this. So, @ahartikainen is the ```make_label``` is fixed can I work on this issue now or is this issue dependant on some certain other function which needs to be fixed first? You can work to fix `.sel` method. I think we need to rewrite the whole `make_label` function so you don't need to worry about it yet. Ok thanks @ahartikainen :+1: So, @OriolAbril and @ahartikainen we need to change summary function index_origin parameter to rc_params data.index_orign default parameter instead of None for now and also change the value of data.index_origin in defaultParams in rcparams to 1 instead of 0 over there > we need to change summary function index_origin parameter to rc_params data.index_orign default parameter instead of None `None` is used to indicate the value in rcParams must be used, it used to be hardcoded to 1. This was fixed by @ahartikainen while working on a related issue in summary: > > I'm not sure if `make_label` can be fixed currently. And summary has been fixed. Therefore summary does not need to be modified. > also change the value of data.index_origin in defaultParams in rcparams to 1 instead of 0 over there We want to keep 0 as the default, to follow Python indexation, and internally we will always used 0 indexed values. The option to use 1 as index origin is a visualization aid for users analyzing Stan or Julia generated samples. In these cases, their model would use 4 chains: `1,2,3,4` so having ArviZ plots and summaries show the same names as the ones used by their model can reduce a lot of index confusions. If you feel up to it you can work on the `.sel` method so that in addition to seeing 1 indexed values, users can use the same values shown in plots to index inference data objects. The idea is to correct the indexes provided by users and convert them to 0 based indexes if necessary to keep ArviZ internally always working with 0 based indexing. I feel that a warning is in order though, as labels used for selection may be strings in which case no correction must be done. Working on indexing in sel is probably not a good first issue.

2020-05-22T23:25:42Z

arviz-devs/arviz

arviz-devs__arviz-1209

cf7c19e69af007e2e6f024135269f7c8bcdb03d6

diff --git a/arviz/__init__.py b/arviz/__init__.py --- a/arviz/__init__.py +++ b/arviz/__init__.py @@ -1,6 +1,6 @@ # pylint: disable=wildcard-import,invalid-name,wrong-import-position """ArviZ is a library for exploratory analysis of Bayesian models.""" -__version__ = "0.8.1" +__version__ = "0.8.2" import os import logging diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -98,7 +98,7 @@ def __init__( 0 ].model self.nchains = trace.nchains if hasattr(trace, "nchains") else 1 - if hasattr(trace.report, "n_tune"): + if hasattr(trace.report, "n_draws") and trace.report.n_draws is not None: self.ndraws = trace.report.n_draws self.attrs = { "sampling_time": trace.report.t_sampling, @@ -109,7 +109,8 @@ def __init__( if self.save_warmup: warnings.warn( "Warmup samples will be stored in posterior group and will not be" - " excluded from stats and diagnostics. Please consider using PyMC3>=3.9", + " excluded from stats and diagnostics." + " Please consider using PyMC3>=3.9 and do not slice the trace manually.", UserWarning, ) else:

diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -1,7 +1,6 @@ # pylint: disable=no-member, invalid-name, redefined-outer-name from sys import version_info from typing import Dict, Tuple -import packaging import numpy as np import pytest @@ -399,8 +398,10 @@ def test_no_model_deprecation(self): fails = check_multiple_attrs(test_dict, inference_data) assert not fails + +class TestPyMC3WarmupHandling: @pytest.mark.skipif( - packaging.version.parse(pm.__version__) < packaging.version.parse("3.9"), + not hasattr(pm.backends.base.SamplerReport, "n_draws"), reason="requires pymc3 3.9 or higher", ) @pytest.mark.parametrize("save_warmup", [False, True]) @@ -434,3 +435,58 @@ def test_save_warmup(self, save_warmup): if save_warmup: assert idata.warmup_posterior.dims["chain"] == 2 assert idata.warmup_posterior.dims["draw"] == 100 + + @pytest.mark.skipif( + hasattr(pm.backends.base.SamplerReport, "n_draws"), reason="requires pymc3 3.8 or lower", + ) + def test_save_warmup_issue_1208_before_3_9(self): + with pm.Model(): + pm.Uniform("u1") + pm.Normal("n1") + trace = pm.sample( + tune=100, + draws=200, + chains=2, + cores=1, + step=pm.Metropolis(), + discard_tuned_samples=False, + ) + assert isinstance(trace, pm.backends.base.MultiTrace) + assert len(trace) == 300 + + # <=3.8 did not track n_draws in the sampler report, + # making from_pymc3 fall back to len(trace) and triggering a warning + with pytest.warns(UserWarning, match="Warmup samples"): + idata = from_pymc3(trace, save_warmup=True) + assert idata.posterior.dims["draw"] == 300 + assert idata.posterior.dims["chain"] == 2 + + @pytest.mark.skipif( + not hasattr(pm.backends.base.SamplerReport, "n_draws"), + reason="requires pymc3 3.9 or higher", + ) + def test_save_warmup_issue_1208_after_3_9(self): + with pm.Model(): + pm.Uniform("u1") + pm.Normal("n1") + trace = pm.sample( + tune=100, + draws=200, + chains=2, + cores=1, + step=pm.Metropolis(), + discard_tuned_samples=False, + ) + assert isinstance(trace, pm.backends.base.MultiTrace) + assert len(trace) == 300 + + # from original trace, warmup draws should be separated out + idata = from_pymc3(trace, save_warmup=True) + assert idata.posterior.dims["chain"] == 2 + assert idata.posterior.dims["draw"] == 200 + + # manually sliced trace triggers the same warning as <=3.8 + with pytest.warns(UserWarning, match="Warmup samples"): + idata = from_pymc3(trace[-30:], save_warmup=True) + assert idata.posterior.dims["chain"] == 2 + assert idata.posterior.dims["draw"] == 30

from_pymc3 fails on manually sliced traces **Describe the bug** When the trace is sliced manually, the `n_draws` in the sampler report are `None`, breaking `from_pymc3`. **Expected behavior** `from_pymc3` should just revert to the old strategy. **Additional context** * latest arviz from master * latest pymc3 from master

2020-05-25T08:54:33Z

arviz-devs/arviz

arviz-devs__arviz-1211

6b4de81d85573a50185b1e5c64097c395a282e70

diff --git a/arviz/__init__.py b/arviz/__init__.py --- a/arviz/__init__.py +++ b/arviz/__init__.py @@ -1,6 +1,6 @@ # pylint: disable=wildcard-import,invalid-name,wrong-import-position """ArviZ is a library for exploratory analysis of Bayesian models.""" -__version__ = "0.8.2" +__version__ = "0.8.3" import os import logging diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -1,7 +1,7 @@ """PyMC3-specific conversion code.""" import logging import warnings -from typing import Dict, List, Any, Optional, Iterable, Union, TYPE_CHECKING +from typing import Dict, List, Any, Optional, Iterable, Union, TYPE_CHECKING, Tuple from types import ModuleType import numpy as np @@ -113,6 +113,8 @@ def __init__( " Please consider using PyMC3>=3.9 and do not slice the trace manually.", UserWarning, ) + self.ntune = len(self.trace) - self.ndraws + self.posterior_trace, self.warmup_trace = self.split_trace() else: self.nchains = self.ndraws = 0 @@ -160,6 +162,26 @@ def find_observations(self) -> Optional[Dict[str, Var]]: return {obs.name: obs.observations for obs in self.model.observed_RVs} return None + def split_trace(self) -> Tuple[Union[None, MultiTrace], Union[None, MultiTrace]]: + """Split MultiTrace object into posterior and warmup. + + Returns + ------- + trace_posterior: pymc3.MultiTrace or None + The slice of the trace corresponding to the posterior. If the posterior + trace is empty, None is returned + trace_warmup: pymc3.MultiTrace or None + The slice of the trace corresponding to the warmup. If the warmup trace is + empty or ``save_warmup=False``, None is returned + """ + trace_posterior = None + trace_warmup = None + if self.save_warmup and self.ntune > 0: + trace_warmup = self.trace[: self.ntune] + if self.ndraws > 0: + trace_posterior = self.trace[self.ntune :] + return trace_posterior, trace_warmup + def log_likelihood_vals_point(self, point, var, log_like_fun): """Compute log likelihood for each observed point.""" log_like_val = utils.one_de(log_like_fun(point)) @@ -208,13 +230,14 @@ def posterior_to_xarray(self): data = {} data_warmup = {} for var_name in var_names: - if self.save_warmup: + if self.warmup_trace: data_warmup[var_name] = np.array( - self.trace[: -self.ndraws].get_values(var_name, combine=False, squeeze=False) + self.warmup_trace.get_values(var_name, combine=False, squeeze=False) + ) + if self.posterior_trace: + data[var_name] = np.array( + self.posterior_trace.get_values(var_name, combine=False, squeeze=False) ) - data[var_name] = np.array( - self.trace[-self.ndraws :].get_values(var_name, combine=False, squeeze=False) - ) return ( dict_to_dataset( data, library=self.pymc3, coords=self.coords, dims=self.dims, attrs=self.attrs @@ -239,11 +262,12 @@ def sample_stats_to_xarray(self): name = rename_key.get(stat, stat) if name == "tune": continue - if self.save_warmup: + if self.warmup_trace: data_warmup[name] = np.array( - self.trace[: -self.ndraws].get_sampler_stats(stat, combine=False) + self.warmup_trace.get_sampler_stats(stat, combine=False) ) - data[name] = np.array(self.trace[-self.ndraws :].get_sampler_stats(stat, combine=False)) + if self.posterior_trace: + data[name] = np.array(self.posterior_trace.get_sampler_stats(stat, combine=False)) return ( dict_to_dataset( @@ -260,17 +284,22 @@ def log_likelihood_to_xarray(self): """Extract log likelihood and log_p data from PyMC3 trace.""" if self.predictions or not self.log_likelihood: return None - try: - data = self._extract_log_likelihood(self.trace[-self.ndraws :]) - except TypeError: - warnings.warn( - """Could not compute log_likelihood, it will be omitted. - Check your model object or set log_likelihood=False""" - ) - return None data_warmup = {} - if self.save_warmup: - data_warmup = self._extract_log_likelihood(self.trace[: -self.ndraws]) + data = {} + warn_msg = ( + "Could not compute log_likelihood, it will be omitted. " + "Check your model object or set log_likelihood=False" + ) + if self.posterior_trace: + try: + data = self._extract_log_likelihood(self.posterior_trace) + except TypeError: + warnings.warn(warn_msg) + if self.warmup_trace: + try: + data_warmup = self._extract_log_likelihood(self.warmup_trace) + except TypeError: + warnings.warn(warn_msg) return ( dict_to_dataset(data, library=self.pymc3, dims=self.dims, coords=self.coords), dict_to_dataset(data_warmup, library=self.pymc3, dims=self.dims, coords=self.coords),

diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -1,4 +1,4 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name +# pylint: disable=no-member, invalid-name, redefined-outer-name, protected-access from sys import version_info from typing import Dict, Tuple @@ -405,36 +405,40 @@ class TestPyMC3WarmupHandling: reason="requires pymc3 3.9 or higher", ) @pytest.mark.parametrize("save_warmup", [False, True]) - def test_save_warmup(self, save_warmup): + @pytest.mark.parametrize("chains", [1, 2]) + @pytest.mark.parametrize("tune,draws", [(0, 50), (10, 40), (30, 0)]) + def test_save_warmup(self, save_warmup, chains, tune, draws): with pm.Model(): pm.Uniform("u1") pm.Normal("n1") trace = pm.sample( - tune=100, - draws=200, - chains=2, + tune=tune, + draws=draws, + chains=chains, cores=1, step=pm.Metropolis(), discard_tuned_samples=False, ) assert isinstance(trace, pm.backends.base.MultiTrace) idata = from_pymc3(trace, save_warmup=save_warmup) - prefix = "" if save_warmup else "~" + warmup_prefix = "" if save_warmup and (tune > 0) else "~" + post_prefix = "" if draws > 0 else "~" test_dict = { - "posterior": ["u1", "n1"], - "sample_stats": ["~tune", "accept"], - f"{prefix}warmup_posterior": ["u1", "n1"], - f"{prefix}warmup_sample_stats": ["~tune"], + f"{post_prefix}posterior": ["u1", "n1"], + f"{post_prefix}sample_stats": ["~tune", "accept"], + f"{warmup_prefix}warmup_posterior": ["u1", "n1"], + f"{warmup_prefix}warmup_sample_stats": ["~tune"], "~warmup_log_likelihood": [], "~log_likelihood": [], } fails = check_multiple_attrs(test_dict, idata) assert not fails - assert idata.posterior.dims["chain"] == 2 - assert idata.posterior.dims["draw"] == 200 - if save_warmup: - assert idata.warmup_posterior.dims["chain"] == 2 - assert idata.warmup_posterior.dims["draw"] == 100 + if hasattr(idata, "posterior"): + assert idata.posterior.dims["chain"] == chains + assert idata.posterior.dims["draw"] == draws + if hasattr(idata, "warmup_posterior"): + assert idata.warmup_posterior.dims["chain"] == chains + assert idata.warmup_posterior.dims["draw"] == tune @pytest.mark.skipif( hasattr(pm.backends.base.SamplerReport, "n_draws"), reason="requires pymc3 3.8 or lower", @@ -458,8 +462,16 @@ def test_save_warmup_issue_1208_before_3_9(self): # making from_pymc3 fall back to len(trace) and triggering a warning with pytest.warns(UserWarning, match="Warmup samples"): idata = from_pymc3(trace, save_warmup=True) - assert idata.posterior.dims["draw"] == 300 - assert idata.posterior.dims["chain"] == 2 + test_dict = { + "posterior": ["u1", "n1"], + "sample_stats": ["~tune", "accept"], + "~warmup_posterior": [], + "~warmup_sample_stats": [], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails + assert idata.posterior.dims["draw"] == 300 + assert idata.posterior.dims["chain"] == 2 @pytest.mark.skipif( not hasattr(pm.backends.base.SamplerReport, "n_draws"), @@ -482,11 +494,27 @@ def test_save_warmup_issue_1208_after_3_9(self): # from original trace, warmup draws should be separated out idata = from_pymc3(trace, save_warmup=True) + test_dict = { + "posterior": ["u1", "n1"], + "sample_stats": ["~tune", "accept"], + "warmup_posterior": ["u1", "n1"], + "warmup_sample_stats": ["~tune", "accept"], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails assert idata.posterior.dims["chain"] == 2 assert idata.posterior.dims["draw"] == 200 # manually sliced trace triggers the same warning as <=3.8 with pytest.warns(UserWarning, match="Warmup samples"): idata = from_pymc3(trace[-30:], save_warmup=True) + test_dict = { + "posterior": ["u1", "n1"], + "sample_stats": ["~tune", "accept"], + "~warmup_posterior": [], + "~warmup_sample_stats": [], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails assert idata.posterior.dims["chain"] == 2 assert idata.posterior.dims["draw"] == 30

from_pymc3(save_warmup=True) puts tuning samples into posterior if draws=0 **Describe the bug** When a trace given to `az.from_pymc3(trace, save_warmup=True)` contains tuning samples, but no draws, the tuning samples end up in the posterior group and the warmup group remains empty. **To Reproduce** ``` with pm.Model() as model: x = pm.Normal("x", mu=0, sigma=1) trace = pm.sample( tune=100, draws=0, discard_tuned_samples=False, chains=1, ) idata = az.from_pymc3(trace, save_warmup=True) # >> Sampling 1 chain for 100 tune and 0 draw iterations (100 + 0 draws total) took 0 seconds. print(idata.warmup_posterior.dims) # >> Frozen(SortedKeysDict({'chain': 1, 'draw': 0})) print(idata.posterior.dims) # >> Frozen(SortedKeysDict({'chain': 1, 'draw': 100})) ``` **Expected behavior** Tuning samples should end up in the warmup group - even if there are no further draws. **Additional context** ArviZ master PyMC3 master

The existing test can be parametrized to detect it: Line 403 in `test_data_pymc.py` ```python @pytest.mark.skipif( not hasattr(pm.backends.base.SamplerReport, "n_draws"), reason="requires pymc3 3.9 or higher", ) @pytest.mark.parametrize("save_warmup", [False, True]) @pytest.mark.parametrize("chains", [1, 2]) @pytest.mark.parametrize("tune,draws", [(0, 200), (100, 200), (100, 0)]) def test_save_warmup(self, save_warmup, chains, tune, draws): with pm.Model(): pm.Uniform("u1") pm.Normal("n1") trace = pm.sample( tune=tune, draws=draws, chains=chains, cores=1, step=pm.Metropolis(), discard_tuned_samples=False, ) assert isinstance(trace, pm.backends.base.MultiTrace) idata = from_pymc3(trace, save_warmup=save_warmup) prefix = "" if save_warmup else "~" test_dict = { "posterior": ["u1", "n1"], "sample_stats": ["~tune", "accept"], f"{prefix}warmup_posterior": ["u1", "n1"], f"{prefix}warmup_sample_stats": ["~tune"], "~warmup_log_likelihood": [], "~log_likelihood": [], } fails = check_multiple_attrs(test_dict, idata) assert not fails assert idata.posterior.dims["chain"] == chains assert idata.posterior.dims["draw"] == draws if save_warmup: assert idata.warmup_posterior.dims["chain"] == chains assert idata.warmup_posterior.dims["draw"] == tune ``` It's a slicing problem.. Doing a PR...

2020-05-26T12:37:21Z

arviz-devs/arviz

arviz-devs__arviz-1227

661e057d3281a119db0a5a06b6090d3b6b58de3a

diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -86,7 +86,7 @@ def __init__( "Using `from_pymc3` without the model will be deprecated in a future release. " "Not using the model will return less accurate and less useful results. " "Make sure you use the model argument or call from_pymc3 within a model context.", - PendingDeprecationWarning, + FutureWarning, ) # This next line is brittle and may not work forever, but is a secret diff --git a/arviz/data/io_pyro.py b/arviz/data/io_pyro.py --- a/arviz/data/io_pyro.py +++ b/arviz/data/io_pyro.py @@ -1,5 +1,6 @@ """Pyro-specific conversion code.""" import logging +import warnings import numpy as np from packaging import version import xarray as xr @@ -26,6 +27,7 @@ def __init__( posterior=None, prior=None, posterior_predictive=None, + log_likelihood=True, predictions=None, constant_data=None, predictions_constant_data=None, @@ -62,6 +64,7 @@ def __init__( self.posterior = posterior self.prior = prior self.posterior_predictive = posterior_predictive + self.log_likelihood = log_likelihood self.predictions = predictions self.constant_data = constant_data self.predictions_constant_data = predictions_constant_data @@ -130,6 +133,8 @@ def sample_stats_to_xarray(self): @requires("model") def log_likelihood_to_xarray(self): """Extract log likelihood from Pyro posterior.""" + if not self.log_likelihood: + return None data = {} if self.observations is not None: try: @@ -143,6 +148,10 @@ def log_likelihood_to_xarray(self): data[obs_name] = np.reshape(log_like, shape) except: # pylint: disable=bare-except # cannot get vectorized trace + warnings.warn( + "Could not get vectorized trace, log_likelihood group will be omitted. " + "Check your model vectorization or set log_likelihood=False" + ) return None return dict_to_dataset(data, library=self.pyro, coords=self.coords, dims=self.dims) @@ -273,6 +282,7 @@ def from_pyro( *, prior=None, posterior_predictive=None, + log_likelihood=True, predictions=None, constant_data=None, predictions_constant_data=None, @@ -294,6 +304,8 @@ def from_pyro( Prior samples from a Pyro model posterior_predictive : dict Posterior predictive samples for the posterior + log_likelihood : bool, optional + Calculate and store pointwise log likelihood values. predictions: dict Out of sample predictions constant_data: dict @@ -313,6 +325,7 @@ def from_pyro( posterior=posterior, prior=prior, posterior_predictive=posterior_predictive, + log_likelihood=log_likelihood, predictions=predictions, constant_data=constant_data, predictions_constant_data=predictions_constant_data,

diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -3,6 +3,7 @@ import os from copy import deepcopy import matplotlib.pyplot as plt +from matplotlib import animation from pandas import DataFrame from scipy.stats import gaussian_kde import numpy as np @@ -497,6 +498,8 @@ def test_plot_pair_shapes(marginals, max_subplots): @pytest.mark.parametrize("alpha", [None, 0.2, 1]) @pytest.mark.parametrize("animated", [False, True]) def test_plot_ppc(models, kind, alpha, animated): + if animation and not animation.writers.is_available("ffmpeg"): + pytest.skip("matplotlib animations within ArviZ require ffmpeg") animation_kwargs = {"blit": False} axes = plot_ppc( models.model_1, @@ -516,6 +519,8 @@ def test_plot_ppc(models, kind, alpha, animated): @pytest.mark.parametrize("jitter", [None, 0, 0.1, 1, 3]) @pytest.mark.parametrize("animated", [False, True]) def test_plot_ppc_multichain(kind, jitter, animated): + if animation and not animation.writers.is_available("ffmpeg"): + pytest.skip("matplotlib animations within ArviZ require ffmpeg") data = from_dict( posterior_predictive={ "x": np.random.randn(4, 100, 30), @@ -543,6 +548,8 @@ def test_plot_ppc_multichain(kind, jitter, animated): @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) @pytest.mark.parametrize("animated", [False, True]) def test_plot_ppc_discrete(kind, animated): + if animation and not animation.writers.is_available("ffmpeg"): + pytest.skip("matplotlib animations within ArviZ require ffmpeg") data = from_dict( observed_data={"obs": np.random.randint(1, 100, 15)}, posterior_predictive={"obs": np.random.randint(1, 300, (1, 20, 15))}, @@ -556,6 +563,10 @@ def test_plot_ppc_discrete(kind, animated): assert axes [email protected]( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) def test_plot_ppc_save_animation(models, kind): animation_kwargs = {"blit": False} @@ -577,6 +588,10 @@ def test_plot_ppc_save_animation(models, kind): assert os.path.getsize(path) [email protected]( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) def test_plot_ppc_discrete_save_animation(kind): data = from_dict( @@ -602,6 +617,10 @@ def test_plot_ppc_discrete_save_animation(kind): assert os.path.getsize(path) [email protected]( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) @pytest.mark.parametrize("system", ["Windows", "Darwin"]) def test_non_linux_blit(models, monkeypatch, system, caplog): import platform @@ -657,6 +676,10 @@ def test_plot_ppc_ax(models, kind, fig_ax): assert axes[0] is ax [email protected]( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) def test_plot_ppc_bad_ax(models, fig_ax): _, ax = fig_ax _, ax2 = plt.subplots(1, 2) diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -425,7 +425,7 @@ def test_no_model_deprecation(self): obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable prior = pm.sample_prior_predictive() - with pytest.warns(PendingDeprecationWarning, match="without the model"): + with pytest.warns(FutureWarning, match="without the model"): inference_data = from_pymc3(prior=prior) test_dict = { "prior": ["beta", "obs"], diff --git a/arviz/tests/external_tests/test_data_pyro.py b/arviz/tests/external_tests/test_data_pyro.py --- a/arviz/tests/external_tests/test_data_pyro.py +++ b/arviz/tests/external_tests/test_data_pyro.py @@ -17,6 +17,7 @@ torch = importorskip("torch") pyro = importorskip("pyro") Predictive = pyro.infer.Predictive +dist = pyro.distributions class TestDataPyro: @@ -164,9 +165,6 @@ def test_inference_data_only_posterior_has_log_likelihood(self, data): assert not fails def test_multiple_observed_rv(self): - import pyro.distributions as dist - from pyro.infer import MCMC, NUTS - y1 = torch.randn(10) y2 = torch.randn(10) @@ -175,8 +173,8 @@ def model_example_multiple_obs(y1=None, y2=None): pyro.sample("y1", dist.Normal(x, 1), obs=y1) pyro.sample("y2", dist.Normal(x, 1), obs=y2) - nuts_kernel = NUTS(model_example_multiple_obs) - mcmc = MCMC(nuts_kernel, num_samples=10) + nuts_kernel = pyro.infer.NUTS(model_example_multiple_obs) + mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) mcmc.run(y1=y1, y2=y2) inference_data = from_pyro(mcmc) test_dict = { @@ -190,9 +188,6 @@ def model_example_multiple_obs(y1=None, y2=None): assert not hasattr(inference_data.sample_stats, "log_likelihood") def test_inference_data_constant_data(self): - import pyro.distributions as dist - from pyro.infer import MCMC, NUTS - x1 = 10 x2 = 12 y1 = torch.randn(10) @@ -201,8 +196,8 @@ def model_constant_data(x, y1=None): _x = pyro.sample("x", dist.Normal(1, 3)) pyro.sample("y1", dist.Normal(x * _x, 1), obs=y1) - nuts_kernel = NUTS(model_constant_data) - mcmc = MCMC(nuts_kernel, num_samples=10) + nuts_kernel = pyro.infer.NUTS(model_constant_data) + mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) mcmc.run(x=x1, y1=y1) posterior = mcmc.get_samples() posterior_predictive = Predictive(model_constant_data, posterior)(x1) @@ -232,3 +227,34 @@ def test_inference_data_num_chains(self, predictions_data, chains): inference_data = from_pyro(predictions=predictions, num_chains=chains) nchains = inference_data.predictions.dims["chain"] assert nchains == chains + + @pytest.mark.parametrize("log_likelihood", [True, False]) + def test_log_likelihood(self, log_likelihood): + """Test behaviour when log likelihood cannot be retrieved. + + If log_likelihood=True there is a warning to say log_likelihood group is skipped, + if log_likelihood=False there is no warning and log_likelihood is skipped. + """ + x = torch.randn((10, 2)) + y = torch.randn(10) + + def model_constant_data(x, y=None): + beta = pyro.sample("beta", dist.Normal(torch.ones(2), 3)) + pyro.sample("y", dist.Normal(x.matmul(beta), 1), obs=y) + + nuts_kernel = pyro.infer.NUTS(model_constant_data) + mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) + mcmc.run(x=x, y=y) + if log_likelihood: + with pytest.warns(UserWarning, match="Could not get vectorized trace"): + inference_data = from_pyro(mcmc, log_likelihood=log_likelihood) + else: + inference_data = from_pyro(mcmc, log_likelihood=log_likelihood) + test_dict = { + "posterior": ["beta"], + "sample_stats": ["diverging"], + "~log_likelihood": [], + "observed_data": ["y"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails

Add skip to animation tests if ffmpeg is not installed **Describe the bug** Running test suite without ffmpeg in environment will raise numerous failures **To Reproduce** Steps to reproduce the behavior. Ideally a self-contained snippet of code, or link to a notebook or external code. Please include screenshots/images produced with ArviZ here, or the stacktrace including `arviz` code to help. <img width="1038" alt="image" src="https://user-images.githubusercontent.com/7213793/59773837-2dd71a00-9263-11e9-863c-329798656979.png"> > > FWARNING:matplotlib.animation:MovieWriter ffmpeg unavailable. Trying to use html instead. > INFO:matplotlib.animation:Animation.save using <class 'matplotlib.animation.HTMLWriter'> > > test_plots.py:559 (test_plot_ppc_save_animation[cumulative]) > models = <arviz.tests.test_plots.models.<locals>.Models object at 0x19a9999b0> > kind = 'cumulative' > > @pytest.mark.parametrize("kind", ["density", "cumulative", "scatter"]) > def test_plot_ppc_save_animation(models, kind): > animation_kwargs = {"blit": False} > axes, anim = plot_ppc( > models.model_1, > kind=kind, > animated=True, > animation_kwargs=animation_kwargs, > num_pp_samples=5, > random_seed=3, > ) > assert axes > assert anim > animations_folder = "saved_animations" > os.makedirs(animations_folder, exist_ok=True) > path = os.path.join(animations_folder, "ppc_{}_animation.mp4".format(kind)) > > anim.save(path) > > test_plots.py:576: > _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > ../../../../.miniconda3/envs/arviz/lib/python3.7/site-packages/matplotlib/animation.py:1166: in save > with writer.saving(self._fig, filename, dpi): > ../../../../.miniconda3/envs/arviz/lib/python3.7/contextlib.py:112: in __enter__ > return next(self.gen) > ../../../../.miniconda3/envs/arviz/lib/python3.7/site-packages/matplotlib/animation.py:228: in saving > self.setup(fig, outfile, dpi, *args, **kwargs) > _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > > self = <matplotlib.animation.HTMLWriter object at 0x1ca9415f8> > fig = <Figure size 640x480 with 1 Axes> > outfile = 'saved_animations/ppc_cumulative_animation.mp4', dpi = 100.0 > frame_dir = None > > def setup(self, fig, outfile, dpi, frame_dir=None): > root, ext = os.path.splitext(outfile) > if ext not in ['.html', '.htm']: > > raise ValueError("outfile must be *.htm or *.html") > E ValueError: outfile must be *.htm or *.html > > ../../../../.miniconda3/envs/arviz/lib/python3.7/site-packages/matplotlib/animation.py:857: ValueError **Expected behavior** Tests should be skipped if ffmpeg is not installed to avoid erroneous failures **Additional context** Versions of `arviz` and other libraries used, operating system used, and anything else that may be useful. Cannot get log-likelihood from Logistic Regression in Pyro I'm trying to get log-likelihood estimates via arviz from my logistic regression model My model looks like this: ``` def logistic_regression(X, obs=None): alpha = pyro.sample("alpha", dist.Normal(0, 5.)) # Prior for the bias/intercept beta = pyro.sample("beta", dist.Normal(torch.zeros(X.shape[1]), 5.*torch.ones(X.shape[1]))) # Priors for the regression coeffcients with pyro.plate("data"): logits = alpha + X.matmul(beta) y = pyro.sample("y", dist.Bernoulli(logits=logits), obs=obs) return y ``` I train it via standard MCMC, like this: ``` nuts_kernel = NUTS(logistic_regression,jit_compile=True, ignore_jit_warnings=True) mcmc_logistic = MCMC(nuts_kernel, num_samples=1000, warmup_steps=500, num_chains=2) mcmc_logistic.run(X_train, y_train) mcmc_logistic.summary() ``` And then I would like to convert it to an arviz module. ``` pyro_data_logit = az.from_pyro( mcmc_logistic, prior=prior_logit, posterior_predictive=posterior_predictive_logit ) ``` The problem is that this object does not contain a log-likelihood, which seems weird. The missing log-likehood is problematic as I can use other functionalities such as .loo I'm able to calculate the log-likelihood by hand, like ``` alpha_hat = samples_logistic["alpha"].mean(axis=0).detach().numpy() beta_hat = samples_logistic["beta"].mean(axis=0).detach().numpy() y_hat = sigmoid(alpha_hat + np.dot(X_test, beta_hat)) log_likelihood_logit = stats.binom.logpmf(1, n = 1, p =y_hat) ``` By arviz.frompyro does not accept manually specified log-likelihood as argument (arviz.frompystan accept this argument). I'm using arviz version '0.7.0' and pyro version '1.2.1'

Maybe a skipif after having checked [`is_available`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.animation.MovieWriterRegistry.html#matplotlib.animation.MovieWriterRegistry.is_available)? Hi! Thanks for reporting. `az.from_pyro` uses [`pyro.Predictive.get_vectorized_trace()`](http://docs.pyro.ai/en/stable/inference_algos.html#pyro.infer.predictive.Predictive.get_vectorized_trace) to automatically retrieve and store pointwise log likelihood samples. To get the `log_likelihood` group populated, ArviZ must be able to execute this function, which as stated in Pyro docs: > ... requires that the model has all batch dims correctly annotated via `plate`. In your case, I think you need to annotate the dimension of `beta` `X.shape[1]` with `plate` to allow ArviZ to automatically populate `log_likelihood` group. If this were not to work (I am not a Pyro expert), you can always combine `from_pyro` with `from_dict` and do something like: pyro_data_logit = az.concat( az.from_pyro(...), az.from_dict(log_likelihood={"y": log_likelihood_logit} ) Note that this requires `log_likelihood_logit` to follow ArviZ dimension order: `chain, draw, *shape`, otherwise they will not be annotated correctly.

2020-06-07T21:51:41Z

arviz-devs/arviz

arviz-devs__arviz-1300

9fcae72d11342feea8b9ae2c6d28677a7ffb9278

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -12,7 +12,9 @@ import numpy as np import xarray as xr from xarray.core.options import OPTIONS +from xarray.core.utils import either_dict_or_kwargs +from .base import dict_to_dataset from .base import _extend_xr_method from ..utils import _subset_list, HtmlTemplate from ..rcparams import rcParams @@ -627,6 +629,101 @@ def rename_dims(self, name_dict=None, groups=None, filter_groups=None, inplace=F else: return out + def add_groups(self, group_dict=None, coords=None, dims=None, **kwargs): + """Add new groups to InferenceData object. + + Parameters + ---------- + group_dict: dict of {str : dict or xarray.Dataset}, optional + Groups to be added + coords : dict[str] -> ndarray + Coordinates for the dataset + dims : dict[str] -> list[str] + Dimensions of each variable. The keys are variable names, values are lists of + coordinates. + **kwargs: mapping + The keyword arguments form of group_dict. One of group_dict or kwargs must be provided. + + See Also + -------- + extend : Extend InferenceData with groups from another InferenceData. + concat : Concatenate InferenceData objects. + """ + group_dict = either_dict_or_kwargs(group_dict, kwargs, "add_groups") + if not group_dict: + raise ValueError("One of group_dict or kwargs must be provided.") + repeated_groups = [group for group in group_dict.keys() if group in self._groups] + if repeated_groups: + raise ValueError("{} group(s) already exists.".format(repeated_groups)) + for group, dataset in group_dict.items(): + if group not in SUPPORTED_GROUPS_ALL: + warnings.warn( + "The group {} is not defined in the InferenceData scheme".format(group), + UserWarning, + ) + if dataset is None: + continue + elif isinstance(dataset, dict): + if ( + group in ("observed_data", "constant_data", "predictions_constant_data") + or group not in SUPPORTED_GROUPS_ALL + ): + warnings.warn( + "the default dims 'chain' and 'draw' will be added automatically", + UserWarning, + ) + dataset = dict_to_dataset(dataset, coords=coords, dims=dims) + elif isinstance(dataset, xr.DataArray): + if dataset.name is None: + dataset.name = "x" + dataset = dataset.to_dataset() + elif not isinstance(dataset, xr.Dataset): + raise ValueError( + "Arguments to add_groups() must be xr.Dataset, xr.Dataarray or dicts\ + (argument '{}' was type '{}')".format( + group, type(dataset) + ) + ) + if dataset: + setattr(self, group, dataset) + if group.startswith(WARMUP_TAG): + self._groups_warmup.append(group) + else: + self._groups.append(group) + + def extend(self, other, join="left"): + """Extend InferenceData with groups from another InferenceData. + + Parameters + ---------- + other : InferenceData + InferenceData to be added + join : {'left', 'right'}, default 'left' + Defines how the two decide which group to keep when the same group is + present in both objects. 'left' will discard the group in ``other`` whereas 'right' + will keep the group in ``other`` and discard the one in ``self``. + + See Also + -------- + add_groups : Add new groups to InferenceData object. + concat : Concatenate InferenceData objects. + + """ + if not isinstance(other, InferenceData): + raise ValueError("Extending is possible between two InferenceData objects only.") + if join not in ("left", "right"): + raise ValueError("join must be either 'left' or 'right', found {}".format(join)) + for group in other._groups_all: # pylint: disable=protected-access + if hasattr(self, group): + if join == "left": + continue + if group not in SUPPORTED_GROUPS_ALL: + warnings.warn( + "{} group is not defined in the InferenceData scheme".format(group), UserWarning + ) + dataset = getattr(other, group) + setattr(self, group, dataset) + set_index = _extend_xr_method(xr.Dataset.set_index) get_index = _extend_xr_method(xr.Dataset.get_index) reset_index = _extend_xr_method(xr.Dataset.reset_index) @@ -886,6 +983,11 @@ def concat(*args, dim=None, copy=True, inplace=False, reset_dim=True): A new InferenceData object by default. When `inplace==True` merge args to first arg and return `None` + See Also + -------- + add_groups : Add new groups to InferenceData object. + extend : Extend InferenceData with groups from another InferenceData. + Examples -------- Use ``concat`` method to concatenate InferenceData objects. This will concatenates over @@ -977,8 +1079,9 @@ def concat(*args, dim=None, copy=True, inplace=False, reset_dim=True): if group in args_groups or group in arg0_groups: msg = ( "Concatenating overlapping groups is not supported unless `dim` is defined." + " Valid dimensions are `chain` and `draw`. Alternatively, use extend to" + " combine InferenceData with overlapping groups" ) - msg += " Valid dimensions are `chain` and `draw`." raise TypeError(msg) group_data = getattr(arg, group) args_groups[group] = deepcopy(group_data) if copy else group_data

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -584,6 +584,63 @@ def test_repr_html(self): assert escape(idata.__repr__()) in html xr.set_options(display_style=display_style) + def test_add_groups(self, data_random): + data = np.random.normal(size=(4, 500, 8)) + idata = data_random + idata.add_groups({"prior": {"a": data[..., 0], "b": data}}) + assert "prior" in idata._groups # pylint: disable=protected-access + assert isinstance(idata.prior, xr.Dataset) + assert hasattr(idata, "prior") + + idata.add_groups(posterior_warmup={"a": data[..., 0], "b": data}) + assert "posterior_warmup" in idata._groups # pylint: disable=protected-access + assert isinstance(idata.posterior_warmup, xr.Dataset) + assert hasattr(idata, "posterior_warmup") + + def test_add_groups_warning(self, data_random): + data = np.random.normal(size=(4, 500, 8)) + idata = data_random + with pytest.warns(UserWarning, match="The group.+not defined in the InferenceData scheme"): + idata.add_groups({"new_group": idata.posterior}) + with pytest.warns(UserWarning, match="the default dims.+will be added automatically"): + idata.add_groups(constant_data={"a": data[..., 0], "b": data}) + assert idata.new_group.equals(idata.posterior) + + def test_add_groups_error(self, data_random): + idata = data_random + with pytest.raises(ValueError, match="One of.+must be provided."): + idata.add_groups() + with pytest.raises(ValueError, match="Arguments.+xr.Dataset, xr.Dataarray or dicts"): + idata.add_groups({"new_group": "new_group"}) + with pytest.raises(ValueError, match="group.+already exists"): + idata.add_groups({"posterior": idata.posterior}) + + def test_extend(self, data_random): + idata = data_random + idata2 = create_data_random(groups=["prior", "prior_predictive", "observed_data"], seed=7) + idata.extend(idata2) + assert hasattr(idata, "prior") + assert hasattr(idata, "prior_predictive") + assert idata.prior.equals(idata2.prior) + assert not idata.observed_data.equals(idata2.observed_data) + assert idata.prior_predictive.equals(idata2.prior_predictive) + + idata.extend(idata2, join="right") + assert idata.prior.equals(idata2.prior) + assert idata.observed_data.equals(idata2.observed_data) + assert idata.prior_predictive.equals(idata2.prior_predictive) + + def test_extend_errors_warnings(self, data_random): + idata = data_random + idata2 = create_data_random(groups=["prior", "prior_predictive", "observed_data"], seed=7) + with pytest.raises(ValueError, match="Extending.+InferenceData objects only."): + idata.extend("something") + with pytest.raises(ValueError, match="join must be either"): + idata.extend(idata2, join="outer") + idata2.add_groups(new_group=idata2.prior) + with pytest.warns(UserWarning): + idata.extend(idata2) + class TestNumpyToDataArray: def test_1d_dataset(self):

add merge/add group function ## Tell us about it `az.concat` currently raises an error if there are repeated groups which is perfectly fine, however, there are situations where one wants to add the different groups to the original idata object and discard the repeated groups. related to #1066 ## Thoughts on implementation several options, some of them complementary: 1. add an `idata.merge` function so that idata wiht groups post, sample_stats, obs_data can be merged with idata with groups post_pred and obs_data discarding the obs_data in one of the two (could have left/right modes) 2. add an `idata.add_groups` so that we can pass it dicts (calls dict_to_dataset) or datasets as kwargs and they are added to idata (I think kwargs is important to allow special usecases and support any group_name, alternatively a mapping {group_name: ds/dict} could be used) 3. make `idata.concat` check equality of datasets with isclose on both vals and coords (but not on attrs! they will surely have different creation time)

tagging @percygautam

2020-07-16T18:03:34Z

arviz-devs/arviz

arviz-devs__arviz-1348

6411cdee7e244b5583afb233627e69ec9099d827

diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -46,6 +46,7 @@ def plot_forest( ridgeplot_overlap, ridgeplot_alpha, ridgeplot_kind, + ridgeplot_truncate, ridgeplot_quantiles, textsize, ess, @@ -119,10 +120,13 @@ def plot_forest( ) elif kind == "ridgeplot": plot_handler.ridgeplot( + hdi_prob, ridgeplot_overlap, linewidth, + markersize, ridgeplot_alpha, ridgeplot_kind, + ridgeplot_truncate, ridgeplot_quantiles, axes[0, 0], ) @@ -270,27 +274,49 @@ def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var): ) return ax - def ridgeplot(self, mult, linewidth, alpha, ridgeplot_kind, ridgeplot_quantiles, ax): + def ridgeplot( + self, + hdi_prob, + mult, + linewidth, + markersize, + alpha, + ridgeplot_kind, + ridgeplot_truncate, + ridgeplot_quantiles, + ax, + ): """Draw ridgeplot for each plotter. Parameters ---------- + hdi_prob : float + Probability for the highest density interval. mult : float How much to multiply height by. Set this to greater than 1 to have some overlap. linewidth : float Width of line on border of ridges + markersize : float + Size of marker in center of forestplot line alpha : float Transparency of ridges - kind : string + ridgeplot_kind : string By default ("auto") continuous variables are plotted using KDEs and discrete ones using histograms. To override this use "hist" to plot histograms and "density" for KDEs + ridgeplot_truncate: bool + Whether to truncate densities according to the value of hdi_prop. Defaults to True + ridgeplot_quantiles: list + Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. + Defaults to None. ax : Axes Axes to draw on """ if alpha is None: alpha = 1.0 for plotter in list(self.plotters.values())[::-1]: - for x, y_min, y_max, y_q, color in list(plotter.ridgeplot(mult, ridgeplot_kind))[::-1]: + for x, y_min, y_max, hdi_, y_q, color in plotter.ridgeplot( + hdi_prob, mult, ridgeplot_kind + ): if alpha == 0: border = color facecolor = None @@ -298,50 +324,66 @@ def ridgeplot(self, mult, linewidth, alpha, ridgeplot_kind, ridgeplot_quantiles, border = "black" facecolor = color if x.dtype.kind == "i": + if ridgeplot_truncate: + y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] + x = x[(x >= hdi_[0]) & (x <= hdi_[1])] + else: + facecolor = color + alpha = [alpha if ci else 0 for ci in ((x >= hdi_[0]) & (x <= hdi_[1]))] + y_min = np.ones_like(x) * y_min ax.vbar( x=x, top=y_max - y_min, bottom=y_min, + width=0.9, + line_color=border, + color=facecolor, fill_alpha=alpha, - fill_color=facecolor, ) else: - if ridgeplot_quantiles is None: - patch = ax.patch( - np.concatenate([x, x[::-1]]), - np.concatenate([y_min, y_max[::-1]]), - fill_color=color, - fill_alpha=alpha, + tr_x = x[(x >= hdi_[0]) & (x <= hdi_[1])] + tr_y_min = np.ones_like(tr_x) * y_min + tr_y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] + y_min = np.ones_like(x) * y_min + patch = ax.patch( + np.concatenate([tr_x, tr_x[::-1]]), + np.concatenate([tr_y_min, tr_y_max[::-1]]), + fill_color=color, + fill_alpha=alpha, + line_width=0, + ) + patch.level = "overlay" + if ridgeplot_truncate: + ax.line( + x, y_max, line_dash="solid", line_width=linewidth, line_color=border + ) + ax.line( + x, y_min, line_dash="solid", line_width=linewidth, line_color=border + ) + else: + ax.line( + tr_x, + tr_y_max, line_dash="solid", line_width=linewidth, line_color=border, ) - patch.level = "overlay" - else: - quantiles = sorted(np.clip(ridgeplot_quantiles, 0, 1)) - if quantiles[0] != 0: - quantiles = [0] + quantiles - if quantiles[-1] != 1: - quantiles = quantiles + [1] - - for quant_0, quant_1 in zip(quantiles[:-1], quantiles[1:]): - idx = (y_q > quant_0) & (y_q < quant_1) - if idx.sum(): - patch_x = np.concatenate( - (x[idx], [x[idx][-1]], x[idx][::-1], [x[idx][0]]) - ) - patch_y = np.concatenate( - ( - y_min[idx], - [y_min[idx][-1]], - y_max[idx][::-1], - [y_max[idx][0]], - ) - ) - patch = ax.patch( - patch_x, patch_y, fill_color=color, fill_alpha=alpha, - ) - patch.level = "overlay" + ax.line( + tr_x, + tr_y_min, + line_dash="solid", + line_width=linewidth, + line_color=border, + ) + if ridgeplot_quantiles is not None: + quantiles = [x[np.sum(y_q < quant)] for quant in ridgeplot_quantiles] + ax.diamond( + quantiles, + np.ones_like(quantiles) * y_min[0], + line_color="black", + fill_color="black", + size=markersize, + ) return ax @@ -351,7 +393,7 @@ def forestplot(self, hdi_prob, quartiles, linewidth, markersize, ax, rope): Parameters ---------- hdi_prob : float - How wide each line should be + Probability for the highest density interval. Width of each line. quartiles : bool Whether to mark quartiles linewidth : float @@ -551,15 +593,21 @@ def treeplot(self, qlist, hdi_prob): ntiles[0], ntiles[-1] = hdi(values.flatten(), hdi_prob, multimodal=False) yield y, label, ntiles, color - def ridgeplot(self, mult, ridgeplot_kind): + def ridgeplot(self, hdi_prob, mult, ridgeplot_kind): """Get data for each ridgeplot for the variable.""" - xvals, yvals, pdfs, pdfs_q, colors = [], [], [], [], [] + xvals, hdi_vals, yvals, pdfs, pdfs_q, colors = [], [], [], [], [], [] + for y, *_, values, color in self.iterator(): yvals.append(y) colors.append(color) values = values.flatten() values = values[np.isfinite(values)] + if hdi_prob != 1: + hdi_ = hdi(values, hdi_prob, multimodal=False) + else: + hdi_ = min(values), max(values) + if ridgeplot_kind == "auto": kind = "hist" if np.all(np.mod(values, 1) == 0) else "density" else: @@ -571,16 +619,17 @@ def ridgeplot(self, mult, ridgeplot_kind): x = x[:-1] elif kind == "density": x, density = kde(values) - density_q = density.cumsum() / density.sum() + + density_q = density.cumsum() / density.sum() xvals.append(x) pdfs.append(density) - pdfs_q.append((density_q)) + pdfs_q.append(density_q) + hdi_vals.append(hdi_) scaling = max(np.max(j) for j in pdfs) - for y, x, pdf, pdf_q, color in zip(yvals, xvals, pdfs, pdfs_q, colors): - y = y * np.ones_like(x) - yield x, y, mult * pdf / scaling + y, pdf_q, color + for y, x, hdi_val, pdf, pdf_q, color in zip(yvals, xvals, hdi_vals, pdfs, pdfs_q, colors): + yield x, y, mult * pdf / scaling + y, hdi_val, pdf_q, color def ess(self): """Get effective n data for the variable.""" diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -40,6 +40,7 @@ def plot_forest( ridgeplot_overlap, ridgeplot_alpha, ridgeplot_kind, + ridgeplot_truncate, ridgeplot_quantiles, textsize, ess, @@ -96,10 +97,13 @@ def plot_forest( ) elif kind == "ridgeplot": plot_handler.ridgeplot( + hdi_prob, ridgeplot_overlap, linewidth, + markersize, ridgeplot_alpha, ridgeplot_kind, + ridgeplot_truncate, ridgeplot_quantiles, axes[0], ) @@ -233,20 +237,37 @@ def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var): ) return ax - def ridgeplot(self, mult, linewidth, alpha, ridgeplot_kind, ridgeplot_quantiles, ax): + def ridgeplot( + self, + hdi_prob, + mult, + linewidth, + markersize, + alpha, + ridgeplot_kind, + ridgeplot_truncate, + ridgeplot_quantiles, + ax, + ): """Draw ridgeplot for each plotter. Parameters ---------- + hdi_prob : float + Probability for the highest density interval. mult : float How much to multiply height by. Set this to greater than 1 to have some overlap. linewidth : float Width of line on border of ridges + markersize : float + Size of marker in center of forestplot line alpha : float Transparency of ridges ridgeplot_kind : string By default ("auto") continuous variables are plotted using KDEs and discrete ones using histograms. To override this use "hist" to plot histograms and "density" for KDEs + ridgeplot_truncate: bool + Whether to truncate densities according to the value of hdi_prop. Defaults to True ridgeplot_quantiles: list Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. Defaults to None. @@ -257,39 +278,64 @@ def ridgeplot(self, mult, linewidth, alpha, ridgeplot_kind, ridgeplot_quantiles, alpha = 1.0 zorder = 0 for plotter in self.plotters.values(): - for x, y_min, y_max, y_q, color in plotter.ridgeplot(mult, ridgeplot_kind): + for x, y_min, y_max, hdi_, y_q, color in plotter.ridgeplot( + hdi_prob, mult, ridgeplot_kind + ): if alpha == 0: border = color facecolor = "None" else: border = "k" - facecolor = to_rgba(color, alpha) if x.dtype.kind == "i": + if ridgeplot_truncate: + facecolor = to_rgba(color, alpha) + y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] + x = x[(x >= hdi_[0]) & (x <= hdi_[1])] + else: + facecolor = [ + to_rgba(color, alpha) if ci else "None" + for ci in ((x >= hdi_[0]) & (x <= hdi_[1])) + ] + y_min = np.ones_like(x) * y_min ax.bar( x, y_max - y_min, bottom=y_min, linewidth=linewidth, ec=border, - fc=facecolor, + color=facecolor, + alpha=None, zorder=zorder, ) else: - if ridgeplot_quantiles is not None: - idx = [np.sum(y_q < quant) for quant in ridgeplot_quantiles] - ax.fill_between( - x, - y_min, - y_max, - where=np.isin(x, x[idx], invert=True, assume_unique=True), - alpha=alpha, - color=color, - zorder=zorder, + tr_x = x[(x >= hdi_[0]) & (x <= hdi_[1])] + tr_y_min = np.ones_like(tr_x) * y_min + tr_y_max = y_max[(x >= hdi_[0]) & (x <= hdi_[1])] + y_min = np.ones_like(x) * y_min + if ridgeplot_truncate: + ax.plot( + tr_x, tr_y_max, "-", linewidth=linewidth, color=border, zorder=zorder + ) + ax.plot( + tr_x, tr_y_min, "-", linewidth=linewidth, color=border, zorder=zorder ) else: ax.plot(x, y_max, "-", linewidth=linewidth, color=border, zorder=zorder) ax.plot(x, y_min, "-", linewidth=linewidth, color=border, zorder=zorder) - ax.fill_between(x, y_min, y_max, alpha=alpha, color=color, zorder=zorder) + ax.fill_between( + tr_x, tr_y_max, tr_y_min, alpha=alpha, color=color, zorder=zorder + ) + + if ridgeplot_quantiles is not None: + quantiles = [x[np.sum(y_q < quant)] for quant in ridgeplot_quantiles] + ax.plot( + quantiles, + np.ones_like(quantiles) * y_min[0], + "d", + mfc=border, + mec=border, + ms=markersize, + ) zorder -= 1 return ax @@ -301,7 +347,7 @@ def forestplot( Parameters ---------- hdi_prob : float - How wide each line should be + Probability for the highest density interval. Width of each line. quartiles : bool Whether to mark quartiles xt_textsize : float @@ -498,37 +544,41 @@ def treeplot(self, qlist, hdi_prob): ntiles[0], ntiles[-1] = hdi(values.flatten(), hdi_prob, multimodal=False) yield y, label, ntiles, color - def ridgeplot(self, mult, ridgeplot_kind): + def ridgeplot(self, hdi_prob, mult, ridgeplot_kind): """Get data for each ridgeplot for the variable.""" - xvals, yvals, pdfs, pdfs_q, colors = [], [], [], [], [] + xvals, hdi_vals, yvals, pdfs, pdfs_q, colors = [], [], [], [], [], [] for y, *_, values, color in self.iterator(): yvals.append(y) colors.append(color) values = values.flatten() values = values[np.isfinite(values)] + if hdi_prob != 1: + hdi_ = hdi(values, hdi_prob, multimodal=False) + else: + hdi_ = min(values), max(values) + if ridgeplot_kind == "auto": kind = "hist" if np.all(np.mod(values, 1) == 0) else "density" else: kind = ridgeplot_kind if kind == "hist": - bins = get_bins(values) - _, density, x = histogram(values, bins=bins) - density_q = density.cumsum() / density.sum() + _, density, x = histogram(values, bins=get_bins(values)) x = x[:-1] elif kind == "density": x, density = kde(values) - density_q = density.cumsum() / density.sum() + + density_q = density.cumsum() / density.sum() xvals.append(x) pdfs.append(density) - pdfs_q.append((density_q)) + pdfs_q.append(density_q) + hdi_vals.append(hdi_) scaling = max(np.max(j) for j in pdfs) - for y, x, pdf, pdf_q, color in zip(yvals, xvals, pdfs, pdfs_q, colors): - y = y * np.ones_like(x) - yield x, y, mult * pdf / scaling + y, pdf_q, color + for y, x, hdi_val, pdf, pdf_q, color in zip(yvals, xvals, hdi_vals, pdfs, pdfs_q, colors): + yield x, y, mult * pdf / scaling + y, hdi_val, pdf_q, color def ess(self): """Get effective n data for the variable.""" diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -26,6 +26,7 @@ def plot_forest( ridgeplot_alpha=None, ridgeplot_overlap=2, ridgeplot_kind="auto", + ridgeplot_truncate=True, ridgeplot_quantiles=None, figsize=None, ax=None, @@ -37,8 +38,7 @@ def plot_forest( ): """Forest plot to compare HDI intervals from a number of distributions. - Generates a forest plot of 100*(hdi_prob)% HDI intervals from - a trace or list of traces. + Generates a forest plot of 100*(hdi_prob)% HDI intervals from a trace or list of traces. Parameters ---------- @@ -48,30 +48,28 @@ def plot_forest( kind: str Choose kind of plot for main axis. Supports "forestplot" or "ridgeplot" model_names: list[str], optional - List with names for the models in the list of data. Useful when - plotting more that one dataset + List with names for the models in the list of data. Useful when plotting more that one + dataset var_names: list[str], optional - List of variables to plot (defaults to None, which results in all - variables plotted) Prefix the variables by `~` when you want to exclude them - from the plot. + List of variables to plot (defaults to None, which results in all variables plotted) + Prefix the variables by `~` when you want to exclude them from the plot. filter_vars: {None, "like", "regex"}, optional, default=None - If `None` (default), interpret var_names as the real variables names. If "like", - interpret var_names as substrings of the real variables names. If "regex", - interpret var_names as regular expressions on the real variables names. A la - `pandas.filter`. + If `None` (default), interpret var_names as the real variables names. If "like", interpret + var_names as substrings of the real variables names. If "regex", interpret var_names as + regular expressions on the real variables names. A la `pandas.filter`. transform: callable Function to transform data (defaults to None i.e.the identity function) coords: dict, optional Coordinates of var_names to be plotted. Passed to `Dataset.sel` combined: bool - Flag for combining multiple chains into a single chain. If False (default), - chains will be plotted separately. + Flag for combining multiple chains into a single chain. If False (default), chains will be + plotted separately. hdi_prob: float, optional Plots highest posterior density interval for chosen percentage of density. Defaults to 0.94. rope: tuple or dictionary of tuples - Lower and upper values of the Region Of Practical Equivalence. If a list with one - interval only is provided, the ROPE will be displayed across the y-axis. If more than one - interval is provided the length of the list should match the number of variables. + Lower and upper values of the Region Of Practical Equivalence. If a list with one interval + only is provided, the ROPE will be displayed across the y-axis. If more than one interval is + provided the length of the list should match the number of variables. quartiles: bool, optional Flag for plotting the interquartile range, in addition to the hdi_prob intervals. Defaults to True @@ -81,9 +79,9 @@ def plot_forest( Flag for plotting the effective sample size. Defaults to False colors: list or string, optional list with valid matplotlib colors, one color per model. Alternative a string can be passed. - If the string is `cycle`, it will automatically chose a color per model from the - matplotlibs cycle. If a single color is passed, eg 'k', 'C2', 'red' this color will be used - for all models. Defauls to 'cycle'. + If the string is `cycle`, it will automatically chose a color per model from the matplotlibs + cycle. If a single color is passed, eg 'k', 'C2', 'red' this color will be used for all + models. Defauls to 'cycle'. textsize: float Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize. @@ -99,6 +97,8 @@ def plot_forest( ridgeplot_kind: string By default ("auto") continuous variables are plotted using KDEs and discrete ones using histograms. To override this use "hist" to plot histograms and "density" for KDEs + ridgeplot_truncate: bool + Whether to truncate densities according to the value of hdi_prop. Defaults to True ridgeplot_quantiles: list Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. Defaults to None. @@ -153,6 +153,22 @@ def plot_forest( >>> colors='white', >>> figsize=(9, 7)) >>> axes[0].set_title('Estimated theta for 8 schools model') + + Ridgeplot non-truncated and with quantiles + + .. plot:: + :context: close-figs + + >>> axes = az.plot_forest(non_centered_data, + >>> kind='ridgeplot', + >>> var_names=['theta'], + >>> combined=True, + >>> ridgeplot_truncate=False, + >>> ridgeplot_quantiles=[.25, .5, .75], + >>> ridgeplot_overlap=0.7, + >>> colors='white', + >>> figsize=(9, 7)) + >>> axes[0].set_title('Estimated theta for 8 schools model') """ if credible_interval: hdi_prob = credible_interval_warning(credible_interval, hdi_prob) @@ -207,6 +223,7 @@ def plot_forest( ridgeplot_overlap=ridgeplot_overlap, ridgeplot_alpha=ridgeplot_alpha, ridgeplot_kind=ridgeplot_kind, + ridgeplot_truncate=ridgeplot_truncate, ridgeplot_quantiles=ridgeplot_quantiles, textsize=textsize, ess=ess,

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -450,6 +450,14 @@ def test_plot_ess_no_divergences(models): ({"var_names": "mu", "rope": (-1, 1)}, 1), ({"r_hat": True, "quartiles": False}, 2), ({"var_names": ["mu"], "colors": "black", "ess": True, "combined": True}, 2), + ( + { + "kind": "ridgeplot", + "ridgeplot_truncate": False, + "ridgeplot_quantiles": [0.25, 0.5, 0.75], + }, + 1, + ), ({"kind": "ridgeplot", "r_hat": True, "ess": True}, 3), ({"kind": "ridgeplot", "r_hat": True, "ess": True, "ridgeplot_alpha": 0}, 3), ( diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -237,6 +237,15 @@ def test_plot_trace_futurewarning(models, prop): ({"var_names": "mu", "rope": (-1, 1)}, 1), ({"r_hat": True, "quartiles": False}, 2), ({"var_names": ["mu"], "colors": "C0", "ess": True, "combined": True}, 2), + ( + { + "kind": "ridgeplot", + "ridgeplot_truncate": False, + "ridgeplot_quantiles": [0.25, 0.5, 0.75], + }, + 1, + ), + ({"kind": "ridgeplot", "r_hat": True, "ess": True}, 3), ({"kind": "ridgeplot", "r_hat": True, "ess": True}, 3), ({"kind": "ridgeplot", "r_hat": True, "ess": True, "ridgeplot_alpha": 0}, 3), (

plot_forest; HPD not visible with kind="ridgeplot" Not sure if a bug or just not a feature that's available, but in using plot_forest() I'm able to see the HPD when using kind="forestplot" but not with kind="ridgeplot". One of the things I liked about bayesplot in R was being able to see the the HPD of the marginal posteriors with the mcmc_areas() function. Is that possible with plot_forest(kind="ridgeplot")? Specifying hdi_prob seems to have no effect with the ridgeplot. (Appreciate the development effort on arviz btw, been a huge help since switching over to Python for my bayesian modeling)

Thanks for taking the time to report this. I had the impression this was implemented, even more I remember working on this. It seems I never push the changes, haha. I will do it. And glad to hear you find ArviZ useful!

2020-08-12T21:42:18Z

arviz-devs/arviz

arviz-devs__arviz-1370

5a6bbeee1b777f926cb658ed46c3e80f521c6b3c

diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -198,7 +198,10 @@ def log_likelihood_vals_point(self, point, var, log_like_fun): """Compute log likelihood for each observed point.""" log_like_val = utils.one_de(log_like_fun(point)) if var.missing_values: - log_like_val = np.where(var.observations.mask, np.nan, log_like_val) + mask = var.observations.mask + if np.ndim(mask) > np.ndim(log_like_val): + mask = np.any(mask, axis=-1) + log_like_val = np.where(mask, np.nan, log_like_val) return log_like_val @requires("trace")

diff --git a/arviz/tests/external_tests/test_data_cmdstanpy.py b/arviz/tests/external_tests/test_data_cmdstanpy.py --- a/arviz/tests/external_tests/test_data_cmdstanpy.py +++ b/arviz/tests/external_tests/test_data_cmdstanpy.py @@ -51,8 +51,8 @@ class Data: runset_obj = RunSet(args) runset_obj._csv_files = filepaths # pylint: disable=protected-access obj = CmdStanMCMC(runset_obj) - obj._validate_csv_files() # pylint: disable=protected-access - obj._assemble_sample() # pylint: disable=protected-access + obj.validate_csv_files() # pylint: disable=protected-access + obj._assemble_draws() # pylint: disable=protected-access return Data diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -288,6 +288,29 @@ def test_missing_data_model(self): fails = check_multiple_attrs(test_dict, inference_data) assert not fails + def test_mv_missing_data_model(self): + data = ma.masked_values([[1, 2], [2, 2], [-1, 4], [2, -1], [-1, -1]], value=-1) + + model = pm.Model() + with model: + mu = pm.Normal("mu", 0, 1, shape=2) + sd_dist = pm.HalfNormal.dist(1.0) + chol, *_ = pm.LKJCholeskyCov("chol_cov", n=2, eta=1, sd_dist=sd_dist, compute_corr=True) + pm.MvNormal("y", mu=mu, chol=chol, observed=data) + trace = pm.sample(100, chains=2) + + # make sure that data is really missing + (y_missing,) = model.missing_values + assert y_missing.tag.test_value.shape == (4,) + inference_data = from_pymc3(trace=trace, model=model) + test_dict = { + "posterior": ["mu", "chol_cov"], + "observed_data": ["y"], + "log_likelihood": ["y"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + @pytest.mark.parametrize("log_likelihood", [True, False, ["y1"]]) def test_multiple_observed_rv(self, log_likelihood): y1_data = np.random.randn(10)

InferenceData conversion does not work with multivariate observation that contains missing value **To Reproduce** ```python import numpy as np import pymc3 as pm data = np.ma.masked_values([[1, 2], [-1, 4], [2, -1], [-1, -1]], value=-1) with pm.Model(): mu = pm.Normal('mu', 0, 1, shape=2) sd_dist = pm.HalfCauchy.dist(beta=2.5) chol, corr, sigmas = pm.LKJCholeskyCov( 'chol_cov', n=2, eta=1, sd_dist=sd_dist, compute_corr=True) obs = pm.MvNormal('obs', mu=mu, chol=chol, observed=data) trace0_ = pm.sample() ``` returns: ```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-82-a388a356e068> in <module> 11 12 obs = pm.MvNormal('obs', mu=mu, chol=chol, observed=data) ---> 13 trace0_ = pm.sample() ~/miniconda3/lib/python3.7/site-packages/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs) 611 if idata_kwargs: 612 ikwargs.update(idata_kwargs) --> 613 idata = arviz.from_pymc3(trace, **ikwargs) 614 615 if compute_convergence_checks: ~/miniconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py in from_pymc3(trace, prior, posterior_predictive, log_likelihood, coords, dims, model, save_warmup) 529 dims=dims, 530 model=model, --> 531 save_warmup=save_warmup, 532 ).to_inference_data() 533 ~/miniconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py in to_inference_data(self) 461 "posterior": self.posterior_to_xarray(), 462 "sample_stats": self.sample_stats_to_xarray(), --> 463 "log_likelihood": self.log_likelihood_to_xarray(), 464 "posterior_predictive": self.posterior_predictive_to_xarray(), 465 "predictions": self.predictions_to_xarray(), ~/miniconda3/lib/python3.7/site-packages/arviz/data/base.py in wrapped(cls, *args, **kwargs) 35 if all([getattr(cls, prop_i) is None for prop_i in prop]): 36 return None ---> 37 return func(cls, *args, **kwargs) 38 39 return wrapped ~/miniconda3/lib/python3.7/site-packages/arviz/data/base.py in wrapped(cls, *args, **kwargs) 35 if all([getattr(cls, prop_i) is None for prop_i in prop]): 36 return None ---> 37 return func(cls, *args, **kwargs) 38 39 return wrapped ~/miniconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py in log_likelihood_to_xarray(self) 303 if self.posterior_trace: 304 try: --> 305 data = self._extract_log_likelihood(self.posterior_trace) 306 except TypeError: 307 warnings.warn(warn_msg) ~/miniconda3/lib/python3.7/site-packages/arviz/data/base.py in wrapped(cls, *args, **kwargs) 35 if all([getattr(cls, prop_i) is None for prop_i in prop]): 36 return None ---> 37 return func(cls, *args, **kwargs) 38 39 return wrapped ~/miniconda3/lib/python3.7/site-packages/arviz/data/base.py in wrapped(cls, *args, **kwargs) 35 if all([getattr(cls, prop_i) is None for prop_i in prop]): 36 return None ---> 37 return func(cls, *args, **kwargs) 38 39 return wrapped ~/miniconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py in _extract_log_likelihood(self, trace) 227 log_like_chain = [ 228 self.log_likelihood_vals_point(point, var, log_like_fun) --> 229 for point in trace.points([chain]) 230 ] 231 log_likelihood_dict.insert(var.name, np.stack(log_like_chain), chain) ~/miniconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py in <listcomp>(.0) 227 log_like_chain = [ 228 self.log_likelihood_vals_point(point, var, log_like_fun) --> 229 for point in trace.points([chain]) 230 ] 231 log_likelihood_dict.insert(var.name, np.stack(log_like_chain), chain) ~/miniconda3/lib/python3.7/site-packages/arviz/data/io_pymc3.py in log_likelihood_vals_point(self, point, var, log_like_fun) 197 log_like_val = utils.one_de(log_like_fun(point)) 198 if var.missing_values: --> 199 log_like_val = np.where(var.observations.mask, np.nan, log_like_val) 200 return log_like_val 201 <__array_function__ internals> in where(*args, **kwargs) ValueError: operands could not be broadcast together with shapes (4,2) () (4,) ```

Yes, we really should support this. Any ideas for a good interface for this? Would this be related to sparse matrices?

2020-09-04T16:36:34Z

arviz-devs/arviz

arviz-devs__arviz-1390

1b9a194879e1be2b3832e8fbddad2cc13abf2b09

diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -266,22 +266,25 @@ def label_idxs(): return label_idxs() - def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var): + def display_multiple_ropes(self, rope, ax, y, linewidth, var_name, selection): """Display ROPE when more than one interval is provided.""" - vals = dict(rope[rope_var][0])["rope"] - ax.line( - vals, - (y + 0.05, y + 0.05), - line_width=linewidth * 2, - color=[ - color - for _, color in zip( - range(3), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) + for sel in rope.get(var_name, []): + # pylint: disable=line-too-long + if all(k in selection and selection[k] == v for k, v in sel.items() if k != "rope"): + vals = sel["rope"] + ax.line( + vals, + (y + 0.05, y + 0.05), + line_width=linewidth * 2, + color=[ + color + for _, color in zip( + range(3), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"]) + ) + ][2], + line_alpha=0.7, ) - ][2], - line_alpha=0.7, - ) - return ax + return ax def ridgeplot( self, @@ -452,9 +455,9 @@ def forestplot(self, hdi_prob, quartiles, linewidth, markersize, ax, rope): qlist = [endpoint, 50, 100 - endpoint] for plotter in self.plotters.values(): - for y, rope_var, values, color in plotter.treeplot(qlist, hdi_prob): + for y, selection, values, color in plotter.treeplot(qlist, hdi_prob): if isinstance(rope, dict): - self.display_multiple_ropes(rope, ax, y, linewidth, rope_var) + self.display_multiple_ropes(rope, ax, y, linewidth, plotter.var_name, selection) mid = len(values) // 2 param_iter = zip( @@ -560,6 +563,7 @@ def iterator(self): skip_dims = set() label_dict = OrderedDict() + selection_list = [] for name, grouped_datum in zip(self.model_names, grouped_data): for _, sub_data in grouped_datum: datum_iter = xarray_var_iter( @@ -569,6 +573,7 @@ def iterator(self): reverse_selections=True, ) for _, selection, values in datum_iter: + selection_list.append(selection) label = make_label(self.var_name, selection, position="beside") if label not in label_dict: label_dict[label] = OrderedDict() @@ -577,14 +582,16 @@ def iterator(self): label_dict[label][name].append(values) y = self.y_start - for label, model_data in label_dict.items(): + for idx, (label, model_data) in enumerate(label_dict.items()): for model_name, value_list in model_data.items(): if model_name: row_label = "{}: {}".format(model_name, label) else: row_label = label for values in value_list: - yield y, row_label, label, values, self.model_color[model_name] + yield y, row_label, label, selection_list[idx], values, self.model_color[ + model_name + ] y += self.chain_offset y += self.var_offset y += self.group_offset @@ -592,7 +599,7 @@ def iterator(self): def labels_ticks_and_vals(self): """Get labels, ticks, values, and colors for the variable.""" y_ticks = defaultdict(list) - for y, label, _, vals, color in self.iterator(): + for y, label, _, _, vals, color in self.iterator(): y_ticks[label].append((y, vals, color)) labels, ticks, vals, colors = [], [], [], [] for label, data in y_ticks.items(): @@ -604,10 +611,10 @@ def labels_ticks_and_vals(self): def treeplot(self, qlist, hdi_prob): """Get data for each treeplot for the variable.""" - for y, _, label, values, color in self.iterator(): + for y, _, _, selection, values, color in self.iterator(): ntiles = np.percentile(values.flatten(), qlist) ntiles[0], ntiles[-1] = hdi(values.flatten(), hdi_prob, multimodal=False) - yield y, label, ntiles, color + yield y, selection, ntiles, color def ridgeplot(self, hdi_prob, mult, ridgeplot_kind): """Get data for each ridgeplot for the variable.""" diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -230,19 +230,22 @@ def label_idxs(): return label_idxs() - def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var): + def display_multiple_ropes(self, rope, ax, y, linewidth, var_name, selection): """Display ROPE when more than one interval is provided.""" - vals = dict(rope[rope_var][0])["rope"] - ax.plot( - vals, - (y + 0.05, y + 0.05), - lw=linewidth * 2, - color="C2", - solid_capstyle="round", - zorder=0, - alpha=0.7, - ) - return ax + for sel in rope.get(var_name, []): + # pylint: disable=line-too-long + if all(k in selection and selection[k] == v for k, v in sel.items() if k != "rope"): + vals = sel["rope"] + ax.plot( + vals, + (y + 0.05, y + 0.05), + lw=linewidth * 2, + color="C2", + solid_capstyle="round", + zorder=0, + alpha=0.7, + ) + return ax def ridgeplot( self, @@ -376,9 +379,9 @@ def forestplot( qlist = [endpoint, 50, 100 - endpoint] for plotter in self.plotters.values(): - for y, rope_var, values, color in plotter.treeplot(qlist, hdi_prob): + for y, selection, values, color in plotter.treeplot(qlist, hdi_prob): if isinstance(rope, dict): - self.display_multiple_ropes(rope, ax, y, linewidth, rope_var) + self.display_multiple_ropes(rope, ax, y, linewidth, plotter.var_name, selection) mid = len(values) // 2 param_iter = zip( @@ -502,6 +505,7 @@ def iterator(self): skip_dims = set() label_dict = OrderedDict() + selection_list = [] for name, grouped_datum in zip(self.model_names, grouped_data): for _, sub_data in grouped_datum: datum_iter = xarray_var_iter( @@ -511,6 +515,7 @@ def iterator(self): reverse_selections=True, ) for _, selection, values in datum_iter: + selection_list.append(selection) label = make_label(self.var_name, selection, position="beside") if label not in label_dict: label_dict[label] = OrderedDict() @@ -519,14 +524,16 @@ def iterator(self): label_dict[label][name].append(values) y = self.y_start - for label, model_data in label_dict.items(): + for idx, (label, model_data) in enumerate(label_dict.items()): for model_name, value_list in model_data.items(): if model_name: row_label = "{}: {}".format(model_name, label) else: row_label = label for values in value_list: - yield y, row_label, label, values, self.model_color[model_name] + yield y, row_label, label, selection_list[idx], values, self.model_color[ + model_name + ] y += self.chain_offset y += self.var_offset y += self.group_offset @@ -534,7 +541,7 @@ def iterator(self): def labels_ticks_and_vals(self): """Get labels, ticks, values, and colors for the variable.""" y_ticks = defaultdict(list) - for y, label, _, vals, color in self.iterator(): + for y, label, _, _, vals, color in self.iterator(): y_ticks[label].append((y, vals, color)) labels, ticks, vals, colors = [], [], [], [] for label, data in y_ticks.items(): @@ -546,10 +553,10 @@ def labels_ticks_and_vals(self): def treeplot(self, qlist, hdi_prob): """Get data for each treeplot for the variable.""" - for y, _, label, values, color in self.iterator(): + for y, _, _, selection, values, color in self.iterator(): ntiles = np.percentile(values.flatten(), qlist) ntiles[0], ntiles[-1] = hdi(values.flatten(), hdi_prob, multimodal=False) - yield y, label, ntiles, color + yield y, selection, ntiles, color def ridgeplot(self, hdi_prob, mult, ridgeplot_kind): """Get data for each ridgeplot for the variable.""" diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -136,10 +136,23 @@ def plot_forest( >>> var_names=["^the"], >>> filter_vars="regex", >>> combined=True, - >>> ridgeplot_overlap=3, >>> figsize=(9, 7)) >>> axes[0].set_title('Estimated theta for 8 schools model') + Forestpĺot with ropes + + .. plot:: + :context: close-figs + + >>> rope = {'theta': [{'school': 'Choate', 'rope': (2, 4)}], 'mu': [{'rope': (-2, 2)}]} + >>> axes = az.plot_forest(non_centered_data, + >>> rope=rope, + >>> var_names='~tau', + >>> combined=True, + >>> figsize=(9, 7)) + >>> axes[0].set_title('Estimated theta for 8 schools model') + + Ridgeplot .. plot::

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -490,7 +490,10 @@ def test_plot_ess_no_divergences(models): ( { "var_names": ["mu", "tau"], - "rope": {"mu": [{"rope": (-0.1, 0.1)}], "tau": [{"rope": (0.2, 0.5)}]}, + "rope": { + "mu": [{"rope": (-0.1, 0.1)}], + "theta": [{"school": "Choate", "rope": (0.2, 0.5)}], + }, }, 1, ), diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -279,8 +279,11 @@ def test_plot_trace_futurewarning(models, prop): ({"kind": "ridgeplot", "r_hat": True, "ess": True, "ridgeplot_alpha": 0}, 3), ( { - "var_names": ["mu", "tau"], - "rope": {"mu": [{"rope": (-0.1, 0.1)}], "tau": [{"rope": (0.2, 0.5)}]}, + "var_names": ["mu", "theta"], + "rope": { + "mu": [{"rope": (-0.1, 0.1)}], + "theta": [{"school": "Choate", "rope": (0.2, 0.5)}], + }, }, 1, ),

Is it possible to have a different rope for every variable dimension Hello, I have been looking at the forest_plot documentation and examples and it seems pretty clear to me that you can have a different rope for each variable. But say I have a different rope for each dimension of a variable say mu 0, mu 1... which all correspond to the same: ```python mu_o = pm.Normal('mu_o', mu=data_fo.mean(), sd=sd_prior, dims='resid') ``` Thank you all very much and keep up the good work! Sergio

There may be a bug in the `rope` code. There are examples of the usage you ask for in the documentation of `plot_posterior`, and I have been able to use it with: rope={"mu": [{"mu_dim": 2, "rope": (-1, 1)}, {"mu_dim": 4, "rope": (0, 3)}]}  but it does not work for `plot_forest` cc @aloctavodia Following on the other issue #1381 . I have created this forestplot: ```python az.plot_forest( (posterior.sel(state="o"), posterior.sel(state="fo")), var_names="mu", combined=True, colors=["C0", "C1"], model_names=["o", "fo"], textsize=9, #rope=rope ) ``` If I add a rope like the one you suggested: ```python rope={"mu": [{"mu_dim": 'A42', "rope": (-1, 1)}, {"mu_dim": 'T33', "rope": (0, 3)}]} ``` I get this error: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-124-7b15dd7be9eb> in <module> ----> 1 az.plot_forest( 2 (posterior.sel(state="o"), posterior.sel(state="fo")), 3 var_names="mu", 4 combined=True, 5 colors=["C0", "C1"], ~/data_partition/bin2/anaconda3/envs/nmr_assign_state/lib/python3.8/site-packages/arviz/plots/forestplot.py in plot_forest(data, kind, model_names, var_names, filter_vars, transform, coords, combined, hdi_prob, rope, quartiles, ess, r_hat, colors, textsize, linewidth, markersize, ridgeplot_alpha, ridgeplot_overlap, ridgeplot_kind, ridgeplot_quantiles, figsize, ax, backend, backend_config, backend_kwargs, show, credible_interval) 226 # TODO: Add backend kwargs 227 plot = get_plotting_function("plot_forest", "forestplot", backend) --> 228 axes = plot(**plot_forest_kwargs) 229 return axes ~/data_partition/bin2/anaconda3/envs/nmr_assign_state/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/forestplot.py in plot_forest(ax, datasets, var_names, model_names, combined, colors, figsize, width_ratios, linewidth, markersize, kind, ncols, hdi_prob, quartiles, rope, ridgeplot_overlap, ridgeplot_alpha, ridgeplot_kind, ridgeplot_quantiles, textsize, ess, r_hat, backend_kwargs, show) 91 axes = np.atleast_1d(axes) 92 if kind == "forestplot": ---> 93 plot_handler.forestplot( 94 hdi_prob, quartiles, xt_labelsize, titlesize, linewidth, markersize, axes[0], rope, 95 ) ~/data_partition/bin2/anaconda3/envs/nmr_assign_state/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/forestplot.py in forestplot(self, hdi_prob, quartiles, xt_labelsize, titlesize, linewidth, markersize, ax, rope) 325 for y, rope_var, values, color in plotter.treeplot(qlist, hdi_prob): 326 if isinstance(rope, dict): --> 327 self.display_multiple_ropes(rope, ax, y, linewidth, rope_var) 328 329 mid = len(values) // 2 ~/data_partition/bin2/anaconda3/envs/nmr_assign_state/lib/python3.8/site-packages/arviz/plots/backends/matplotlib/forestplot.py in display_multiple_ropes(self, rope, ax, y, linewidth, rope_var) 221 def display_multiple_ropes(self, rope, ax, y, linewidth, rope_var): 222 """Display ROPE when more than one interval is provided.""" --> 223 vals = dict(rope[rope_var][0])["rope"] 224 ax.plot( 225 vals, KeyError: 'mu T112' ``` Really appreciate your help I am quite sure this is a bug. Given that plot_posterior accepts the rope dict, we should see where they differ and fix the forest plot version so it works and behaves like the one in `plot_posterior`. It may also be a good idea once this is done to add a link in the description of `rope` argument in forest plot to plot_posterior to help understand the rope scalar/list/dict possibilities Yes, the documentation is a bit confusing. What would you like me to do? Can you reproduce this unexpected behaviour in a simple test-case? I think the first step would be creating a minimal example to reproduce the bug. If my intuition is correct, using the same data and rope argument as in [`plot_posterior`](https://arviz-devs.github.io/arviz/generated/arviz.plot_posterior.html#arviz.plot_posterior) docs should serve this goal. This example in particular looks like the one that will show this issue: ``` rope = {'mu': [{'rope': (-2, 2)}], 'theta': [{'school': 'Choate', 'rope': (2, 4)}]} az.plot_posterior(data, var_names=['mu', 'theta'], rope=rope) ``` The next step should probably be comparing the source codes of `plot_posterior` and `plot_forest`, I don't know why but instead of having the same behaviour when interpreting the rope argument, one works correctly and the other crashes. Hopefully, fixing the differences between the two will solve the issue as `plot_posterior` seems to work correctly. I am affraid I don't have much knowledge to work on this... If there is anything I can help you with let me know.

2020-09-21T15:16:33Z

arviz-devs/arviz

arviz-devs__arviz-1403

40589dd9658afff4e905ef3b61a65ffeb7e66b24

diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py --- a/arviz/plots/backends/bokeh/densityplot.py +++ b/arviz/plots/backends/bokeh/densityplot.py @@ -63,7 +63,7 @@ def plot_density( rows, cols, figsize=figsize, - squeeze=True, + squeeze=False, backend_kwargs=backend_kwargs, ) else:

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -112,10 +112,26 @@ def test_plot_density_no_subset(): "c": np.random.normal(size=200), } ) - axes = plot_density([model_ab, model_bc]) + axes = plot_density([model_ab, model_bc], backend="bokeh", show=False) assert axes.size == 3 +def test_plot_density_one_var(): + """Test plot_density works when there is only one variable (#1401).""" + model_ab = from_dict( + { + "a": np.random.normal(size=200), + } + ) + model_bc = from_dict( + { + "a": np.random.normal(size=200), + } + ) + axes = plot_density([model_ab, model_bc], backend="bokeh", show=False) + assert axes.size == 1 + + def test_plot_density_bad_kwargs(models): obj = [getattr(models, model_fit) for model_fit in ["model_1", "model_2"]] with pytest.raises(ValueError):

Error plotting a single variable with plot_density and bokeh backend **Describe the bug** Over in ArviZ.jl, we use the Julia equivalent to the below snippet to test Bokeh integration for `plot_density`. It worked fine until recently, where we now get an error with bokeh only but not matplotlib, though I'm not certain whether a change in arviz or bokeh is responsible. **To Reproduce** ```python >>> import arviz >>> import numpy as np >>> import matplotlib.pyplot as plt >>> arr1 = np.random.randn(4, 100) >>> arr2 = np.random.randn(4, 100) >>> arviz.plot_density([{"x": arr1}, {"x": arr2}], var_names = ["x"]) # matplotlib works fine >>> plt.show() ``` <img src=https://user-images.githubusercontent.com/8673634/94775414-9bce2480-0374-11eb-8938-f74a486f97de.png width=400></img> ```python >>> arviz.plot_density([{"x": arr1}, {"x": arr2}], var_names = ["x"], backend="bokeh") # errors Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/saxen/.julia/conda/3/lib/python3.8/site-packages/arviz/plots/densityplot.py", line 252, in plot_density ax = plot(**plot_density_kwargs) File "/Users/saxen/.julia/conda/3/lib/python3.8/site-packages/arviz/plots/backends/bokeh/densityplot.py", line 74, in plot_density for label, ax_ in zip(all_labels, (item for item in ax.flatten() if item is not None)) AttributeError: 'Figure' object has no attribute 'flatten' ``` **Additional context** Relevant package versions in the conda environment used: ``` arviz 0.10.0 py_0 conda-forge bokeh 2.2.1 py38h32f6830_0 conda-forge matplotlib 3.1.3 py38_0 conda-forge numpy 1.19.1 py38h3b9f5b6_0 ```

2020-10-01T16:48:50Z

arviz-devs/arviz

arviz-devs__arviz-1419

0e4e188add3f6a66dba01d8f4cc1a077ccfcf5d4

diff --git a/arviz/plots/backends/bokeh/rankplot.py b/arviz/plots/backends/bokeh/rankplot.py --- a/arviz/plots/backends/bokeh/rankplot.py +++ b/arviz/plots/backends/bokeh/rankplot.py @@ -23,10 +23,31 @@ def plot_rank( colors, ref_line, labels, + ref_line_kwargs, + bar_kwargs, + vlines_kwargs, + marker_vlines_kwargs, backend_kwargs, show, ): """Bokeh rank plot.""" + if ref_line_kwargs is None: + ref_line_kwargs = {} + ref_line_kwargs.setdefault("line_dash", "dashed") + ref_line_kwargs.setdefault("line_color", "black") + + if bar_kwargs is None: + bar_kwargs = {} + bar_kwargs.setdefault("line_color", "white") + + if vlines_kwargs is None: + vlines_kwargs = {} + vlines_kwargs.setdefault("line_width", 2) + vlines_kwargs.setdefault("line_dash", "solid") + + if marker_vlines_kwargs is None: + marker_vlines_kwargs = {} + if backend_kwargs is None: backend_kwargs = {} @@ -62,6 +83,7 @@ def plot_rank( gap = 1 width = bin_ary[1] - bin_ary[0] + bar_kwargs.setdefault("width", width) # Center the bins bin_ary = (bin_ary[1:] + bin_ary[:-1]) / 2 @@ -74,34 +96,30 @@ def plot_rank( x=bin_ary, top=y_ticks[-1] + counts, bottom=y_ticks[-1], - width=width, fill_color=colors[idx], - line_color="white", + **bar_kwargs, ) if ref_line: - hline = Span( - location=y_ticks[-1] + counts.mean(), line_dash="dashed", line_color="black" - ) + hline = Span(location=y_ticks[-1] + counts.mean(), **ref_line_kwargs) ax.add_layout(hline) if labels: ax.yaxis.axis_label = "Chain" elif kind == "vlines": ymin = np.full(len(all_counts), all_counts.mean()) for idx, counts in enumerate(all_counts): - ax.circle(bin_ary, counts, fill_color=colors[idx], line_color=colors[idx]) - - x_locations = [(bin, bin) for bin in bin_ary] - y_locations = [(ymin[idx], counts_) for counts_ in counts] - ax.multi_line( - x_locations, - y_locations, - line_dash="solid", + ax.circle( + bin_ary, + counts, + fill_color=colors[idx], line_color=colors[idx], - line_width=3, + **marker_vlines_kwargs, ) + x_locations = [(bin, bin) for bin in bin_ary] + y_locations = [(ymin[idx], counts_) for counts_ in counts] + ax.multi_line(x_locations, y_locations, line_color=colors[idx], **vlines_kwargs) if ref_line: - hline = Span(location=all_counts.mean(), line_dash="dashed", line_color="black") + hline = Span(location=all_counts.mean(), **ref_line_kwargs) ax.add_layout(hline) if labels: diff --git a/arviz/plots/backends/matplotlib/rankplot.py b/arviz/plots/backends/matplotlib/rankplot.py --- a/arviz/plots/backends/matplotlib/rankplot.py +++ b/arviz/plots/backends/matplotlib/rankplot.py @@ -20,10 +20,32 @@ def plot_rank( colors, ref_line, labels, + ref_line_kwargs, + bar_kwargs, + vlines_kwargs, + marker_vlines_kwargs, backend_kwargs, show, ): """Matplotlib rankplot..""" + if ref_line_kwargs is None: + ref_line_kwargs = {} + ref_line_kwargs.setdefault("linestyle", "--") + ref_line_kwargs.setdefault("color", "k") + + if bar_kwargs is None: + bar_kwargs = {} + bar_kwargs.setdefault("align", "center") + + if vlines_kwargs is None: + vlines_kwargs = {} + vlines_kwargs.setdefault("lw", 2) + + if marker_vlines_kwargs is None: + marker_vlines_kwargs = {} + marker_vlines_kwargs.setdefault("marker", "o") + marker_vlines_kwargs.setdefault("lw", 0) + if backend_kwargs is None: backend_kwargs = {} @@ -52,6 +74,8 @@ def plot_rank( gap = all_counts.max() * 1.05 width = bin_ary[1] - bin_ary[0] + bar_kwargs.setdefault("width", width) + bar_kwargs.setdefault("edgecolor", ax.get_facecolor()) # Center the bins bin_ary = (bin_ary[1:] + bin_ary[:-1]) / 2 @@ -63,23 +87,22 @@ def plot_rank( bin_ary, counts, bottom=y_ticks[-1], - width=width, - align="center", color=colors[idx], - edgecolor=ax.get_facecolor(), + **bar_kwargs, ) if ref_line: - ax.axhline(y=y_ticks[-1] + counts.mean(), linestyle="--", color="k") + ax.axhline(y=y_ticks[-1] + counts.mean(), **ref_line_kwargs) if labels: ax.set_ylabel("Chain", fontsize=ax_labelsize) elif kind == "vlines": ymin = all_counts.mean() + for idx, counts in enumerate(all_counts): - ax.plot(bin_ary, counts, "o", color=colors[idx]) - ax.vlines(bin_ary, ymin, counts, lw=2, colors=colors[idx]) + ax.plot(bin_ary, counts, color=colors[idx], **marker_vlines_kwargs) + ax.vlines(bin_ary, ymin, counts, colors=colors[idx], **vlines_kwargs) ax.set_ylim(0, all_counts.mean() * 2) if ref_line: - ax.axhline(y=all_counts.mean(), linestyle="--", color="k") + ax.axhline(y=ymin, **ref_line_kwargs) if labels: ax.set_xlabel("Rank (all chains)", fontsize=ax_labelsize) diff --git a/arviz/plots/rankplot.py b/arviz/plots/rankplot.py --- a/arviz/plots/rankplot.py +++ b/arviz/plots/rankplot.py @@ -24,6 +24,10 @@ def plot_rank( figsize=None, ax=None, backend=None, + ref_line_kwargs=None, + bar_kwargs=None, + vlines_kwargs=None, + marker_vlines_kwargs=None, backend_kwargs=None, show=None, ): @@ -80,6 +84,18 @@ def plot_rank( its own array of plot areas (and return it). backend: str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + ref_line_kwargs : dict, optional + Reference line keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.axhline` or + :meth:`bokeh:bokeh.model.Span`. + bar_kwargs : dict, optional + Bars keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.bar` or + :meth:`bokeh:bokeh.plotting.figure.Figure.vbar`. + vlines_kwargs : dict, optional + Vlines keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.vlines` or + :meth:`bokeh:bokeh.plotting.figure.Figure.multi_line`. + marker_vlines_kwargs : dict, optional + Marker for the vlines keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.plot` or + :meth:`bokeh:bokeh.plotting.figure.Figure.circle`. backend_kwargs: bool, optional These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. @@ -121,6 +137,13 @@ def plot_rank( >>> az.plot_rank(centered_data, var_names="mu", kind='vlines', ax=ax[0]) >>> az.plot_rank(noncentered_data, var_names="mu", kind='vlines', ax=ax[1]) + Change the aesthetics using kwargs + + .. plot:: + :context: close-figs + + >>> az.plot_rank(noncentered_data, var_names="mu", kind="vlines", + >>> vlines_kwargs={'lw':0}, marker_vlines_kwargs={'lw':3}); """ if transform is not None: data = transform(data) @@ -161,6 +184,10 @@ def plot_rank( colors=colors, ref_line=ref_line, labels=labels, + ref_line_kwargs=ref_line_kwargs, + bar_kwargs=bar_kwargs, + vlines_kwargs=vlines_kwargs, + marker_vlines_kwargs=marker_vlines_kwargs, backend_kwargs=backend_kwargs, show=show, )

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -1030,8 +1030,19 @@ def test_plot_posterior_point_estimates(models, point_estimate): {"var_names": "mu"}, {"var_names": ("mu", "tau"), "coords": {"theta_dim_0": [0, 1]}}, {"var_names": "mu", "ref_line": True}, + { + "var_names": "mu", + "ref_line_kwargs": {"line_width": 2, "line_color": "red"}, + "bar_kwargs": {"width": 50}, + }, {"var_names": "mu", "ref_line": False}, {"var_names": "mu", "kind": "vlines"}, + { + "var_names": "mu", + "kind": "vlines", + "vlines_kwargs": {"line_width": 0}, + "marker_vlines_kwargs": {"radius": 20}, + }, ], ) def test_plot_rank(models, kwargs): diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -848,8 +848,19 @@ def test_plot_autocorr_var_names(models, var_names): {"var_names": "mu"}, {"var_names": ("mu", "tau"), "coords": {"theta_dim_0": [0, 1]}}, {"var_names": "mu", "ref_line": True}, + { + "var_names": "mu", + "ref_line_kwargs": {"lw": 2, "color": "C2"}, + "bar_kwargs": {"width": 0.7}, + }, {"var_names": "mu", "ref_line": False}, {"var_names": "mu", "kind": "vlines"}, + { + "var_names": "mu", + "kind": "vlines", + "vlines_kwargs": {"lw": 0}, + "marker_vlines_kwargs": {"lw": 3}, + }, ], ) def test_plot_rank(models, kwargs):

plot_rank ignores backend_kwargs Using `plot_rank` on many a trace with many coefficients can require changing the line widths and marker sizes (e.g. when using `kind="vlines"`). This usage however has no effect: `az.plot_rank(many_coefficient_trace, backend_kwargs={'linewidth':0.5, 'markersize':1, 's':1})` The docstring says > These are kwargs specific to the backend being used. For additional documentation check the plotting method of the backend. , which seems to suggest passing this dictionary is the correct usage. How can this be changed to accept kwargs, or could an example usage be added?

Hi, `backend_kwargs` are related to figure/axes generation. They are passed to https://docs.bokeh.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure or to https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html (documentation of this with links is a work in progress, see #1188 ). The kwargs used to customize the drawing are generally passed as `plot_kwargs`, `line_kwargs`... in `plot_rank` however, these do not seem to be available. If it's ok, I'll change the title of the issue to `plot_rank` is missing plot customization kwargs so we remember to add them. We should look at the code and see which customization kwargs would make sense here then add them to the funcion

2020-10-15T16:01:00Z

arviz-devs/arviz

arviz-devs__arviz-1435

d6603e67b3a700c08afc24fdeba234b62b6f5c43

diff --git a/arviz/plots/backends/bokeh/compareplot.py b/arviz/plots/backends/bokeh/compareplot.py --- a/arviz/plots/backends/bokeh/compareplot.py +++ b/arviz/plots/backends/bokeh/compareplot.py @@ -105,8 +105,16 @@ def plot_compare( ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_ic", "black")) if insample_dev: + scale = comp_df[f"{information_criterion}_scale"][0] + p_ic = comp_df[f"p_{information_criterion}"] + if scale == "log": + correction = p_ic + elif scale == "negative_log": + correction = -p_ic + elif scale == "deviance": + correction = -(2 * p_ic) ax.circle( - comp_df[information_criterion] - (2 * comp_df["p_" + information_criterion]), + comp_df[information_criterion] + correction, yticks_pos[::2], line_color=plot_kwargs.get("color_insample_dev", "black"), fill_color=plot_kwargs.get("color_insample_dev", "black"), diff --git a/arviz/plots/backends/matplotlib/compareplot.py b/arviz/plots/backends/matplotlib/compareplot.py --- a/arviz/plots/backends/matplotlib/compareplot.py +++ b/arviz/plots/backends/matplotlib/compareplot.py @@ -82,8 +82,16 @@ def plot_compare( ) if insample_dev: + scale = comp_df[f"{information_criterion}_scale"][0] + p_ic = comp_df[f"p_{information_criterion}"] + if scale == "log": + correction = p_ic + elif scale == "negative_log": + correction = -p_ic + elif scale == "deviance": + correction = -(2 * p_ic) ax.plot( - comp_df[information_criterion] - (2 * comp_df["p_" + information_criterion]), + comp_df[information_criterion] + correction, yticks_pos[::2], color=plot_kwargs.get("color_insample_dev", "k"), marker=plot_kwargs.get("marker_insample_dev", "o"),

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -326,16 +326,6 @@ def test_plot_compare(models, kwargs): assert axes -def test_plot_compare_manual(models): - """Test compare plot without scale column""" - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) - - # remove "scale" column - del model_compare["loo_scale"] - axes = plot_compare(model_compare, backend="bokeh", show=False) - assert axes - - def test_plot_compare_no_ic(models): """Check exception is raised if model_compare doesn't contain a valid information criterion""" model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -937,16 +937,6 @@ def test_plot_compare(models, kwargs): assert axes -def test_plot_compare_manual(models): - """Test compare plot without scale column""" - model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) - - # remove "scale" column - del model_compare["loo_scale"] - axes = plot_compare(model_compare) - assert axes - - def test_plot_compare_no_ic(models): """Check exception is raised if model_compare doesn't contain a valid information criterion""" model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2})

plot_compare issue with pWAIC The plot_compare method seems to add the pWAIC values to the in-sample deviance to get WAIC values regardless of scale (deviance or log). Shouldn't the pWAIC be subtracted in the log scale, where a higher score is better? Otherwise, for example with two models m1 and m2 with the same in-sample deviance of 20: if m1 has pWAIC of 10, m2 has pWAIC of 5 then m1 WAIC is 30 and m2 WAIC is 25 so m1 is preferred. However, with the same in-sample deviance the model with the lower pWAIC should be preferred i.e. m2. Example from my work:  I might be getting confused about this and my example isn't well explained, sorry.

2020-11-02T12:47:47Z

arviz-devs/arviz

arviz-devs__arviz-1559

718eb31ed844c3bb71d564736af54e203ca0dc3a

diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -124,14 +124,12 @@ def plot_ppc( plot_kwargs = {"color": color, "alpha": alpha, "linewidth": 0.5 * linewidth} if dtype == "i": plot_kwargs["drawstyle"] = "steps-pre" - ax_i.plot( - [], color=color, label="{} predictive {}".format(group.capitalize(), pp_var_name) - ) + ax_i.plot([], color=color, label="{} predictive".format(group.capitalize())) if observed: if dtype == "f": plot_kde( obs_vals, - label="Observed {}".format(var_name), + label="Observed", plot_kwargs={"color": "k", "linewidth": linewidth, "zorder": 3}, fill_kwargs={"alpha": 0}, ax=ax_i, @@ -144,7 +142,7 @@ def plot_ppc( ax_i.plot( bin_edges, hist, - label="Observed {}".format(var_name), + label="Observed", color="k", linewidth=linewidth, zorder=3, @@ -179,7 +177,7 @@ def plot_ppc( ax_i.plot(x_s, y_s, **plot_kwargs) if mean: - label = "{} predictive mean {}".format(group.capitalize(), pp_var_name) + label = "{} predictive mean".format(group.capitalize()) if dtype == "f": rep = len(pp_densities) len_density = len(pp_densities[0]) @@ -224,7 +222,7 @@ def plot_ppc( *_empirical_cdf(obs_vals), color="k", linewidth=linewidth, - label="Observed {}".format(var_name), + label="Observed", drawstyle=drawstyle, zorder=3 ) @@ -253,7 +251,7 @@ def plot_ppc( drawstyle=drawstyle, linewidth=linewidth ) - ax_i.plot([], color=color, label="Posterior predictive {}".format(pp_var_name)) + ax_i.plot([], color=color, label="Posterior predictive") if mean: ax_i.plot( *_empirical_cdf(pp_vals.flatten()), @@ -261,7 +259,7 @@ def plot_ppc( linestyle="--", linewidth=linewidth * 1.5, drawstyle=drawstyle, - label="Posterior predictive mean {}".format(pp_var_name) + label="Posterior predictive mean" ) ax_i.set_yticks([0, 0.5, 1]) @@ -276,7 +274,7 @@ def plot_ppc( "linewidth": linewidth * 1.5, "zorder": 3, }, - label="Posterior predictive mean {}".format(pp_var_name), + label="Posterior predictive mean", ax=ax_i, legend=legend, ) @@ -290,7 +288,7 @@ def plot_ppc( hist, color=color, linewidth=linewidth * 1.5, - label="Posterior predictive mean {}".format(pp_var_name), + label="Posterior predictive mean", zorder=3, linestyle="--", drawstyle="steps-pre", @@ -316,7 +314,7 @@ def plot_ppc( color="k", markersize=markersize, alpha=alpha, - label="Observed {}".format(var_name), + label="Observed", zorder=4, ) @@ -340,9 +338,7 @@ def plot_ppc( vals, yvals, "o", zorder=2, color=color, markersize=markersize, alpha=alpha ) - ax_i.plot( - [], color=color, marker="o", label="Posterior predictive {}".format(pp_var_name) - ) + ax_i.plot([], color=color, marker="o", label="Posterior predictive") ax_i.set_yticks([])

diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -794,6 +794,14 @@ def test_plot_ppc_bad_ax(models, fig_ax): plot_ppc(models.model_1, ax=ax2) +def test_plot_legend(models): + axes = plot_ppc(models.model_1) + legend_texts = axes.get_legend().get_texts() + result = [i.get_text() for i in legend_texts] + expected = ["Posterior predictive", "Observed", "Posterior predictive mean"] + assert result == expected + + @pytest.mark.parametrize("var_names", (None, "mu", ["mu", "tau"])) def test_plot_violin(models, var_names): axes = plot_violin(models.model_1, var_names=var_names)

plot_ppc only plots one legend if there are multiple `observed` **To Reproduce** ```python import seaborn as sns import pymc3 as pm import pandas as pd import arviz as az iris = sns.load_dataset("iris") y_s = pd.Categorical(iris["species"]).codes x_n = "sepal_length" x_0 = iris[x_n].values with pm.Model() as lda: μ = pm.Normal("μ", mu=0, sd=10, shape=2) σ = pm.HalfNormal("σ", 10) setosa = pm.Normal("setosa", mu=μ[0], sd=σ, observed=x_0[:50]) versicolor = pm.Normal("versicolor", mu=μ[1], sd=σ, observed=x_0[50:]) bd = pm.Deterministic("bd", (μ[0] + μ[1]) / 2) trace_lda = pm.sample(1000, return_inferencedata=True) with lda: ppc = pm.sample_posterior_predictive(trace=trace_lda) az.plot_ppc(az.from_pymc3(posterior_predictive=ppc, model=lda)) ```  **Expected behavior** I think I'd have expected both subplots to have a legend **Additional context** ```python > az.__version__, pm.__version__ ('0.11.1', '3.11.0') ```

We can change it to optionally have more than one legend, but I think this default makes sense at it reduces redundancy (and the data/ink ratio). Sure, but the second subplot is about versicolor (whilst the first one setosa), so is it really redundant to have an extra legend when the first one reads "observed setosa"? Yes, if you have that information on the x_label. Of course the current behavior is problematic, because we have one legend that is supposed to work for both plots but is referring only to the first. So I suggest to have as default one legend and remove from the legend the name of the variable. Agreed, that would be good - I'll work on a PR next week Thanks @MarcoGorelli!

2021-02-13T13:58:07Z

arviz-devs/arviz

arviz-devs__arviz-1579

cb4b9e49b670206e158166a6cf0396b8e4eff818

diff --git a/arviz/data/io_cmdstanpy.py b/arviz/data/io_cmdstanpy.py --- a/arviz/data/io_cmdstanpy.py +++ b/arviz/data/io_cmdstanpy.py @@ -50,6 +50,9 @@ def __init__( self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup + if self.log_likelihood is None and "log_lik" in self.posterior.stan_vars_cols: + self.log_likelihood = ["log_lik"] + import cmdstanpy # pylint: disable=import-error self.cmdstanpy = cmdstanpy @@ -92,8 +95,20 @@ def posterior_to_xarray(self): coords = deepcopy(self.coords) if self.coords is not None else {} return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=coords, dims=dims), - dict_to_dataset(data_warmup, library=self.cmdstanpy, coords=coords, dims=dims), + dict_to_dataset( + data, + library=self.cmdstanpy, + coords=coords, + dims=dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=coords, + dims=dims, + index_origin=self.index_origin, + ), ) @requires("posterior") @@ -257,6 +272,13 @@ def log_likelihood_to_xarray(self): valid_cols, self.save_warmup, ) + if isinstance(self.log_likelihood, dict): + data = {obs_name: data[lik_name] for obs_name, lik_name in self.log_likelihood.items()} + if data_warmup: + data_warmup = { + obs_name: data_warmup[lik_name] + for obs_name, lik_name in self.log_likelihood.items() + } return ( dict_to_dataset( data, @@ -445,9 +467,19 @@ def posterior_to_xarray_pre_v_0_9_68(self): ) return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=self.coords, dims=self.dims), dict_to_dataset( - data_warmup, library=self.cmdstanpy, coords=self.coords, dims=self.dims + data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ), ) @@ -471,9 +503,19 @@ def sample_stats_to_xarray_pre_v_0_9_68(self, fit): if data_warmup: data_warmup[name] = data_warmup.pop(s_param).astype(dtypes.get(s_param, float)) return ( - dict_to_dataset(data, library=self.cmdstanpy, coords=self.coords, dims=self.dims), dict_to_dataset( - data_warmup, library=self.cmdstanpy, coords=self.coords, dims=self.dims + data, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, + ), + dict_to_dataset( + data_warmup, + library=self.cmdstanpy, + coords=self.coords, + dims=self.dims, + index_origin=self.index_origin, ), ) @@ -484,7 +526,9 @@ def _as_set(spec): return [] if isinstance(spec, str): return [spec] - else: + try: + return set(spec.values()) + except AttributeError: return set(spec) @@ -496,7 +540,7 @@ def _filter(names, spec): for item in spec: names.remove(item) elif isinstance(spec, dict): - for item in spec.keys(): + for item in spec.values(): names.remove(item) return names @@ -527,6 +571,7 @@ def _unpack_fit(fit, items, save_warmup): else: num_warmup = fit.num_draws_warmup + nchains = fit.chains draws = np.swapaxes(fit.draws(inc_warmup=save_warmup), 0, 1) sample = {} sample_warmup = {} @@ -534,22 +579,23 @@ def _unpack_fit(fit, items, save_warmup): for item in items: if item in fit.stan_vars_cols: col_idxs = fit.stan_vars_cols[item] + if len(col_idxs) == 1: + raw_draws = draws[..., col_idxs[0]] + else: + raw_draws = fit.stan_variable(item, inc_warmup=save_warmup) + raw_draws = np.swapaxes( + raw_draws.reshape((-1, nchains, *raw_draws.shape[1:]), order="F"), 0, 1 + ) elif item in fit.sampler_vars_cols: col_idxs = fit.sampler_vars_cols[item] + raw_draws = draws[..., col_idxs[0]] else: raise ValueError("fit data, unknown variable: {}".format(item)) if save_warmup: - if len(col_idxs) == 1: - sample_warmup[item] = np.squeeze(draws[:num_warmup, :, col_idxs], axis=2) - sample[item] = np.squeeze(draws[num_warmup:, :, col_idxs], axis=2) - else: - sample_warmup[item] = draws[:num_warmup, :, col_idxs] - sample[item] = draws[num_warmup:, :, col_idxs] + sample_warmup[item] = raw_draws[:, :num_warmup, ...] + sample[item] = raw_draws[:, num_warmup:, ...] else: - if len(col_idxs) == 1: - sample[item] = np.squeeze(draws[:, :, col_idxs], axis=2) - else: - sample[item] = draws[:, :, col_idxs] + sample[item] = raw_draws return sample, sample_warmup @@ -680,8 +726,12 @@ def from_cmdstanpy( Constant data used in the sampling. predictions_constant_data : dict Constant data for predictions used in the sampling. - log_likelihood : str, list of str - Pointwise log_likelihood for the data. + log_likelihood : str, list of str, dict of {str: str} + Pointwise log_likelihood for the data. If a dict, its keys should represent var_names + from the corresponding observed data and its values the stan variable where the + data is stored. By default, if a variable ``log_lik`` is present in the Stan model, + it will be retrieved as pointwise log likelihood values. Use ``False`` to avoid this + behaviour. index_origin : int, optional Starting value of integer coordinate values. Defaults to the value in rcParam ``data.index_origin``.

diff --git a/arviz/tests/external_tests/test_data_cmdstanpy.py b/arviz/tests/external_tests/test_data_cmdstanpy.py --- a/arviz/tests/external_tests/test_data_cmdstanpy.py +++ b/arviz/tests/external_tests/test_data_cmdstanpy.py @@ -40,19 +40,22 @@ def _create_test_data(): parameters { real mu; real<lower=0> tau; - real eta[J]; + real eta[2, J / 2]; } transformed parameters { real theta[J]; - for (j in 1:J) - theta[j] = mu + tau * eta[j]; + for (j in 1:J/2) { + theta[j] = mu + tau * eta[1, j]; + theta[j + 4] = mu + tau * eta[2, j]; + } } model { mu ~ normal(0, 5); tau ~ cauchy(0, 5); - eta ~ normal(0, 1); + eta[1] ~ normal(0, 1); + eta[2] ~ normal(0, 1); y ~ normal(theta, sigma); } @@ -174,14 +177,13 @@ def get_inference_data(self, data, eight_schools_params): observed_data={"y": eight_schools_params["y"]}, constant_data={"y": eight_schools_params["y"]}, predictions_constant_data={"y": eight_schools_params["y"]}, - log_likelihood="log_lik", + log_likelihood={"y": "log_lik"}, coords={"school": np.arange(eight_schools_params["J"])}, dims={ - "theta": ["school"], "y": ["school"], "log_lik": ["school"], "y_hat": ["school"], - "eta": ["school"], + "theta": ["school"], }, ) @@ -202,10 +204,10 @@ def get_inference_data2(self, data, eight_schools_params): "log_lik_dim": np.arange(eight_schools_params["J"]), }, dims={ - "theta": ["school"], + "eta": ["extra_dim", "half school"], "y": ["school"], "y_hat": ["school"], - "eta": ["school"], + "theta": ["school"], "log_lik": ["log_lik_dim"], }, ) @@ -218,8 +220,17 @@ def get_inference_data3(self, data, eight_schools_params): prior=data.obj, prior_predictive=["y_hat", "log_lik"], observed_data={"y": eight_schools_params["y"]}, - coords={"school": np.arange(eight_schools_params["J"])}, - dims={"theta": ["school"], "y": ["school"], "y_hat": ["school"], "eta": ["school"]}, + coords={ + "school": np.arange(eight_schools_params["J"]), + "half school": ["a", "b", "c", "d"], + "extra_dim": ["x", "y"], + }, + dims={ + "eta": ["extra_dim", "half school"], + "y": ["school"], + "y_hat": ["school"], + "theta": ["school"], + }, ) def get_inference_data4(self, data, eight_schools_params): @@ -248,11 +259,11 @@ def get_inference_data_warmup_true_is_true(self, data, eight_schools_params): log_likelihood="log_lik", coords={"school": np.arange(eight_schools_params["J"])}, dims={ - "theta": ["school"], + "eta": ["extra_dim", "half school"], "y": ["school"], "log_lik": ["school"], "y_hat": ["school"], - "eta": ["school"], + "theta": ["school"], }, save_warmup=True, ) @@ -271,11 +282,11 @@ def get_inference_data_warmup_false_is_true(self, data, eight_schools_params): log_likelihood="log_lik", coords={"school": np.arange(eight_schools_params["J"])}, dims={ - "theta": ["school"], + "eta": ["extra_dim", "half school"], "y": ["school"], "log_lik": ["school"], "y_hat": ["school"], - "eta": ["school"], + "theta": ["school"], }, save_warmup=True, ) @@ -294,11 +305,11 @@ def get_inference_data_warmup_true_is_false(self, data, eight_schools_params): log_likelihood="log_lik", coords={"school": np.arange(eight_schools_params["J"])}, dims={ - "theta": ["school"], + "eta": ["extra_dim", "half school"], "y": ["school"], "log_lik": ["school"], "y_hat": ["school"], - "eta": ["school"], + "theta": ["school"], }, save_warmup=False, ) @@ -322,7 +333,7 @@ def test_inference_data(self, data, eight_schools_params): "observed_data": ["y"], "constant_data": ["y"], "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], + "log_likelihood": ["y", "~log_lik"], "prior": ["theta"], } fails = check_multiple_attrs(test_dict, inference_data1) @@ -352,10 +363,15 @@ def test_inference_data(self, data, eight_schools_params): fails = check_multiple_attrs(test_dict, inference_data3) assert not fails # inference_data 4 - test_dict = {"posterior": ["theta"], "prior": ["theta"]} + test_dict = { + "posterior": ["eta", "mu", "theta"], + "prior": ["theta"], + "log_likelihood": ["log_lik"], + } fails = check_multiple_attrs(test_dict, inference_data4) assert not fails assert len(inference_data4.posterior.theta.shape) == 3 # pylint: disable=no-member + assert len(inference_data4.posterior.eta.shape) == 4 # pylint: disable=no-member assert len(inference_data4.posterior.mu.shape) == 2 # pylint: disable=no-member def test_inference_data_warmup(self, data, eight_schools_params): @@ -394,13 +410,13 @@ def test_inference_data_warmup(self, data, eight_schools_params): "predictions_constant_data": ["y"], "log_likelihood": ["log_lik"], "prior": ["theta"], + "~warmup_posterior": [], + "~warmup_predictions": [], + "~warmup_log_likelihood": [], + "~warmup_prior": [], } fails = check_multiple_attrs(test_dict, inference_data_false_is_true) assert not fails - assert "warmup_posterior" not in inference_data_false_is_true - assert "warmup_predictions" not in inference_data_false_is_true - assert "warmup_log_likelihood" not in inference_data_false_is_true - assert "warmup_prior" not in inference_data_false_is_true # inference_data no warmup test_dict = { "posterior": ["theta"], @@ -410,13 +426,13 @@ def test_inference_data_warmup(self, data, eight_schools_params): "predictions_constant_data": ["y"], "log_likelihood": ["log_lik"], "prior": ["theta"], + "~warmup_posterior": [], + "~warmup_predictions": [], + "~warmup_log_likelihood": [], + "~warmup_prior": [], } fails = check_multiple_attrs(test_dict, inference_data_true_is_false) assert not fails - assert "warmup_posterior" not in inference_data_true_is_false - assert "warmup_predictions" not in inference_data_true_is_false - assert "warmup_log_likelihood" not in inference_data_true_is_false - assert "warmup_prior" not in inference_data_true_is_false # inference_data no warmup test_dict = { "posterior": ["theta"], @@ -424,14 +440,12 @@ def test_inference_data_warmup(self, data, eight_schools_params): "observed_data": ["y"], "constant_data": ["y"], "predictions_constant_data": ["y"], - "log_likelihood": ["log_lik"], + "log_likelihood": ["y"], "prior": ["theta"], + "~warmup_posterior": [], + "~warmup_predictions": [], + "~warmup_log_likelihood": [], + "~warmup_prior": [], } fails = check_multiple_attrs(test_dict, inference_data_false_is_false) assert not fails - assert "warmup_posterior" not in inference_data_false_is_false - assert "warmup_predictions" not in inference_data_false_is_false - assert "warmup_log_likelihood" not in inference_data_false_is_false - assert ( - "warmup_prior" not in inference_data_false_is_false - ) # pylint: disable=redefined-outer-name diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-1.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,121 +28,121 @@ # stepsize_jitter = 0 (Default) # id = 1 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\ae9n1ga_.json +# file = /tmp/tmp2ipdytqf/_pz629gf.json # init = 2 (Default) # random -# seed = 66991 +# seed = 81267 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-1-azqdj9ca.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-1-wo553htr.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 # Adaptation terminated -# Step size = 0.381249 +# Step size = 0.385518 # Diagonal elements of inverse mass matrix: -# 9.5193, 1.58369, 0.948532, 1.02511, 0.940046, 0.821932, 0.870007, 0.746886, 0.869037, 0.859392 --6.90053,0.958437,0.381249,3,7,0,9.92441,4.04135,1.33077,-0.0719923,0.490877,2.28447,0.249098,-0.600358,0.204217,1.53678,-0.445723,3.94554,4.69459,7.08146,4.37284,3.24241,4.31312,6.08645,3.44819,-4.9128,-3.27615,-3.89003,-3.34535,-3.22726,-3.36219,-3.93119,-3.92217,16.2526,7.98879,25.7583,6.02204,9.3322,8.57438,4.63324,-3.05003 --8.3131,0.970554,0.381249,3,15,0,12.5518,3.60788,6.11393,-0.822935,0.421611,-2.10946,-0.391857,-2.01014,-0.552989,-0.345897,0.0558374,-1.42349,6.18558,-9.2892,1.21209,-8.68199,0.226939,1.49308,3.94926,-5.55086,-3.23798,-3.76878,-3.45526,-3.48044,-3.3193,-4.58392,-3.90933,5.46613,8.46368,-10.3903,2.44517,-21.5847,2.64333,1.18381,-11.6144 --9.45625,0.675886,0.381249,3,7,0,14.6077,-0.42445,0.163285,2.48584,-0.352001,0.0219701,0.199781,0.0354488,-0.00143004,0.437418,0.442866,-0.0185506,-0.481927,-0.420863,-0.391829,-0.418662,-0.424684,-0.353027,-0.352137,-5.37152,-3.58124,-3.70452,-3.54262,-3.11825,-3.32522,-4.90569,-4.04477,-3.18568,5.45013,2.55981,10.0132,-8.99661,-17.5315,10.228,40.11 --6.74809,0.989516,0.381249,3,7,0,12.8709,2.16906,0.179036,-0.12873,-0.130149,0.686472,0.528952,-0.647099,-0.128289,1.16795,-0.893591,2.14601,2.14576,2.29196,2.26376,2.05321,2.14609,2.37817,2.00908,-5.11239,-3.39288,-3.74622,-3.40953,-3.17371,-3.32226,-4.44173,-3.96335,-31.5456,12.3574,-22.1157,16.2919,12.342,-18.2487,-22.9587,-3.59553 --3.62472,0.850412,0.381249,3,7,0,9.03015,2.03475,8.47004,1.05461,0.150237,0.979599,0.110902,0.397396,0.922533,0.657116,0.691998,10.9674,3.30727,10.332,2.97409,5.40071,9.84864,7.60055,7.896,-4.27168,-3.33163,-4.03868,-3.38381,-3.36906,-3.64038,-3.76227,-3.8353,12.4739,10.1587,-8.818,18.0483,18.1176,13.8228,8.41847,42.0065 --8.51391,0.948021,0.381249,4,15,0,11.8909,8.57016,1.76222,2.03314,-0.272068,-1.96838,0.539092,-1.40958,-0.338699,-0.173605,-1.00309,12.153,8.09072,5.10144,9.52016,6.08617,7.9733,8.26423,6.80249,-4.18505,-3.22156,-3.81972,-3.34308,-3.42612,-3.51777,-3.69545,-3.851,-5.81774,9.48504,19.1826,22.7925,9.70181,1.58634,23.2371,8.25918 --9.4665,0.61595,0.381249,4,15,0,13.086,-0.144604,0.0641931,-0.32415,0.277254,1.37133,-0.273731,0.5216,-0.550672,-0.283953,1.49486,-0.165412,-0.126806,-0.0565742,-0.162176,-0.111121,-0.179953,-0.162832,-0.0486444,-5.38986,-3.55175,-3.70845,-3.5288,-3.12104,-3.32259,-4.87097,-4.03334,-8.31082,-13.6788,9.92762,5.72322,-9.5082,3.58627,18.9704,0.630547 --4.56491,0.956129,0.381249,3,15,0,12.3962,8.50675,9.77596,0.894399,-0.352056,-1.43704,-0.18963,-0.509782,-0.0571914,0.648632,-1.31738,17.2504,5.06506,-5.54166,6.65293,3.52314,7.94765,14.8478,-4.3719,-3.88378,-3.26459,-3.70414,-3.31733,-3.24245,-3.5163,-3.27121,-4.22295,19.2933,9.18762,13.902,-6.07455,-8.6677,-2.37482,23.6575,2.18832 --5.16079,0.945818,0.381249,4,15,0,8.5765,6.29436,1.41806,-0.933565,0.546761,1.40519,-0.142043,-0.760905,-0.359795,0.0137282,0.659237,4.97051,7.06969,8.287,6.09293,5.21535,5.78415,6.31382,7.22919,-4.80556,-3.22585,-3.94035,-3.32023,-3.35462,-3.41141,-3.90436,-3.84443,-32.7994,28.1928,0.676009,8.18695,10.3789,17.5057,5.87815,15.6191 --6.26564,1,0.381249,4,15,0,10.326,4.20505,6.85395,0.97311,-0.288807,-0.638523,0.543147,1.95908,0.177668,0.303074,-0.278493,10.8747,2.22558,-0.171363,7.92775,17.6325,5.42278,6.2823,2.29627,-4.27871,-3.38824,-3.70715,-3.32039,-5.25919,-3.39766,-3.90805,-3.95462,-26.5066,7.8004,-0.0182613,5.05059,18.7709,7.65897,7.66456,-11.6919 --5.61718,0.982522,0.381249,4,15,0,12.6517,6.64491,3.32553,-0.884606,0.593056,0.581918,-0.301878,-2.17829,-0.392812,0.488992,0.331675,3.70313,8.61713,8.58009,5.64101,-0.599045,5.3386,8.27107,7.7479,-4.93885,-3.22343,-3.95344,-3.32447,-3.11716,-3.39462,-3.69478,-3.83721,5.51506,10.1702,-0.790291,-3.51846,-0.560962,12.0225,16.7357,6.93087 --4.14458,0.999754,0.381249,3,15,0,8.17578,3.55799,5.87246,-0.39053,-0.364201,-0.505564,0.302661,-0.924431,-0.401664,1.47738,1.316,1.26462,1.41923,0.589084,5.33535,-1.87069,1.19923,12.2339,11.2862,-5.21539,-3.43806,-3.71669,-3.32828,-3.12084,-3.317,-3.38777,-3.8101,1.36266,-0.832305,5.28386,27.6917,-14.5191,6.41872,21.499,-18.0125 --5.76434,0.981456,0.381249,4,15,0,7.61565,2.89528,2.50235,-1.19114,-0.232304,-0.498737,0.631829,-0.303422,1.13905,0.909279,1.49516,-0.0853568,2.31398,1.64727,4.47634,2.13602,5.74559,5.17062,6.6367,-5.37985,-3.38318,-3.73371,-3.34315,-3.17687,-3.40989,-4.04449,-3.8537,-0.783931,-3.39368,2.82166,6.24125,20.0374,17.2747,2.61416,28.4833 --7.21899,0.953414,0.381249,3,7,0,11.4552,4.93155,3.05056,2.68632,0.605251,0.337834,0.3001,-0.131522,-1.15142,-0.696543,-1.0541,13.1263,6.7779,5.96213,5.84702,4.53033,1.41908,2.8067,1.71596,-4.1186,-3.22899,-3.8484,-3.32233,-3.30496,-3.31756,-4.37571,-3.97252,21.8239,9.46193,-13.5549,19.9647,4.5295,-17.2332,7.34771,10.7974 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+-7.48523,0.950119,0.385518,5,39,0,11.7693,1.50404,0.738203,1.2646,-1.14504,-0.881476,-0.529194,0.194484,-1.04242,1.17359,0.877372,2.43758,0.853334,1.64761,2.37039,0.658769,1.11339,0.734522,2.15172,-5.07907,-3.4769,-3.73372,-3.4054,-3.13315,-3.31689,-4.71201,-3.95898,-1.73658,10.8703,23.8903,5.29939,2.35226,-14.346,28.4556,-3.65272 +-7.29664,0.986551,0.385518,3,15,0,11.7876,8.83,1.12759,1.06794,-0.171176,-0.204102,-0.482476,-0.554099,1.24103,-0.375854,2.01153,10.0342,8.59985,8.2052,8.40619,8.63698,8.28596,10.2294,11.0982,-4.34425,-3.22332,-3.93675,-3.325,-3.68944,-3.53619,-3.52344,-3.81057,17.9722,9.05458,-2.48611,9.22641,1.6735,-0.947614,5.29048,0.862101 +-5.96692,0.989533,0.385518,3,7,0,10.9597,9.34948,13.6781,-0.494972,-0.728989,-0.815078,0.241453,-0.581316,-0.375488,-0.480268,0.310744,2.57923,-1.7992,1.3982,2.78035,-0.621676,12.6521,4.21353,13.5999,-5.06302,-3.70165,-3.72931,-3.39041,-3.11705,-3.87787,-4.17186,-3.81326,11.0313,3.22052,-1.97456,3.48576,-5.52664,10.76,3.19276,4.31117 +-6.04693,1,0.385518,3,7,0,8.05874,10.8004,2.2575,0.362243,-1.50873,-0.089532,-0.685658,-0.382761,0.942418,0.671715,-0.673368,11.6182,10.5983,9.93631,12.3168,7.39443,9.25252,12.9279,9.28027,-4.22335,-3.25528,-4.01838,-3.43364,-3.55114,-3.59826,-3.35015,-3.82073,-2.5582,20.6438,26.3398,25.0129,-1.3185,-2.72148,12.6062,15.5287 +-6.41578,0.776287,0.385518,4,23,0,10.9607,9.06737,0.671532,0.355793,-1.07882,-0.0371514,-0.0953811,-0.758793,-0.0389723,1.06442,-1.04848,9.3063,9.04242,8.55782,9.78216,8.34291,9.00332,9.0412,8.36328,-4.40355,-3.22696,-3.95243,-3.34882,-3.65499,-3.58152,-3.62282,-3.82972,-12.2664,1.43154,8.89884,4.41676,5.20958,0.968425,-5.4337,-4.03021 +-2.50112,0.841596,0.385518,3,7,0,7.24577,1.05021,12.6362,1.00451,0.209848,-0.306386,-0.0617685,0.419931,0.533195,0.180316,0.728383,13.7434,-2.82135,6.35655,3.32872,3.7019,0.269693,7.78778,10.2542,-4.07866,-3.80703,-3.86251,-3.37253,-3.25263,-3.31904,-3.74297,-3.81401,28.3318,-9.2508,-1.5294,-8.34022,-2.82224,6.25377,-2.8107,-1.27704 # -# Elapsed Time: 0.213 seconds (Warm-up) -# 0.053 seconds (Sampling) -# 0.266 seconds (Total) +# Elapsed Time: 0.02366 seconds (Warm-up) +# 0.00441 seconds (Sampling) +# 0.02807 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-2.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,121 +28,121 @@ # stepsize_jitter = 0 (Default) # id = 2 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\ae9n1ga_.json +# file = /tmp/tmp2ipdytqf/_pz629gf.json # init = 2 (Default) # random -# seed = 66991 +# seed = 81267 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-2-fmm4ub3c.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-2-gjhvdtyv.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 # Adaptation terminated -# Step size = 0.363235 +# Step size = 0.40918 # Diagonal elements of inverse mass matrix: -# 9.99791, 1.47964, 0.872801, 0.771319, 0.893751, 0.897378, 0.746145, 0.965774, 0.880585, 0.870741 --5.51706,1,0.363235,3,7,0,7.02038,6.38935,1.54005,1.36077,0.101732,0.505469,-1.09415,-1.38386,0.616845,-0.0717758,-0.376011,8.48501,6.54602,7.1678,4.70431,4.25813,7.33932,6.27881,5.81027,-4.47329,-3.23209,-3.89345,-3.33861,-3.28683,-3.4829,-3.90846,-3.86843,13.3657,22.3077,5.12384,7.77606,5.36791,9.18223,2.26082,8.35397 --8.12182,0.921256,0.363235,4,15,0,13.5875,2.71951,0.4937,-0.223205,-0.17417,-0.48891,2.09139,1.00292,-1.26771,0.931507,0.556179,2.60931,2.63352,2.47814,3.75203,3.21465,2.09364,3.1794,2.9941,-5.05963,-3.36552,-3.75014,-3.36043,-3.22581,-3.32178,-4.31978,-3.93447,12.4627,3.09424,-6.63969,-2.09445,-0.978068,0.32619,-5.55771,21.6517 --8.5946,0.742664,0.363235,3,7,0,12.9736,1.3959,9.0014,1.69015,1.47535,1.91446,-0.666528,-0.0215094,0.719956,-0.0304407,1.29941,16.6096,14.6761,18.6287,-4.60379,1.20228,7.87651,1.12189,13.0924,-3.9153,-3.44438,-4.6052,-3.87323,-3.1461,-3.51223,-4.64588,-3.81115,14.8459,14.6487,20.6808,-9.69252,-12.2337,5.5344,2.26376,12.0379 --7.88729,0.9852,0.363235,4,15,0,14.0299,0.806093,1.5206,0.526903,1.40067,-0.212751,1.42906,0.0683111,-0.192595,0.0158651,-2.20443,1.6073,2.93595,0.482584,2.97913,0.909967,0.513234,0.830218,-2.54597,-5.17493,-3.34975,-3.71522,-3.38364,-3.13868,-3.31781,-4.69553,-4.13583,-1.48751,-7.57971,-7.43727,7.9868,15.0179,10.9514,-7.27631,-11.9221 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+-7.07183,0.976088,0.40918,3,15,0,10.2783,4.29125,6.8153,-1.34054,-0.243969,-0.464196,-1.11754,1.13508,-0.467312,0.345144,0.93361,-4.84493,1.12762,12.0271,6.64351,2.62853,-3.32511,1.10638,10.6541,-6.0243,-3.45767,-4.13257,-3.31736,-3.19744,-3.39413,-4.6485,-3.81211,4.73095,-2.98624,19.1795,1.03106,-6.04577,-3.7636,-2.62356,12.9275 +-10.0368,0.820975,0.40918,3,7,0,13.3282,4.88167,0.421502,1.3449,-0.532117,-1.231,-0.442666,-0.367974,2.7552,0.93214,-0.475818,5.44855,4.3628,4.72657,5.27457,4.65738,4.69509,6.04299,4.68111,-4.75714,-3.28767,-3.80813,-3.32914,-3.31373,-3.37325,-3.93637,-3.89197,11.0563,-5.60207,12.019,22.5203,4.78757,9.19113,7.58015,4.91578 +-5.879,1,0.40918,3,7,0,13.174,9.64592,7.30351,0.162132,-0.368014,-1.59928,0.752016,0.17479,-0.00648439,-0.149038,-0.303708,10.8301,-2.0344,10.9225,8.55742,6.95813,15.1383,9.59856,7.42779,-4.28212,-3.72497,-4.07011,-3.32686,-3.5071,-4.14283,-3.57444,-3.84157,6.69957,-1.29268,25.7062,6.33501,4.11189,28.7527,-10.4756,11.9959 +-4.86273,1,0.40918,3,7,0,8.18144,4.67487,0.716522,0.00565509,0.9166,0.0638742,-0.534166,0.83944,-0.368249,0.705838,0.525396,4.67892,4.72064,5.27635,5.18062,5.33163,4.29213,4.41101,5.05133,-4.83559,-3.27529,-3.82531,-3.33051,-3.36363,-3.36162,-4.14483,-3.88382,15.3587,12.5415,31.584,-19.4061,34.2822,2.55665,-1.17017,22.3613 +-12.9882,0.855619,0.40918,3,7,0,14.623,3.4773,2.88631,-0.089531,1.90787,-1.27192,0.911883,-0.67347,-1.33091,2.049,2.48278,3.21889,-0.19384,1.53346,9.39136,8.984,6.10927,-0.36412,10.6434,-4.99166,-3.55722,-3.73167,-3.34046,-3.73147,-3.4247,-4.90773,-3.81215,-29.6327,-11.012,4.40883,5.6878,3.56145,-5.91092,6.40165,47.3433 # -# Elapsed Time: 0.21 seconds (Warm-up) -# 0.047 seconds (Sampling) -# 0.257 seconds (Total) +# Elapsed Time: 0.032638 seconds (Warm-up) +# 0.00743 seconds (Sampling) +# 0.040068 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-3.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,121 +28,121 @@ # stepsize_jitter = 0 (Default) # id = 3 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\ae9n1ga_.json +# file = /tmp/tmp2ipdytqf/_pz629gf.json # init = 2 (Default) # random -# seed = 66991 +# seed = 81267 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-3-fa_dawzq.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-3-llkr9wpb.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 # Adaptation terminated -# Step size = 0.442385 +# Step size = 0.427277 # Diagonal elements of inverse mass matrix: -# 10.1811, 1.33609, 0.885451, 0.965106, 0.953699, 0.925806, 0.691821, 0.857663, 1.04392, 0.867476 --6.77677,0.519123,0.442385,3,7,0,12.2757,1.72813,2.92904,2.65118,0.159545,1.20225,-0.389475,0.359336,-0.102668,1.29177,0.207848,9.49354,2.19544,5.24955,0.587342,2.78064,1.42741,5.51176,2.33692,-4.38808,-3.38999,-3.82445,-3.48676,-3.20439,-3.31759,-4.0013,-3.95341,-7.14982,-9.81027,21.7107,-9.15302,18.9277,5.27425,10.0318,-3.86935 --7.69287,0.569945,0.442385,2,7,0,14.3994,8.42648,1.18369,1.40009,0.0648852,1.44402,-0.271908,-1.03524,0.684275,1.30637,1.11323,10.0838,8.50328,10.1358,8.10462,7.20107,9.23645,9.97282,9.7442,-4.3403,-3.22279,-4.02854,-3.32188,-3.53133,-3.59716,-3.5437,-3.81716,20.9038,2.33109,-6.15296,18.8366,2.872,11.6274,4.06628,23.9465 --5.11228,1,0.442385,3,7,0,9.17725,4.02552,6.4128,-0.653115,0.843504,-1.372,-0.146871,0.442682,-0.00954319,-0.549837,-0.069621,-0.162779,9.43474,-4.77284,3.08366,6.86435,3.96432,0.499522,3.57905,-5.38953,-3.23182,-3.69767,-3.38021,-3.49794,-3.35314,-4.75286,-3.91874,17.1861,15.6674,14.8228,1.86845,10.99,18.8049,6.74148,-13.1777 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+-5.70956,0.996705,0.427277,3,7,0,8.8117,1.64589,8.01624,0.577923,0.233696,1.53043,-0.946013,0.385203,0.617561,-1.27605,0.652598,6.27866,13.9142,4.73377,-8.58325,3.51925,-5.93758,6.5964,6.87727,-4.67547,-3.39641,-3.80835,-4.3203,-3.24224,-3.51572,-3.87173,-3.84981,0.538523,29.3714,-4.88568,-10.8225,11.9627,10.1641,12.7226,3.42322 +-5.71715,0.8533,0.427277,3,7,0,10.149,1.64021,3.36414,-0.0914467,-0.425009,-0.707049,1.03612,-0.482345,2.25718,0.229707,-0.578774,1.33257,-0.738404,0.0175309,2.41298,0.210419,5.12587,9.23369,-0.306869,-5.20733,-3.60332,-3.70931,-3.40378,-3.12521,-3.38718,-3.60576,-4.04304,20.2996,-9.80301,-12.559,-2.83557,3.94986,12.4174,14.1874,-8.58131 +-5.43334,0.975626,0.427277,3,7,0,9.71174,6.58683,5.0604,1.59441,-0.486602,1.74761,-0.584236,-0.157756,-0.43303,0.209085,0.804114,14.6552,15.4304,5.78852,7.64488,4.12443,3.63036,4.39553,10.656,-4.02273,-3.49758,-3.84238,-3.31855,-3.27826,-3.34542,-4.14693,-3.8121,37.3641,13.5893,26.2486,28.8347,10.1804,0.271595,-23.2863,16.9546 +-7.063,0.894759,0.427277,3,7,0,12.6227,5.07936,1.80732,-0.538163,-0.889065,-0.918783,1.81753,-1.72836,0.231252,-0.205443,0.690958,4.10673,3.41883,1.95566,4.70806,3.47254,8.36423,5.49731,6.32815,-4.89563,-3.32646,-3.73949,-3.33854,-3.23964,-3.54093,-4.00311,-3.85896,12.2228,19.448,6.83914,1.23396,17.908,10.3777,10.2191,-12.9826 +-7.16514,0.925436,0.427277,5,39,0,14.3806,3.1861,1.05063,-1.4443,-1.77498,0.640387,0.972553,0.00715867,0.872698,-0.785647,-0.221477,1.66868,3.85891,3.19362,2.36068,1.32126,4.20789,4.10298,2.95341,-5.16774,-3.30727,-3.76645,-3.40577,-3.14942,-3.35936,-4.18716,-3.93561,-0.822112,6.56459,-26.8935,4.29027,0.869777,3.47466,1.61115,-19.873 # -# Elapsed Time: 0.195 seconds (Warm-up) -# 0.036 seconds (Sampling) -# 0.231 seconds (Total) +# Elapsed Time: 0.020332 seconds (Warm-up) +# 0.004139 seconds (Sampling) +# 0.024471 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_nowarmup-4.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,121 +28,121 @@ # stepsize_jitter = 0 (Default) # id = 4 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\ae9n1ga_.json +# file = /tmp/tmp2ipdytqf/_pz629gf.json # init = 2 (Default) # random -# seed = 66991 +# seed = 81267 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-4-5_vzf_pq.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-4-_7_vglm2.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1.1,eta.2.1,eta.1.2,eta.2.2,eta.1.3,eta.2.3,eta.1.4,eta.2.4,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 # Adaptation terminated -# Step size = 0.427277 +# Step size = 0.369164 # Diagonal elements of inverse mass matrix: -# 10.6565, 1.23678, 0.964941, 0.842079, 0.89328, 1.07166, 0.885401, 0.969848, 0.850369, 0.853605 --7.71355,0.938143,0.427277,3,15,0,13.2444,2.81525,2.13062,2.67531,-0.05751,-1.04325,-0.110494,1.22218,0.846643,0.347742,-0.680733,8.5153,2.69272,0.592493,2.57983,5.41924,4.61912,3.55616,1.36487,-4.47066,-3.36236,-3.71673,-3.39757,-3.37053,-3.37096,-4.26465,-3.98386,15.5954,28.9917,13.5131,10.3738,15.6212,6.99981,-9.94175,-6.43094 --3.04994,0.757885,0.427277,3,7,0,8.69164,1.11559,13.327,0.550094,0.219674,-1.09411,0.526044,-0.0866322,-0.170472,0.649723,-0.696702,8.44667,4.04318,-13.4655,8.12616,-0.0389535,-1.15629,9.77443,-8.16933,-4.47662,-3.29981,-3.90545,-3.32207,-3.12186,-3.33605,-3.55982,-4.43709,6.28394,-1.73964,3.36253,5.9862,-3.0894,-4.37855,16.5437,-17.8173 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+-5.67346,0.982425,0.369164,3,15,0,8.92791,4.71261,2.46567,0.791174,0.700934,-1.10685,0.817554,0.820516,-0.597322,-0.429301,-1.14297,6.66338,1.98349,6.73573,3.65409,6.44088,6.72842,3.23981,1.89442,-4.63866,-3.40252,-3.87665,-3.36309,-3.45793,-3.45243,-4.31084,-3.96691,-7.49043,-5.54055,-0.193541,4.11438,19.1878,5.54598,23.6038,2.84796 # -# Elapsed Time: 0.205 seconds (Warm-up) -# 0.032 seconds (Sampling) -# 0.237 seconds (Total) +# Elapsed Time: 0.021656 seconds (Warm-up) +# 0.0047 seconds (Sampling) +# 0.026356 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-1.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,621 +28,621 @@ # stepsize_jitter = 0 (Default) # id = 1 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\fdo0f5kc.json +# file = /tmp/tmp2ipdytqf/x8rvpwpf.json # init = 2 (Default) # random -# seed = 42813 +# seed = 31709 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-1-ri9gm546.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-1-lu7otosq.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --6.50256,1.0666e-98,1,1,1,0,13.3961,-0.898803,3.90309,1.31949,-1.00843,1.22308,0.140399,0.511532,0.958604,0.425203,-0.714559,4.25127,-4.83479,3.87501,-0.350813,1.09775,2.84272,0.760805,-3.68779,-4.88033,-4.04518,-3.78384,-3.54012,-3.14333,-3.33087,-4.70747,-4.1891,-12.7018,-8.79833,18.8885,5.61451,-3.80707,24.1716,1.64341,-13.2963 --6.50256,0,2.33506,0,1,1,12.7796,-0.898803,3.90309,1.31949,-1.00843,1.22308,0.140399,0.511532,0.958604,0.425203,-0.714559,4.25127,-4.83479,3.87501,-0.350813,1.09775,2.84272,0.760805,-3.68779,-4.88033,-4.04518,-3.78384,-3.54012,-3.14333,-3.33087,-4.70747,-4.1891,14.1886,-3.13492,4.06806,-12.288,2.88473,22.593,-14.1769,-4.93905 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+-4.94753,0.979901,0.289495,4,15,0,9.18783,5.34352,10.1522,0.209323,-0.512977,-0.484104,-1.42263,-0.507954,0.893691,-0.0626177,1.63493,7.46861,0.428813,0.186687,4.70782,0.135695,-9.09926,14.4164,21.9416,-4.56374,-3.50814,-3.71136,-3.33855,-3.12412,-3.7383,-3.28573,-3.96183,19.27,-7.44073,31.0883,6.18554,-10.2342,-17.6043,17.7583,31.8866 # Adaptation terminated -# Step size = 0.508851 +# Step size = 0.298701 # Diagonal elements of inverse mass matrix: -# 13.0226, 1.52763, 0.935471, 0.731183, 1.00608, 0.787048, 0.598159, 0.769613, 0.711506, 1.17378 --7.33564,1,0.508851,3,7,0,11.518,1.76652,1.43462,-0.379885,1.01648,1.24227,-1.70368,0.988268,0.823402,-0.129851,-0.646082,1.22153,3.22479,3.54871,-0.677625,3.18432,2.9478,1.58024,0.839637,-5.22051,-3.33554,-3.77529,-3.56041,-3.22424,-3.33251,-4.56957,-4.00152,4.05187,-7.03315,17.7491,22.024,-10.0878,0.509758,6.97607,29.1283 --5.74346,0.970822,0.508851,3,7,0,12.0216,3.57816,7.54545,1.2818,-0.931613,-0.322152,1.62612,-1.08676,-1.10587,0.5377,0.194379,13.2499,-3.45128,1.14738,15.848,-4.62191,-4.76612,7.63534,5.04483,-4.11046,-3.87718,-3.72512,-3.64033,-3.19714,-3.45422,-3.75865,-3.88396,23.6415,-0.617279,-0.287285,19.8868,0.241203,-14.7373,4.24153,24.4422 --6.81567,1,0.508851,3,7,0,8.44867,3.46809,1.68826,-0.63921,1.58283,0.075865,-1.50165,1.0765,1.10483,0.517539,0.111713,2.38894,6.14033,3.59617,0.932912,5.2855,5.33333,4.34183,3.6567,-5.0846,-3.23882,-3.77651,-3.46894,-3.36004,-3.39443,-4.15425,-3.91673,6.73155,20.9515,24.6791,6.35758,8.65353,8.34836,2.95733,-30.1005 --4.60967,0.992258,0.508851,3,7,0,9.42628,1.80563,3.38136,0.257537,-1.25136,0.764577,-0.490432,0.0516302,0.547457,0.976447,0.709012,2.67646,-2.42568,4.39094,0.147307,1.98021,3.65678,5.10735,4.20306,-5.05206,-3.765,-3.79822,-3.51088,-3.17099,-3.346,-4.05263,-3.90313,2.50684,5.26279,8.89331,14.7197,17.5564,-10.8906,14.4232,7.21723 --5.27613,0.9685,0.508851,3,7,0,8.11598,2.7441,4.70805,1.25565,-1.44768,-1.20305,0.756888,-0.508834,-0.809008,0.344721,-0.124649,8.65574,-4.07166,-2.9199,6.30757,0.348484,-1.06475,4.36706,2.15725,-4.45855,-3.95015,-3.69154,-3.31882,-3.12739,-3.33445,-4.15081,-3.95882,13.2013,6.53978,-29.6023,6.84331,7.6259,-22.1711,13.7848,7.62024 --13.8717,0.229184,0.508851,3,7,0,19.6861,1.12221,0.577078,2.06325,-0.158114,-0.727393,2.54764,-1.93593,-0.076505,-1.51645,1.45069,2.31287,1.03096,0.702446,2.59239,0.00502509,1.07806,0.247098,1.95937,-5.09327,-3.46436,-3.7183,-3.39711,-3.1224,-3.31686,-4.79735,-3.96489,0.195671,-0.273402,10.5806,14.6919,-10.2795,4.48388,-4.33343,11.152 --8.58525,1,0.508851,3,7,0,17.6044,3.08446,0.475318,0.458713,0.889998,0.827369,0.712977,-1.13644,-0.125892,-1.63126,1.75661,3.30249,3.50749,3.47772,3.42335,2.54429,3.02462,2.30909,3.91941,-4.98247,-3.32244,-3.77348,-3.3697,-3.19371,-3.33377,-4.45255,-3.91008,-17.492,7.61434,-1.86685,5.30044,12.2492,2.98922,3.33255,13.6014 --5.91846,0.965928,0.508851,3,7,0,13.8764,5.94174,5.12008,0.0891167,-0.734351,-1.46724,-0.637386,0.913868,-0.111539,1.85482,0.608535,6.39802,2.1818,-1.57062,2.67827,10.6208,5.37065,15.4386,9.05748,-4.66398,-3.39078,-3.69552,-3.39401,-3.94976,-3.39577,-3.25433,-3.82267,-10.5139,7.67213,-8.62044,4.31274,29.8155,-4.76915,24.8138,-33.0616 --7.11987,0.224366,0.508851,3,7,0,11.2822,5.33427,0.0845312,-0.280301,-0.510427,-0.247022,-0.0797691,0.352793,1.26012,0.869162,0.496856,5.31057,5.29112,5.31338,5.32752,5.36409,5.44079,5.40774,5.37627,-4.77101,-3.25821,-3.82651,-3.32839,-3.36617,-3.39832,-4.01435,-3.87702,8.35723,-10.3897,9.28413,4.9107,-5.34699,19.3574,-1.35707,-5.30589 --6.31807,0.761388,0.508851,3,7,0,9.40799,3.96196,33.3096,0.32409,0.156843,-0.27845,-0.220211,-0.553536,-0.872927,0.386913,-0.0628541,14.7573,9.18635,-5.31311,-3.37317,-14.4761,-25.1149,16.8499,1.86832,-4.0167,-3.22856,-3.70198,-3.76147,-4.23719,-6.13496,-3.22814,-3.96772,8.55975,10.5807,-6.70228,-0.683479,-16.0473,-7.15605,17.7435,30.3051 --5.28314,0.192997,0.508851,3,7,0,11.8796,4.30998,0.777613,-0.356687,-0.943853,0.470751,0.291992,0.259541,0.583244,-0.0194222,-1.37951,4.03261,3.57602,4.67604,4.53703,4.5118,4.76351,4.29487,3.23725,-4.90351,-3.31938,-3.80661,-3.3419,-3.30369,-3.37536,-4.16068,-3.92781,13.5879,0.667574,31.4576,4.3156,-2.38214,-4.45426,3.58649,-0.00363568 --4.2197,0.998463,0.508851,3,7,0,6.72618,2.76655,9.28243,0.38439,1.22792,-0.357027,0.524564,-0.470188,-0.644179,-0.365563,0.809019,6.33462,14.1646,-0.547528,7.63578,-1.59793,-3.213,-0.626764,10.2762,-4.67007,-3.41154,-3.70327,-3.3185,-3.11837,-3.39018,-4.95631,-3.8139,-6.56249,0.262528,37.9845,-15.6503,-4.71115,-6.5652,-16.565,20.0088 --4.97588,0.46897,0.508851,3,7,0,10.3136,2.74475,3.75284,1.02382,1.11312,-0.272724,0.568861,-0.0104229,0.389286,-0.641109,1.60444,6.587,6.92213,1.72126,4.87959,2.70563,4.20568,0.338767,8.76596,-4.64591,-3.22733,-3.73506,-3.33541,-3.20093,-3.3593,-4.78112,-3.82545,11.7411,12.5937,10.1335,26.1716,7.29339,-9.51251,-2.72332,-6.5471 --4.22918,0.929936,0.508851,3,7,0,7.95918,5.16472,5.99621,-0.208185,-0.246171,-0.195015,-0.156059,-0.0387631,0.551834,-1.01205,0.376763,3.91641,3.68863,3.99537,4.22896,4.93229,8.47364,-0.903747,7.42387,-4.91592,-3.31446,-3.7871,-3.34856,-3.3334,-3.54764,-5.00828,-3.84163,12.1953,-4.34094,32.0326,12.2779,10.7506,13.7902,-6.70161,-14.8553 --8.41345,0.881346,0.508851,3,7,0,9.54561,3.91143,0.599085,0.478817,-0.230936,-1.46796,1.51849,0.657285,0.712541,-0.271208,-1.94739,4.19828,3.77308,3.032,4.82113,4.3052,4.3383,3.74895,2.74478,-4.88593,-3.31086,-3.76259,-3.33645,-3.2899,-3.36288,-4.23699,-3.9415,-16.262,-4.39085,-2.88349,8.8084,16.3482,13.3947,13.3579,-5.65237 --6.42347,0.717727,0.508851,3,7,0,12.0285,8.20884,1.8143,0.802921,-1.1256,-1.12208,0.515076,-0.224987,1.04103,1.3643,0.759053,9.66557,6.16666,6.17305,9.14334,7.80064,10.0976,10.6841,9.58598,-4.37399,-3.23833,-3.85587,-3.33582,-3.59426,-3.65884,-3.48914,-3.8183,3.14071,5.83811,8.93373,25.8715,-7.16393,24.1495,7.43005,52.09 --5.43396,0.99649,0.508851,3,7,0,10.1717,0.424014,2.1163,0.06302,1.126,0.739258,-0.122057,-0.506849,-1.24657,-0.367596,-0.458173,0.557383,2.80697,1.9885,0.165706,-0.648629,-2.2141,-0.353928,-0.545616,-5.30054,-3.35636,-3.74013,-3.50984,-3.11693,-3.35952,-4.90586,-4.0522,-8.5823,-13.5687,3.42024,18.2429,2.69229,-10.5678,1.64536,-13.1513 --5.85044,0.935809,0.508851,3,7,0,9.98875,7.84831,1.19107,0.244917,0.0635522,-0.813879,-0.822884,1.51886,0.574226,-0.167683,0.541328,8.14002,7.924,6.87892,6.8682,9.65738,8.53225,7.64859,8.49307,-4.50347,-3.22155,-3.88214,-3.31691,-3.81727,-3.55128,-3.75728,-3.82829,20.278,18.8674,11.1242,5.98698,7.44274,4.08234,28.0204,-5.33868 --4.82198,0.772893,0.508851,3,7,0,13.7602,3.33265,2.33798,-0.573107,-0.225071,-0.968223,-0.889463,1.09452,-0.461938,0.409984,0.759565,1.99274,2.80644,1.06897,1.25311,5.89163,2.25265,4.29119,5.1085,-5.13005,-3.35639,-3.72386,-3.45331,-3.40934,-3.32332,-4.16118,-3.8826,11.1958,2.3644,9.55021,-7.30189,14.1678,-12.824,0.01226,34.7902 --6.12713,0.799042,0.508851,3,7,0,14.2457,10.2258,3.34074,0.876037,0.212737,-0.326991,1.01841,-1.18583,0.452567,-0.351314,-1.23993,13.1524,10.9365,9.13337,13.628,6.26423,11.7377,9.05211,6.08346,-4.11688,-3.26464,-3.97906,-3.49836,-3.4419,-3.79327,-3.62185,-3.86333,35.2324,-1.29157,3.34112,29.128,3.91658,13.8319,41.1834,7.13511 --7.21421,0.973774,0.508851,3,7,0,10.376,1.45521,1.26941,-0.9034,-0.853786,0.290046,-1.63337,1.24144,-0.166302,-0.503223,0.760971,0.308426,0.371405,1.8234,-0.618205,3.03111,1.2441,0.816414,2.42119,-5.33104,-3.5125,-3.73697,-3.55666,-3.21647,-3.31708,-4.6979,-3.95091,-28.1716,-10.4419,18.9159,-7.97407,-2.93398,-15.2722,-0.843038,-12.8995 --8.03614,0.93013,0.508851,3,7,0,13.3104,9.98671,1.67114,0.181087,-1.41776,1.2437,0.737482,0.217686,-1.58592,0.531951,-1.00883,10.2893,7.61742,12.0651,11.2191,10.3505,7.3364,10.8757,8.3008,-4.32403,-3.22226,-4.1348,-3.39039,-3.91143,-3.48274,-3.4753,-3.83043,27.5134,-9.43109,0.141071,15.1107,15.5436,12.6054,16.2221,9.10074 --8.78889,0.979283,0.508851,3,7,0,12.9666,0.354889,3.04215,-0.475738,0.932738,-2.1382,-0.711363,0.870752,1.12574,-0.710362,1.18608,-1.09238,3.19242,-6.14984,-1.80919,3.00385,3.77955,-1.80614,3.96313,-5.5078,-3.33709,-3.71091,-3.6375,-3.21512,-3.34876,-5.18294,-3.90899,3.15899,10.7416,-0.633471,-7.51982,8.83261,3.6753,-8.68552,10.2075 --7.36645,1,0.508851,3,7,0,11.108,6.56703,4.81174,1.06023,-1.0098,1.93925,0.783928,-0.88181,-0.961532,0.848933,-1.31125,11.6686,1.70813,15.8982,10.3391,2.32399,1.94039,10.6519,0.257625,-4.21969,-3.41946,-4.38907,-3.36291,-3.18437,-3.32049,-3.4915,-4.02209,31.0311,-4.62395,28.3691,22.5712,-7.24485,-1.56323,-5.50774,-0.860911 --7.06019,0.865605,0.508851,3,7,0,10.4469,5.62941,4.65817,0.896709,0.786608,-2.1931,-0.837721,-0.414494,0.850554,-0.328339,1.48321,9.80643,9.29356,-4.58641,1.72716,3.69862,9.59143,4.09995,12.5385,-4.36256,-3.22989,-3.69644,-3.43172,-3.25244,-3.62184,-4.18758,-3.80976,-1.88576,14.3954,52.5468,1.25449,-12.4218,16.1508,1.02881,17.1433 --6.85039,0.985299,0.508851,3,7,0,10.441,4.06784,4.3205,0.130213,0.326902,-2.59322,-0.542793,0.572225,-0.344744,-0.000969498,1.3394,4.63042,5.48022,-7.13617,1.7227,6.54014,2.57837,4.06365,9.85473,-4.84063,-3.25327,-3.72494,-3.43192,-3.46711,-3.32713,-4.19263,-3.81641,-25.141,16.2088,-3.48507,-8.73741,19.0319,2.53532,-10.7169,24.8682 --3.34066,0.405409,0.508851,3,7,0,11.601,3.53033,4.00191,0.761016,0.189548,-0.651701,-0.683023,-0.290712,-1.05993,-0.140786,-0.098572,6.57585,4.28888,0.922281,0.796935,2.36693,-0.711406,2.96692,3.13585,-4.64698,-3.29039,-3.72157,-3.47583,-3.18614,-3.32894,-4.35149,-3.93057,-8.35122,16.3042,-23.8238,-18.9656,-18.0113,3.39453,8.50721,-1.21786 --6.18286,0.866083,0.508851,3,7,0,9.59203,4.00005,1.68546,1.0481,0.319392,0.305235,1.92568,0.857427,1.10413,-0.178727,0.321552,5.76659,4.53838,4.51452,7.24571,5.44521,5.86102,3.69882,4.54202,-4.72549,-3.28144,-3.80182,-3.31708,-3.37259,-3.41448,-4.24414,-3.89515,-4.76846,-8.30266,22.9672,4.24154,3.96085,-2.82249,9.25672,-19.3251 --6.76741,0.916012,0.508851,3,7,0,10.6362,3.51323,6.17324,-0.972642,-0.377003,-0.350729,-1.73953,-0.705897,-0.797547,1.50914,-0.393261,-2.49112,1.1859,1.3481,-7.22529,-0.844441,-1.41022,12.8295,1.08554,-5.69301,-3.45368,-3.72845,-4.15303,-3.11631,-3.34084,-3.35519,-3.99315,9.7476,8.4005,-30.4231,-3.80592,-11.5016,2.75457,13.3733,4.83075 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+-4.29673,0.973607,0.298701,4,15,0,11.8066,2.33458,5.06701,0.936671,-0.114264,0.154244,-0.636432,1.15673,0.0170147,0.692202,-1.06583,7.0807,3.11614,8.19576,5.84197,1.75561,-0.890222,2.4208,-3.06596,-4.59947,-3.34078,-3.93634,-3.32238,-3.16304,-3.3316,-4.43508,-4.15959,-15.9001,0.851256,26.9712,8.42796,8.3954,-12.4332,10.0616,-8.11024 +-7.00182,0.883983,0.298701,4,15,0,12.0094,-1.0203,4.32209,0.788839,0.31646,0.857686,0.408764,-2.29977,1.61322,0.629391,0.614493,2.38914,2.6867,-10.9601,1.69999,0.34747,0.746419,5.95218,1.6356,-5.08458,-3.36268,-3.81528,-3.43291,-3.12737,-3.3171,-3.94727,-3.97508,21.6381,2.95945,-8.71215,5.85179,-4.75696,-1.97892,-9.22513,-0.800425 # -# Elapsed Time: 0.297 seconds (Warm-up) -# 0.036 seconds (Sampling) -# 0.333 seconds (Total) +# Elapsed Time: 0.042168 seconds (Warm-up) +# 0.010171 seconds (Sampling) +# 0.052339 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-2.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,621 +28,621 @@ # stepsize_jitter = 0 (Default) # id = 2 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\fdo0f5kc.json +# file = /tmp/tmp2ipdytqf/x8rvpwpf.json # init = 2 (Default) # random -# seed = 42813 +# seed = 31709 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-2-um2trvs6.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-2-6qogngy4.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --10.1438,0.860407,1,2,3,0,16.2912,1.96986,0.271417,0.128526,-1.25819,-1.06317,-1.06716,0.681899,1.17102,1.56195,1.67022,2.00474,1.62836,1.6813,1.68021,2.15494,2.28769,2.3938,2.42318,-5.12866,-3.42451,-3.73433,-3.43378,-3.17761,-3.32369,-4.43929,-3.95085,16.9584,7.56753,-28.2047,-2.52002,9.00976,-1.72017,-1.35401,15.0023 --10.1438,0,11.1609,0,1,1,13.7608,1.96986,0.271417,0.128526,-1.25819,-1.06317,-1.06716,0.681899,1.17102,1.56195,1.67022,2.00474,1.62836,1.6813,1.68021,2.15494,2.28769,2.3938,2.42318,-5.12866,-3.42451,-3.73433,-3.43378,-3.17761,-3.32369,-4.43929,-3.95085,8.6586,-4.64643,9.18491,6.12244,-8.49633,-6.78867,5.00834,5.23644 --10.1438,0.0631739,1.74953,1,2,1,14.0197,1.96986,0.271417,0.128526,-1.25819,-1.06317,-1.06716,0.681899,1.17102,1.56195,1.67022,2.00474,1.62836,1.6813,1.68021,2.15494,2.28769,2.3938,2.42318,-5.12866,-3.42451,-3.73433,-3.43378,-3.17761,-3.32369,-4.43929,-3.95085,19.7165,-14.5041,-18.5669,2.61976,11.3751,-27.0042,19.28,-7.87558 --4.91758,0.99434,0.195613,4,15,0,12.4058,3.01493,1.38305,-1.03522,0.540251,-0.902652,1.00689,0.271599,0.318301,0.685079,0.489172,1.58318,3.76213,1.76652,4.40751,3.39057,3.45516,3.96243,3.69148,-5.17776,-3.31132,-3.7359,-3.34461,-3.23516,-3.34174,-4.20679,-3.91584,13.8744,2.02202,-6.72434,8.7481,12.7115,-2.74623,25.4513,-13.3465 --4.9632,0.997249,0.256533,4,15,0,6.89512,2.83866,8.99782,1.16057,-0.694257,0.51275,-0.36208,-0.112707,-0.389706,0.40621,-1.53053,13.2813,-3.40814,7.4523,-0.419271,1.82455,-0.667842,6.49367,-10.9328,-4.10841,-3.87225,-3.90491,-3.54429,-3.16541,-3.32833,-3.8835,-4.6209,16.36,-1.35014,8.39896,-10.7622,8.78376,-8.5245,-2.56139,-0.986214 --6.74731,0.926928,0.393862,4,15,0,10.6638,0.0912447,0.981191,-0.184609,0.651419,-0.448038,0.271359,-0.200087,-0.245331,-0.504874,2.03824,-0.0898924,0.730411,-0.348366,0.3575,-0.105079,-0.149472,-0.404133,2.09114,-5.38042,-3.48576,-3.70526,-3.49916,-3.12111,-3.32229,-4.91508,-3.96083,14.9931,-12.0435,11.8605,16.685,11.5451,-11.0834,-29.1453,19.6384 --7.47672,0.975332,0.53239,3,15,0,13.378,3.79645,0.193548,-0.175041,1.1422,-0.0341776,-0.423246,1.65322,-0.0677104,-0.694727,-0.809936,3.76257,4.01752,3.78984,3.71453,4.11643,3.78335,3.66199,3.63969,-4.93244,-3.30082,-3.78157,-3.36144,-3.27775,-3.34885,-4.24942,-3.91717,-11.199,13.0779,8.88259,16.1096,5.41126,9.17495,0.299821,-6.36443 --11.3833,0.549838,0.875219,2,7,0,15.9123,2.33625,5.25407,-0.339133,-1.26579,2.00121,0.34377,1.50807,-2.32189,0.275512,-0.0838689,0.554425,-4.31429,12.8508,4.14245,10.2598,-9.86311,3.78381,1.8956,-5.3009,-3.97973,-4.18224,-3.35058,-3.89877,-3.80447,-4.23202,-3.96687,6.8299,-8.56793,28.5934,-7.82661,30.1431,-21.391,11.5544,-3.99329 --10.5499,0.93659,0.389466,4,23,0,19.9095,4.29055,1.35511,1.37646,-0.564585,-0.106392,0.26688,-0.118764,-3.62459,-0.185135,-0.517407,6.1558,3.52548,4.14638,4.6522,4.12961,-0.621159,4.03967,3.58941,-4.68736,-3.32163,-3.79127,-3.33961,-3.27859,-3.32769,-4.19598,-3.91847,-1.80444,-2.93187,-13.3211,-6.70594,12.3779,-1.80458,8.92422,-10.1389 --7.43292,1,0.590174,3,7,0,12.6745,4.71231,0.568625,-0.802677,-0.0114222,-0.741257,2.00205,0.652465,0.768552,0.657797,0.965377,4.25589,4.70581,4.29081,5.85072,5.08332,5.14933,5.08635,5.26125,-4.87984,-3.27578,-3.79535,-3.32229,-3.3446,-3.38798,-4.05534,-3.87939,-11.5508,7.2358,-16.8826,-12.7804,-4.24754,-16.821,11.8589,-14.2316 --6.54891,0.46013,1.10649,2,7,0,13.1011,7.3972,0.970976,0.905625,-0.838824,0.287626,-0.983363,-1.13167,-1.22517,0.735019,-0.85613,8.27654,6.58272,7.67647,6.44237,6.29837,6.20758,8.11088,6.56591,-4.49147,-3.23157,-3.91416,-3.31812,-3.44497,-3.4289,-3.7105,-3.85488,41.8301,4.02435,9.83513,12.806,7.82467,23.7047,-13.813,14.8317 --7.49017,0.991168,0.379138,4,23,0,9.76185,-0.436463,1.79835,0.93379,-0.577321,-0.196325,-1.90956,-0.376939,0.741746,0.759652,1.19471,1.24282,-1.47469,-0.789525,-3.87053,-1.11433,0.89746,0.929662,1.71205,-5.21798,-3.67037,-3.70107,-3.80513,-3.11624,-3.31688,-4.67851,-3.97265,18.612,-12.1496,13.3037,13.962,6.90913,-5.06214,-21.1543,-25.9429 --8.56515,0.942569,0.699086,3,7,0,12.3318,-3.95776,6.0006,1.33351,1.67805,0.234378,1.94059,1.10656,-0.602611,0.397367,-0.197263,4.04413,6.11152,-2.55135,7.68692,2.68227,-7.57379,-1.57332,-5.14146,-4.90229,-3.23936,-3.69192,-3.31878,-3.19986,-3.62059,-5.1371,-4.26275,-16.179,-2.07966,-36.5401,30.2359,9.03116,-12.7811,2.4242,1.40358 --8.56515,4.05625e-51,1.10602,1,1,0,12.9422,-3.95776,6.0006,1.33351,1.67805,0.234378,1.94059,1.10656,-0.602611,0.397367,-0.197263,4.04413,6.11152,-2.55135,7.68692,2.68227,-7.57379,-1.57332,-5.14146,-4.90229,-3.23936,-3.69192,-3.31878,-3.19986,-3.62059,-5.1371,-4.26275,-12.2823,3.62731,-18.1582,9.3071,7.13933,-5.46128,8.14596,-18.4385 --7.46721,0.998066,0.0924045,5,31,0,12.9685,-3.01724,4.95852,0.675501,-1.12119,-0.724913,-0.314376,0.343779,0.919813,1.33812,1.30549,0.332254,-8.57669,-6.61173,-4.57608,-1.3126,1.54368,3.61787,3.45609,-5.32811,-4.59546,-3.71701,-3.87058,-3.11677,-3.31806,-4.25575,-3.92196,-10.6737,-1.76345,9.74461,-7.90317,-2.44466,5.87827,2.07155,12.0594 --5.22405,0.995658,0.175808,4,15,0,9.55537,-2.51171,3.8891,1.45966,0.592923,-0.847072,0.0219667,0.185473,0.281025,0.479461,-0.124525,3.16504,-0.205771,-5.80606,-2.42628,-1.79039,-1.41878,-0.647036,-2.996,-4.9976,-3.5582,-3.70691,-3.684,-3.12002,-3.34101,-4.96008,-4.15635,-0.277656,4.67434,-3.47525,-3.86005,5.2964,0.364383,-5.36979,-18.848 --8.25742,0.920442,0.330106,3,7,0,13.6313,-5.12397,2.76583,0.245552,1.17644,0.763541,0.482081,0.497109,-1.00016,0.687784,-0.337287,-4.44482,-1.87015,-3.01215,-3.79062,-3.74906,-7.89025,-3.22169,-6.05685,-5.96625,-3.70862,-3.69153,-3.79798,-3.16281,-3.64343,-5.47332,-4.31247,-3.39332,-1.7745,-13.3952,-6.52985,4.53377,-8.81434,17.8695,0.361984 --8.24492,0.865027,0.48907,3,7,0,13.4781,-4.63823,2.98188,0.374885,0.284635,1.22976,-0.04226,1.39702,-0.428934,0.457304,-0.47752,-3.52037,-3.78949,-0.971223,-4.76425,-0.472483,-5.91727,-3.27461,-6.06214,-5.83484,-3.91648,-3.69957,-3.88872,-3.11788,-3.51456,-5.48457,-4.31277,-20.0511,-4.83628,3.17606,-16.5172,-16.3549,-7.49821,2.79685,-7.2303 --9.93417,0.628324,0.609708,2,7,0,15.0208,-3.45008,10.188,0.54368,-1.01232,0.0750159,-0.316951,-1.45413,0.37294,0.480803,-0.304209,2.08892,-13.7635,-2.68582,-6.67917,-18.2647,0.349416,1.44833,-6.54935,-5.11895,-5.58978,-3.69172,-4.09006,-4.9561,-3.31858,-4.59131,-4.3403,11.6175,-12.4024,-2.41881,-6.04622,-23.3521,-13.0825,4.12746,-14.109 --13.3264,1,0.372169,3,7,0,17.3037,-1.69006,5.9518,0.344295,2.17213,-0.96414,0.60853,2.03183,-0.866422,1.37723,2.85831,0.35911,11.238,-7.42842,1.93179,10.403,-6.84683,6.50691,15.322,-5.32481,-3.27395,-3.72983,-3.42298,-3.9188,-3.57127,-3.88198,-3.82634,-25.4167,-3.55556,-41.0844,-1.62391,13.6189,-31.2862,11.2392,15.3548 --13.3264,0.0780241,0.69415,2,3,0,17.8219,-1.69006,5.9518,0.344295,2.17213,-0.96414,0.60853,2.03183,-0.866422,1.37723,2.85831,0.35911,11.238,-7.42842,1.93179,10.403,-6.84683,6.50691,15.322,-5.32481,-3.27395,-3.72983,-3.42298,-3.9188,-3.57127,-3.88198,-3.82634,16.2757,4.36304,-21.0631,-9.11563,-0.345663,-28.4526,7.35339,-3.55776 --10.7272,0.998806,0.0839706,6,127,0,21.8115,3.72397,2.65671,-0.0960207,-2.13151,0.532749,-1.51778,-2.0664,1.8447,0.800343,-0.535138,3.46887,-1.93882,5.13933,-0.308317,-1.76585,8.62479,5.85025,2.30227,-4.96427,-3.71542,-3.82092,-3.53754,-3.11978,-3.55707,-3.95961,-3.95444,14.6226,-1.15085,8.48053,-18.2142,-12.3481,19.6424,6.57936,17.6653 --5.291,1,0.156596,4,15,0,12.832,3.79964,1.3613,0.538591,-0.89848,0.529171,-0.273756,0.0210134,1.03469,-0.673389,-1.19011,4.53282,2.57654,4.52,3.42697,3.82824,5.20817,2.88295,2.17953,-4.85079,-3.36859,-3.80198,-3.36959,-3.26006,-3.39001,-4.36415,-3.95814,-6.80105,-3.54221,-13.8693,-0.662639,7.1365,-5.34116,6.52536,1.32334 --5.29194,0.993672,0.290138,4,31,0,9.08591,8.35659,6.03152,0.107736,0.927589,-0.555555,0.276954,-0.591404,-1.26605,1.40012,1.28576,9.0064,13.9514,5.00574,10.027,4.78952,0.720352,16.8015,16.1117,-4.42867,-3.39862,-3.81671,-3.3547,-3.32307,-3.31716,-3.22871,-3.8354,-19.4014,7.15636,3.23858,19.9775,-8.36658,-13.0996,19.8697,19.302 --4.35479,0.829024,0.522534,3,15,0,10.5012,5.1285,6.90507,0.737545,-0.884745,-0.171446,-0.173154,0.0133812,0.367927,0.102444,-1.4694,10.2213,-0.980731,3.94465,3.93285,5.2209,7.66906,5.83588,-5.01782,-4.32939,-3.62479,-3.78572,-3.35571,-3.35505,-3.50062,-3.96135,-4.25623,-0.236085,0.00536241,15.529,-2.88901,13.8238,20.0116,0.418962,-16.8528 --4.26865,0.501179,0.582206,3,7,0,7.39501,4.47763,0.947006,-0.546348,0.710458,-0.272807,-0.798486,0.17945,0.366388,0.597781,-0.432771,3.96023,5.15043,4.21928,3.72146,4.64757,4.8246,5.04373,4.06779,-4.91123,-3.26212,-3.79332,-3.36125,-3.31305,-3.37728,-4.06085,-3.90641,4.7,6.01357,-9.79827,7.32252,15.3208,-14.4825,1.80859,46.699 --8.09103,0.938477,0.255843,4,15,0,13.5348,2.69324,1.90453,1.1362,-0.771405,0.741818,0.0202713,2.40336,-0.46742,0.797569,-1.2192,4.85716,1.22407,4.10605,2.73184,7.27051,1.80302,4.21223,0.371233,-4.81719,-3.45109,-3.79015,-3.39211,-3.53839,-3.3195,-4.17204,-4.018,-13.5855,-22.1147,-7.3925,14.1293,-0.171654,-0.928877,-16.0669,34.1476 --6.63981,0.964552,0.389413,3,15,0,13.9322,3.98772,6.12408,-0.565844,-1.08433,-0.133946,-0.703514,-0.18152,0.0465295,-0.782992,-1.42204,0.52245,-2.6528,3.16743,-0.320653,2.87608,4.27267,-0.807383,-4.72098,-5.3048,-3.78893,-3.76582,-3.53829,-3.2089,-3.36109,-4.99011,-4.24078,-20.8996,-2.33671,28.8119,-10.0661,13.0955,8.09658,-12.6739,17.9851 --3.9232,0.85527,0.632917,3,7,0,9.09007,3.24038,11.0625,1.3061,0.090783,-0.250435,0.61471,-0.973808,-0.739864,-0.223033,0.675658,17.6891,4.24467,0.469941,10.0406,-7.53237,-4.94437,0.773081,10.7149,-3.86324,-3.29204,-3.71504,-3.35504,-3.37957,-3.46285,-4.70536,-3.81186,25.9694,8.16142,-9.75027,6.55429,-4.41023,6.76735,-13.5752,-16.4054 --5.85787,0.390176,0.754566,3,15,0,10.7362,4.02329,11.3966,1.46289,0.47595,-0.482902,-0.960911,-0.978809,-1.18722,1.12649,-0.910245,20.6952,9.44748,-1.48013,-6.92779,-7.13177,-9.5069,16.8614,-6.35037,-3.74557,-3.232,-3.69604,-4.11842,-3.34825,-3.77301,-3.22801,-4.32896,25.4626,-0.16691,-12.9276,-13.3766,0.902617,-1.5896,21.9257,1.02589 --9.03885,0.956661,0.250968,4,15,0,13.6178,5.48209,1.15797,1.49922,-1.69744,-1.50742,0.30807,-1.45515,1.566,-0.312976,0.458503,7.21814,3.51651,3.73655,5.83882,3.79707,7.29546,5.11967,6.01302,-4.58673,-3.32203,-3.78016,-3.32241,-3.25821,-3.48061,-4.05104,-3.86462,11.791,2.88803,11.8925,2.07338,4.04433,-14.335,29.9933,-12.8496 --3.68062,0.941845,0.395941,3,15,0,10.8368,6.24984,7.76981,1.70336,-0.378897,-0.0442645,-0.339896,0.259455,-0.332423,0.306668,-0.792198,19.4847,3.30588,5.90591,3.60891,8.26576,3.66697,8.6326,0.094613,-3.78812,-3.3317,-3.84644,-3.36435,-3.64613,-3.34623,-3.66026,-4.02804,14.3237,21.823,-3.18305,9.08015,1.52058,18.8994,11.6529,-11.3904 --9.09783,0.305928,0.595739,3,7,0,10.4283,5.29448,2.8913,-1.68401,-0.240774,-1.89911,1.1101,-1.74745,0.469317,1.74741,-0.0513764,0.425485,4.59833,-0.19642,8.5041,0.242073,6.65141,10.3468,5.14593,-5.31666,-3.27938,-3.70688,-3.32618,-3.12569,-3.44881,-3.51438,-3.88181,-11.6359,20.4499,-12.9581,1.11059,-4.65477,-0.932768,10.3948,9.61965 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+-8.53305,0.808924,0.247993,4,15,0,15.7002,11.7455,5.11434,0.25912,-1.6056,-0.0165903,-0.0626547,-1.70006,0.393035,-2.1685,0.220696,13.0707,11.6607,3.05081,0.655071,3.53392,11.4251,13.7556,12.8742,-4.12228,-3.28853,-3.76304,-3.48319,-3.24305,-3.76593,-3.3116,-3.81049,-11.7231,19.1941,-21.5097,22.2197,-0.307206,9.69775,38.956,-2.60776 +-4.9706,0.909518,0.260187,4,15,0,12.1795,5.95128,0.701246,-0.117135,-0.959264,0.234583,-0.800751,0.348871,-0.167572,-0.707969,-0.81899,5.86914,6.11578,6.19593,5.45482,5.2786,5.38976,5.83377,5.37697,-4.71538,-3.23928,-3.85669,-3.3267,-3.3595,-3.39646,-3.96161,-3.877,-4.21884,-7.97024,16.1821,-20.7682,7.70494,-1.87408,-1.29072,36.6638 +-5.89382,0.926166,0.349851,3,15,0,7.99444,5.31393,0.488607,0.211477,-0.782328,0.96318,-1.23572,0.68776,-0.416046,0.239524,-0.634761,5.41726,5.78454,5.64997,5.43096,4.93168,4.71015,5.11064,5.00378,-4.76028,-3.24606,-3.83766,-3.32701,-3.33335,-3.37371,-4.0522,-3.88485,7.51938,8.15017,-4.01702,20.7974,2.45949,-5.82045,-1.61168,-17.3443 +-7.49734,0.584703,0.487922,3,7,0,12.3098,3.50017,3.45592,0.252117,0.567104,1.30903,-1.15928,1.36655,0.0966106,-1.67182,1.3485,4.37147,8.02407,8.22285,-2.27749,5.46004,-0.506197,3.83405,8.16049,-4.86767,-3.22153,-3.93753,-3.6725,-3.37377,-3.32621,-4.22489,-3.83206,17.766,5.34673,15.7029,-8.92491,8.24988,-22.3564,4.96425,13.228 +-6.97752,0.994391,0.294429,4,15,0,11.983,4.40713,4.63609,1.68936,-1.23498,-1.27049,1.64582,-0.502768,1.43558,0.0543794,-0.310088,12.2391,-1.48298,2.07625,4.65924,-1.31836,12.0373,11.0626,2.96953,-4.179,-3.67116,-3.74186,-3.33948,-3.11679,-3.82023,-3.46216,-3.93516,-18.5037,-13.2815,-9.3866,-3.86884,11.6343,11.0952,1.95328,-26.5462 +-9.22926,0.777865,0.482639,3,7,0,10.6075,4.00732,1.76005,-1.07305,2.07104,1.51813,-1.1928,0.421457,-1.32436,0.217783,0.739512,2.11869,6.6793,4.7491,4.39062,7.65243,1.90793,1.67638,5.30889,-5.11553,-3.23024,-3.80881,-3.34497,-3.57829,-3.32024,-4.55383,-3.8784,27.519,6.72892,36.3969,-3.31846,8.83864,8.94087,-4.23739,58.8636 +-8.36422,0.970557,0.466705,3,7,0,14.156,4.16698,1.56426,0.0928929,2.33384,-1.27713,-1.02101,0.957121,-0.977198,-0.110303,0.0265707,4.31229,2.16922,5.66417,3.99444,7.81771,2.56986,2.63839,4.20855,-4.87389,-3.39151,-3.83814,-3.35416,-3.59611,-3.32702,-4.40142,-3.90299,16.7879,-6.15576,22.439,15.2614,10.8246,7.95488,-7.70073,-6.86893 +-6.92298,0.368702,0.715396,2,7,0,11.7834,1.16309,0.444134,-0.507681,1.39049,-1.01701,-1.02569,0.0371941,-0.269253,-0.199834,0.546858,0.937614,0.711402,1.17961,1.07434,1.78065,0.707549,1.04351,1.40597,-5.25448,-3.48714,-3.72565,-3.46193,-3.16389,-3.31719,-4.65914,-3.98251,-24.2935,-8.35139,-12.2758,1.13072,5.03007,-0.39863,-3.82081,-9.83258 +-6.49962,0.988048,0.261379,4,15,0,12.0534,11.1491,7.15407,1.24266,-1.98121,0.814627,0.104468,-0.545557,0.644678,-0.537078,-0.0560875,20.0392,16.977,7.24617,7.30683,-3.02462,11.8965,15.7612,10.7479,-3.76782,-3.62446,-3.89657,-3.31722,-3.14147,-3.80747,-3.24658,-3.81173,30.7599,28.4978,15.1501,8.53135,-4.88899,32.7254,12.0107,-2.21285 # Adaptation terminated -# Step size = 0.376854 +# Step size = 0.404623 # Diagonal elements of inverse mass matrix: -# 11.9041, 1.60384, 0.800009, 0.994846, 1.31884, 0.908467, 0.955974, 1.01903, 0.817898, 0.785695 --5.45902,0.907529,0.376854,3,7,0,7.36169,6.58034,3.62687,-0.834677,0.0560847,-1.1316,0.152676,-1.54851,0.926427,1.00679,-0.604181,3.55308,6.78375,2.47618,7.13408,0.964096,9.94037,10.2318,4.38906,-4.9551,-3.22892,-3.7501,-3.31691,-3.13998,-3.64712,-3.52324,-3.8987,20.0496,11.7887,-9.63196,-3.21752,20.4176,-2.8998,6.04978,-20.6497 --2.4989,0.999012,0.376854,3,7,0,6.24928,5.06716,5.42872,0.549105,0.45205,0.63438,-0.417259,-0.462338,-0.0572255,0.182256,0.528033,8.04809,7.52121,8.51103,2.80198,2.55725,4.7565,6.05658,7.9337,-4.51161,-3.22267,-3.95032,-3.38966,-3.19427,-3.37514,-3.93475,-3.83483,10.9275,-10.2673,-2.2405,-0.753094,1.91546,20.2858,-0.664753,22.5307 --6.78826,0.903874,0.376854,3,7,0,7.78775,-1.76209,1.26968,0.806553,1.255,0.820632,0.0814434,0.456268,-0.818367,0.302064,0.75591,-0.738022,-0.16863,-0.720146,-1.65868,-1.18277,-2.80116,-1.37856,-0.802322,-5.46226,-3.55516,-3.70168,-3.62664,-3.11637,-3.37654,-5.09917,-4.06224,-15.4175,-9.85203,5.02246,3.37666,-3.82228,8.39429,0.206705,-34.9992 --5.91018,0.997787,0.376854,3,15,0,10.5843,5.98912,7.49353,-0.561329,0.318178,-0.0492218,-1.23035,-0.122951,-1.74387,0.67034,0.960516,1.78278,8.3734,5.62028,-3.23058,5.06779,-7.07864,11.0123,13.1868,-5.15442,-3.22222,-3.83666,-3.74933,-3.34343,-3.58652,-3.46566,-3.81148,-17.8035,1.04326,2.43884,-11.0155,-8.07728,-29.2703,-0.297796,45.7927 --6.82633,0.982561,0.376854,4,15,0,11.3555,3.62429,2.09655,2.29666,-0.70678,-0.0946103,0.789144,0.39998,1.07901,-0.0701011,-1.13472,8.43935,2.14249,3.42593,5.27877,4.46287,5.88648,3.47732,1.24528,-4.47725,-3.39308,-3.77218,-3.32908,-3.30038,-3.4155,-4.27607,-3.9878,-0.152628,-5.28479,-5.93162,14.1086,2.75813,13.1146,-2.46666,7.82782 --8.0337,0.187158,0.376854,3,7,0,13.4243,2.12377,6.98456,2.13462,-1.22872,0.194215,0.625491,0.236884,1.13648,-0.68108,-0.7622,17.0332,-6.45829,3.48027,6.49255,3.77829,10.0615,-2.63327,-3.19986,-3.89426,-4.26674,-3.77355,-3.3179,-3.2571,-3.65614,-5.35018,-4.16585,40.7178,-14.7717,1.84601,6.62022,15.0037,15.7688,28.6477,-15.2407 --6.06818,1,0.376854,3,7,0,11.0927,8.13151,0.366027,0.187354,-0.55515,0.119454,0.453501,0.816668,0.936305,-0.245637,0.683838,8.20008,7.92831,8.17523,8.2975,8.43043,8.47422,8.0416,8.38181,-4.49818,-3.22155,-3.93544,-3.32379,-3.66513,-3.54768,-3.71737,-3.82951,32.7756,3.51002,-18.5649,22.8153,10.4705,3.85615,10.5929,25.2664 --13.9733,0.863215,0.376854,3,7,0,17.807,5.29397,0.395657,-1.53968,0.322087,-1.40777,1.08694,0.853848,0.90241,-1.19154,-3.31233,4.68478,5.42141,4.73698,5.72402,5.6318,5.65102,4.82253,3.98342,-4.83499,-3.25477,-3.80844,-3.32356,-3.38765,-3.40622,-4.08975,-3.90849,25.5025,12.3454,8.06295,1.15851,2.62823,2.37236,12.3616,-3.92349 --12.6154,0.997457,0.376854,3,15,0,16.6019,6.44361,0.704867,-1.55848,-0.513661,-1.71446,0.659648,0.354212,1.52845,-1.40556,-2.63021,5.3451,6.08155,5.23515,6.90858,6.69329,7.52097,5.45288,4.58967,-4.76753,-3.23993,-3.82398,-3.31687,-3.48151,-3.49255,-4.00867,-3.89405,-17.2215,7.18251,5.08449,2.66665,-6.39613,1.13329,21.4984,5.03961 --13.6566,0.943528,0.376854,3,7,0,20.4251,1.58464,0.681419,-1.11016,-1.36875,-3.42852,0.251358,-0.232645,1.79159,-0.752827,-0.512781,0.828158,0.651949,-0.751611,1.75592,1.42611,2.80546,1.07165,1.23522,-5.26768,-3.49149,-3.7014,-3.43047,-3.1525,-3.3303,-4.65437,-3.98814,-38.1425,-16.584,-5.8176,-2.40913,11.8168,7.07996,-6.55658,1.8013 --7.17437,0.987912,0.376854,3,15,0,18.3135,6.462,2.82538,-0.357137,0.926788,-0.0734725,1.44472,-2.23865,-0.500884,-0.424481,1.08527,5.45295,9.08053,6.25441,10.5439,0.136957,5.04681,5.26268,9.5283,-4.7567,-3.22736,-3.8588,-3.36873,-3.12414,-3.38451,-4.03272,-3.81874,-26.5466,22.3714,11.7174,32.8515,4.97832,-0.361657,7.73573,29.8371 --11.2632,0.936916,0.376854,4,15,0,15.0618,-0.23034,3.01587,1.09483,-0.658563,-0.437946,-1.78347,0.0997897,2.41876,0.490553,-1.9355,3.07151,-2.21648,-1.55113,-5.60907,0.070613,7.06434,1.2491,-6.06757,-5.00794,-3.74341,-3.69563,-3.97381,-3.12324,-3.4688,-4.62449,-4.31307,15.2297,-7.17797,-9.72296,0.780395,-12.4825,29.8238,-0.136481,-0.0385683 --6.6098,1,0.376854,3,7,0,13.8528,0.741465,1.94355,-0.0122022,-0.994107,1.42659,-1.2094,0.139045,0.411329,0.664891,-1.02644,0.717749,-1.19063,3.51412,-1.60907,1.0117,1.5409,2.03371,-1.25347,-5.28104,-3.64386,-3.77441,-3.6231,-3.14114,-3.31804,-4.49614,-4.08038,-3.9008,-11.2234,5.13455,14.6285,5.00513,-3.44728,2.7984,2.35406 --5.43465,0.974972,0.376854,4,15,0,11.7906,8.07076,1.84479,0.360091,1.1229,-0.33128,1.12811,-0.939934,-0.0277172,-0.106372,1.11901,8.73505,10.1423,7.45962,10.1519,6.33678,8.01963,7.87452,10.1351,-4.45174,-3.24447,-3.90521,-3.35788,-3.44844,-3.52045,-3.73415,-3.81468,-3.33676,12.8768,47.5757,12.9183,-10.7752,18.0451,4.8964,2.67825 --7.60472,0.959309,0.376854,3,15,0,11.4005,9.65513,2.27682,-0.740693,-0.147034,-0.911284,1.7862,-0.802284,-0.840235,0.6151,1.41283,7.96871,9.32036,7.5803,13.722,7.82848,7.74207,11.0556,12.8719,-4.51866,-3.23024,-3.91017,-3.50355,-3.59729,-3.50467,-3.46265,-3.81048,15.8088,18.2394,20.3498,4.09821,5.84604,15.7977,28.353,9.70343 --6.14905,0.982891,0.376854,3,7,0,12.8528,9.39147,3.03713,-0.078524,-0.323526,-0.247636,1.55369,-0.576664,-1.14115,1.1398,0.963879,9.15298,8.40888,8.63937,14.1102,7.64007,5.92564,12.8532,12.3189,-4.41634,-3.22236,-3.95613,-3.52574,-3.57697,-3.41709,-3.35397,-3.80947,17.0974,-15.0632,13.7447,8.1708,3.90267,12.9722,-4.56963,7.82105 --7.94896,0.980348,0.376854,4,15,0,11.5736,-0.924835,2.3345,1.84623,0.94588,-0.13722,-1.16982,0.702397,1.44254,0.367937,-0.820525,3.38519,1.28332,-1.24517,-3.65579,0.714914,2.44278,-0.0658853,-2.84035,-4.97341,-3.44709,-3.69754,-3.78603,-3.13432,-3.32544,-4.8534,-4.14918,18.2029,19.7809,-5.55417,-10.7204,-0.653685,13.4533,9.92676,-8.20249 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+-4.05708,0.565008,0.404623,3,7,0,6.587,6.74274,1.28664,0.666944,-0.431897,0.646043,-0.184375,0.320133,0.398808,-0.955611,-0.0980765,7.60085,7.57396,7.15463,5.51321,6.18704,6.50551,7.25586,6.61655,-4.55171,-3.22243,-3.89293,-3.32597,-3.43501,-3.44208,-3.79871,-3.85403,22.3624,6.64819,19.4835,6.01702,18.6667,11.3409,6.66054,7.30597 +-7.10276,0.85934,0.404623,3,7,0,8.84306,2.2129,6.53203,-1.35485,-0.988984,0.555304,0.115552,-0.480266,-1.08358,0.0110199,0.155788,-6.63706,5.84016,-0.924216,2.28488,-4.24718,2.96768,-4.86508,3.23051,-6.29305,-3.24485,-3.69994,-3.4087,-3.18125,-3.33283,-5.83558,-3.92799,-14.1509,-8.31662,-28.5804,1.48308,1.93046,3.45483,7.87207,-18.4928 +-9.63333,0.952515,0.404623,3,7,0,12.5219,7.33307,0.187511,0.530101,1.47137,-1.44195,-0.565557,1.39845,-0.595014,0.239224,1.32932,7.43247,7.06269,7.5953,7.37793,7.60897,7.22702,7.2215,7.58233,-4.56704,-3.22592,-3.91079,-3.31742,-3.57366,-3.47706,-3.8024,-3.83943,-15.4096,21.297,-16.3349,9.51875,12.0108,1.17058,-10.8054,22.0126 +-9.47001,0.972126,0.404623,3,7,0,14.8514,4.95371,3.71188,-0.779143,1.12309,-0.900559,1.26192,1.91961,-0.508963,1.00048,1.43378,2.06163,1.61095,12.0791,8.66739,9.12248,9.63781,3.0645,10.2757,-5.1221,-3.42562,-4.13563,-3.32832,-3.74866,-3.62515,-4.33687,-3.8139,15.936,-0.139161,28.7263,6.69072,13.2012,17.755,8.21879,6.73271 +-10.0231,0.998731,0.404623,3,7,0,15.5634,0.0147585,0.756786,-0.166526,-0.904897,1.44473,1.58429,2.26949,0.151043,0.841247,-0.241884,-0.111266,1.10811,1.73227,0.651402,-0.670055,1.21373,0.129066,-0.168296,-5.38308,-3.45901,-3.73527,-3.48338,-3.11684,-3.31702,-4.81838,-4.03781,-13.9955,-4.97614,14.3811,-0.903628,8.85237,-3.36246,-4.96789,-8.01378 # -# Elapsed Time: 0.278 seconds (Warm-up) -# 0.048 seconds (Sampling) -# 0.326 seconds (Total) +# Elapsed Time: 0.023495 seconds (Warm-up) +# 0.004659 seconds (Sampling) +# 0.028154 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-3.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,621 +28,621 @@ # stepsize_jitter = 0 (Default) # id = 3 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\fdo0f5kc.json +# file = /tmp/tmp2ipdytqf/x8rvpwpf.json # init = 2 (Default) # random -# seed = 42813 +# seed = 31709 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-3-3nda9m2m.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-3-rh2l2pkd.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --12.678,0,2,0,1,1,19.1103,-1.97706,0.281165,1.07855,1.90646,-1.20198,1.95357,-1.18919,-0.11587,-0.983402,-0.0730637,-1.67381,-1.44103,-2.31502,-1.42779,-2.31142,-2.00964,-2.25356,-1.9976,-5.58373,-3.66719,-3.69244,-3.61034,-3.12678,-3.35426,-5.27256,-4.11168,8.94166,-8.74877,2.70552,-3.50372,7.4675,-2.76646,-14.0425,-3.40437 --12.678,3.75998e-66,2.33506,1,1,0,15.4469,-1.97706,0.281165,1.07855,1.90646,-1.20198,1.95357,-1.18919,-0.11587,-0.983402,-0.0730637,-1.67381,-1.44103,-2.31502,-1.42779,-2.31142,-2.00964,-2.25356,-1.9976,-5.58373,-3.66719,-3.69244,-3.61034,-3.12678,-3.35426,-5.27256,-4.11168,-2.63152,16.5635,7.10893,19.0502,5.42489,34.3782,-3.31296,-5.32286 --9.51814,0.999967,0.230236,4,15,0,18.8387,0.782373,0.496658,0.0419843,-1.56007,-0.644363,1.3468,0.304374,-1.23353,-1.83135,0.096913,0.803225,0.00755083,0.462345,1.45127,0.933543,0.16973,-0.127182,0.830506,-5.27069,-3.54092,-3.71494,-3.44406,-3.13924,-3.31968,-4.8645,-4.00184,22.634,-13.1901,22.4619,-4.24873,4.81809,13.4267,1.29016,6.47811 --6.77414,0.99505,0.23977,4,15,0,13.6147,-1.00143,7.50129,0.941967,2.27557,0.617699,-0.584504,-0.0528762,0.925145,1.31495,0.536294,6.06455,16.0683,3.63212,-5.38596,-1.39807,5.93836,8.86242,3.02147,-4.69624,-3.54701,-3.77744,-3.95077,-3.11714,-3.41761,-3.639,-3.93371,8.68354,26.8622,-25.2507,-7.1358,4.84619,-13.7266,-24.7428,17.1077 --4.36524,1,0.319748,3,7,0,9.56305,-1.07445,8.95734,1.08232,1.1557,-0.612084,-0.250437,-0.474135,0.682334,1.6403,0.758626,8.62024,9.27759,-6.5571,-3.3177,-5.32144,5.03745,13.6183,5.72082,-4.4616,-3.22968,-3.71624,-3.75673,-3.23144,-3.38419,-3.31752,-3.87016,10.3624,11.1709,17.6127,-7.00685,-8.5978,-0.657823,18.669,5.66934 --7.28915,0.286963,0.499727,3,7,0,9.48635,-1.7645,1.07251,-0.284122,-0.655439,0.711415,1.14234,0.786953,-0.784042,-0.618343,-0.275636,-2.06923,-2.46747,-1.0015,-0.539335,-0.920488,-2.6054,-2.42768,-2.06013,-5.63623,-3.76936,-3.69933,-3.55172,-3.1162,-3.37055,-5.30798,-4.11438,-8.42697,0.439837,-21.8662,-8.49448,-22.0646,-2.00652,0.354681,-8.81962 --5.56832,0.997372,0.0958107,6,127,0,12.752,4.20407,8.82787,0.934406,-0.664424,-2.24087,0.623262,0.371066,0.232868,0.724212,0.271205,12.4529,-1.66139,-15.578,9.70614,7.47979,6.25979,10.5973,6.59823,-4.16413,-3.68824,-4.00053,-3.34709,-3.56003,-3.43115,-3.49552,-3.85434,16.2816,-5.5194,-28.2008,1.48981,11.1252,6.17944,9.38387,12.2983 --6.90677,0.981762,0.163961,5,31,0,11.6863,1.25828,0.888644,-0.551534,1.1195,1.9477,-0.43313,-0.677793,0.0802594,-0.118351,-0.329016,0.768163,2.25312,2.98909,0.873382,0.655964,1.3296,1.15311,0.965903,-5.27493,-3.38666,-3.76158,-3.47194,-3.13309,-3.31728,-4.64061,-3.9972,-14.0544,2.29921,5.47087,2.0149,5.85379,4.47961,1.54529,4.18477 --7.0885,0.959207,0.278987,3,15,0,7.74084,1.12832,1.0425,-0.400969,1.22248,2.06286,-0.611247,-0.61137,0.027251,0.285526,-0.308668,0.710305,2.40276,3.27886,0.491088,0.49096,1.15673,1.42598,0.806529,-5.28194,-3.37817,-3.76853,-3.4919,-3.12989,-3.31694,-4.59501,-4.00266,8.65863,11.8021,10.6773,-4.0531,-2.37274,-11.6623,-7.89374,-26.0798 --8.8357,0.911901,0.453328,3,7,0,14.9251,2.39937,3.28734,0.133283,-0.70975,2.57341,0.109406,0.58497,-1.46173,-0.175163,-1.20979,2.83752,0.0661827,10.859,2.75903,4.32236,-2.40584,1.82355,-1.57762,-5.03399,-3.53625,-4.06667,-3.39116,-3.29102,-3.36477,-4.52991,-4.0938,20.9707,12.4728,7.37492,10.5078,3.88657,-8.07136,-1.01006,13.9133 --5.74358,0.797438,0.643025,3,7,0,15.4745,2.12355,2.42262,-0.364931,1.10248,1.34728,0.340327,0.535203,0.242721,0.160608,-1.44616,1.23946,4.79444,5.38752,2.94804,3.42015,2.71158,2.51265,-1.37994,-5.21838,-3.2729,-3.82893,-3.38468,-3.23677,-3.32894,-4.42081,-4.08558,-2.72343,17.2147,19.4106,4.94085,8.00143,-13.9091,16.3228,16.0703 --7.7977,0.941333,0.639831,3,7,0,9.45254,1.82322,1.76496,-0.0404823,0.606291,2.05513,1.06568,0.596725,1.4114,0.104782,-1.13702,1.75177,2.8933,5.45044,3.70409,2.87641,4.31428,2.00815,-0.183578,-5.15803,-3.35192,-3.831,-3.36172,-3.20892,-3.36222,-4.50022,-4.03838,21.9173,-1.43633,13.6816,-7.64794,24.0432,33.6842,8.94777,-9.2546 --7.89962,0.346616,1.00683,2,3,0,12.7484,2.33552,3.45699,0.165352,0.690873,2.16752,-1.64855,-0.331268,-0.402592,1.57083,-0.778766,2.90714,4.72386,9.8286,-3.36349,1.19033,0.943761,7.76588,-0.356669,-5.02621,-3.27519,-4.01296,-3.76064,-3.14578,-3.31685,-3.74521,-4.04494,14.4884,-0.198412,-33.8538,-10.2742,7.54306,-0.774102,14.8784,10.2962 --5.28029,0.979877,0.245485,4,15,0,14.9212,3.67936,2.11531,0.8895,-0.22027,1.68392,-0.615959,-0.588259,0.455909,0.72195,0.99689,5.56093,3.21342,7.24137,2.37642,2.43501,4.64375,5.20651,5.78809,-4.7459,-3.33608,-3.89638,-3.40517,-3.189,-3.3717,-4.03989,-3.86886,8.0508,-0.49256,22.3419,-4.13972,16.9731,15.6053,-4.78767,8.94512 --5.41486,0.982109,0.438981,3,7,0,9.13626,3.54203,2.81792,0.851913,-0.359745,1.64001,-0.144188,0.367273,0.338299,1.02361,1.42413,5.94265,2.52829,8.16344,3.13571,4.57697,4.49532,6.42648,7.55512,-4.70816,-3.37122,-3.93493,-3.37854,-3.30815,-3.36732,-3.89126,-3.8398,6.51994,13.8766,5.64203,10.5913,3.22283,-6.77796,7.82432,3.57164 --6.60172,0.900891,0.787142,3,7,0,9.44897,3.57136,2.33177,1.0248,-1.65409,0.127562,-0.739721,-0.513589,-1.48034,-0.623613,0.868324,5.96097,-0.285605,3.8688,1.8465,2.37379,0.119531,2.11723,5.5961,-4.70636,-3.56478,-3.78368,-3.42658,-3.18643,-3.32004,-4.48283,-3.8726,18.8202,-3.01033,11.0711,8.74532,20.1736,1.9441,8.78237,25.7844 --7.31931,0.530694,1.09275,2,3,0,12.669,6.01165,2.08973,-0.761443,1.37063,-0.847863,-0.753172,0.100367,1.58706,1.21346,1.31085,4.42044,8.87591,4.23985,4.43773,6.2214,9.32819,8.54746,8.75098,-4.86253,-3.22536,-3.7939,-3.34396,-3.43807,-3.60344,-3.66828,-3.8256,-0.713854,5.23344,-2.89417,15.0407,5.28488,17.3247,1.62291,5.0375 --8.3738,0.983938,0.488011,4,23,0,12.7038,0.956403,4.22276,1.21625,-1.09322,0.474149,1.18283,0.252614,-1.68337,0.0519724,-1.79931,6.09236,-3.66001,2.95862,5.95124,2.02313,-6.15205,1.17587,-6.64168,-4.69353,-3.9013,-3.76087,-3.32138,-3.17258,-3.52821,-4.63678,-4.34559,1.31912,-6.14336,42.735,18.1672,28.3539,7.53289,7.56249,22.2801 --8.23957,0.189814,0.872343,2,3,0,15.0057,0.742918,0.575429,1.06156,-0.267586,0.721443,-0.183379,-0.926945,-2.11138,-0.747969,0.204529,1.35377,0.588942,1.15806,0.637397,0.209527,-0.47203,0.312515,0.86061,-5.20481,-3.49614,-3.7253,-3.48412,-3.12519,-3.32579,-4.78576,-4.0008,-4.29072,-6.23988,15.3068,21.8473,6.43218,-18.0768,-6.95401,14.7444 --8.77916,0.997497,0.142089,4,31,0,11.7772,0.617076,0.535001,1.98782,-0.585606,0.71406,0.378995,-0.00167338,-1.75594,-0.190445,0.824187,1.68056,0.303777,0.999099,0.819839,0.616181,-0.322355,0.515188,1.05802,-5.16635,-3.51768,-3.72276,-3.47466,-3.13229,-3.32406,-4.75012,-3.99407,-18.1618,10.7655,-11.6332,-12.0491,0.294803,-4.54458,-6.99182,-5.35984 --3.79679,0.991262,0.265137,4,15,0,10.6587,1.38678,4.81679,0.583185,-0.227578,-0.0649221,0.315837,-0.353106,-0.353127,-0.805012,-0.151071,4.19586,0.290587,1.07406,2.9081,-0.314057,-0.314158,-2.49079,0.659102,-4.88618,-3.5187,-3.72395,-3.38602,-3.11907,-3.32397,-5.32089,-4.00779,19.4312,-3.01505,10.5813,10.4429,-16.5013,-9.80058,10.7661,-35.3156 --3.4391,0.962501,0.481093,3,7,0,6.50052,1.40923,5.375,0.748892,0.381088,0.759893,0.483816,0.136038,-0.29816,1.58012,0.886068,5.43452,3.45757,5.49365,4.00974,2.14043,-0.193382,9.90235,6.17184,-4.75855,-3.32469,-3.83243,-3.35378,-3.17704,-3.32272,-3.54938,-3.86173,3.651,12.4074,3.00887,22.9773,2.44153,12.0468,18.8404,-8.23857 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+-5.48403,0.934929,0.487377,3,7,0,11.8117,5.39354,8.88938,0.98283,-0.693654,-0.0129001,-1.05112,1.20644,1.01626,-0.249414,1.77537,14.1303,5.27887,16.118,3.17641,-0.77261,-3.95024,14.4274,21.1755,-4.05448,-3.25855,-4.40539,-3.37725,-3.11648,-3.41809,-3.28534,-3.93923,14.2199,1.32258,28.7531,16.4695,1.50985,-11.5843,27.967,36.9389 +-3.89102,0.82705,0.692249,3,7,0,8.8088,6.65052,7.50849,1.09357,-1.32356,-0.831657,-0.441604,-0.13332,1.16368,-0.167913,0.783395,14.8616,0.406043,5.6495,5.38976,-3.28739,3.33475,15.388,12.5326,-4.01058,-3.50986,-3.83765,-3.32755,-3.14846,-3.33936,-3.25564,-3.80975,39.6294,5.46288,32.3546,1.21834,-7.41397,18.136,3.0468,37.4265 +-3.89102,0.192268,0.756119,3,7,0,7.87928,6.65052,7.50849,1.09357,-1.32356,-0.831657,-0.441604,-0.13332,1.16368,-0.167913,0.783395,14.8616,0.406043,5.6495,5.38976,-3.28739,3.33475,15.388,12.5326,-4.01058,-3.50986,-3.83765,-3.32755,-3.14846,-3.33936,-3.25564,-3.80975,-25.5218,-0.789958,-1.26312,-4.33436,5.5409,3.73308,23.6477,16.2457 +-3.66799,0.984883,0.182889,5,47,0,8.4571,1.06396,11.613,0.820501,0.436766,0.0239397,-1.00216,-0.62947,0.938451,-0.299449,-0.201558,10.5924,1.34197,-6.24606,-2.41353,6.13611,-10.5741,11.9622,-1.27673,-4.30038,-3.44317,-3.71211,-3.68301,-3.43051,-3.87039,-3.4038,-4.08133,5.66043,10.6641,12.558,-30.5739,-4.91084,-16.4988,-7.1387,17.2244 # Adaptation terminated -# Step size = 0.471367 +# Step size = 0.339982 # Diagonal elements of inverse mass matrix: -# 10.7236, 1.73513, 1.04721, 0.792423, 0.887697, 0.922656, 0.817726, 0.88704, 1.00112, 0.678474 --4.88161,0.944712,0.471367,3,7,0,8.12347,5.63651,1.71421,-0.297402,-0.561491,0.617434,0.0145136,1.41901,-0.906897,0.132988,0.479511,5.1267,4.674,6.69492,5.66139,8.06899,4.0819,5.86448,6.45849,-4.78963,-3.27684,-3.8751,-3.32424,-3.62386,-3.35608,-3.95788,-3.8567,-6.98617,-1.11176,11.5736,10.4953,11.5669,0.672047,-3.80239,6.28931 --5.6557,0.903407,0.471367,3,7,0,10.5498,0.792321,4.15632,0.15982,0.203153,-0.00171606,0.721724,-0.676866,1.68713,1.8895,-0.40296,1.45658,1.63669,0.785189,3.79203,-2.02095,7.80457,8.6457,-0.882509,-5.19266,-3.42398,-3.71951,-3.35936,-3.1226,-3.50816,-3.65904,-4.06542,23.9942,13.7272,-13.5219,9.52634,-7.66396,10.7384,23.3587,-12.5967 --5.74711,0.99922,0.471367,3,7,0,10.351,4.06827,1.84983,1.08678,0.207902,0.159091,0.850403,1.12547,-1.39207,-0.516097,0.952243,6.07863,4.45285,4.36256,5.64137,6.15019,1.49317,3.11357,5.82975,-4.69487,-3.28444,-3.7974,-3.32446,-3.43175,-3.31784,-4.32955,-3.86806,-4.70384,-13.2682,4.4179,4.63278,8.65422,20.7842,15.0706,-16.9771 --4.17577,0.991698,0.471367,3,7,0,8.35891,6.02445,2.74916,-0.529091,0.181898,-0.426025,-1.42246,-0.824093,-0.0542137,0.806478,0.374186,4.5699,6.52451,4.85324,2.11389,3.75889,5.87541,8.24158,7.05314,-4.84692,-3.23241,-3.81198,-3.41549,-3.25596,-3.41506,-3.69766,-3.84707,14.9537,12.4939,21.0402,-5.67012,13.4272,-13.6424,17.0698,15.115 --6.47866,0.78101,0.471367,3,7,0,9.85372,11.1943,3.23599,-0.389716,0.0548641,-1.30058,-0.681553,-0.801456,0.890561,1.09575,-0.0216662,9.9332,11.3719,6.98565,8.98882,8.60081,14.0762,14.7401,11.1242,-4.35234,-3.27837,-3.88628,-3.33318,-3.68515,-4.02339,-3.27466,-3.81049,28.4346,20.766,33.3285,14.7761,-1.04983,-9.38476,31.6903,41.9923 --9.65234,0.93115,0.471367,3,7,0,14.3755,-0.147751,0.957986,1.16111,0.417426,0.140729,1.14717,-0.48435,-1.57363,-0.509816,2.30052,0.964576,0.252137,-0.0129346,0.951224,-0.611751,-1.65527,-0.636147,2.05612,-5.25124,-3.52167,-3.70895,-3.46802,-3.11709,-3.34597,-4.95805,-3.9619,-23.2065,-2.32204,2.79957,7.21221,10.7622,3.46485,-13.4758,-0.677454 --8.84285,0.436643,0.471367,3,7,0,16.1083,-0.475276,3.13392,0.405435,1.30148,0.0383573,1.38785,-0.6389,-1.91534,-0.652813,1.59576,0.795325,3.60348,-0.355067,3.87413,-2.47754,-6.47781,-2.52114,4.52571,-5.27164,-3.31817,-3.70519,-3.35721,-3.12964,-3.5479,-5.32711,-3.89552,20.4286,-15.7449,35.1165,4.59057,-3.98891,3.65926,15.23,27.1229 --6.3332,0.994963,0.471367,3,7,0,11.6502,8.93668,6.65589,-0.283973,-0.802779,0.851031,-0.062246,-0.322182,-0.208434,2.04789,-0.738502,7.04659,3.59347,14.6011,8.52238,6.79227,7.54936,22.5672,4.02129,-4.60265,-3.31861,-4.2966,-3.32641,-3.49097,-3.49408,-3.32582,-3.90755,-2.32909,15.5229,25.2764,12.8174,7.33416,18.909,26.9028,-4.177 --4.59419,0.78989,0.471367,3,7,0,12.6638,1.32325,2.48823,-0.0312603,0.759591,-1.00602,0.159784,0.00585273,0.106885,-0.941305,0.622437,1.24546,3.21328,-1.17996,1.72082,1.33781,1.5892,-1.01893,2.87201,-5.21767,-3.33609,-3.698,-3.432,-3.1499,-3.31827,-5.03012,-3.93789,16.4457,-19.6817,-7.21322,10.0174,12.3518,3.17214,1.29346,6.12634 --6.42237,0.936984,0.471367,3,7,0,8.52988,7.91704,1.19703,0.0566544,1.18274,-0.307722,-0.915425,0.444548,1.00685,0.579987,1.38671,7.98486,9.33283,7.54869,6.82125,8.44918,9.12227,8.61131,9.57699,-4.51722,-3.23041,-3.90886,-3.31697,-3.66732,-3.58944,-3.66226,-3.81837,9.06617,17.5817,-8.81236,-13.1882,23.4843,13.1653,19.3647,40.0978 --5.35174,0.997728,0.471367,3,7,0,8.26649,6.56408,1.06957,0.378451,0.793704,-0.529857,-0.945233,0.162504,1.1062,0.0490766,1.02615,6.96886,7.413,5.99735,5.55308,6.73789,7.74724,6.61657,7.66162,-4.6099,-3.22325,-3.84964,-3.32548,-3.48576,-3.50495,-3.86944,-3.83836,-8.37739,-5.66566,19.1691,15.4425,19.1827,0.902783,13.0684,32.0074 --7.83481,0.890927,0.471367,3,7,0,10.0617,0.289422,0.405354,1.13691,-0.184292,-1.42185,-0.367705,0.127426,-0.958911,-0.486734,1.11209,0.750275,0.214719,-0.286932,0.140372,0.341075,-0.0992766,0.0921225,0.740211,-5.27709,-3.52458,-3.7059,-3.51127,-3.12726,-3.32183,-4.82498,-4.00496,28.8624,0.596198,8.50816,-23.7726,-7.68525,33.8632,-5.18707,-8.26812 --8.88646,0.589749,0.471367,3,7,0,14.9014,1.14818,0.193509,1.97194,0.410663,-0.722446,0.431988,0.419937,-1.01866,-0.767381,0.805366,1.52977,1.22765,1.00838,1.23178,1.22944,0.951061,0.999686,1.30403,-5.18404,-3.45085,-3.72291,-3.45432,-3.14684,-3.31684,-4.66658,-3.98586,-6.83656,-5.279,18.9507,6.12599,-5.58339,9.98471,5.35012,17.1935 --9.19782,0.957499,0.471367,3,7,0,14.3855,0.507667,0.25218,-1.57933,0.0180248,1.69795,0.536546,1.00488,0.254049,-0.863659,-0.369141,0.109391,0.512212,0.935857,0.642973,0.761078,0.571733,0.289869,0.414577,-5.35562,-3.50186,-3.72178,-3.48382,-3.13531,-3.31759,-4.78977,-4.01644,-3.27585,14.6351,-8.12364,7.55185,6.0018,10.9459,-7.40485,19.5898 --6.06481,0.996661,0.471367,3,7,0,12.2048,8.36022,7.93599,1.96674,0.137878,-1.86368,-0.304733,-1.07986,-0.524714,1.28333,-0.268517,23.9683,9.45442,-6.42994,5.94186,-0.209546,4.19609,18.5447,6.22927,-3.66311,-3.2321,-3.7145,-3.32146,-3.12002,-3.35904,-3.22301,-3.8607,29.716,2.55078,1.06018,8.92958,5.74443,5.12096,5.41398,14.354 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+-8.91872,0.997605,0.339982,4,15,0,15.8487,7.37484,4.53662,-1.71563,-0.304097,-0.0882439,-0.731616,0.516063,-1.48385,1.58743,-0.595855,-0.408347,6.97451,9.71602,14.5764,5.99527,4.05577,0.643169,4.67167,-5.4204,-3.22678,-4.00734,-3.55403,-3.41822,-3.35542,-4.72782,-3.89219,17.84,1.92896,20.0142,4.79733,0.119525,10.7656,4.91147,10.7076 +-7.99843,0.866727,0.339982,4,15,0,13.2826,1.79006,0.790653,1.59771,0.312465,-0.827791,0.950488,-0.933931,1.21544,-1.52806,-0.0413145,3.05329,1.13556,1.05164,0.581888,2.03711,2.54156,2.75105,1.75739,-5.00996,-3.45713,-3.72359,-3.48705,-3.1731,-3.32665,-4.38418,-3.97121,-7.88038,0.960447,1.12096,0.42686,15.7738,19.79,-4.06051,34.2331 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+-9.03212,0.946514,0.339982,3,7,0,11.1039,-0.869433,0.398167,-1.0013,0.128054,2.06384,-0.0431922,-0.375424,0.382653,-0.977789,0.775827,-1.26812,-0.0476777,-1.01891,-1.25876,-0.818446,-0.886631,-0.717073,-0.560524,-5.53059,-3.54535,-3.69919,-3.59868,-3.11637,-3.33154,-4.97317,-4.05278,2.52906,22.8681,-10.9436,3.33716,10.4131,4.73562,-10.6136,39.5003 +-6.11181,1,0.339982,3,7,0,9.98708,0.346473,0.76484,-0.918743,0.50009,1.16499,-0.475955,0.0635154,0.286067,-0.790265,0.509886,-0.356219,1.2375,0.395052,-0.257954,0.728962,-0.0175566,0.565268,0.736454,-5.41382,-3.45018,-3.71404,-3.53451,-3.13462,-3.32111,-4.74137,-4.00509,1.19141,4.80758,-7.0614,-7.4226,-3.56803,-15.28,-1.01936,-36.0832 +-5.56054,0.869735,0.339982,4,15,0,9.81823,7.48722,3.42373,0.479786,0.972044,-1.66431,0.123808,-0.593005,0.408759,0.849546,-0.0858158,9.12987,1.78906,5.45693,10.3958,10.8152,7.9111,8.8867,7.19341,-4.41828,-3.4144,-3.83121,-3.36449,-3.97789,-3.5142,-3.63678,-3.84496,7.98756,-20.5902,-21.0232,4.78475,9.18614,-0.207916,13.1947,30.2204 +-6.30561,0.9476,0.339982,3,15,0,12.5392,1.22003,7.59806,2.05946,-1.47285,0.869274,0.0369718,-1.40592,0.979333,-0.507283,-0.197309,16.8679,7.82482,-9.46224,-2.63433,-9.97074,1.50094,8.66105,-0.279135,-3.90237,-3.22168,-3.77309,-3.70039,-3.61292,-3.31787,-3.6576,-4.04199,27.4187,1.98953,2.1891,-6.46639,-2.56308,2.80804,26.5199,11.9164 +-7.31851,0.83151,0.339982,4,15,0,15.1824,7.24188,1.78433,0.287162,2.10035,-0.955953,-0.22028,0.885023,-0.611844,0.924395,0.0478374,7.75427,5.53614,8.82106,8.89131,10.9896,6.84883,6.15015,7.32724,-4.53785,-3.25188,-3.96445,-3.33161,-4.00351,-3.45819,-3.92362,-3.84301,-4.64711,23.385,9.36954,45.8526,1.16713,-17.25,-5.2994,-2.72355 # -# Elapsed Time: 0.311 seconds (Warm-up) -# 0.05 seconds (Sampling) -# 0.361 seconds (Total) +# Elapsed Time: 0.024466 seconds (Warm-up) +# 0.004564 seconds (Sampling) +# 0.02903 seconds (Total) # diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv --- a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv +++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools_warmup-4.csv @@ -1,5 +1,5 @@ # stan_version_major = 2 -# stan_version_minor = 24 +# stan_version_minor = 20 # stan_version_patch = 0 # model = stan_test_data_model # method = sample (Default) @@ -28,621 +28,621 @@ # stepsize_jitter = 0 (Default) # id = 4 # data -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\fdo0f5kc.json +# file = /tmp/tmp2ipdytqf/x8rvpwpf.json # init = 2 (Default) # random -# seed = 42813 +# seed = 31709 # output -# file = C:\Users\user\AppData\Local\Temp\tmpi921ksrd\stan_test_data-202010140133-4-9p_qyufb.csv +# file = /tmp/tmp2ipdytqf/stan_test_data-202102230057-4-h3yaihdn.csv # diagnostic_file = (Default) # refresh = 100 (Default) -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8 --9.6858,0,2,0,1,1,15.5941,1.49651,1.59,-0.488743,1.56105,-1.82573,1.45527,0.308196,-1.88253,1.23852,0.784357,0.719407,3.97858,-1.4064,3.8104,1.98654,-1.49672,3.46576,2.74364,-5.28083,-3.30238,-3.69649,-3.35887,-3.17122,-3.34259,-4.27774,-3.94153,-26.5729,16.6299,1.49487,5.51926,2.78776,-4.39852,-2.22528,-8.56978 --9.6858,0,2.33506,0,1,1,12.1696,1.49651,1.59,-0.488743,1.56105,-1.82573,1.45527,0.308196,-1.88253,1.23852,0.784357,0.719407,3.97858,-1.4064,3.8104,1.98654,-1.49672,3.46576,2.74364,-5.28083,-3.30238,-3.69649,-3.35887,-3.17122,-3.34259,-4.27774,-3.94153,-12.0841,8.06171,0.115793,-3.53732,7.78581,8.24489,5.16804,-3.03244 --4.13182,0.993277,0.230236,4,15,0,12.1508,2.22168,5.25072,1.32744,0.892111,-0.453732,0.616567,0.654841,1.13304,0.919962,0.471636,9.19171,6.9059,-0.160742,5.4591,5.66006,8.17097,7.05214,4.69811,-4.4131,-3.22751,-3.70727,-3.32665,-3.38997,-3.52932,-3.8208,-3.89159,18.9036,18.2485,9.269,-7.60824,17.4225,-6.11543,1.18117,7.36017 --3.27811,0.996135,0.235534,4,31,0,6.57706,4.48989,4.08137,0.0827223,-0.308544,0.292644,0.25432,-0.40814,-0.335459,-0.0927454,-1.20483,4.82751,3.23061,5.68428,5.52786,2.82412,3.12076,4.11136,-0.427458,-4.82024,-3.33526,-3.83883,-3.32579,-3.20643,-3.33542,-4.186,-4.04765,-25.1406,10.8867,30.0679,15.7234,9.20586,9.07441,-16.4433,12.3854 --4.97837,0.958599,0.314668,3,7,0,9.4468,4.08728,5.19966,1.9348,0.706011,-1.08871,-0.182912,-0.788389,0.478069,1.08801,-1.06399,14.1476,7.75829,-1.57363,3.1362,-0.0120787,6.57307,9.74456,-1.44511,-4.05341,-3.22182,-3.6955,-3.37852,-3.12219,-3.44518,-3.56229,-4.08828,17.2257,18.3375,1.36922,-9.97543,-8.73714,7.86132,-11.903,15.205 --5.9683,0.977361,0.434379,3,7,0,8.11507,4.21605,7.93983,1.20556,0.774304,-0.736258,0.835696,-1.49267,0.864941,1.84786,-0.509899,13.788,10.3639,-1.62971,10.8513,-7.63551,11.0835,18.8877,0.16754,-4.07584,-3.24946,-3.69519,-3.37813,-3.38795,-3.73699,-3.22546,-4.02537,-5.49047,5.35023,-10.084,23.7995,-5.43435,22.3007,26.8497,-5.84195 --4.10454,1,0.687011,3,7,0,7.33676,4.14293,4.29091,0.521235,0.850977,-0.622048,1.12272,-1.33316,0.437119,0.594575,-0.594817,6.3795,7.79439,1.47378,8.9604,-1.57755,6.01857,6.6942,1.59062,-4.66576,-3.22173,-3.73062,-3.33271,-3.11822,-3.42091,-3.86063,-3.97653,27.2379,10.4292,13.7561,14.4011,-16.1693,18.2505,-19.7901,21.9246 --6.00002,0.0501885,1.22471,2,3,0,13.2969,5.10034,0.567949,1.53602,-0.451345,0.446036,-0.283151,0.999234,-0.356562,-0.254697,0.838245,5.97272,4.844,5.35366,4.93952,5.66785,4.89783,4.95568,5.57642,-4.70521,-3.27133,-3.82782,-3.33438,-3.39061,-3.37962,-4.07229,-3.87299,40.1201,-1.42007,-9.13463,-1.43454,13.143,-9.79764,-3.40667,-7.62017 --5.53659,0.998467,0.113722,5,31,0,9.91071,3.59124,7.4112,-1.08014,-0.260205,-1.20927,0.668777,-1.06,0.615931,1.00702,-0.289495,-4.41389,1.66281,-5.37093,8.54768,-4.26461,8.15603,11.0545,1.44574,-5.96179,-3.42232,-3.70251,-3.32673,-3.18195,-3.52844,-3.46272,-3.98121,2.42966,6.40406,10.929,12.6839,7.73648,-2.68016,7.8634,-20.9903 --10.9278,0.912431,0.208273,5,31,0,15.1811,-3.62957,0.31008,-0.682671,1.15646,-1.22069,-1.18142,-0.891824,-0.796138,-0.391102,0.392435,-3.84125,-3.27097,-4.00808,-3.9959,-3.9061,-3.87643,-3.75084,-3.50788,-5.88002,-3.8567,-3.69351,-3.81646,-3.1683,-3.4151,-5.58702,-4.18044,5.85594,-5.23403,-2.67487,-19.1958,-6.7265,-12.8421,-15.5763,1.36607 --6.88749,0.991161,0.295602,4,15,0,16.3397,2.05871,0.711025,0.833695,-0.802178,1.07128,1.14475,1.23057,0.746997,0.54047,-0.460207,2.65148,1.48834,2.82041,2.87265,2.93367,2.58984,2.44299,1.73149,-5.05487,-3.43353,-3.75769,-3.38723,-3.21168,-3.32728,-4.43163,-3.97203,26.3645,9.63515,-5.67921,-7.11956,-2.03083,5.33603,-18.5217,1.40574 --6.06109,0.791694,0.542846,3,7,0,14.5756,3.95482,3.40108,-0.716072,1.11218,-1.03163,-0.585202,-1.34055,-1.45499,0.407681,0.929222,1.5194,7.73744,0.44616,1.9645,-0.604515,-0.993716,5.34137,7.11518,-5.18526,-3.22187,-3.71472,-3.42161,-3.11713,-3.33326,-4.02273,-3.84613,17.7153,10.8502,-4.21393,-6.49008,-8.84389,8.28074,37.9967,-16.5125 --7.4137,0.890706,0.533489,3,7,0,10.969,6.58936,3.01609,1.56982,-1.34997,0.923201,0.209477,1.02348,1.21448,0.627974,-1.09051,11.3241,2.51774,9.37381,7.22116,9.67625,10.2523,8.48338,3.30029,-4.24496,-3.3718,-3.99057,-3.31704,-3.81976,-3.67058,-3.67435,-3.92611,14.7346,22.137,7.65921,11.3341,21.7708,1.03907,29.0355,0.385837 --5.47107,0.951054,0.718223,3,7,0,11.9819,8.33726,1.46162,-0.967665,-0.0863702,-1.18033,-1.12351,-0.205615,0.172541,0.405668,-0.468256,6.9229,8.21102,6.61206,6.69511,8.03673,8.58945,8.93019,7.65285,-4.6142,-3.22175,-3.87198,-3.31722,-3.62025,-3.55485,-3.63283,-3.83847,21.9575,31.125,-26.5189,-2.2079,5.71528,6.61416,10.4739,9.09106 --5.47107,0.00641999,1.16705,2,3,0,9.55284,8.33726,1.46162,-0.967665,-0.0863702,-1.18033,-1.12351,-0.205615,0.172541,0.405668,-0.468256,6.9229,8.21102,6.61206,6.69511,8.03673,8.58945,8.93019,7.65285,-4.6142,-3.22175,-3.87198,-3.31722,-3.62025,-3.55485,-3.63283,-3.83847,-15.2553,15.8547,-10.0101,16.0288,4.37442,28.6957,24.7824,20.7754 --4.06194,0.997717,0.101186,5,31,0,9.83306,7.5564,1.61345,1.09138,0.244469,0.666089,0.453743,0.204483,-0.301884,-0.0945349,0.0346138,9.31729,7.95084,8.6311,8.28849,7.88633,7.06933,7.40388,7.61225,-4.40264,-3.22154,-3.95575,-3.32369,-3.60361,-3.46905,-3.78291,-3.83902,9.94127,24.341,4.09682,5.34897,0.0191557,-0.720219,0.166966,7.026 --5.04929,0.975429,0.191932,3,7,0,10.1598,7.48591,12.1987,1.30903,1.04193,0.234102,-0.703213,-0.135278,0.477112,0.678253,-0.243957,23.4543,20.196,10.3416,-1.09235,5.83571,13.3061,15.7597,4.50997,-3.67291,-3.96524,-4.03918,-3.58744,-3.4046,-3.94261,-3.24662,-3.89589,26.5416,27.9984,-8.95156,5.21506,8.99797,21.6773,21.3179,24.2399 --5.0704,0.991687,0.337709,4,15,0,8.48556,4.64205,2.79736,0.192897,-1.2544,0.82051,-0.90089,0.770887,0.455476,0.390124,-0.796909,5.18165,1.13304,6.93731,2.12193,6.7985,5.91618,5.73336,2.4128,-4.78405,-3.4573,-3.8844,-3.41516,-3.49157,-3.4167,-3.97388,-3.95115,7.0729,-24.0089,37.5606,7.56026,0.769042,8.32135,6.60382,-17.6816 --7.71492,0.711021,0.619784,3,15,0,12.7243,7.74446,0.507132,-0.575448,-1.20759,0.277161,0.26215,0.797601,1.7291,0.963448,0.542029,7.45263,7.13205,7.88502,7.8774,8.14895,8.62134,8.23306,8.01934,-4.5652,-3.22529,-3.92294,-3.32001,-3.63285,-3.55685,-3.69849,-3.83376,24.3563,1.70678,26.5288,10.248,5.06086,-1.69472,7.46186,14.1391 --9.46942,0.702245,0.485028,3,7,0,17.3262,7.92036,1.36327,-1.61047,-1.35304,1.099,-0.923028,0.85863,1.42893,1.34218,0.612733,5.72486,6.0758,9.41859,6.66203,9.09091,9.86839,9.75011,8.75569,-4.72962,-3.24004,-3.99274,-3.31731,-3.74472,-3.64183,-3.56183,-3.82555,-4.67985,-0.592055,21.5007,11.3722,21.1467,18.4488,0.0882065,21.2862 --10.9615,0.936657,0.371531,3,7,0,17.4993,8.01098,2.70429,0.327916,0.803972,1.24746,-2.44406,1.65821,1.21727,1.23932,-0.616359,8.89776,10.1851,11.3845,1.40155,12.4952,11.3028,11.3624,6.34417,-4.43787,-3.2454,-4.09565,-3.44635,-4.24037,-3.75546,-3.44181,-3.85868,0.88744,8.7427,3.13168,3.03546,4.95434,5.23322,9.3196,-7.63718 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+-6.79908,0.317747,0.862429,2,3,0,10.3678,1.41696,3.81896,-0.180051,-0.742721,1.4149,0.156367,-0.451063,2.50416,0.201391,1.42453,0.729352,6.8204,-0.305632,2.18607,-1.41946,2.01412,10.9802,6.85719,-5.27963,-3.22848,-3.70571,-3.41259,-3.11725,-3.32108,-3.46791,-3.85013,20.5634,6.53921,-11.8879,-5.98546,-0.458243,8.30767,8.21848,6.42336 +-6.02392,0.997976,0.26244,4,15,0,11.5752,2.76592,5.65889,0.269961,-0.619783,-0.351081,-1.14945,-0.466436,2.39061,0.120404,1.52718,4.2936,0.779194,0.12641,3.44727,-0.741361,-3.73871,16.2941,11.4081,-4.87586,-3.48222,-3.71062,-3.36899,-3.11658,-3.40962,-3.23607,-3.80985,15.0622,-8.73528,-0.379044,-5.4656,-0.5572,-2.32316,30.2564,9.23969 +-7.12765,0.924505,0.442838,3,15,0,9.79347,5.34446,0.557421,0.588151,-0.0980776,0.0153352,0.962812,0.106961,-1.69768,-0.146219,-1.55717,5.67231,5.35301,5.40409,5.26296,5.28979,5.88116,4.39814,4.47647,-4.73482,-3.25656,-3.82947,-3.3293,-3.36037,-3.41529,-4.14658,-3.89666,4.50419,16.7229,-3.57569,3.48371,2.29822,3.24219,3.15623,23.6065 +-7.76049,0.852472,0.618229,3,7,0,13.9178,4.78942,0.474197,0.524858,0.0799262,0.811577,2.32898,0.56347,-0.744069,-0.123477,-0.700401,5.0383,5.17427,5.05661,4.73087,4.82732,5.89381,4.43658,4.45729,-4.79863,-3.26145,-3.8183,-3.33811,-3.32578,-3.4158,-4.14135,-3.89711,28.7515,2.372,-9.70119,4.6775,5.73365,-2.7664,13.0404,14.9878 +-9.35059,0.463484,0.719563,2,7,0,13.8643,10.1456,0.861007,-1.50484,-0.57572,1.1976,-0.00842597,-1.51873,0.389287,-0.218763,-1.62813,8.84994,11.1768,8.83799,9.95726,9.64992,10.1384,10.4808,8.74378,-4.44193,-3.27198,-3.96523,-3.35297,-3.81629,-3.66192,-3.50422,-3.82567,2.35104,9.2777,-7.09802,15.3534,13.2595,24.7164,-14.2178,-12.7084 +-7.7343,0.980981,0.323531,4,15,0,14.1507,5.90573,0.420652,1.13952,1.72613,0.408416,0.782748,0.583886,-0.261059,1.02549,0.910736,6.38507,6.07753,6.15134,6.3371,6.63183,6.23499,5.79591,6.28883,-4.66522,-3.24,-3.8551,-3.31865,-3.4757,-3.43008,-3.96622,-3.85965,14.1011,1.52228,18.6674,14.9983,-1.54324,-2.89088,2.60725,-14.9607 +-5.46676,0.854159,0.5152,3,7,0,10.912,9.58009,2.20707,-0.314878,-0.402162,1.06118,-0.453308,-0.970252,0.20413,1.00581,0.483059,8.88514,11.9222,7.43868,11.8,8.69249,8.57961,10.0306,10.6462,-4.43894,-3.29844,-3.90435,-3.41204,-3.69607,-3.55423,-3.53908,-3.81214,18.957,27.2057,26.1589,-12.324,3.7666,0.685077,11.8511,19.0874 +-4.08374,1,0.600748,3,7,0,7.83901,0.0104924,7.11243,1.94868,0.0380325,-0.305578,0.371236,0.723505,0.7635,0.394666,-0.244497,13.8704,-2.16291,5.15637,2.81753,0.280996,2.65088,5.44083,-1.72847,-4.07065,-3.73795,-3.82146,-3.38912,-3.12629,-3.3281,-4.01019,-4.10016,16.9482,9.21238,23.6655,-0.438905,-9.90762,-7.54524,-2.26621,-7.74847 +-4.08374,0.00482967,0.990292,2,7,0,12.0637,0.0104924,7.11243,1.94868,0.0380325,-0.305578,0.371236,0.723505,0.7635,0.394666,-0.244497,13.8704,-2.16291,5.15637,2.81753,0.280996,2.65088,5.44083,-1.72847,-4.07065,-3.73795,-3.82146,-3.38912,-3.12629,-3.3281,-4.01019,-4.10016,47.5189,-5.7527,33.1614,-16.7015,-0.526181,-1.62922,-8.17529,13.6866 +-7.39284,0.988832,0.152862,4,15,0,9.40933,0.467311,5.29418,2.44886,0.139144,-1.52021,0.0985774,0.584874,0.791412,0.521561,-0.857122,13.432,-7.58098,3.56374,3.22855,1.20397,0.989198,4.65719,-4.07045,-4.0986,-4.43536,-3.77567,-3.37561,-3.14615,-3.31683,-4.11168,-4.20786,-3.05281,-13.505,-13.6225,4.96518,6.23859,-0.431019,-0.581437,16.6038 # Adaptation terminated -# Step size = 0.421219 +# Step size = 0.436713 # Diagonal elements of inverse mass matrix: -# 13.3539, 1.01227, 0.850668, 0.916738, 0.958114, 0.683339, 0.74495, 0.905992, 0.772253, 0.946218 --6.84336,0.966538,0.421219,3,7,0,10.2295,4.86902,3.38118,-0.158458,0.0316127,1.33482,-0.95114,0.616302,-0.40855,2.30013,-0.998249,4.33324,4.9759,9.38228,1.65304,6.95284,3.48763,12.6462,1.49376,-4.87169,-3.26725,-3.99098,-3.43497,-3.50658,-3.34241,-3.36484,-3.97965,8.63553,5.1349,11.2785,8.82464,11.6015,-15.6877,11.6974,0.827816 --4.67817,0.995987,0.421219,3,7,0,9.24717,2.25512,4.23065,0.36803,0.00365295,0.833945,-0.854427,0.624093,0.982956,1.16329,-0.728012,3.81213,2.27057,5.78325,-1.35966,4.89544,6.41366,7.17659,-0.824841,-4.92711,-3.38566,-3.8422,-3.60561,-3.33071,-3.43794,-3.80726,-4.06313,-15.8924,-1.3056,-13.7901,-4.44665,-18.1145,0.706098,8.48738,4.09662 --4.10784,0.910446,0.421219,3,15,0,11.3383,5.05747,4.45582,0.684564,0.672383,0.895867,-1.00242,-0.166512,0.443777,0.756038,-0.935322,8.10776,8.05348,9.04929,0.590886,4.31552,7.03486,8.42624,0.889843,-4.50632,-3.22154,-3.97509,-3.48657,-3.29058,-3.46733,-3.67981,-3.9998,-0.372633,8.50948,-7.59578,5.883,-2.26883,-0.332696,-10.0358,-19.752 --6.79091,0.87796,0.421219,3,7,0,9.99332,4.70638,2.36469,0.113717,-2.19221,0.0369365,0.199191,-1.64915,-1.02582,0.675907,0.00605949,4.97528,-0.477515,4.79372,5.1774,0.806635,2.28064,6.30469,4.72071,-4.80507,-3.58086,-3.81016,-3.33056,-3.13631,-3.32361,-3.90542,-3.89108,-4.15279,-9.77897,14.4448,-7.89858,10.4637,8.62503,9.12031,31.8978 --6.02531,0.993208,0.421219,3,7,0,8.90645,4.48966,12.151,1.16686,1.94246,0.18702,0.509531,0.107623,0.275969,0.434506,0.256501,18.6681,28.0924,6.76213,10.6809,5.79738,7.84295,9.76933,7.6064,-3.82051,-5.24005,-3.87766,-3.37282,-3.40138,-3.51033,-3.56024,-3.8391,27.8665,21.2131,5.47256,8.47243,20.8371,11.7816,20.6188,25.7603 --5.94687,1,0.421219,3,7,0,8.89709,-0.0455336,1.56298,0.84223,-1.09003,0.951538,0.0808742,0.575682,0.557354,1.18469,-0.134556,1.27086,-1.74924,1.44171,0.0808715,0.854249,0.825602,1.80612,-0.255843,-5.21465,-3.69676,-3.73006,-3.51466,-3.13739,-3.31696,-4.53273,-4.04111,10.296,-2.60344,-11.4238,-8.49782,10.2849,1.08815,-11.559,-1.70252 --6.83216,0.922268,0.421219,4,15,0,12.6567,-3.2546,4.27818,-0.521889,-0.0963222,0.157438,-0.548141,0.485716,0.93622,1.31909,1.06844,-5.48733,-3.66668,-2.58105,-5.59964,-1.17662,0.750719,2.38869,1.31639,-6.11899,-3.90208,-3.69187,-3.97283,-3.11636,-3.31709,-4.44009,-3.98545,-17.5451,-5.84632,16.8414,-2.4447,1.74615,-3.17967,0.737064,43.0555 --6.31123,0.929346,0.421219,3,7,0,12.4165,2.14033,4.60242,1.26849,0.282745,-0.83771,-0.47639,1.19248,1.24029,2.06457,0.28438,7.97847,3.44164,-1.71517,-0.052224,7.62862,7.84868,11.6424,3.44916,-4.51779,-3.32542,-3.69475,-3.52235,-3.57575,-3.51065,-3.42362,-3.92214,25.8865,9.90384,12.8097,15.9038,-3.79296,18.4493,21.0862,20.6257 --5.3362,0.980393,0.421219,3,7,0,9.68744,0.370224,4.15967,0.445722,0.175798,1.2462,-0.92825,0.751758,0.405098,0.939964,1.13877,2.22428,1.10148,5.554,-3.49099,3.49729,2.0553,4.28016,5.10712,-5.10341,-3.45947,-3.83444,-3.77163,-3.24101,-3.32144,-4.16269,-3.88263,-8.81259,-0.579909,-18.1852,3.85786,11.4628,-5.68782,16.2893,40.2412 --8.64399,0.921087,0.421219,3,7,0,12.659,5.20433,3.78903,1.07699,-0.0565486,1.75171,-1.90884,-1.6985,-1.52073,0.527791,0.394381,9.28509,4.99007,11.8416,-2.02834,-1.23134,-0.55776,7.20415,6.69865,-4.40532,-3.26682,-4.12175,-3.65366,-3.11649,-3.32686,-3.80428,-3.85268,-10.2281,-2.6322,16.5033,-9.47102,-5.01319,3.7061,12.1213,-3.14054 --9.33341,0.994527,0.421219,4,15,0,13.0767,4.79229,2.01686,-0.376561,0.0778305,-1.7878,2.30397,1.2927,1.56926,0.20313,-0.390569,4.03282,4.94926,1.18656,9.43906,7.39948,7.95727,5.20197,4.00457,-4.90349,-3.26806,-3.72576,-3.34142,-3.55167,-3.51685,-4.04047,-3.90796,27.4814,8.80113,-21.0596,2.21277,18.7201,20.9351,-1.33299,2.36273 --10.5709,0.824515,0.421219,4,15,0,14.4192,8.83839,0.185727,0.350615,-0.0778317,-0.572897,-0.641065,-1.52029,1.44067,-1.64256,-1.37921,8.90351,8.82393,8.73199,8.71932,8.55603,9.10596,8.53332,8.58223,-4.43738,-3.22492,-3.96035,-3.32905,-3.67985,-3.58835,-3.66961,-3.82734,-6.73215,14.3269,-7.30824,26.6138,9.34256,-15.1566,-5.6108,13.5193 --9.00928,0.998801,0.421219,3,7,0,15.0597,4.6419,0.497794,-0.298304,0.634846,-0.270038,-0.507208,-1.41297,-0.0398156,-1.20317,-2.46255,4.49341,4.95792,4.50748,4.38941,3.93853,4.62208,4.04297,3.41606,-4.8549,-3.26779,-3.80161,-3.345,-3.26671,-3.37105,-4.19552,-3.92302,14.7619,5.10349,-0.920547,20.5269,10.181,16.3518,14.2867,27.3045 --6.06098,0.99661,0.421219,4,15,0,12.4098,0.988142,4.62263,1.33599,-0.211516,-0.977347,1.85203,-0.53962,-1.11205,1.07703,-0.435649,7.16391,0.0103817,-3.52977,9.54941,-1.50632,-4.15244,5.96686,-1.0257,-4.59175,-3.54069,-3.69208,-3.34369,-3.11775,-3.42653,-3.94551,-4.07115,16.7705,0.87902,11.7241,-15.7694,-10.3518,-18.8318,8.77907,-10.8259 --2.91488,0.957625,0.421219,3,7,0,9.71308,4.83636,5.19271,0.803761,0.334931,0.567268,0.286608,-0.916465,-0.0724461,1.1825,0.775814,9.01006,6.57556,7.78202,6.32464,0.0774311,4.46017,10.9767,8.86494,-4.42836,-3.23167,-3.91858,-3.31872,-3.12333,-3.36631,-3.46816,-3.82448,3.2177,6.04405,16.3196,19.0651,18.554,-2.0874,11.7884,8.19613 --4.18358,0.947941,0.421219,3,7,0,5.60971,3.49415,2.83946,0.433331,0.848052,-0.450982,-0.347148,-1.13303,-0.539764,-0.540249,0.937277,4.72457,5.90215,2.2136,2.50844,0.276962,1.96151,1.96013,6.1555,-4.83087,-3.24353,-3.74462,-3.4002,-3.12623,-3.32065,-4.50791,-3.86202,22.1779,-22.6756,7.25064,-19.4083,0.345886,8.49066,10.9829,30.1725 --5.5954,0.871312,0.421219,3,7,0,11.5886,4.56275,2.9081,0.18022,-1.09533,0.315151,-0.643519,1.29167,0.852279,1.35604,-0.46035,5.08685,1.37742,5.47924,2.69133,8.31906,7.04126,8.50625,3.22401,-4.79368,-3.44082,-3.83195,-3.39355,-3.65224,-3.46765,-3.67218,-3.92817,9.58081,1.6818,12.1141,4.056,16.0829,20.8201,11.939,-10.9185 --5.28352,0.98021,0.421219,3,15,0,7.49695,4.86785,4.35811,-0.571253,-0.539094,-0.510921,-0.268968,1.40526,0.741164,0.857733,-0.64158,2.37827,2.51842,2.6412,3.69566,10.9922,8.09793,8.60595,2.07178,-5.08582,-3.37176,-3.75368,-3.36195,-4.00389,-3.52502,-3.66276,-3.96142,-8.10757,-4.31463,42.6726,22.5258,3.46142,7.04393,8.25663,22.5263 --5.88492,0.951973,0.421219,3,15,0,10.4431,-0.295645,5.89242,1.71152,0.305756,1.28208,-0.748429,-0.202677,0.390134,0.119438,-0.826551,9.78936,1.506,7.25893,-4.7057,-1.4899,2.00319,0.408133,-5.16603,-4.36394,-3.43238,-3.89708,-3.88305,-3.11764,-3.32099,-4.76889,-4.26405,58.4519,9.89142,13.1794,1.69807,-7.62854,1.41667,9.53697,-4.719 --6.08627,0.928662,0.421219,3,7,0,12.3117,2.13687,7.49464,1.70991,-0.404871,1.68832,-0.465066,-0.624007,0.52743,0.929514,-0.86842,14.952,-0.897494,14.7902,-1.34863,-2.53983,6.08977,9.10324,-4.37162,-4.00532,-3.61735,-4.30967,-3.60485,-3.1308,-3.42388,-3.61729,-4.22294,13.5823,13.1112,4.32406,-11.3493,7.3297,1.31333,-4.63977,-23.8109 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+-8.78781,0.939713,0.436713,3,7,0,14.0557,7.16821,4.38902,-0.525051,-0.843532,-1.5068,0.539268,-1.52679,2.18386,-0.0626235,1.67902,4.86375,0.554807,0.467086,6.89335,3.46592,9.53507,16.7532,14.5374,-4.81651,-3.49868,-3.71501,-3.31688,-3.23928,-3.61786,-3.2293,-3.81925,6.97256,-2.32261,14.3272,4.8157,-6.55892,10.1342,14.9615,17.2824 +-4.90252,1,0.436713,3,7,0,11.1868,8.58925,6.60234,1.25539,-0.925871,1.31644,0.046576,0.120696,0.107203,-0.865333,-0.173341,16.8778,17.2808,9.38613,2.87603,2.47634,8.89676,9.29704,7.4448,-3.90189,-3.65219,-3.99117,-3.38711,-3.19076,-3.57451,-3.60023,-3.84133,-25.8184,15.9418,17.0387,-3.58259,15.5283,12.2942,19.9725,-13.2686 +-7.7406,0.970133,0.436713,3,7,0,10.6304,-2.31031,2.86949,-0.163729,1.00896,-0.720878,-0.180005,-0.437933,1.54325,1.70591,0.84247,-2.78013,-4.37887,-3.56696,2.58479,0.584895,-2.82683,2.11802,0.107151,-5.73236,-3.98771,-3.69216,-3.39739,-3.13167,-3.37735,-4.48271,-4.02758,8.72359,2.58623,28.1673,0.892796,-9.51284,3.02114,7.80321,-3.03994 +-6.24377,0.923775,0.436713,3,7,0,13.6827,3.33142,9.22052,0.793958,-0.849003,0.971866,0.224636,0.285373,-0.710093,-0.350898,1.79805,10.6521,12.2925,5.9627,0.0959552,-4.49683,5.40268,-3.21601,19.9103,-4.29576,-3.31365,-3.84842,-3.5138,-3.19164,-3.39693,-5.47212,-3.90587,27.8098,27.4832,16.3894,10.7169,-11.1983,-1.14117,-5.66651,-10.0507 +-5.02565,1,0.436713,3,7,0,8.66406,8.67861,1.88381,0.297162,0.17331,-0.867329,-1.05028,0.462701,0.684949,0.953253,-0.0303146,9.23841,7.04473,9.55025,10.4744,9.00509,6.70008,9.96892,8.6215,-4.40921,-3.22609,-3.99916,-3.36671,-3.73408,-3.45109,-3.54401,-3.82692,0.476372,3.22476,22.9414,18.8303,11.227,13.7054,16.9439,3.92959 # -# Elapsed Time: 0.241 seconds (Warm-up) -# 0.039 seconds (Sampling) -# 0.28 seconds (Total) +# Elapsed Time: 0.042753 seconds (Warm-up) +# 0.00453 seconds (Sampling) +# 0.047283 seconds (Total) #

az.from_cmdstanpy fails from latest version of cmdstanpy (v0.9.68) for 2d params **Describe the bug** After updating to the latest version of cmdstanpy, the `az.from_cmdstanpy` fails when dims provided had length > 1. The error message is : ```python ValueError: different number of dimensions on data and dims: 3 vs 4 ``` The stacktrace is provided below. The problem appears to be that `az.data.io_cmdstanpy._unpack_fit` returns a flattened result for each parameter, rather than mirroring the shape of the parameter along the lines of what `cmdstanpy.CmdStanMCMC.stan_variable` does. This does not cause an error for scalar or 1d parameters, but does cause an error for 2+d parameters where the dims are provided. **To Reproduce** See [this gist](https://gist.github.com/jburos/d39a6d1d9db7517483dcd05813996e85) **Expected behavior** The InferenceData object should be created from a CmdStanMCMC object. **Additional context** #### Relevant parts of the stacktrace: ```python ~/projects/workflow2/workflow/models/stanmodel.py in prepare_inference_data(cls, fit, coords, prior_fit, stan_data, **kwargs) 244 if prior_fit is not None: 245 input_args = dict(prior=prior_fit, **input_args) --> 246 idata = az.from_cmdstanpy(**input_args) 247 # add information about the stan model class, etc. 248 run_id = cls._get_run_id(idata) ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/io_cmdstanpy.py in from_cmdstanpy(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, coords, dims, save_warmup) 657 InferenceData object 658 """ --> 659 return CmdStanPyConverter( 660 posterior=posterior, 661 posterior_predictive=posterior_predictive, ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/io_cmdstanpy.py in to_inference_data(self) 333 save_warmup=self.save_warmup, 334 **{ --> 335 "posterior": self.posterior_to_xarray(), 336 "sample_stats": self.sample_stats_to_xarray(), 337 "posterior_predictive": self.posterior_predictive_to_xarray(), ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/base.py in wrapped(cls, *args, **kwargs) 44 if all([getattr(cls, prop_i) is None for prop_i in prop]): 45 return None ---> 46 return func(cls, *args, **kwargs) 47 48 return wrapped ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/io_cmdstanpy.py in posterior_to_xarray(self) 93 94 return ( ---> 95 dict_to_dataset(data, library=self.cmdstanpy, coords=coords, dims=dims), 96 dict_to_dataset(data_warmup, library=self.cmdstanpy, coords=coords, dims=dims), 97 ) ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/base.py in dict_to_dataset(data, attrs, library, coords, dims, skip_event_dims) 238 data_vars = {} 239 for key, values in data.items(): --> 240 data_vars[key] = numpy_to_data_array( 241 values, var_name=key, coords=coords, dims=dims.get(key), skip_event_dims=skip_event_dims 242 ) ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/base.py in numpy_to_data_array(ary, var_name, coords, dims, skip_event_dims) 197 # filter coords based on the dims 198 coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in dims} --> 199 return xr.DataArray(ary, coords=coords, dims=dims) 200 201 ~/.local/share/virtualenvs/workflow2-PgZLfFHB/lib/python3.8/site-packages/xarray/core/dataarray.py in __init__(self, data, coords, dims, name, attrs, indexes, fastpath) 401 data = _check_data_shape(data, coords, dims) 402 data = as_compatible_data(data) --> 403 coords, dims = _infer_coords_and_dims(data.shape, coords, dims) 404 variable = Variable(dims, data, attrs, fastpath=True) 405 indexes = dict( ~/.local/share/virtualenvs/workflow2-PgZLfFHB/lib/python3.8/site-packages/xarray/core/dataarray.py in _infer_coords_and_dims(shape, coords, dims) 119 dims = tuple(dims) 120 elif len(dims) != len(shape): --> 121 raise ValueError( 122 "different number of dimensions on data " 123 "and dims: %s vs %s" % (len(shape), len(dims)) ValueError: different number of dimensions on data and dims: 3 vs 4 ``` #### System info Arviz version: `0.11.1` cmdstanpy version: `0.9.68` Python: `3.8.7 (default, Feb 3 2021, 06:31:03) \n[Clang 12.0.0 (clang-1200.0.32.29)]` `from_cmdstanpy` doesn't capture log_likelihood when provided as a dict **Describe the bug** When using `from_cmdstanpy(CmdStanMCMC, log_likelihood = {'output_name': 'log_lik_parameter_name'})`, the log_likelihood group isn't returned in the az.InferenceData object. However, if I use `from_cmdstanpy(CmdStanMCMC, log_likelihood = ['log_lik_parameter_name'])`, the log_likelihood group is created correctly. In both cases the log_likelihood is correctly filtered out from the `posterior` draws, per [these lines](https://github.com/arviz-devs/arviz/blob/master/arviz/data/io_cmdstanpy.py#L249-L256). However, the `log_likelihood_to_xarray` method does not address the scenario where the user provides the log_likelihood as a dict ([per these lines](https://github.com/arviz-devs/arviz/blob/master/arviz/data/io_cmdstanpy.py#L211-L220)). **To Reproduce** I updated a gist [here](https://gist.github.com/jburos/d39a6d1d9db7517483dcd05813996e85/1f1223cadbe22e8a227ffaf59b6657b72f88c675) to create a CmdStan fit for the same eight_schools model in the documentation. The gist creates az.InferenceData objects in two ways: ```python idata_from_cmdstanpy = az.from_cmdstanpy(fit_from_cmdstanpy, dims=dims, coords=coords, log_likelihood = ['log_lik']) idata2_from_cmdstanpy = az.from_cmdstanpy(fit_from_cmdstanpy, dims=dims, coords=coords, log_likelihood = {'y': 'log_lik'}) ``` Output: ```python In [2]: idata_from_cmdstanpy Out[2]: Inference data with groups: > posterior > log_likelihood > sample_stats In [3]: idata2_from_cmdstanpy Out[3]: Inference data with groups: > posterior > sample_stats ``` **Expected behavior** Both methods of describing `log_likelihood` output should be supported **Additional context** ```python In [4]: az.__version__ Out[4]: '0.10.0' ```

Is this with the latest release or with the development version? (both will show 0.11.1) I think we got a PR in that fixes this, but it has not yet been released I just released a new version to include the changes that update the cmdstanpy converter. Let us know if there are more issues. I expect the new release to be available on pypi in 30-40 mins and on conda-forge in a few hours. > Is this with the latest release or with the development version? (both will show 0.11.1) > > I think we got a PR in that fixes this, but it has not yet been released This was with the latest development version, as of yesterday. Today's development version now appears to work, thanks so much. Great! I will close the issue then, let us know if there are any other problems :smile: Arg. I spoke too soon, I'm still seeing this behavior on `cmdstanpy` version 0.9.68 & `arviz` 0.11.2. ```python ----> 1 idata2_from_cmdstanpy = az.from_cmdstanpy(fit_from_cmdstanpy, dims=dims_all, coords=coords, posterior_predictive = ["y_hat"], log_likelihood = ['log_lik']) ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/io_cmdstanpy.py in from_cmdstanpy(posterior, posterior_predictive, predictions, prior, prior_predictive, observed_data, constant_data, predictions_constant_data, log_likelihood, index_origin, coords, dims, save_warmup) 699 InferenceData object 700 """ --> 701 return CmdStanPyConverter( 702 posterior=posterior, 703 posterior_predictive=posterior_predictive, ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/io_cmdstanpy.py in to_inference_data(self) 371 save_warmup=self.save_warmup, 372 **{ --> 373 "posterior": self.posterior_to_xarray(), 374 "sample_stats": self.sample_stats_to_xarray(), 375 "posterior_predictive": self.posterior_predictive_to_xarray(), ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/base.py in wrapped(cls, *args, **kwargs) 45 if all([getattr(cls, prop_i) is None for prop_i in prop]): 46 return None ---> 47 return func(cls, *args, **kwargs) 48 49 return wrapped ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/io_cmdstanpy.py in posterior_to_xarray(self) 93 94 return ( ---> 95 dict_to_dataset(data, library=self.cmdstanpy, coords=coords, dims=dims), 96 dict_to_dataset(data_warmup, library=self.cmdstanpy, coords=coords, dims=dims), 97 ) ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/base.py in dict_to_dataset(data, attrs, library, coords, dims, default_dims, index_origin, skip_event_dims) 284 data_vars = {} 285 for key, values in data.items(): --> 286 data_vars[key] = numpy_to_data_array( 287 values, 288 var_name=key, ~/.local/share/virtualenvs/workflow2-PgZLfFHB/src/arviz/arviz/data/base.py in numpy_to_data_array(ary, var_name, coords, dims, default_dims, index_origin, skip_event_dims) 231 # filter coords based on the dims 232 coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in dims} --> 233 return xr.DataArray(ary, coords=coords, dims=dims) 234 235 ~/.local/share/virtualenvs/workflow2-PgZLfFHB/lib/python3.8/site-packages/xarray/core/dataarray.py in __init__(self, data, coords, dims, name, attrs, indexes, fastpath) 401 data = _check_data_shape(data, coords, dims) 402 data = as_compatible_data(data) --> 403 coords, dims = _infer_coords_and_dims(data.shape, coords, dims) 404 variable = Variable(dims, data, attrs, fastpath=True) 405 indexes = dict( ~/.local/share/virtualenvs/workflow2-PgZLfFHB/lib/python3.8/site-packages/xarray/core/dataarray.py in _infer_coords_and_dims(shape, coords, dims) 119 dims = tuple(dims) 120 elif len(dims) != len(shape): --> 121 raise ValueError( 122 "different number of dimensions on data " 123 "and dims: %s vs %s" % (len(shape), len(dims)) ValueError: different number of dimensions on data and dims: 3 vs 4 ``` log_likelihood accepts `str` or a `list of str`. (See docstring) What would be the purpose of that dict? Ah, interesting. I was looking at [this pystan schema example in the User Guide](https://arviz-devs.github.io/arviz/schema/PyStan_schema_example.html): ```python idata_stan = az.from_pystan( posterior=posterior, prior=prior, posterior_predictive=["slack_comments_hat","github_commits_hat"], prior_predictive=["slack_comments_hat","github_commits_hat"], observed_data=["slack_comments","github_commits"], constant_data=["time_since_joined"], log_likelihood={ "slack_comments": "log_likelihood_slack_comments", "github_commits": "log_likelihood_github_commits" }, predictions=["slack_comments_pred", "github_commits_pred"], predictions_constant_data=["time_since_joined_pred"], coords={"developer": names, "candidate developer" : candidate_devs}, dims={ "slack_comments": ["developer"], "github_commits" : ["developer"], "slack_comments_hat": ["developer"], "github_commits_hat": ["developer"], "time_since_joined": ["developer"], "slack_comments_pred" : ["candidate developer"], "github_commits_pred" : ["candidate developer"], "time_since_joined_pred" : ["candidate developer"], } ) ``` Which includes a `dict` for the scenario where there are multiple observed values, each with a different log_likelihood. Ok, yeah. There could an option to rename values.

2021-02-22T01:15:18Z

arviz-devs/arviz

arviz-devs__arviz-1588

c562fad36aed92a4d007bc1d80c13d69dafe18e7

diff --git a/arviz/data/base.py b/arviz/data/base.py --- a/arviz/data/base.py +++ b/arviz/data/base.py @@ -59,7 +59,7 @@ def wrapped(cls: RequiresArgTypeT) -> Optional[RequiresReturnTypeT]: """Return None if not all props are available.""" for prop in self.props: prop = [prop] if isinstance(prop, str) else prop - if all([getattr(cls, prop_i) is None for prop_i in prop]): + if all((getattr(cls, prop_i) is None for prop_i in prop)): return None return func(cls) diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -43,7 +43,7 @@ def fixed_eq(self, other): """Use object identity for MultiObservedRV equality.""" return self is other - if tuple([int(x) for x in pm.__version__.split(".")]) < (3, 9): # type: ignore + if tuple((int(x) for x in pm.__version__.split("."))) < (3, 9): # type: ignore pm.model.MultiObservedRV.__eq__ = fixed_eq # type: ignore diff --git a/arviz/plots/backends/bokeh/energyplot.py b/arviz/plots/backends/bokeh/energyplot.py --- a/arviz/plots/backends/bokeh/energyplot.py +++ b/arviz/plots/backends/bokeh/energyplot.py @@ -62,7 +62,7 @@ def plot_energy( if (fill_color[0].startswith("C") and len(fill_color[0]) == 2) and ( fill_color[1].startswith("C") and len(fill_color[1]) == 2 ): - fill_color = tuple([_colors[int(color[1:]) % 10] for color in fill_color]) + fill_color = tuple((_colors[int(color[1:]) % 10] for color in fill_color)) elif fill_color[0].startswith("C") and len(fill_color[0]) == 2: fill_color = tuple([_colors[int(fill_color[0][1:]) % 10]] + list(fill_color[1:])) elif fill_color[1].startswith("C") and len(fill_color[1]) == 2: diff --git a/arviz/plots/backends/matplotlib/energyplot.py b/arviz/plots/backends/matplotlib/energyplot.py --- a/arviz/plots/backends/matplotlib/energyplot.py +++ b/arviz/plots/backends/matplotlib/energyplot.py @@ -52,7 +52,7 @@ def plot_energy( if (fill_color[0].startswith("C") and len(fill_color[0]) == 2) and ( fill_color[1].startswith("C") and len(fill_color[1]) == 2 ): - fill_color = tuple([_colors[int(color[1:]) % 10] for color in fill_color]) + fill_color = tuple((_colors[int(color[1:]) % 10] for color in fill_color)) elif fill_color[0].startswith("C") and len(fill_color[0]) == 2: fill_color = tuple([_colors[int(fill_color[0][1:]) % 10]] + list(fill_color[1:])) elif fill_color[1].startswith("C") and len(fill_color[1]) == 2: diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -108,7 +108,7 @@ def plot_ppc( ) if animated: fig = axes[0].get_figure() - if not all([ax.get_figure() is fig for ax in axes]): + if not all((ax.get_figure() is fig for ax in axes)): raise ValueError("All axes must be on the same figure for animation to work") for i, ax_i in enumerate(np.ravel(axes)[:length_plotters]):

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -449,7 +449,7 @@ def test_del(self, use): assert not fails # assert _groups attribute contains all groups groups = getattr(idata, "_groups") - assert all([group in groups for group in test_dict]) + assert all((group in groups for group in test_dict)) # Use del method if use == "del": diff --git a/arviz/tests/base_tests/test_rcparams.py b/arviz/tests/base_tests/test_rcparams.py --- a/arviz/tests/base_tests/test_rcparams.py +++ b/arviz/tests/base_tests/test_rcparams.py @@ -102,7 +102,7 @@ def test_rcparams_repr_str(): str_str = rcParams.__str__() assert repr_str.startswith("RcParams") for string in (repr_str, str_str): - assert all([key in string for key in rcParams.keys()]) + assert all((key in string for key in rcParams.keys())) ### Test arvizrc.template file is up to date ### @@ -110,10 +110,10 @@ def test_rctemplate_updated(): fname = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../../arvizrc.template") rc_pars_template = read_rcfile(fname) rc_defaults = rc_params(ignore_files=True) - assert all([key in rc_pars_template.keys() for key in rc_defaults.keys()]), [ + assert all((key in rc_pars_template.keys() for key in rc_defaults.keys())), [ key for key in rc_defaults.keys() if key not in rc_pars_template ] - assert all([value == rc_pars_template[key] for key, value in rc_defaults.items()]), [ + assert all((value == rc_pars_template[key] for key, value in rc_defaults.items())), [ key for key, value in rc_defaults.items() if value != rc_pars_template[key] ]

Enable pylint use-a-generator check I guess that latest pylint has added a `use-a-generator` check that did not use to exist. I disabled it in #1577 from `.pylintrc` but maybe we should enable the check and either fix the code or disable on a line per line basis. Same for `consider-using-generator` Steps to fix the issue: 1. Remove these two checks from the list of disabled checks in pylintrc: https://github.com/arviz-devs/arviz/blob/main/.pylintrc#L75-L76 2. Run pylint on arviz (or what is the same, run [`scripts/lint.sh`](https://github.com/arviz-devs/arviz/blob/main/scripts/lint.sh)) 3. Go to each of the files and lines where pylint shows a warning and try to fix the issue. There is also the possibility of false positives, I expect most occurences to be valid complains, but some may have to still be ignored. This ignores should be done on a line basis, not from `pylintrc`

Hey @OriolAbril , I'd like to work on this issue... thanks, for the steps as well. Great, just assigned you to the issue, as usual, let us know if you need any help

2021-02-27T20:33:56Z

arviz-devs/arviz

arviz-devs__arviz-1602

c4005b178c600f949d90dbe0258b9c8a1200626d

diff --git a/arviz/plots/backends/bokeh/ppcplot.py b/arviz/plots/backends/bokeh/ppcplot.py --- a/arviz/plots/backends/bokeh/ppcplot.py +++ b/arviz/plots/backends/bokeh/ppcplot.py @@ -1,5 +1,6 @@ """Bokeh Posterior predictive plot.""" import numpy as np +from bokeh.models.annotations import Legend from ....stats.density_utils import get_bins, histogram, kde from ...kdeplot import plot_kde @@ -86,6 +87,7 @@ def plot_ppc( var_name, sel, isel, obs_vals = obs_plotters[i] pp_var_name, _, _, pp_vals = pp_plotters[i] dtype = predictive_dataset[pp_var_name].dtype.kind + legend_it = [] # flatten non-specified dimensions obs_vals = obs_vals.flatten() @@ -111,14 +113,19 @@ def plot_ppc( pp_xs.append(bin_edges) if dtype == "f": - ax_i.multi_line(pp_xs, pp_densities, **plot_kwargs) + multi_line = ax_i.multi_line(pp_xs, pp_densities, **plot_kwargs) + legend_it.append(("{} predictive".format(group.capitalize()), [multi_line])) else: + all_steps = [] for x_s, y_s in zip(pp_xs, pp_densities): - ax_i.step(x_s, y_s, **plot_kwargs) + step = ax_i.step(x_s, y_s, **plot_kwargs) + all_steps.append(step) + legend_it.append(("{} predictive".format(group.capitalize()), all_steps)) if observed: + label = "Observed" if dtype == "f": - plot_kde( + _, glyph = plot_kde( obs_vals, plot_kwargs={"line_color": "black", "line_width": linewidth}, fill_kwargs={"alpha": 0}, @@ -126,20 +133,24 @@ def plot_ppc( backend="bokeh", backend_kwargs={}, show=False, + return_glyph=True, ) + legend_it.append((label, glyph)) else: bins = get_bins(obs_vals) _, hist, bin_edges = histogram(obs_vals, bins=bins) hist = np.concatenate((hist[:1], hist)) - ax_i.step( + step = ax_i.step( bin_edges, hist, line_color="black", line_width=linewidth, mode="center", ) + legend_it.append((label, [step])) if mean: + label = "{} predictive mean".format(group.capitalize()) if dtype == "f": rep = len(pp_densities) len_density = len(pp_densities[0]) @@ -150,19 +161,20 @@ def plot_ppc( new_x -= (new_x[1] - new_x[0]) / 2 for irep in range(rep): new_d[irep][bins[irep]] = pp_densities[irep] - ax_i.line( + line = ax_i.line( new_x, new_d.mean(0), color=color, line_dash="dashed", line_width=linewidth, ) + legend_it.append((label, [line])) else: vals = pp_vals.flatten() bins = get_bins(vals) _, hist, bin_edges = histogram(vals, bins=bins) hist = np.concatenate((hist[:1], hist)) - ax_i.step( + step = ax_i.step( bin_edges, hist, line_color=color, @@ -170,12 +182,14 @@ def plot_ppc( line_dash="dashed", mode="center", ) + legend_it.append((label, [step])) ax_i.yaxis.major_tick_line_color = None ax_i.yaxis.minor_tick_line_color = None ax_i.yaxis.major_label_text_font_size = "0pt" elif kind == "cumulative": if observed: + label = "Observed" if dtype == "f": glyph = ax_i.line( *_empirical_cdf(obs_vals), @@ -183,39 +197,45 @@ def plot_ppc( line_width=linewidth, ) glyph.level = "overlay" + legend_it.append((label, [glyph])) else: - ax_i.step( + step = ax_i.step( *_empirical_cdf(obs_vals), line_color="black", line_width=linewidth, mode="center", ) + legend_it.append((label, [step])) pp_densities = np.empty((2 * len(pp_sampled_vals), pp_sampled_vals[0].size)) for idx, vals in enumerate(pp_sampled_vals): vals = np.array([vals]).flatten() pp_x, pp_density = _empirical_cdf(vals) pp_densities[2 * idx] = pp_x pp_densities[2 * idx + 1] = pp_density - ax_i.multi_line( + multi_line = ax_i.multi_line( list(pp_densities[::2]), list(pp_densities[1::2]), line_alpha=alpha, line_color=color, line_width=linewidth, ) + legend_it.append(("{} predictive".format(group.capitalize()), [multi_line])) if mean: - ax_i.line( + label = "{} predictive mean".format(group.capitalize()) + line = ax_i.line( *_empirical_cdf(pp_vals.flatten()), color=color, line_dash="dashed", line_width=linewidth, ) + legend_it.append((label, [line])) elif kind == "scatter": if mean: + label = "{} predictive mean".format(group.capitalize()) if dtype == "f": - plot_kde( + _, glyph = plot_kde( pp_vals.flatten(), plot_kwargs={ "line_color": color, @@ -226,13 +246,15 @@ def plot_ppc( backend="bokeh", backend_kwargs={}, show=False, + return_glyph=True, ) + legend_it.append((label, glyph)) else: vals = pp_vals.flatten() bins = get_bins(vals) _, hist, bin_edges = histogram(vals, bins=bins) hist = np.concatenate((hist[:1], hist)) - ax_i.step( + step = ax_i.step( bin_edges, hist, color=color, @@ -240,6 +262,7 @@ def plot_ppc( line_dash="dashed", mode="center", ) + legend_it.append((label, [step])) jitter_scale = 0.1 y_rows = np.linspace(0, 0.1, num_pp_samples + 1) @@ -247,6 +270,7 @@ def plot_ppc( scale_high = jitter_scale * jitter if observed: + label = "Observed" obs_yvals = np.zeros_like(obs_vals, dtype=np.float64) if jitter: obs_yvals += np.random.uniform( @@ -260,18 +284,34 @@ def plot_ppc( line_alpha=alpha, ) glyph.level = "overlay" + legend_it.append((label, [glyph])) + all_scatter = [] for vals, y in zip(pp_sampled_vals, y_rows[1:]): vals = np.ravel(vals) yvals = np.full_like(vals, y, dtype=np.float64) if jitter: yvals += np.random.uniform(low=scale_low, high=scale_high, size=len(vals)) - ax_i.scatter(vals, yvals, fill_color=color, size=markersize, fill_alpha=alpha) + scatter = ax_i.scatter( + vals, yvals, fill_color=color, size=markersize, fill_alpha=alpha + ) + all_scatter.append(scatter) + legend_it.append(("{} predictive".format(group.capitalize()), all_scatter)) ax_i.yaxis.major_tick_line_color = None ax_i.yaxis.minor_tick_line_color = None ax_i.yaxis.major_label_text_font_size = "0pt" + if legend: + legend = Legend( + items=legend_it, + location="top_left", + orientation="vertical", + ) + ax_i.add_layout(legend) + if textsize is not None: + ax_i.legend.label_text_font_size = f"{textsize}pt" + ax_i.legend.click_policy = "hide" ax_i.xaxis.axis_label = labeller.make_pp_label(var_name, pp_var_name, sel, isel) show_layout(axes, show)

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -890,6 +890,17 @@ def test_plot_ppc(models, kind, alpha, observed): assert axes +def test_plot_ppc_textsize(models): + axes = plot_ppc( + models.model_1, + textsize=10, + random_seed=3, + backend="bokeh", + show=False, + ) + assert axes + + @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) @pytest.mark.parametrize("jitter", [None, 0, 0.1, 1, 3]) def test_plot_ppc_multichain(kind, jitter):

Show legend in ppc plot (backend bokeh) ## Tell us about it xref https://github.com/arviz-devs/arviz/pull/1559#issuecomment-778762939 The matplotlib plots have legend, making it clear what each line/colour type represents. The bokeh plots don't seem to have them, and adding them would make the meaning of the plot clearer

Hey can I take this issue? Thanks yep, assigning it to you

2021-03-05T19:58:09Z

arviz-devs/arviz

arviz-devs__arviz-1619

59575274a5921acae24930733b7125c96ff8e97b

diff --git a/arviz/data/io_numpyro.py b/arviz/data/io_numpyro.py --- a/arviz/data/io_numpyro.py +++ b/arviz/data/io_numpyro.py @@ -91,7 +91,10 @@ def arbitrary_element(dct): for i, v in enumerate(tree_flatten_samples) } self._samples = samples - self.nchains, self.ndraws = posterior.num_chains, posterior.num_samples + self.nchains, self.ndraws = ( + posterior.num_chains, + posterior.num_samples // posterior.thinning, + ) self.model = self.posterior.sampler.model # model arguments and keyword arguments self._args = self.posterior._args # pylint: disable=protected-access

diff --git a/arviz/tests/external_tests/test_data_numpyro.py b/arviz/tests/external_tests/test_data_numpyro.py --- a/arviz/tests/external_tests/test_data_numpyro.py +++ b/arviz/tests/external_tests/test_data_numpyro.py @@ -227,3 +227,29 @@ def test_inference_data_num_chains(self, predictions_data, chains): inference_data = from_numpyro(predictions=predictions, num_chains=chains) nchains = inference_data.predictions.dims["chain"] assert nchains == chains + + @pytest.mark.parametrize("nchains", [1, 2]) + @pytest.mark.parametrize("thin", [1, 2, 3, 5, 10]) + def test_mcmc_with_thinning(self, nchains, thin): + import numpyro + import numpyro.distributions as dist + from numpyro.infer import MCMC, NUTS + + x = np.random.normal(10, 3, size=100) + + def model(x): + numpyro.sample( + "x", + dist.Normal( + numpyro.sample("loc", dist.Uniform(0, 20)), + numpyro.sample("scale", dist.Uniform(0, 20)), + ), + obs=x, + ) + + nuts_kernel = NUTS(model) + mcmc = MCMC(nuts_kernel, num_warmup=100, num_samples=400, num_chains=nchains, thinning=thin) + mcmc.run(PRNGKey(0), x=x) + + inference_data = from_numpyro(mcmc) + assert inference_data.posterior["loc"].shape == (nchains, 400 // thin)

`az.from_numpyro` crashes when running MCMC with `thinning != 1` **Describe the bug** When using `az.from_numpyro` to import an MCMC run from `numpyro`, a `ValueError` is thrown if the MCMC was run with, e.g., `thinning=2`. **To Reproduce** Running ```python import arviz as az import numpy as np import numpyro import numpyro.distributions as dist from jax import random from numpyro.infer import MCMC, NUTS data = np.random.normal(10, 3, size=100) def model(data): numpyro.sample( 'x', dist.Normal( numpyro.sample('loc', dist.Uniform(0, 20)), numpyro.sample('scale', dist.Uniform(0, 20)), ), obs=data, ) kernel = NUTS(model) mcmc = MCMC(NUTS(model), 100, 200, thinning=2) mcmc.run(random.PRNGKey(0), data=data) mcmc.print_summary() az.from_numpyro(mcmc) # crash ``` will crash with ```python ValueError: cannot reshape array of size 10000 into shape (1,200,100) ``` Setting `thinning=1` will prevent the crash. **Expected behavior** I would expect `az.from_numpyro(mcmc)` to not crash. **Additional context** I am using arviz `0.11.2` and numpyro `0.6.0`.

Thanks for reporting, it looks like the culprit is this line: https://github.com/arviz-devs/arviz/blob/59575274a5921acae24930733b7125c96ff8e97b/arviz/data/io_numpyro.py#L94 which gathers info from the numpyro object in order to be able to reshape the samples accordingly afterwards (which is where the error is triggered). Taking thinning into account when defining `self.ndraws` should fix the issue. It looks like `thinning` is stored in the same way as `num_chains` and `num_draws`: https://github.com/pyro-ppl/numpyro/blob/master/numpyro/infer/mcmc.py#L245-L250 Do you want to submit a PR for this @kpj ? I can help f you decide to do so, otherwise I'll label it as `beginner` and wait for someone to work on this Thanks for the information! I'd be happy to submit a PR. Would the fix be as easy as dividing `self.ndraws` by `posterior.thinning` in the line after https://github.com/arviz-devs/arviz/blob/59575274a5921acae24930733b7125c96ff8e97b/arviz/data/io_numpyro.py#L94? > Would the fix be as easy as dividing self.ndraws by posterior.thinning in the line after I think so

2021-03-18T19:00:39Z

arviz-devs/arviz

arviz-devs__arviz-1629

7ec650f75846119d511daf3ecc1d06f1eb6a085e

diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -104,6 +104,12 @@ def __init__( # way to access the model from the trace. self.attrs = None if trace is not None: + if isinstance(self.trace, InferenceData): + raise ValueError( + "Using the `InferenceData` as a `trace` argument won't work. " + "Please use the `arviz.InferenceData.extend` method to extend the " + "`InferenceData` with groups from another `InferenceData`." + ) if self.model is None: self.model = list(self.trace._straces.values())[ # pylint: disable=protected-access 0

diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -10,6 +10,7 @@ from arviz import ( # pylint: disable=wrong-import-position InferenceData, + from_dict, from_pymc3, from_pymc3_predictions, ) @@ -107,7 +108,7 @@ def test_from_pymc(self, data, eight_schools_params, chains, draws): ) def test_from_pymc_predictions(self, data, eight_schools_params): - "Test that we can add predictions to a previously-existing InferenceData." + """Test that we can add predictions to a previously-existing InferenceData.""" test_dict = { "posterior": ["mu", "tau", "eta", "theta"], "sample_stats": ["diverging", "lp"], @@ -139,6 +140,15 @@ def test_from_pymc_predictions(self, data, eight_schools_params): assert ivalues.shape[0] == 1 # one chain in predictions assert np.all(np.isclose(ivalues[0], values)) + def test_from_pymc_trace_inference_data(self): + """Check if the error is raised successfully after passing InferenceData as trace""" + idata = from_dict( + posterior={"A": np.random.randn(2, 10, 2), "B": np.random.randn(2, 10, 5, 2)} + ) + assert isinstance(idata, InferenceData) + with pytest.raises(ValueError): + from_pymc3(trace=idata, model=pm.Model()) + def test_from_pymc_predictions_new(self, data, eight_schools_params): # check creating new inference_data, posterior_predictive = self.make_predictions_inference_data(

Check for InferenceData input on from_pymc3 `pymc3.sample` has a keyword argument that makes the function return an InferenceData object, `from_pymc3` is called under the hood by pymc3. This can generate confusion when doing also posterior predictive checks or similar postprocessing, and it is common to end up calling `from_pymc3` manually again but using the `InferenceData` as `trace` argument, which does not work nor should it work. We should check for InferenceData input on io_pymc3 to make sure the input is not an inferencedata and if it is raise an error pointing people out to https://arviz-devs.github.io/arviz/api/generated/arviz.InferenceData.extend.html#arviz.InferenceData.extend.

Hi @OriolAbril I'd like to work on this... can you assign this to me? Hey @OriolAbril , sorry for the delay, I got stuck somewhere else. Anyways, even though you described it perfectly I just wanted to make sure that I need to enable type checking for `io_pymc3`'s input and if in case it's of type `InferenceData`, it should raise the error as you mentioned... Am I missing anything here? That is all.

2021-03-24T17:21:17Z

arviz-devs/arviz

arviz-devs__arviz-1632

60988ff29c67a0801f7244bcaa7a8c07866cbfcb

diff --git a/arviz/data/base.py b/arviz/data/base.py --- a/arviz/data/base.py +++ b/arviz/data/base.py @@ -1,6 +1,7 @@ """Low level converters usually used by other functions.""" import datetime import functools +import re import warnings from copy import deepcopy from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union @@ -438,3 +439,47 @@ def _make_json_serializable(data: dict) -> dict: f"Value associated with variable `{type(value)}` is not JSON serializable." ) return ret + + +def infer_stan_dtypes(stan_code): + """Infer Stan integer variables from generated quantities block.""" + # Remove old deprecated comments + stan_code = "\n".join( + line if "#" not in line else line[: line.find("#")] for line in stan_code.splitlines() + ) + pattern_remove_comments = re.compile( + r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"', re.DOTALL | re.MULTILINE + ) + stan_code = re.sub(pattern_remove_comments, "", stan_code) + + # Check generated quantities + if "generated quantities" not in stan_code: + return {} + + # Extract generated quantities block + gen_quantities_location = stan_code.index("generated quantities") + block_start = gen_quantities_location + stan_code[gen_quantities_location:].index("{") + + curly_bracket_count = 0 + block_end = None + for block_end, char in enumerate(stan_code[block_start:], block_start + 1): + if char == "{": + curly_bracket_count += 1 + elif char == "}": + curly_bracket_count -= 1 + + if curly_bracket_count == 0: + break + + stan_code = stan_code[block_start:block_end] + + stan_integer = r"int" + stan_limits = r"(?:\<[^\>]+\>)*" # ignore group: 0 or more <....> + stan_param = r"([^;=\s\[]+)" # capture group: ends= ";", "=", "[" or whitespace + stan_ws = r"\s*" # 0 or more whitespace + stan_ws_one = r"\s+" # 1 or more whitespace + pattern_int = re.compile( + "".join((stan_integer, stan_ws_one, stan_limits, stan_ws, stan_param)), re.IGNORECASE + ) + dtypes = {key.strip(): "int" for key in re.findall(pattern_int, stan_code)} + return dtypes diff --git a/arviz/data/io_cmdstan.py b/arviz/data/io_cmdstan.py --- a/arviz/data/io_cmdstan.py +++ b/arviz/data/io_cmdstan.py @@ -1,16 +1,18 @@ +# pylint: disable=too-many-lines """CmdStan-specific conversion code.""" import logging import os import re from collections import defaultdict from glob import glob +from pathlib import Path from typing import Dict, List, Optional, Union import numpy as np from .. import utils from ..rcparams import rcParams -from .base import CoordSpec, DimSpec, dict_to_dataset, requires +from .base import CoordSpec, DimSpec, dict_to_dataset, infer_stan_dtypes, requires from .inference_data import InferenceData _log = logging.getLogger(__name__) @@ -83,9 +85,19 @@ def __init__( self.index_origin = index_origin if dtypes is None: - self.dtypes = {} - else: - self.dtypes = dtypes + dtypes = {} + elif isinstance(dtypes, str): + dtypes_path = Path(dtypes) + if dtypes_path.exists(): + with dtypes_path.open("r") as f_obj: + model_code = f_obj.read() + else: + model_code = dtypes + + dtypes = infer_stan_dtypes(model_code) + + self.dtypes = dtypes + # populate posterior and sample_stats self._parse_posterior() self._parse_prior() @@ -963,8 +975,9 @@ def from_cmdstan( save_warmup : bool Save warmup iterations into InferenceData object, if found in the input files. If not defined, use default defined by the rcParams. - dtypes : dict + dtypes : dict or str A dictionary containing dtype information (int, float) for parameters. + If input is a string, it is assumed to be a model code or path to model code file. Returns ------- diff --git a/arviz/data/io_cmdstanpy.py b/arviz/data/io_cmdstanpy.py --- a/arviz/data/io_cmdstanpy.py +++ b/arviz/data/io_cmdstanpy.py @@ -3,11 +3,12 @@ import re from collections import defaultdict from copy import deepcopy +from pathlib import Path import numpy as np from ..rcparams import rcParams -from .base import dict_to_dataset, make_attrs, requires +from .base import dict_to_dataset, infer_stan_dtypes, make_attrs, requires from .inference_data import InferenceData _log = logging.getLogger(__name__) @@ -34,6 +35,7 @@ def __init__( coords=None, dims=None, save_warmup=None, + dtypes=None, ): self.posterior = posterior # CmdStanPy CmdStanMCMC object self.posterior_predictive = posterior_predictive @@ -52,6 +54,24 @@ def __init__( self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup + import cmdstanpy # pylint: disable=import-error + + if dtypes is None: + dtypes = {} + elif isinstance(dtypes, cmdstanpy.model.CmdStanModel): + model_code = dtypes.code() + dtypes = infer_stan_dtypes(model_code) + elif isinstance(dtypes, str): + dtypes_path = Path(dtypes) + if dtypes_path.exists(): + with dtypes_path.open("r") as f_obj: + model_code = f_obj.read() + else: + model_code = dtypes + dtypes = infer_stan_dtypes(model_code) + + self.dtypes = dtypes + if hasattr(self.posterior, "stan_vars_cols"): if self.log_likelihood is True and "log_lik" in self.posterior.stan_vars_cols: self.log_likelihood = ["log_lik"] @@ -66,8 +86,6 @@ def __init__( if isinstance(self.log_likelihood, bool): self.log_likelihood = None - import cmdstanpy # pylint: disable=import-error - self.cmdstanpy = cmdstanpy @requires("posterior") @@ -101,6 +119,7 @@ def posterior_to_xarray(self): self.posterior, items, self.save_warmup, + self.dtypes, ) # copy dims and coords - Mitzi question: why??? @@ -139,7 +158,12 @@ def stats_to_xarray(self, fit): if not hasattr(fit, "sampler_vars_cols"): return self.sample_stats_to_xarray_pre_v_0_9_68(fit) - dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64} + dtypes = { + "divergent__": bool, + "n_leapfrog__": np.int64, + "treedepth__": np.int64, + **self.dtypes, + } items = list(fit.sampler_vars_cols.keys()) rename_dict = { "divergent": "diverging", @@ -153,6 +177,7 @@ def stats_to_xarray(self, fit): fit, items, self.save_warmup, + self.dtypes, ) for item in items: name = re.sub("__$", "", item) @@ -198,6 +223,7 @@ def predictive_to_xarray(self, names, fit): fit, predictive, self.save_warmup, + self.dtypes, ) else: # pre_v_0_9_68 valid_cols = _filter_columns(fit.column_names, predictive) @@ -206,6 +232,7 @@ def predictive_to_xarray(self, names, fit): fit.column_names, valid_cols, self.save_warmup, + self.dtypes, ) return ( @@ -236,6 +263,7 @@ def predictions_to_xarray(self): self.posterior, predictions, self.save_warmup, + self.dtypes, ) else: # pre_v_0_9_68 columns = self.posterior.column_names @@ -245,6 +273,7 @@ def predictions_to_xarray(self): columns, valid_cols, self.save_warmup, + self.dtypes, ) return ( @@ -275,6 +304,7 @@ def log_likelihood_to_xarray(self): self.posterior, log_likelihood, self.save_warmup, + self.dtypes, ) else: # pre_v_0_9_68 columns = self.posterior.column_names @@ -284,6 +314,7 @@ def log_likelihood_to_xarray(self): columns, valid_cols, self.save_warmup, + self.dtypes, ) if isinstance(self.log_likelihood, dict): data = {obs_name: data[lik_name] for obs_name, lik_name in self.log_likelihood.items()} @@ -325,6 +356,7 @@ def prior_to_xarray(self): self.prior, items, self.save_warmup, + self.dtypes, ) else: # pre_v_0_9_68 columns = self.prior.column_names @@ -339,6 +371,7 @@ def prior_to_xarray(self): columns, valid_cols, self.save_warmup, + self.dtypes, ) return ( @@ -477,6 +510,7 @@ def posterior_to_xarray_pre_v_0_9_68(self): columns, valid_cols, self.save_warmup, + self.dtypes, ) return ( @@ -506,6 +540,7 @@ def sample_stats_to_xarray_pre_v_0_9_68(self, fit): columns, valid_cols, self.save_warmup, + self.dtypes, ) for s_param in list(data.keys()): s_param_, *_ = s_param.split(".") @@ -562,7 +597,7 @@ def _filter_columns(columns, spec): return [col for col in columns if col.split("[")[0].split(".")[0] in spec] -def _unpack_fit(fit, items, save_warmup): +def _unpack_fit(fit, items, save_warmup, dtypes): """Transform fit to dictionary containing ndarrays. Parameters @@ -570,6 +605,7 @@ def _unpack_fit(fit, items, save_warmup): data: cmdstanpy.CmdStanMCMC items: list save_warmup: bool + dtypes: dict Returns ------- @@ -603,6 +639,7 @@ def _unpack_fit(fit, items, save_warmup): raw_draws = draws[..., col_idxs[0]] else: raise ValueError("fit data, unknown variable: {}".format(item)) + raw_draws = raw_draws.astype(dtypes.get(item)) if save_warmup: sample_warmup[item] = raw_draws[:, :num_warmup, ...] sample[item] = raw_draws[:, num_warmup:, ...] @@ -612,7 +649,7 @@ def _unpack_fit(fit, items, save_warmup): return sample, sample_warmup -def _unpack_frame(fit, columns, valid_cols, save_warmup): +def _unpack_frame(fit, columns, valid_cols, save_warmup, dtypes): """Transform fit to dictionary containing ndarrays. Called when fit object created by cmdstanpy version < 0.9.68 @@ -623,6 +660,7 @@ def _unpack_frame(fit, columns, valid_cols, save_warmup): columns: list valid_cols: list save_warmup: bool + dtypes: dict Returns ------- @@ -696,6 +734,13 @@ def _unpack_frame(fit, columns, valid_cols, save_warmup): sample[key][shape_loc] = np.swapaxes(data[..., i], 0, 1) if save_warmup: sample_warmup[key][shape_loc] = np.swapaxes(data_warmup[..., i], 0, 1) + + for key, dtype in dtypes.items(): + if key in sample: + sample[key] = sample[key].astype(dtype) + if save_warmup: + if key in sample_warmup: + sample_warmup[key] = sample_warmup[key].astype(dtype) return sample, sample_warmup @@ -714,6 +759,7 @@ def from_cmdstanpy( coords=None, dims=None, save_warmup=None, + dtypes=None, ): """Convert CmdStanPy data into an InferenceData object. @@ -755,6 +801,10 @@ def from_cmdstanpy( save_warmup : bool Save warmup iterations into InferenceData object, if found in the input files. If not defined, use default defined by the rcParams. + dtypes: dict or str or cmdstanpy.CmdStanModel + A dictionary containing dtype information (int, float) for parameters. + If input is a string, it is assumed to be a model code or path to model code file. + Model code can extracted from cmdstanpy.CmdStanModel object. Returns ------- @@ -774,4 +824,5 @@ def from_cmdstanpy( coords=coords, dims=dims, save_warmup=save_warmup, + dtypes=dtypes, ).to_inference_data() diff --git a/arviz/data/io_pystan.py b/arviz/data/io_pystan.py --- a/arviz/data/io_pystan.py +++ b/arviz/data/io_pystan.py @@ -1,4 +1,4 @@ -# pylint: disable=too-many-instance-attributes +# pylint: disable=too-many-instance-attributes,too-many-lines """PyStan-specific conversion code.""" import re import warnings @@ -10,7 +10,7 @@ from .. import _log from ..rcparams import rcParams -from .base import dict_to_dataset, generate_dims_coords, make_attrs, requires +from .base import dict_to_dataset, generate_dims_coords, infer_stan_dtypes, make_attrs, requires from .inference_data import InferenceData try: @@ -40,6 +40,7 @@ def __init__( coords=None, dims=None, save_warmup=None, + dtypes=None, ): self.posterior = posterior self.posterior_predictive = posterior_predictive @@ -55,6 +56,7 @@ def __init__( self.coords = coords self.dims = dims self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup + self.dtypes = dtypes if ( self.log_likelihood is True @@ -94,7 +96,9 @@ def posterior_to_xarray(self): ignore = posterior_predictive + predictions + log_likelihood + ["lp__"] - data, data_warmup = get_draws(posterior, ignore=ignore, warmup=self.save_warmup) + data, data_warmup = get_draws( + posterior, ignore=ignore, warmup=self.save_warmup, dtypes=self.dtypes + ) attrs = get_attrs(posterior) return ( dict_to_dataset( @@ -113,7 +117,9 @@ def sample_stats_to_xarray(self): data, data_warmup = get_sample_stats(posterior, warmup=self.save_warmup) # lp__ - stat_lp, stat_lp_warmup = get_draws(posterior, variables="lp__", warmup=self.save_warmup) + stat_lp, stat_lp_warmup = get_draws( + posterior, variables="lp__", warmup=self.save_warmup, dtypes=self.dtypes + ) data["lp"] = stat_lp["lp__"] if stat_lp_warmup: data_warmup["lp"] = stat_lp_warmup["lp__"] @@ -141,7 +147,10 @@ def log_likelihood_to_xarray(self): if isinstance(log_likelihood, (list, tuple)): log_likelihood = {name: name for name in log_likelihood} log_likelihood_draws, log_likelihood_draws_warmup = get_draws( - fit, variables=list(log_likelihood.values()), warmup=self.save_warmup + fit, + variables=list(log_likelihood.values()), + warmup=self.save_warmup, + dtypes=self.dtypes, ) data = { obs_var_name: log_likelihood_draws[log_like_name] @@ -175,7 +184,7 @@ def posterior_predictive_to_xarray(self): posterior = self.posterior posterior_predictive = self.posterior_predictive data, data_warmup = get_draws( - posterior, variables=posterior_predictive, warmup=self.save_warmup + posterior, variables=posterior_predictive, warmup=self.save_warmup, dtypes=self.dtypes ) return ( dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims), @@ -188,7 +197,9 @@ def predictions_to_xarray(self): """Convert predictions samples to xarray.""" posterior = self.posterior predictions = self.predictions - data, data_warmup = get_draws(posterior, variables=predictions, warmup=self.save_warmup) + data, data_warmup = get_draws( + posterior, variables=predictions, warmup=self.save_warmup, dtypes=self.dtypes + ) return ( dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims), dict_to_dataset(data_warmup, library=self.pystan, coords=self.coords, dims=self.dims), @@ -207,7 +218,7 @@ def prior_to_xarray(self): ignore = prior_predictive + ["lp__"] - data, _ = get_draws(prior, ignore=ignore, warmup=False) + data, _ = get_draws(prior, ignore=ignore, warmup=False, dtypes=self.dtypes) attrs = get_attrs(prior) return dict_to_dataset( data, library=self.pystan, attrs=attrs, coords=self.coords, dims=self.dims @@ -220,7 +231,7 @@ def sample_stats_prior_to_xarray(self): data, _ = get_sample_stats(prior, warmup=False) # lp__ - stat_lp, _ = get_draws(prior, variables="lp__", warmup=False) + stat_lp, _ = get_draws(prior, variables="lp__", warmup=False, dtypes=self.dtypes) data["lp"] = stat_lp["lp__"] attrs = get_attrs(prior) @@ -234,7 +245,7 @@ def prior_predictive_to_xarray(self): """Convert prior_predictive samples to xarray.""" prior = self.prior prior_predictive = self.prior_predictive - data, _ = get_draws(prior, variables=prior_predictive, warmup=False) + data, _ = get_draws(prior, variables=prior_predictive, warmup=False, dtypes=self.dtypes) return dict_to_dataset(data, library=self.pystan, coords=self.coords, dims=self.dims) @requires("posterior") @@ -309,6 +320,7 @@ def __init__( log_likelihood=None, coords=None, dims=None, + dtypes=None, ): self.posterior = posterior self.posterior_model = posterior_model @@ -325,6 +337,7 @@ def __init__( ) self.coords = coords self.dims = dims + self.dtypes = dtypes if ( self.log_likelihood is True @@ -365,7 +378,7 @@ def posterior_to_xarray(self): ignore = posterior_predictive + predictions + log_likelihood - data = get_draws_stan3(posterior, model=posterior_model, ignore=ignore) + data = get_draws_stan3(posterior, model=posterior_model, ignore=ignore, dtypes=self.dtypes) attrs = get_attrs_stan3(posterior, model=posterior_model) return dict_to_dataset( data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims @@ -376,7 +389,7 @@ def sample_stats_to_xarray(self): """Extract sample_stats from posterior.""" posterior = self.posterior posterior_model = self.posterior_model - data = get_sample_stats_stan3(posterior, ignore="lp__") + data = get_sample_stats_stan3(posterior, ignore="lp__", dtypes=self.dtypes) data["lp"] = get_sample_stats_stan3(posterior, variables="lp__")["lp"] attrs = get_attrs_stan3(posterior, model=posterior_model) @@ -397,7 +410,7 @@ def log_likelihood_to_xarray(self): if isinstance(log_likelihood, (list, tuple)): log_likelihood = {name: name for name in log_likelihood} log_likelihood_draws = get_draws_stan3( - fit, model=model, variables=list(log_likelihood.values()) + fit, model=model, variables=list(log_likelihood.values()), dtypes=self.dtypes ) data = { obs_var_name: log_likelihood_draws[log_like_name] @@ -414,7 +427,9 @@ def posterior_predictive_to_xarray(self): posterior = self.posterior posterior_model = self.posterior_model posterior_predictive = self.posterior_predictive - data = get_draws_stan3(posterior, model=posterior_model, variables=posterior_predictive) + data = get_draws_stan3( + posterior, model=posterior_model, variables=posterior_predictive, dtypes=self.dtypes + ) return dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims) @requires("posterior") @@ -424,7 +439,9 @@ def predictions_to_xarray(self): posterior = self.posterior posterior_model = self.posterior_model predictions = self.predictions - data = get_draws_stan3(posterior, model=posterior_model, variables=predictions) + data = get_draws_stan3( + posterior, model=posterior_model, variables=predictions, dtypes=self.dtypes + ) return dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims) @requires("prior") @@ -441,7 +458,7 @@ def prior_to_xarray(self): ignore = prior_predictive - data = get_draws_stan3(prior, model=prior_model, ignore=ignore) + data = get_draws_stan3(prior, model=prior_model, ignore=ignore, dtypes=self.dtypes) attrs = get_attrs_stan3(prior, model=prior_model) return dict_to_dataset( data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims @@ -452,7 +469,7 @@ def sample_stats_prior_to_xarray(self): """Extract sample_stats_prior from prior.""" prior = self.prior prior_model = self.prior_model - data = get_sample_stats_stan3(prior) + data = get_sample_stats_stan3(prior, dtypes=self.dtypes) attrs = get_attrs_stan3(prior, model=prior_model) return dict_to_dataset( data, library=self.stan, attrs=attrs, coords=self.coords, dims=self.dims @@ -465,7 +482,9 @@ def prior_predictive_to_xarray(self): prior = self.prior prior_model = self.prior_model prior_predictive = self.prior_predictive - data = get_draws_stan3(prior, model=prior_model, variables=prior_predictive) + data = get_draws_stan3( + prior, model=prior_model, variables=prior_predictive, dtypes=self.dtypes + ) return dict_to_dataset(data, library=self.stan, coords=self.coords, dims=self.dims) @requires("posterior_model") @@ -546,7 +565,7 @@ def to_inference_data(self): ) -def get_draws(fit, variables=None, ignore=None, warmup=False): +def get_draws(fit, variables=None, ignore=None, warmup=False, dtypes=None): """Extract draws from PyStan fit.""" if ignore is None: ignore = [] @@ -558,7 +577,10 @@ def get_draws(fit, variables=None, ignore=None, warmup=False): msg = "Fit doesn't contain samples." raise AttributeError(msg) - dtypes = infer_dtypes(fit) + if dtypes is None: + dtypes = {} + + dtypes = {**infer_dtypes(fit), **dtypes} if variables is None: variables = fit.sim["pars_oi"] @@ -645,9 +667,11 @@ def get_draws(fit, variables=None, ignore=None, warmup=False): return data, data_warmup -def get_sample_stats(fit, warmup=False): +def get_sample_stats(fit, warmup=False, dtypes=None): """Extract sample stats from PyStan fit.""" - dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64} + if dtypes is None: + dtypes = {} + dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64, **dtypes} rename_dict = { "divergent": "diverging", @@ -765,14 +789,16 @@ def get_attrs(fit): return attrs -def get_draws_stan3(fit, model=None, variables=None, ignore=None): +def get_draws_stan3(fit, model=None, variables=None, ignore=None, dtypes=None): """Extract draws from PyStan3 fit.""" if ignore is None: ignore = [] - dtypes = {} + if dtypes is None: + dtypes = {} + if model is not None: - dtypes = infer_dtypes(fit, model) + dtypes = {**infer_dtypes(fit, model), **dtypes} if variables is None: variables = fit.param_names @@ -800,9 +826,11 @@ def get_draws_stan3(fit, model=None, variables=None, ignore=None): return data -def get_sample_stats_stan3(fit, variables=None, ignore=None): +def get_sample_stats_stan3(fit, variables=None, ignore=None, dtypes=None): """Extract sample stats from PyStan3 fit.""" - dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64} + if dtypes is None: + dtypes = {} + dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64, **dtypes} rename_dict = { "divergent": "diverging", @@ -859,30 +887,14 @@ def infer_dtypes(fit, model=None): Function strips out generated quantities block and searchs for `int` dtypes after stripping out comments inside the block. """ - pattern_remove_comments = re.compile( - r'//.*?$|/\*.*?\*/|\'(?:\\.|[^\\\'])*\'|"(?:\\.|[^\\"])*"', re.DOTALL | re.MULTILINE - ) - stan_integer = r"int" - stan_limits = r"(?:\<[^\>]+\>)*" # ignore group: 0 or more <....> - stan_param = r"([^;=\s\[]+)" # capture group: ends= ";", "=", "[" or whitespace - stan_ws = r"\s*" # 0 or more whitespace - pattern_int = re.compile( - "".join((stan_integer, stan_ws, stan_limits, stan_ws, stan_param)), re.IGNORECASE - ) if model is None: stan_code = fit.get_stancode() model_pars = fit.model_pars else: stan_code = model.program_code model_pars = fit.param_names - # remove deprecated comments - stan_code = "\n".join( - line if "#" not in line else line[: line.find("#")] for line in stan_code.splitlines() - ) - stan_code = re.sub(pattern_remove_comments, "", stan_code) - stan_code = stan_code.split("generated quantities")[-1] - dtypes = re.findall(pattern_int, stan_code) - dtypes = {item.strip(): "int" for item in dtypes if item.strip() in model_pars} + + dtypes = {key: item for key, item in infer_stan_dtypes(stan_code).items() if key in model_pars} return dtypes @@ -903,6 +915,7 @@ def from_pystan( posterior_model=None, prior_model=None, save_warmup=None, + dtypes=None, ): """Convert PyStan data into an InferenceData object. @@ -953,7 +966,12 @@ def from_pystan( PyStan3 specific model object. Needed for automatic dtype parsing. save_warmup : bool Save warmup iterations into InferenceData object. If not defined, use default - defined by the rcParams. + defined by the rcParams. Not supported in PyStan3. + dtypes: dict + A dictionary containing dtype information (int, float) for parameters. + By default dtype information is extracted from the model code. + Model code is extracted from fit object in PyStan 2 and from model object + in PyStan 3. Returns ------- @@ -981,6 +999,7 @@ def from_pystan( log_likelihood=log_likelihood, coords=coords, dims=dims, + dtypes=dtypes, ).to_inference_data() else: return PyStanConverter( @@ -996,4 +1015,5 @@ def from_pystan( coords=coords, dims=dims, save_warmup=save_warmup, + dtypes=dtypes, ).to_inference_data()

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -28,7 +28,7 @@ to_netcdf, ) -from ...data.base import dict_to_dataset, generate_dims_coords, make_attrs +from ...data.base import dict_to_dataset, generate_dims_coords, infer_stan_dtypes, make_attrs from ...data.datasets import LOCAL_DATASETS, REMOTE_DATASETS, RemoteFileMetadata from ..helpers import ( # pylint: disable=unused-import chains, @@ -331,6 +331,22 @@ def test_inference_concat_keeps_all_fields(): assert not fails_c2 [email protected]( + "model_code,expected", + [ + ("data {int y;} models {y ~ poisson(3);} generated quantities {int X;}", {"X": "int"}), + ( + "data {real y;} models {y ~ normal(0,1);} generated quantities {int Y; real G;}", + {"Y": "int"}, + ), + ], +) +def test_infer_stan_dtypes(model_code, expected): + """Test different examples for dtypes in Stan models.""" + res = infer_stan_dtypes(model_code) + assert res == expected + + class TestInferenceData: # pylint: disable=too-many-public-methods def test_addition(self): idata1 = from_dict( diff --git a/arviz/tests/base_tests/test_data_zarr.py b/arviz/tests/base_tests/test_data_zarr.py --- a/arviz/tests/base_tests/test_data_zarr.py +++ b/arviz/tests/base_tests/test_data_zarr.py @@ -6,7 +6,6 @@ import numpy as np import pytest - from arviz import InferenceData, from_dict from ..helpers import ( # pylint: disable=unused-import @@ -14,8 +13,8 @@ check_multiple_attrs, draws, eight_schools_params, - running_on_ci, importorskip, + running_on_ci, ) zarr = importorskip("zarr") # pylint: disable=invalid-name diff --git a/arviz/tests/base_tests/test_diagnostics.py b/arviz/tests/base_tests/test_diagnostics.py --- a/arviz/tests/base_tests/test_diagnostics.py +++ b/arviz/tests/base_tests/test_diagnostics.py @@ -9,8 +9,8 @@ from ...data import from_cmdstan, load_arviz_data from ...rcparams import rcParams -from ...stats import bfmi, ess, mcse, rhat from ...sel_utils import xarray_var_iter +from ...stats import bfmi, ess, mcse, rhat from ...stats.diagnostics import ( _ess, _ess_quantile, diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py --- a/arviz/tests/base_tests/test_plot_utils.py +++ b/arviz/tests/base_tests/test_plot_utils.py @@ -8,22 +8,20 @@ from ...data import from_dict from ...plots.backends.matplotlib import dealiase_sel_kwargs, matplotlib_kwarg_dealiaser from ...plots.plot_utils import ( + compute_ranks, filter_plotters_list, format_sig_figs, get_plotting_function, make_2d, set_bokeh_circular_ticks_labels, vectorized_to_hex, - compute_ranks, ) -from ...sel_utils import xarray_to_ndarray, xarray_sel_iter - from ...rcparams import rc_context +from ...sel_utils import xarray_sel_iter, xarray_to_ndarray from ...stats.density_utils import get_bins from ...utils import get_coords from ..helpers import running_on_ci - # Check if Bokeh is installed bokeh_installed = importlib.util.find_spec("bokeh") is not None # pylint: disable=invalid-name diff --git a/arviz/tests/external_tests/test_data_cmdstanpy.py b/arviz/tests/external_tests/test_data_cmdstanpy.py --- a/arviz/tests/external_tests/test_data_cmdstanpy.py +++ b/arviz/tests/external_tests/test_data_cmdstanpy.py @@ -1,6 +1,7 @@ # pylint: disable=redefined-outer-name import os import sys +import tempfile from glob import glob import numpy as np @@ -134,6 +135,7 @@ def filepaths(self, data_directory): def data(self, filepaths): # Skip tests if cmdstanpy not installed cmdstanpy = importorskip("cmdstanpy") + CmdStanModel = cmdstanpy.CmdStanModel # pylint: disable=invalid-name CmdStanMCMC = cmdstanpy.CmdStanMCMC # pylint: disable=invalid-name RunSet = cmdstanpy.stanfit.RunSet # pylint: disable=invalid-name CmdStanArgs = cmdstanpy.model.CmdStanArgs # pylint: disable=invalid-name @@ -164,6 +166,13 @@ class Data: obj_warmup.validate_csv_files() # pylint: disable=protected-access obj_warmup._assemble_draws() # pylint: disable=protected-access + _model_code = """model { real y; } generated quantities { int eta; int theta[N]; }""" + _tmp_dir = tempfile.TemporaryDirectory(prefix="arviz_tests_") + _stan_file = os.path.join(_tmp_dir.name, "stan_model_test.stan") + with open(_stan_file, "w") as f: + f.write(_model_code) + model = CmdStanModel(stan_file=_stan_file, compile=False) + return Data def get_inference_data(self, data, eight_schools_params): @@ -232,6 +241,7 @@ def get_inference_data3(self, data, eight_schools_params): "theta": ["school"], "log_lik": ["log_lik_dim"], }, + dtypes=data.model, ) def get_inference_data4(self, data, eight_schools_params): @@ -245,6 +255,7 @@ def get_inference_data4(self, data, eight_schools_params): observed_data={"y": eight_schools_params["y"]}, coords=None, dims=None, + dtypes={"eta": int, "theta": int}, ) def get_inference_data5(self, data, eight_schools_params): @@ -258,6 +269,7 @@ def get_inference_data5(self, data, eight_schools_params): observed_data={"y": eight_schools_params["y"]}, coords=None, dims=None, + dtypes=data.model.code(), ) def get_inference_data_warmup_true_is_true(self, data, eight_schools_params): @@ -341,7 +353,8 @@ def test_inference_data(self, data, eight_schools_params): inference_data2 = self.get_inference_data2(data, eight_schools_params) inference_data3 = self.get_inference_data3(data, eight_schools_params) inference_data4 = self.get_inference_data4(data, eight_schools_params) - inference_data5 = self.get_inference_data4(data, eight_schools_params) + inference_data5 = self.get_inference_data5(data, eight_schools_params) + # inference_data 1 test_dict = { "posterior": ["theta"], @@ -354,6 +367,7 @@ def test_inference_data(self, data, eight_schools_params): } fails = check_multiple_attrs(test_dict, inference_data1) assert not fails + # inference_data 2 test_dict = { "posterior_predictive": ["y_hat"], @@ -368,6 +382,7 @@ def test_inference_data(self, data, eight_schools_params): } fails = check_multiple_attrs(test_dict, inference_data2) assert not fails + # inference_data 3 test_dict = { "posterior_predictive": ["y_hat"], @@ -379,6 +394,9 @@ def test_inference_data(self, data, eight_schools_params): } fails = check_multiple_attrs(test_dict, inference_data3) assert not fails + assert inference_data3.posterior.eta.dtype.kind == "i" # pylint: disable=no-member + assert inference_data3.posterior.theta.dtype.kind == "i" # pylint: disable=no-member + # inference_data 4 test_dict = { "posterior": ["eta", "mu", "theta"], @@ -390,6 +408,9 @@ def test_inference_data(self, data, eight_schools_params): assert len(inference_data4.posterior.theta.shape) == 3 # pylint: disable=no-member assert len(inference_data4.posterior.eta.shape) == 4 # pylint: disable=no-member assert len(inference_data4.posterior.mu.shape) == 2 # pylint: disable=no-member + assert inference_data4.posterior.eta.dtype.kind == "i" # pylint: disable=no-member + assert inference_data4.posterior.theta.dtype.kind == "i" # pylint: disable=no-member + # inference_data 5 test_dict = { "posterior": ["eta", "mu", "theta"], @@ -397,6 +418,8 @@ def test_inference_data(self, data, eight_schools_params): "log_likelihood": ["log_lik"], } fails = check_multiple_attrs(test_dict, inference_data5) + assert inference_data5.posterior.eta.dtype.kind == "i" # pylint: disable=no-member + assert inference_data5.posterior.theta.dtype.kind == "i" # pylint: disable=no-member def test_inference_data_warmup(self, data, eight_schools_params): inference_data_true_is_true = self.get_inference_data_warmup_true_is_true( diff --git a/arviz/tests/external_tests/test_data_pystan.py b/arviz/tests/external_tests/test_data_pystan.py --- a/arviz/tests/external_tests/test_data_pystan.py +++ b/arviz/tests/external_tests/test_data_pystan.py @@ -136,6 +136,7 @@ def get_inference_data5(self, data): log_likelihood=False, prior_model=data.model, save_warmup=pystan_version() == 2, + dtypes={"eta": int}, ) def test_sampler_stats(self, data, eight_schools_params): @@ -221,6 +222,7 @@ def test_inference_data(self, data, eight_schools_params): ) fails = check_multiple_attrs(test_dict, inference_data5) assert not fails + assert inference_data5.posterior.eta.dtype.kind == "i" def test_invalid_fit(self, data): if pystan_version() == 2:

dtypes with cmdstanpy We should take a look at how we handle dtypes in `from_cmdstanpy` (or directly on cmdstanpy). See https://github.com/teddygroves/figure_skating/pull/1 for an example. Note this is more of an issue _candidate_ for now. I will try to play around a bit and see where things are going wrong, but I am posting anyways because 1) someone may already know the answer and I will not need to look into this and 2) maybe someone beats me into looking, I can't promise to look into this soon and we may want to fix this before the next release.

io_cmdstan uses explicit dict https://github.com/arviz-devs/arviz/blob/main/arviz/data/io_cmdstan.py io_pystan access model_code to infer dtypes. CmdStanPy does not handle dtypes.

2021-03-25T17:20:06Z

arviz-devs/arviz

arviz-devs__arviz-1647

d10e5b3f1d71152f06e6d138807118c36cbd122b

diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -648,7 +648,7 @@ def loo(data, pointwise=None, var_name=None, reff=None, scale=None): log_likelihood = _get_log_likelihood(inference_data, var_name=var_name) pointwise = rcParams["stats.ic_pointwise"] if pointwise is None else pointwise - log_likelihood = log_likelihood.stack(sample=("chain", "draw")) + log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) shape = log_likelihood.shape n_samples = shape[-1] n_data_points = np.product(shape[:-1]) @@ -692,7 +692,7 @@ def loo(data, pointwise=None, var_name=None, reff=None, scale=None): warn_mg = True ufunc_kwargs = {"n_dims": 1, "ravel": False} - kwargs = {"input_core_dims": [["sample"]]} + kwargs = {"input_core_dims": [["__sample__"]]} loo_lppd_i = scale_value * _wrap_xarray_ufunc( _logsumexp, log_weights, ufunc_kwargs=ufunc_kwargs, **kwargs ) @@ -752,6 +752,14 @@ def psislw(log_weights, reff=1.0): """ Pareto smoothed importance sampling (PSIS). + Notes + ----- + If the ``log_weights`` input is an :class:`~xarray.DataArray` with a dimension + named ``__sample__`` (recommended) ``psislw`` will interpret this dimension as samples, + and all other dimensions as dimensions of the observed data, looping over them to + calculate the psislw of each observation. If no ``__sample__`` dimension is present or + the input is a numpy array, the last dimension will be interpreted as ``__sample__``. + Parameters ---------- log_weights: array @@ -778,13 +786,17 @@ def psislw(log_weights, reff=1.0): In [1]: import arviz as az ...: data = az.load_arviz_data("centered_eight") - ...: log_likelihood = data.sample_stats.log_likelihood.stack(sample=("chain", "draw")) + ...: log_likelihood = data.sample_stats.log_likelihood.stack( + ...: __sample__=("chain", "draw") + ...: ) ...: az.psislw(-log_likelihood, reff=0.8) """ - if hasattr(log_weights, "sample"): - n_samples = len(log_weights.sample) - shape = [size for size, dim in zip(log_weights.shape, log_weights.dims) if dim != "sample"] + if hasattr(log_weights, "__sample__"): + n_samples = len(log_weights.__sample__) + shape = [ + size for size, dim in zip(log_weights.shape, log_weights.dims) if dim != "__sample__" + ] else: n_samples = log_weights.shape[-1] shape = log_weights.shape[:-1] @@ -799,7 +811,7 @@ def psislw(log_weights, reff=1.0): # define kwargs func_kwargs = {"cutoff_ind": cutoff_ind, "cutoffmin": cutoffmin, "k_min": k_min, "out": out} ufunc_kwargs = {"n_dims": 1, "n_output": 2, "ravel": False, "check_shape": False} - kwargs = {"input_core_dims": [["sample"]], "output_core_dims": [["sample"], []]} + kwargs = {"input_core_dims": [["__sample__"]], "output_core_dims": [["__sample__"], []]} log_weights, pareto_shape = _wrap_xarray_ufunc( _psislw, log_weights, @@ -808,7 +820,7 @@ def psislw(log_weights, reff=1.0): **kwargs, ) if isinstance(log_weights, xr.DataArray): - log_weights = log_weights.rename("log_weights").rename(sample="sample") + log_weights = log_weights.rename("log_weights") if isinstance(pareto_shape, xr.DataArray): pareto_shape = pareto_shape.rename("pareto_shape") return log_weights, pareto_shape @@ -1415,13 +1427,13 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): else: raise TypeError('Valid scale values are "deviance", "log", "negative_log"') - log_likelihood = log_likelihood.stack(sample=("chain", "draw")) + log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) shape = log_likelihood.shape n_samples = shape[-1] n_data_points = np.product(shape[:-1]) ufunc_kwargs = {"n_dims": 1, "ravel": False} - kwargs = {"input_core_dims": [["sample"]]} + kwargs = {"input_core_dims": [["__sample__"]]} lppd_i = _wrap_xarray_ufunc( _logsumexp, log_likelihood, @@ -1431,7 +1443,7 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): **kwargs, ) - vars_lpd = log_likelihood.var(dim="sample") + vars_lpd = log_likelihood.var(dim="__sample__") warn_mg = False if np.any(vars_lpd > 0.4): warnings.warn( @@ -1535,7 +1547,7 @@ def loo_pit(idata=None, *, y=None, y_hat=None, log_weights=None): In [1]: T = data.observed_data.obs - data.posterior.mu.median(dim=("chain", "draw")) ...: T_hat = data.posterior_predictive.obs - data.posterior.mu - ...: T_hat = T_hat.stack(sample=("chain", "draw")) + ...: T_hat = T_hat.stack(__sample__=("chain", "draw")) ...: az.loo_pit(idata=data, y=T**2, y_hat=T_hat**2) """ @@ -1561,7 +1573,7 @@ def loo_pit(idata=None, *, y=None, y_hat=None, log_weights=None): elif not isinstance(y, (np.ndarray, xr.DataArray)): raise ValueError(f"y must be of types array, DataArray or str, not {type(y)}") if isinstance(y_hat, str): - y_hat = idata.posterior_predictive[y_hat].stack(sample=("chain", "draw")).values + y_hat = idata.posterior_predictive[y_hat].stack(__sample__=("chain", "draw")).values elif not isinstance(y_hat, (np.ndarray, xr.DataArray)): raise ValueError(f"y_hat must be of types array, DataArray or str, not {type(y_hat)}") if log_weights is None: @@ -1572,10 +1584,10 @@ def loo_pit(idata=None, *, y=None, y_hat=None, log_weights=None): log_likelihood = _get_log_likelihood(idata) else: log_likelihood = _get_log_likelihood(idata) - log_likelihood = log_likelihood.stack(sample=("chain", "draw")) + log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) posterior = convert_to_dataset(idata, group="posterior") n_chains = len(posterior.chain) - n_samples = len(log_likelihood.sample) + n_samples = len(log_likelihood.__sample__) ess_p = ess(posterior, method="mean") # this mean is over all data variables reff = ( @@ -1608,7 +1620,7 @@ def loo_pit(idata=None, *, y=None, y_hat=None, log_weights=None): ) kwargs = { - "input_core_dims": [[], ["sample"], ["sample"]], + "input_core_dims": [[], ["__sample__"], ["__sample__"]], "output_core_dims": [[]], "join": "left", }

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -718,9 +718,9 @@ def test_plot_loo_pit_label(models, args): if args.get("y_hat") == "str": y_hat = "y" elif args.get("y_hat") == "DataArray": - y_hat = models.model_1.posterior_predictive.y.stack(sample=("chain", "draw")) + y_hat = models.model_1.posterior_predictive.y.stack(__sample__=("chain", "draw")) elif args.get("y_hat") == "ndarray": - y_hat = models.model_1.posterior_predictive.y.stack(sample=("chain", "draw")).values + y_hat = models.model_1.posterior_predictive.y.stack(__sample__=("chain", "draw")).values else: y_hat = None diff --git a/arviz/tests/base_tests/test_stats.py b/arviz/tests/base_tests/test_stats.py --- a/arviz/tests/base_tests/test_stats.py +++ b/arviz/tests/base_tests/test_stats.py @@ -486,7 +486,7 @@ def test_loo_print(centered_eight, scale): def test_psislw(centered_eight): pareto_k = loo(centered_eight, pointwise=True, reff=0.7)["pareto_k"] log_likelihood = get_log_likelihood(centered_eight) - log_likelihood = log_likelihood.stack(sample=("chain", "draw")) + log_likelihood = log_likelihood.stack(__sample__=("chain", "draw")) assert_allclose(pareto_k, psislw(-log_likelihood, 0.7)[1]) @@ -544,9 +544,9 @@ def test_loo_pit(centered_eight, args): y_hat = args.get("y_hat", None) log_weights = args.get("log_weights", None) y_arr = centered_eight.observed_data.obs - y_hat_arr = centered_eight.posterior_predictive.obs.stack(sample=("chain", "draw")) - log_like = get_log_likelihood(centered_eight).stack(sample=("chain", "draw")) - n_samples = len(log_like.sample) + y_hat_arr = centered_eight.posterior_predictive.obs.stack(__sample__=("chain", "draw")) + log_like = get_log_likelihood(centered_eight).stack(__sample__=("chain", "draw")) + n_samples = len(log_like.__sample__) ess_p = ess(centered_eight.posterior, method="mean") reff = np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples log_weights_arr = psislw(-log_like, reff=reff)[0] @@ -584,9 +584,9 @@ def test_loo_pit_multidim(multidim_models, args): log_weights = args.get("log_weights", None) idata = multidim_models.model_1 y_arr = idata.observed_data.y - y_hat_arr = idata.posterior_predictive.y.stack(sample=("chain", "draw")) - log_like = get_log_likelihood(idata).stack(sample=("chain", "draw")) - n_samples = len(log_like.sample) + y_hat_arr = idata.posterior_predictive.y.stack(__sample__=("chain", "draw")) + log_like = get_log_likelihood(idata).stack(__sample__=("chain", "draw")) + n_samples = len(log_like.__sample__) ess_p = ess(idata.posterior, method="mean") reff = np.hstack([ess_p[v].values.flatten() for v in ess_p.data_vars]).mean() / n_samples log_weights_arr = psislw(-log_like, reff=reff)[0]

az.loo fails when "sample" already exists as a dimension **Describe the bug** `az.loo` fails if there is a dimension named `sample` in the `log_likelihood` data variable. It looks like the `log_likelihood.stack(sample=("chain", "draw"))` line is the culprit. **To Reproduce** ```python import arviz as az data = az.load_arviz_data("centered_eight") data.sample_stats = data.sample_stats.rename({"school": "sample"}) az.loo(data) ``` <details> <summary>Error</summary> ``` Traceback (most recent call last): File "tmp/loo_issue.py", line 5, in <module> az.loo(data) File "/Users/gibs/miniconda3/envs/birdman/lib/python3.7/site-packages/arviz/stats/stats.py", line 618, in loo log_likelihood = log_likelihood.stack(sample=("chain", "draw")) File "/Users/gibs/miniconda3/envs/birdman/lib/python3.7/site-packages/xarray/core/dataarray.py", line 2035, in stack ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs) File "/Users/gibs/miniconda3/envs/birdman/lib/python3.7/site-packages/xarray/core/dataset.py", line 3578, in stack result = result._stack_once(dims, new_dim) File "/Users/gibs/miniconda3/envs/birdman/lib/python3.7/site-packages/xarray/core/dataset.py", line 3524, in _stack_once stacked_var = exp_var.stack(**{new_dim: dims}) File "/Users/gibs/miniconda3/envs/birdman/lib/python3.7/site-packages/xarray/core/variable.py", line 1554, in stack result = result._stack_once(dims, new_dim) File "/Users/gibs/miniconda3/envs/birdman/lib/python3.7/site-packages/xarray/core/variable.py", line 1507, in _stack_once "cannot create a new dimension with the same " ValueError: cannot create a new dimension with the same name as an existing dimension ``` </details> **Expected behavior** I expected that the computation would proceed as in the [documentation page](https://arviz-devs.github.io/arviz/api/generated/arviz.loo.html): ``` Computed from 2000 by 8 log-likelihood matrix Estimate SE elpd_loo -30.81 1.43 p_loo 0.95 - ``` **Additional context** This was with `arviz` commit 5957527 installed through `pip install git+git://github.com/arviz-devs/arviz.git` and `xarray` version `0.17.0`. --- I realize that I can just change the name of my dimension from `sample` to something else to address this. However, I think it would be nice to not have to rename things, especially as the temporary dataset is not returned at all.

we could use `sample__` or something less probable to be used. It is true it is not returned but it is used to indicate xarray which dimension should be summed over: https://github.com/arviz-devs/arviz/blob/main/arviz/stats/stats.py#L662 `__sample__`? To fix this issue, one has to rename the `sample` dim created when stacking in `loo` and `loo_pit` to `__sample__` to avoid name collisions. > To fix this issue, one has to rename the `sample` dim created when stacking in `loo` and `loo_pit` to `__sample__` to avoid name collisions. Thanks for the suggestion... I would like to work on the fix, can you assign it to me?

2021-03-31T00:57:52Z

arviz-devs/arviz

arviz-devs__arviz-1660

93f8f1c219dc2a012e6a7e149b6a031f2de688e7

diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -1,657 +1,55 @@ +# pylint: disable=unused-import """PyMC3-specific conversion code.""" -import logging -import warnings -from types import ModuleType -from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union +import pkg_resources +import packaging -import numpy as np -import xarray as xr +__all__ = ["from_pymc3", "from_pymc3_predictions"] -from .. import utils -from ..rcparams import rcParams -from .base import CoordSpec, DimSpec, dict_to_dataset, generate_dims_coords, make_attrs, requires -from .inference_data import InferenceData, concat +try: + pymc3_version = pkg_resources.get_distribution("pymc3").version + PYMC3_V4 = packaging.version.parse(pymc3_version) >= packaging.version.parse("4.0") +except pkg_resources.DistributionNotFound: + PYMC3_V4 = False -if TYPE_CHECKING: - from typing import Set # pylint: disable=ungrouped-imports - import pymc3 as pm - - try: - import aesara # pylint: disable=unused-import - except ImportError: - import theano as aesara # pylint: disable=unused-import - from pymc3 import Model, MultiTrace # pylint: disable=invalid-name +if not PYMC3_V4: + from .io_pymc3_3x import from_pymc3, from_pymc3_predictions else: - MultiTrace = Any # pylint: disable=invalid-name - Model = Any # pylint: disable=invalid-name - -___all__ = [""] - -_log = logging.getLogger(__name__) - -Coords = Dict[str, List[Any]] -Dims = Dict[str, List[str]] -# random variable object ... -Var = Any # pylint: disable=invalid-name - - -def _monkey_patch_pymc3(pm: ModuleType) -> None: # pylint: disable=invalid-name - assert pm.__name__ == "pymc3" - - def fixed_eq(self, other): - """Use object identity for MultiObservedRV equality.""" - return self is other - - if tuple((int(x) for x in pm.__version__.split("."))) < (3, 9): # type: ignore - pm.model.MultiObservedRV.__eq__ = fixed_eq # type: ignore - -class PyMC3Converter: # pylint: disable=too-many-instance-attributes - """Encapsulate PyMC3 specific logic.""" - - model = None # type: Optional[pm.Model] - nchains = None # type: int - ndraws = None # type: int - posterior_predictive = None # Type: Optional[Dict[str, np.ndarray]] - predictions = None # Type: Optional[Dict[str, np.ndarray]] - prior = None # Type: Optional[Dict[str, np.ndarray]] - - def __init__( - self, - *, + def from_pymc3( trace=None, + *, prior=None, posterior_predictive=None, log_likelihood=None, - predictions=None, - coords: Optional[Coords] = None, - dims: Optional[Dims] = None, + coords=None, + dims=None, model=None, - save_warmup: Optional[bool] = None, - density_dist_obs: bool = True, + save_warmup=None, + density_dist_obs=True, ): - import pymc3 - - try: - import aesara # pylint: disable=redefined-outer-name - except ImportError: - import theano as aesara - - _monkey_patch_pymc3(pymc3) - - self.pymc3 = pymc3 - self.aesara = aesara + """Convert pymc3 data into an InferenceData object. - self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup - self.trace = trace - - # this permits us to get the model from command-line argument or from with model: - try: - self.model = self.pymc3.modelcontext(model or self.model) - except TypeError as e: - _log.error("Got error %s trying to find log_likelihood in translation.", e) - self.model = None - - if self.model is None: - warnings.warn( - "Using `from_pymc3` without the model will be deprecated in a future release. " - "Not using the model will return less accurate and less useful results. " - "Make sure you use the model argument or call from_pymc3 within a model context.", - FutureWarning, - ) - - # This next line is brittle and may not work forever, but is a secret - # way to access the model from the trace. - self.attrs = None - if trace is not None: - if isinstance(self.trace, InferenceData): - raise ValueError( - "Using the `InferenceData` as a `trace` argument won't work. " - "Please use the `arviz.InferenceData.extend` method to extend the " - "`InferenceData` with groups from another `InferenceData`." - ) - if self.model is None: - self.model = list(self.trace._straces.values())[ # pylint: disable=protected-access - 0 - ].model - self.nchains = trace.nchains if hasattr(trace, "nchains") else 1 - if hasattr(trace.report, "n_draws") and trace.report.n_draws is not None: - self.ndraws = trace.report.n_draws - self.attrs = { - "sampling_time": trace.report.t_sampling, - "tuning_steps": trace.report.n_tune, - } - else: - self.ndraws = len(trace) - if self.save_warmup: - warnings.warn( - "Warmup samples will be stored in posterior group and will not be" - " excluded from stats and diagnostics." - " Please consider using PyMC3>=3.9 and do not slice the trace manually.", - UserWarning, - ) - self.ntune = len(self.trace) - self.ndraws - self.posterior_trace, self.warmup_trace = self.split_trace() - else: - self.nchains = self.ndraws = 0 - - self.prior = prior - self.posterior_predictive = posterior_predictive - self.log_likelihood = ( - rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood - ) - self.predictions = predictions - - def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: - return next(iter(dct.values())) - - if trace is None: - # if you have a posterior_predictive built with keep_dims, - # you'll lose here, but there's nothing I can do about that. - self.nchains = 1 - get_from = None - if predictions is not None: - get_from = predictions - elif posterior_predictive is not None: - get_from = posterior_predictive - elif prior is not None: - get_from = prior - if get_from is None: - # pylint: disable=line-too-long - raise ValueError( - "When constructing InferenceData must have at least" - " one of trace, prior, posterior_predictive or predictions." - ) - - aelem = arbitrary_element(get_from) - self.ndraws = aelem.shape[0] - - self.coords = {} if coords is None else coords - if hasattr(self.model, "coords"): - self.coords = {**self.model.coords, **self.coords} - - self.dims = {} if dims is None else dims - if hasattr(self.model, "RV_dims"): - model_dims = {k: list(v) for k, v in self.model.RV_dims.items()} - self.dims = {**model_dims, **self.dims} - - self.density_dist_obs = density_dist_obs - self.observations, self.multi_observations = self.find_observations() - - def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]: - """If there are observations available, return them as a dictionary.""" - if self.model is None: - return (None, None) - observations = {} - multi_observations = {} - for obs in self.model.observed_RVs: - if hasattr(obs, "observations"): - observations[obs.name] = obs.observations - elif hasattr(obs, "data") and self.density_dist_obs: - for key, val in obs.data.items(): - multi_observations[key] = val.eval() if hasattr(val, "eval") else val - return observations, multi_observations - - def split_trace(self) -> Tuple[Union[None, MultiTrace], Union[None, MultiTrace]]: - """Split MultiTrace object into posterior and warmup. - - Returns - ------- - trace_posterior: pymc3.MultiTrace or None - The slice of the trace corresponding to the posterior. If the posterior - trace is empty, None is returned - trace_warmup: pymc3.MultiTrace or None - The slice of the trace corresponding to the warmup. If the warmup trace is - empty or ``save_warmup=False``, None is returned + Placeholder for function moved to PyMC3. """ - trace_posterior = None - trace_warmup = None - if self.save_warmup and self.ntune > 0: - trace_warmup = self.trace[: self.ntune] - if self.ndraws > 0: - trace_posterior = self.trace[self.ntune :] - return trace_posterior, trace_warmup - - def log_likelihood_vals_point(self, point, var, log_like_fun): - """Compute log likelihood for each observed point.""" - log_like_val = utils.one_de(log_like_fun(point)) - if var.missing_values: - mask = var.observations.mask - if np.ndim(mask) > np.ndim(log_like_val): - mask = np.any(mask, axis=-1) - log_like_val = np.where(mask, np.nan, log_like_val) - return log_like_val - - def _extract_log_likelihood(self, trace): - """Compute log likelihood of each observation.""" - if self.trace is None: - return None - if self.model is None: - return None - - # If we have predictions, then we have a thinned trace which does not - # support extracting a log likelihood. - if self.log_likelihood is True: - cached = [(var, var.logp_elemwise) for var in self.model.observed_RVs] - else: - cached = [ - (var, var.logp_elemwise) - for var in self.model.observed_RVs - if var.name in self.log_likelihood - ] - try: - log_likelihood_dict = ( - self.pymc3.sampling._DefaultTrace( # pylint: disable=protected-access - len(trace.chains) - ) - ) - except AttributeError as err: - raise AttributeError( - "Installed version of ArviZ requires PyMC3>=3.8. Please upgrade with " - "`pip install pymc3>=3.8` or `conda install -c conda-forge pymc3>=3.8`." - ) from err - for var, log_like_fun in cached: - try: - for k, chain in enumerate(trace.chains): - log_like_chain = [ - self.log_likelihood_vals_point(point, var, log_like_fun) - for point in trace.points([chain]) - ] - log_likelihood_dict.insert(var.name, np.stack(log_like_chain), k) - except TypeError as e: - raise TypeError( - *tuple(["While computing log-likelihood for {var}: "] + list(e.args)) - ) from e - return log_likelihood_dict.trace_dict - - @requires("trace") - def posterior_to_xarray(self): - """Convert the posterior to an xarray dataset.""" - var_names = self.pymc3.util.get_default_varnames( - self.trace.varnames, include_transformed=False - ) - data = {} - data_warmup = {} - for var_name in var_names: - if self.warmup_trace: - data_warmup[var_name] = np.array( - self.warmup_trace.get_values(var_name, combine=False, squeeze=False) - ) - if self.posterior_trace: - data[var_name] = np.array( - self.posterior_trace.get_values(var_name, combine=False, squeeze=False) - ) - return ( - dict_to_dataset( - data, library=self.pymc3, coords=self.coords, dims=self.dims, attrs=self.attrs - ), - dict_to_dataset( - data_warmup, - library=self.pymc3, - coords=self.coords, - dims=self.dims, - attrs=self.attrs, - ), - ) - - @requires("trace") - def sample_stats_to_xarray(self): - """Extract sample_stats from PyMC3 trace.""" - data = {} - rename_key = { - "model_logp": "lp", - "mean_tree_accept": "acceptance_rate", - "depth": "tree_depth", - "tree_size": "n_steps", - } - data = {} - data_warmup = {} - for stat in self.trace.stat_names: - name = rename_key.get(stat, stat) - if name == "tune": - continue - if self.warmup_trace: - data_warmup[name] = np.array( - self.warmup_trace.get_sampler_stats(stat, combine=False) - ) - if self.posterior_trace: - data[name] = np.array(self.posterior_trace.get_sampler_stats(stat, combine=False)) - - return ( - dict_to_dataset( - data, library=self.pymc3, dims=None, coords=self.coords, attrs=self.attrs - ), - dict_to_dataset( - data_warmup, library=self.pymc3, dims=None, coords=self.coords, attrs=self.attrs - ), - ) - - @requires("trace") - @requires("model") - def log_likelihood_to_xarray(self): - """Extract log likelihood and log_p data from PyMC3 trace.""" - if self.predictions or not self.log_likelihood: - return None - data_warmup = {} - data = {} - warn_msg = ( - "Could not compute log_likelihood, it will be omitted. " - "Check your model object or set log_likelihood=False" - ) - if self.posterior_trace: - try: - data = self._extract_log_likelihood(self.posterior_trace) - except TypeError: - warnings.warn(warn_msg) - if self.warmup_trace: - try: - data_warmup = self._extract_log_likelihood(self.warmup_trace) - except TypeError: - warnings.warn(warn_msg) - return ( - dict_to_dataset( - data, library=self.pymc3, dims=self.dims, coords=self.coords, skip_event_dims=True - ), - dict_to_dataset( - data_warmup, - library=self.pymc3, - dims=self.dims, - coords=self.coords, - skip_event_dims=True, - ), + raise NotImplementedError( + "The converter has been moved to PyMC3 codebase, use pymc3.to_inference_data" ) - def translate_posterior_predictive_dict_to_xarray(self, dct) -> xr.Dataset: - """Take Dict of variables to numpy ndarrays (samples) and translate into dataset.""" - data = {} - for k, ary in dct.items(): - shape = ary.shape - if shape[0] == self.nchains and shape[1] == self.ndraws: - data[k] = ary - elif shape[0] == self.nchains * self.ndraws: - data[k] = ary.reshape((self.nchains, self.ndraws, *shape[1:])) - else: - data[k] = utils.expand_dims(ary) - # pylint: disable=line-too-long - _log.warning( - "posterior predictive variable %s's shape not compatible with number of chains and draws. " - "This can mean that some draws or even whole chains are not represented.", - k, - ) - return dict_to_dataset(data, library=self.pymc3, coords=self.coords, dims=self.dims) - - @requires(["posterior_predictive"]) - def posterior_predictive_to_xarray(self): - """Convert posterior_predictive samples to xarray.""" - return self.translate_posterior_predictive_dict_to_xarray(self.posterior_predictive) - - @requires(["predictions"]) - def predictions_to_xarray(self): - """Convert predictions (out of sample predictions) to xarray.""" - return self.translate_posterior_predictive_dict_to_xarray(self.predictions) - - def priors_to_xarray(self): - """Convert prior samples (and if possible prior predictive too) to xarray.""" - if self.prior is None: - return {"prior": None, "prior_predictive": None} - if self.observations is not None: - prior_predictive_vars = list(self.observations.keys()) - prior_vars = [key for key in self.prior.keys() if key not in prior_predictive_vars] - else: - prior_vars = list(self.prior.keys()) - prior_predictive_vars = None - - priors_dict = {} - for group, var_names in zip( - ("prior", "prior_predictive"), (prior_vars, prior_predictive_vars) - ): - priors_dict[group] = ( - None - if var_names is None - else dict_to_dataset( - {k: utils.expand_dims(self.prior[k]) for k in var_names}, - library=self.pymc3, - coords=self.coords, - dims=self.dims, - ) - ) - return priors_dict - - @requires(["observations", "multi_observations"]) - @requires("model") - def observed_data_to_xarray(self): - """Convert observed data to xarray.""" - if self.predictions: - return None - if self.dims is None: - dims = {} - else: - dims = self.dims - observed_data = {} - for name, vals in {**self.observations, **self.multi_observations}.items(): - if hasattr(vals, "get_value"): - vals = vals.get_value() - vals = utils.one_de(vals) - val_dims = dims.get(name) - val_dims, coords = generate_dims_coords( - vals.shape, name, dims=val_dims, coords=self.coords - ) - # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} - observed_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) - return xr.Dataset(data_vars=observed_data, attrs=make_attrs(library=self.pymc3)) - - @requires(["trace", "predictions"]) - @requires("model") - def constant_data_to_xarray(self): - """Convert constant data to xarray.""" - # For constant data, we are concerned only with deterministics and data. - # The constant data vars must be either pm.Data (TensorSharedVariable) or pm.Deterministic - constant_data_vars = {} # type: Dict[str, Var] - for var in self.model.deterministics: - if hasattr(self.aesara, "gof"): - ancestors_func = self.aesara.gof.graph.ancestors # pylint: disable=no-member - else: - ancestors_func = self.aesara.graph.basic.ancestors # pylint: disable=no-member - ancestors = ancestors_func(var.owner.inputs) - # no dependency on a random variable - if not any((isinstance(a, self.pymc3.model.PyMC3Variable) for a in ancestors)): - constant_data_vars[var.name] = var - - def is_data(name, var) -> bool: - assert self.model is not None - return ( - var not in self.model.deterministics - and var not in self.model.observed_RVs - and var not in self.model.free_RVs - and var not in self.model.potentials - and (self.observations is None or name not in self.observations) - ) - - # I don't know how to find pm.Data, except that they are named variables that aren't - # observed or free RVs, nor are they deterministics, and then we eliminate observations. - for name, var in self.model.named_vars.items(): - if is_data(name, var): - constant_data_vars[name] = var - - if not constant_data_vars: - return None - if self.dims is None: - dims = {} - else: - dims = self.dims - constant_data = {} - for name, vals in constant_data_vars.items(): - if hasattr(vals, "get_value"): - vals = vals.get_value() - # this might be a Deterministic, and must be evaluated - elif hasattr(self.model[name], "eval"): - vals = self.model[name].eval() - vals = np.atleast_1d(vals) - val_dims = dims.get(name) - val_dims, coords = generate_dims_coords( - vals.shape, name, dims=val_dims, coords=self.coords - ) - # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} - try: - constant_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) - except ValueError as err: - raise ValueError( - "Error translating constant_data variable %s: %s" % (name, err) - ) from err - return xr.Dataset(data_vars=constant_data, attrs=make_attrs(library=self.pymc3)) - - def to_inference_data(self): - """Convert all available data to an InferenceData object. + def from_pymc3_predictions( + predictions, + posterior_trace=None, + model=None, + coords=None, + dims=None, + idata_orig=None, + inplace=False, + ): + """Translate out-of-sample predictions into ``InferenceData``. - Note that if groups can not be created (e.g., there is no `trace`, so - the `posterior` and `sample_stats` can not be extracted), then the InferenceData - will not have those groups. + Placeholder for function moved to PyMC3. """ - id_dict = { - "posterior": self.posterior_to_xarray(), - "sample_stats": self.sample_stats_to_xarray(), - "log_likelihood": self.log_likelihood_to_xarray(), - "posterior_predictive": self.posterior_predictive_to_xarray(), - "predictions": self.predictions_to_xarray(), - **self.priors_to_xarray(), - "observed_data": self.observed_data_to_xarray(), - } - if self.predictions: - id_dict["predictions_constant_data"] = self.constant_data_to_xarray() - else: - id_dict["constant_data"] = self.constant_data_to_xarray() - return InferenceData(save_warmup=self.save_warmup, **id_dict) - - -def from_pymc3( - trace: Optional[MultiTrace] = None, - *, - prior: Optional[Dict[str, Any]] = None, - posterior_predictive: Optional[Dict[str, Any]] = None, - log_likelihood: Union[bool, Iterable[str], None] = None, - coords: Optional[CoordSpec] = None, - dims: Optional[DimSpec] = None, - model: Optional[Model] = None, - save_warmup: Optional[bool] = None, - density_dist_obs: bool = True, -) -> InferenceData: - """Convert pymc3 data into an InferenceData object. - - All three of them are optional arguments, but at least one of ``trace``, - ``prior`` and ``posterior_predictive`` must be present. - For a usage example read the - :ref:`Creating InferenceData section on from_pymc3 <creating_InferenceData>` - - Parameters - ---------- - trace : pymc3.MultiTrace, optional - Trace generated from MCMC sampling. Output of - :py:func:`pymc3:pymc3.sampling.sample`. - prior : dict, optional - Dictionary with the variable names as keys, and values numpy arrays - containing prior and prior predictive samples. - posterior_predictive : dict, optional - Dictionary with the variable names as keys, and values numpy arrays - containing posterior predictive samples. - log_likelihood : bool or array_like of str, optional - List of variables to calculate `log_likelihood`. Defaults to True which calculates - `log_likelihood` for all observed variables. If set to False, log_likelihood is skipped. - Defaults to the value of rcParam ``data.log_likelihood``. - coords : dict of {str: array-like}, optional - Map of coordinate names to coordinate values - dims : dict of {str: list of str}, optional - Map of variable names to the coordinate names to use to index its dimensions. - model : pymc3.Model, optional - Model used to generate ``trace``. It is not necessary to pass ``model`` if in - ``with`` context. - save_warmup : bool, optional - Save warmup iterations InferenceData object. If not defined, use default - defined by the rcParams. - density_dist_obs : bool, default True - Store variables passed with ``observed`` arg to - :class:`pymc3:pymc.distributions.DensityDist` in the generated InferenceData. - - Returns - ------- - InferenceData - """ - return PyMC3Converter( - trace=trace, - prior=prior, - posterior_predictive=posterior_predictive, - log_likelihood=log_likelihood, - coords=coords, - dims=dims, - model=model, - save_warmup=save_warmup, - density_dist_obs=density_dist_obs, - ).to_inference_data() - - -### Later I could have this return ``None`` if the ``idata_orig`` argument is supplied. But -### perhaps we should have an inplace argument? -def from_pymc3_predictions( - predictions, - posterior_trace: Optional[MultiTrace] = None, - model: Optional[Model] = None, - coords=None, - dims=None, - idata_orig: Optional[InferenceData] = None, - inplace: bool = False, -) -> InferenceData: - """Translate out-of-sample predictions into ``InferenceData``. - - Parameters - ---------- - predictions: Dict[str, np.ndarray] - The predictions are the return value of ``pymc3.sample_posterior_predictive``, - a dictionary of strings (variable names) to numpy ndarrays (draws). - posterior_trace: pm.MultiTrace - This should be a trace that has been thinned appropriately for - ``pymc3.sample_posterior_predictive``. Specifically, any variable whose shape is - a deterministic function of the shape of any predictor (explanatory, independent, etc.) - variables must be *removed* from this trace. - model: pymc3.Model - This argument is *not* optional, unlike in conventional uses of ``from_pymc3``. - The reason is that the posterior_trace argument is likely to supply an incorrect - value of model. - coords: Dict[str, array-like[Any]] - Coordinates for the variables. Map from coordinate names to coordinate values. - dims: Dict[str, array-like[str]] - Map from variable name to ordered set of coordinate names. - idata_orig: InferenceData, optional - If supplied, then modify this inference data in place, adding ``predictions`` and - (if available) ``predictions_constant_data`` groups. If this is not supplied, make a - fresh InferenceData - inplace: boolean, optional - If idata_orig is supplied and inplace is True, merge the predictions into idata_orig, - rather than returning a fresh InferenceData object. - - Returns - ------- - InferenceData: - May be modified ``idata_orig``. - """ - if inplace and not idata_orig: - raise ValueError( - ( - "Do not pass True for inplace unless passing" - "an existing InferenceData as idata_orig" - ) + raise NotImplementedError( + "The converter has been moved to PyMC3 codebase, " + "use pymc3.to_inference_data_predictions" ) - new_idata = PyMC3Converter( - trace=posterior_trace, predictions=predictions, model=model, coords=coords, dims=dims - ).to_inference_data() - if idata_orig is None: - return new_idata - elif inplace: - concat([idata_orig, new_idata], dim=None, inplace=True) - return idata_orig - else: - # if we are not returning in place, then merge the old groups into the new inference - # data and return that. - concat([new_idata, idata_orig], dim=None, copy=True, inplace=True) - return new_idata diff --git a/arviz/data/io_pymc3_3x.py b/arviz/data/io_pymc3_3x.py new file mode 100644 --- /dev/null +++ b/arviz/data/io_pymc3_3x.py @@ -0,0 +1,658 @@ +# pylint: disable=unused-import +"""PyMC3-specific conversion code (PyMC3<4.0).""" +import logging +import warnings +from types import ModuleType +from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union + +import numpy as np +import xarray as xr + +from .. import utils +from ..rcparams import rcParams +from .base import CoordSpec, DimSpec, dict_to_dataset, generate_dims_coords, make_attrs, requires +from .inference_data import InferenceData, concat + +if TYPE_CHECKING: + from typing import Set # pylint: disable=ungrouped-imports + + import pymc3 as pm + + try: + import aesara # pylint: disable=unused-import + except ImportError: + import theano as aesara # pylint: disable=unused-import + from pymc3 import Model, MultiTrace # pylint: disable=invalid-name +else: + MultiTrace = Any # pylint: disable=invalid-name + Model = Any # pylint: disable=invalid-name + +___all__ = [""] + +_log = logging.getLogger(__name__) + +Coords = Dict[str, List[Any]] +Dims = Dict[str, List[str]] +# random variable object ... +Var = Any # pylint: disable=invalid-name + + +def _monkey_patch_pymc3(pm: ModuleType) -> None: # pylint: disable=invalid-name + assert pm.__name__ == "pymc3" + + def fixed_eq(self, other): + """Use object identity for MultiObservedRV equality.""" + return self is other + + if tuple((int(x) for x in pm.__version__.split("."))) < (3, 9): # type: ignore + pm.model.MultiObservedRV.__eq__ = fixed_eq # type: ignore + + +class PyMC3Converter: # pylint: disable=too-many-instance-attributes + """Encapsulate PyMC3 specific logic.""" + + model = None # type: Optional[pm.Model] + nchains = None # type: int + ndraws = None # type: int + posterior_predictive = None # Type: Optional[Dict[str, np.ndarray]] + predictions = None # Type: Optional[Dict[str, np.ndarray]] + prior = None # Type: Optional[Dict[str, np.ndarray]] + + def __init__( + self, + *, + trace=None, + prior=None, + posterior_predictive=None, + log_likelihood=None, + predictions=None, + coords: Optional[Coords] = None, + dims: Optional[Dims] = None, + model=None, + save_warmup: Optional[bool] = None, + density_dist_obs: bool = True, + ): + import pymc3 + + try: + import aesara # pylint: disable=redefined-outer-name + except ImportError: + import theano as aesara + + _monkey_patch_pymc3(pymc3) + + self.pymc3 = pymc3 + self.aesara = aesara + + self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup + self.trace = trace + + # this permits us to get the model from command-line argument or from with model: + try: + self.model = self.pymc3.modelcontext(model or self.model) + except TypeError as e: + _log.error("Got error %s trying to find log_likelihood in translation.", e) + self.model = None + + if self.model is None: + warnings.warn( + "Using `from_pymc3` without the model will be deprecated in a future release. " + "Not using the model will return less accurate and less useful results. " + "Make sure you use the model argument or call from_pymc3 within a model context.", + FutureWarning, + ) + + # This next line is brittle and may not work forever, but is a secret + # way to access the model from the trace. + self.attrs = None + if trace is not None: + if isinstance(self.trace, InferenceData): + raise ValueError( + "Using the `InferenceData` as a `trace` argument won't work. " + "Please use the `arviz.InferenceData.extend` method to extend the " + "`InferenceData` with groups from another `InferenceData`." + ) + if self.model is None: + self.model = list(self.trace._straces.values())[ # pylint: disable=protected-access + 0 + ].model + self.nchains = trace.nchains if hasattr(trace, "nchains") else 1 + if hasattr(trace.report, "n_draws") and trace.report.n_draws is not None: + self.ndraws = trace.report.n_draws + self.attrs = { + "sampling_time": trace.report.t_sampling, + "tuning_steps": trace.report.n_tune, + } + else: + self.ndraws = len(trace) + if self.save_warmup: + warnings.warn( + "Warmup samples will be stored in posterior group and will not be" + " excluded from stats and diagnostics." + " Please consider using PyMC3>=3.9 and do not slice the trace manually.", + UserWarning, + ) + self.ntune = len(self.trace) - self.ndraws + self.posterior_trace, self.warmup_trace = self.split_trace() + else: + self.nchains = self.ndraws = 0 + + self.prior = prior + self.posterior_predictive = posterior_predictive + self.log_likelihood = ( + rcParams["data.log_likelihood"] if log_likelihood is None else log_likelihood + ) + self.predictions = predictions + + def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: + return next(iter(dct.values())) + + if trace is None: + # if you have a posterior_predictive built with keep_dims, + # you'll lose here, but there's nothing I can do about that. + self.nchains = 1 + get_from = None + if predictions is not None: + get_from = predictions + elif posterior_predictive is not None: + get_from = posterior_predictive + elif prior is not None: + get_from = prior + if get_from is None: + # pylint: disable=line-too-long + raise ValueError( + "When constructing InferenceData must have at least" + " one of trace, prior, posterior_predictive or predictions." + ) + + aelem = arbitrary_element(get_from) + self.ndraws = aelem.shape[0] + + self.coords = {} if coords is None else coords + if hasattr(self.model, "coords"): + self.coords = {**self.model.coords, **self.coords} + + self.dims = {} if dims is None else dims + if hasattr(self.model, "RV_dims"): + model_dims = {k: list(v) for k, v in self.model.RV_dims.items()} + self.dims = {**model_dims, **self.dims} + + self.density_dist_obs = density_dist_obs + self.observations, self.multi_observations = self.find_observations() + + def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]: + """If there are observations available, return them as a dictionary.""" + if self.model is None: + return (None, None) + observations = {} + multi_observations = {} + for obs in self.model.observed_RVs: + if hasattr(obs, "observations"): + observations[obs.name] = obs.observations + elif hasattr(obs, "data") and self.density_dist_obs: + for key, val in obs.data.items(): + multi_observations[key] = val.eval() if hasattr(val, "eval") else val + return observations, multi_observations + + def split_trace(self) -> Tuple[Union[None, MultiTrace], Union[None, MultiTrace]]: + """Split MultiTrace object into posterior and warmup. + + Returns + ------- + trace_posterior: pymc3.MultiTrace or None + The slice of the trace corresponding to the posterior. If the posterior + trace is empty, None is returned + trace_warmup: pymc3.MultiTrace or None + The slice of the trace corresponding to the warmup. If the warmup trace is + empty or ``save_warmup=False``, None is returned + """ + trace_posterior = None + trace_warmup = None + if self.save_warmup and self.ntune > 0: + trace_warmup = self.trace[: self.ntune] + if self.ndraws > 0: + trace_posterior = self.trace[self.ntune :] + return trace_posterior, trace_warmup + + def log_likelihood_vals_point(self, point, var, log_like_fun): + """Compute log likelihood for each observed point.""" + log_like_val = utils.one_de(log_like_fun(point)) + if var.missing_values: + mask = var.observations.mask + if np.ndim(mask) > np.ndim(log_like_val): + mask = np.any(mask, axis=-1) + log_like_val = np.where(mask, np.nan, log_like_val) + return log_like_val + + def _extract_log_likelihood(self, trace): + """Compute log likelihood of each observation.""" + if self.trace is None: + return None + if self.model is None: + return None + + # If we have predictions, then we have a thinned trace which does not + # support extracting a log likelihood. + if self.log_likelihood is True: + cached = [(var, var.logp_elemwise) for var in self.model.observed_RVs] + else: + cached = [ + (var, var.logp_elemwise) + for var in self.model.observed_RVs + if var.name in self.log_likelihood + ] + try: + log_likelihood_dict = ( + self.pymc3.sampling._DefaultTrace( # pylint: disable=protected-access + len(trace.chains) + ) + ) + except AttributeError as err: + raise AttributeError( + "Installed version of ArviZ requires PyMC3>=3.8. Please upgrade with " + "`pip install pymc3>=3.8` or `conda install -c conda-forge pymc3>=3.8`." + ) from err + for var, log_like_fun in cached: + try: + for k, chain in enumerate(trace.chains): + log_like_chain = [ + self.log_likelihood_vals_point(point, var, log_like_fun) + for point in trace.points([chain]) + ] + log_likelihood_dict.insert(var.name, np.stack(log_like_chain), k) + except TypeError as e: + raise TypeError( + *tuple(["While computing log-likelihood for {var}: "] + list(e.args)) + ) from e + return log_likelihood_dict.trace_dict + + @requires("trace") + def posterior_to_xarray(self): + """Convert the posterior to an xarray dataset.""" + var_names = self.pymc3.util.get_default_varnames( + self.trace.varnames, include_transformed=False + ) + data = {} + data_warmup = {} + for var_name in var_names: + if self.warmup_trace: + data_warmup[var_name] = np.array( + self.warmup_trace.get_values(var_name, combine=False, squeeze=False) + ) + if self.posterior_trace: + data[var_name] = np.array( + self.posterior_trace.get_values(var_name, combine=False, squeeze=False) + ) + return ( + dict_to_dataset( + data, library=self.pymc3, coords=self.coords, dims=self.dims, attrs=self.attrs + ), + dict_to_dataset( + data_warmup, + library=self.pymc3, + coords=self.coords, + dims=self.dims, + attrs=self.attrs, + ), + ) + + @requires("trace") + def sample_stats_to_xarray(self): + """Extract sample_stats from PyMC3 trace.""" + data = {} + rename_key = { + "model_logp": "lp", + "mean_tree_accept": "acceptance_rate", + "depth": "tree_depth", + "tree_size": "n_steps", + } + data = {} + data_warmup = {} + for stat in self.trace.stat_names: + name = rename_key.get(stat, stat) + if name == "tune": + continue + if self.warmup_trace: + data_warmup[name] = np.array( + self.warmup_trace.get_sampler_stats(stat, combine=False) + ) + if self.posterior_trace: + data[name] = np.array(self.posterior_trace.get_sampler_stats(stat, combine=False)) + + return ( + dict_to_dataset( + data, library=self.pymc3, dims=None, coords=self.coords, attrs=self.attrs + ), + dict_to_dataset( + data_warmup, library=self.pymc3, dims=None, coords=self.coords, attrs=self.attrs + ), + ) + + @requires("trace") + @requires("model") + def log_likelihood_to_xarray(self): + """Extract log likelihood and log_p data from PyMC3 trace.""" + if self.predictions or not self.log_likelihood: + return None + data_warmup = {} + data = {} + warn_msg = ( + "Could not compute log_likelihood, it will be omitted. " + "Check your model object or set log_likelihood=False" + ) + if self.posterior_trace: + try: + data = self._extract_log_likelihood(self.posterior_trace) + except TypeError: + warnings.warn(warn_msg) + if self.warmup_trace: + try: + data_warmup = self._extract_log_likelihood(self.warmup_trace) + except TypeError: + warnings.warn(warn_msg) + return ( + dict_to_dataset( + data, library=self.pymc3, dims=self.dims, coords=self.coords, skip_event_dims=True + ), + dict_to_dataset( + data_warmup, + library=self.pymc3, + dims=self.dims, + coords=self.coords, + skip_event_dims=True, + ), + ) + + def translate_posterior_predictive_dict_to_xarray(self, dct) -> xr.Dataset: + """Take Dict of variables to numpy ndarrays (samples) and translate into dataset.""" + data = {} + for k, ary in dct.items(): + shape = ary.shape + if shape[0] == self.nchains and shape[1] == self.ndraws: + data[k] = ary + elif shape[0] == self.nchains * self.ndraws: + data[k] = ary.reshape((self.nchains, self.ndraws, *shape[1:])) + else: + data[k] = utils.expand_dims(ary) + # pylint: disable=line-too-long + _log.warning( + "posterior predictive variable %s's shape not compatible with number of chains and draws. " + "This can mean that some draws or even whole chains are not represented.", + k, + ) + return dict_to_dataset(data, library=self.pymc3, coords=self.coords, dims=self.dims) + + @requires(["posterior_predictive"]) + def posterior_predictive_to_xarray(self): + """Convert posterior_predictive samples to xarray.""" + return self.translate_posterior_predictive_dict_to_xarray(self.posterior_predictive) + + @requires(["predictions"]) + def predictions_to_xarray(self): + """Convert predictions (out of sample predictions) to xarray.""" + return self.translate_posterior_predictive_dict_to_xarray(self.predictions) + + def priors_to_xarray(self): + """Convert prior samples (and if possible prior predictive too) to xarray.""" + if self.prior is None: + return {"prior": None, "prior_predictive": None} + if self.observations is not None: + prior_predictive_vars = list(self.observations.keys()) + prior_vars = [key for key in self.prior.keys() if key not in prior_predictive_vars] + else: + prior_vars = list(self.prior.keys()) + prior_predictive_vars = None + + priors_dict = {} + for group, var_names in zip( + ("prior", "prior_predictive"), (prior_vars, prior_predictive_vars) + ): + priors_dict[group] = ( + None + if var_names is None + else dict_to_dataset( + {k: utils.expand_dims(self.prior[k]) for k in var_names}, + library=self.pymc3, + coords=self.coords, + dims=self.dims, + ) + ) + return priors_dict + + @requires(["observations", "multi_observations"]) + @requires("model") + def observed_data_to_xarray(self): + """Convert observed data to xarray.""" + if self.predictions: + return None + if self.dims is None: + dims = {} + else: + dims = self.dims + observed_data = {} + for name, vals in {**self.observations, **self.multi_observations}.items(): + if hasattr(vals, "get_value"): + vals = vals.get_value() + vals = utils.one_de(vals) + val_dims = dims.get(name) + val_dims, coords = generate_dims_coords( + vals.shape, name, dims=val_dims, coords=self.coords + ) + # filter coords based on the dims + coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} + observed_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) + return xr.Dataset(data_vars=observed_data, attrs=make_attrs(library=self.pymc3)) + + @requires(["trace", "predictions"]) + @requires("model") + def constant_data_to_xarray(self): + """Convert constant data to xarray.""" + # For constant data, we are concerned only with deterministics and data. + # The constant data vars must be either pm.Data (TensorSharedVariable) or pm.Deterministic + constant_data_vars = {} # type: Dict[str, Var] + for var in self.model.deterministics: + if hasattr(self.aesara, "gof"): + ancestors_func = self.aesara.gof.graph.ancestors # pylint: disable=no-member + else: + ancestors_func = self.aesara.graph.basic.ancestors # pylint: disable=no-member + ancestors = ancestors_func(var.owner.inputs) + # no dependency on a random variable + if not any((isinstance(a, self.pymc3.model.PyMC3Variable) for a in ancestors)): + constant_data_vars[var.name] = var + + def is_data(name, var) -> bool: + assert self.model is not None + return ( + var not in self.model.deterministics + and var not in self.model.observed_RVs + and var not in self.model.free_RVs + and var not in self.model.potentials + and (self.observations is None or name not in self.observations) + ) + + # I don't know how to find pm.Data, except that they are named variables that aren't + # observed or free RVs, nor are they deterministics, and then we eliminate observations. + for name, var in self.model.named_vars.items(): + if is_data(name, var): + constant_data_vars[name] = var + + if not constant_data_vars: + return None + if self.dims is None: + dims = {} + else: + dims = self.dims + constant_data = {} + for name, vals in constant_data_vars.items(): + if hasattr(vals, "get_value"): + vals = vals.get_value() + # this might be a Deterministic, and must be evaluated + elif hasattr(self.model[name], "eval"): + vals = self.model[name].eval() + vals = np.atleast_1d(vals) + val_dims = dims.get(name) + val_dims, coords = generate_dims_coords( + vals.shape, name, dims=val_dims, coords=self.coords + ) + # filter coords based on the dims + coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in val_dims} + try: + constant_data[name] = xr.DataArray(vals, dims=val_dims, coords=coords) + except ValueError as err: + raise ValueError( + "Error translating constant_data variable %s: %s" % (name, err) + ) from err + return xr.Dataset(data_vars=constant_data, attrs=make_attrs(library=self.pymc3)) + + def to_inference_data(self): + """Convert all available data to an InferenceData object. + + Note that if groups can not be created (e.g., there is no `trace`, so + the `posterior` and `sample_stats` can not be extracted), then the InferenceData + will not have those groups. + """ + id_dict = { + "posterior": self.posterior_to_xarray(), + "sample_stats": self.sample_stats_to_xarray(), + "log_likelihood": self.log_likelihood_to_xarray(), + "posterior_predictive": self.posterior_predictive_to_xarray(), + "predictions": self.predictions_to_xarray(), + **self.priors_to_xarray(), + "observed_data": self.observed_data_to_xarray(), + } + if self.predictions: + id_dict["predictions_constant_data"] = self.constant_data_to_xarray() + else: + id_dict["constant_data"] = self.constant_data_to_xarray() + return InferenceData(save_warmup=self.save_warmup, **id_dict) + + +def from_pymc3( + trace=None, + *, + prior=None, + posterior_predictive=None, + log_likelihood=None, + coords=None, + dims=None, + model=None, + save_warmup=None, + density_dist_obs=True, +): + """Convert pymc3 data into an InferenceData object. + + All three of them are optional arguments, but at least one of ``trace``, + ``prior`` and ``posterior_predictive`` must be present. + For a usage example read the + :ref:`Creating InferenceData section on from_pymc3 <creating_InferenceData>` + + Parameters + ---------- + trace : pymc3.MultiTrace, optional + Trace generated from MCMC sampling. Output of + :py:func:`pymc3:pymc3.sampling.sample`. + prior : dict, optional + Dictionary with the variable names as keys, and values numpy arrays + containing prior and prior predictive samples. + posterior_predictive : dict, optional + Dictionary with the variable names as keys, and values numpy arrays + containing posterior predictive samples. + log_likelihood : bool or array_like of str, optional + List of variables to calculate `log_likelihood`. Defaults to True which calculates + `log_likelihood` for all observed variables. If set to False, log_likelihood is skipped. + Defaults to the value of rcParam ``data.log_likelihood``. + coords : dict of {str: array-like}, optional + Map of coordinate names to coordinate values + dims : dict of {str: list of str}, optional + Map of variable names to the coordinate names to use to index its dimensions. + model : pymc3.Model, optional + Model used to generate ``trace``. It is not necessary to pass ``model`` if in + ``with`` context. + save_warmup : bool, optional + Save warmup iterations InferenceData object. If not defined, use default + defined by the rcParams. + density_dist_obs : bool, default True + Store variables passed with ``observed`` arg to + :class:`pymc3:pymc.distributions.DensityDist` in the generated InferenceData. + + Returns + ------- + InferenceData + """ + return PyMC3Converter( + trace=trace, + prior=prior, + posterior_predictive=posterior_predictive, + log_likelihood=log_likelihood, + coords=coords, + dims=dims, + model=model, + save_warmup=save_warmup, + density_dist_obs=density_dist_obs, + ).to_inference_data() + + +### Later I could have this return ``None`` if the ``idata_orig`` argument is supplied. But +### perhaps we should have an inplace argument? +def from_pymc3_predictions( + predictions, + posterior_trace=None, + model=None, + coords=None, + dims=None, + idata_orig=None, + inplace=False, +): + """Translate out-of-sample predictions into ``InferenceData``. + + Parameters + ---------- + predictions: Dict[str, np.ndarray] + The predictions are the return value of ``pymc3.sample_posterior_predictive``, + a dictionary of strings (variable names) to numpy ndarrays (draws). + posterior_trace: pm.MultiTrace + This should be a trace that has been thinned appropriately for + ``pymc3.sample_posterior_predictive``. Specifically, any variable whose shape is + a deterministic function of the shape of any predictor (explanatory, independent, etc.) + variables must be *removed* from this trace. + model: pymc3.Model + This argument is *not* optional, unlike in conventional uses of ``from_pymc3``. + The reason is that the posterior_trace argument is likely to supply an incorrect + value of model. + coords: Dict[str, array-like[Any]] + Coordinates for the variables. Map from coordinate names to coordinate values. + dims: Dict[str, array-like[str]] + Map from variable name to ordered set of coordinate names. + idata_orig: InferenceData, optional + If supplied, then modify this inference data in place, adding ``predictions`` and + (if available) ``predictions_constant_data`` groups. If this is not supplied, make a + fresh InferenceData + inplace: boolean, optional + If idata_orig is supplied and inplace is True, merge the predictions into idata_orig, + rather than returning a fresh InferenceData object. + + Returns + ------- + InferenceData: + May be modified ``idata_orig``. + """ + if inplace and not idata_orig: + raise ValueError( + ( + "Do not pass True for inplace unless passing" + "an existing InferenceData as idata_orig" + ) + ) + new_idata = PyMC3Converter( + trace=posterior_trace, predictions=predictions, model=model, coords=coords, dims=dims + ).to_inference_data() + if idata_orig is None: + return new_idata + elif inplace: + concat([idata_orig, new_idata], dim=None, inplace=True) + return idata_orig + else: + # if we are not returning in place, then merge the old groups into the new inference + # data and return that. + concat([new_idata, idata_orig], dim=None, copy=True, inplace=True) + return new_idata

diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -3,6 +3,7 @@ from typing import Dict, Tuple import numpy as np +import pkg_resources import packaging import pandas as pd import pytest @@ -24,8 +25,20 @@ load_cached_models, ) -# Skip all tests if pymc3 not installed -pm = importorskip("pymc3") +# Skip all tests unless running on pymc3 v3 +try: + pymc3_version = pkg_resources.get_distribution("pymc3").version + PYMC3_V4 = packaging.version.parse(pymc3_version) >= packaging.version.parse("4.0") + PYMC3_installed = True + import pymc3 as pm +except pkg_resources.DistributionNotFound: + PYMC3_V4 = False + PYMC3_installed = False + +pytestmark = pytest.mark.skipif( + not PYMC3_installed or PYMC3_V4, + reason="Run tests only if pymc3 installed and its version is <4.0", +) class TestDataPyMC3:

Remove technical debt in `from_pymc3` and move to pymc3 repo PyMC3 already depends on ArviZ and is moving towards a seamless integration between the two packages (i.e. autocoords and dims, returning `InferenceData` from `pm.sample`, having `from_pymc3` retrieve and use the model context...). Similarly to PyMC4, we thought it would make development of both packages easier to move `from_pymc3` to PyMC3 codebase at some point. This would allow to simplify the code of `from_pymc3` and to update it straight away changes in PyMC3 require it thus eliminating the need for synchronized releases of ArviZ and PyMC3 or the [current monkeypatching](https://github.com/arviz-devs/arviz/blob/master/arviz/data/io_pymc3.py#L33) of PyMC3 done in `io_pymc3`. This issue should be a place for roadmap and discussion on this. cc @AlexAndorra @michaelosthege @rpgoldman I think the first step would be identifying things that could be problematic when going ahead with the merger. Things that come to mind are maybe #1063 or making sure all functions used are public.

2021-04-13T18:37:34Z

arviz-devs/arviz

arviz-devs__arviz-1676

0fbd405c05045c72277baa68437418ebab744099

diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -34,6 +34,7 @@ def plot_forest( var_names, model_names, combined, + combine_dims, colors, figsize, width_ratios, @@ -64,6 +65,7 @@ def plot_forest( var_names=var_names, model_names=model_names, combined=combined, + combine_dims=combine_dims, colors=colors, labeller=labeller, ) @@ -214,7 +216,7 @@ class PlotHandler: # pylint: disable=inconsistent-return-statements - def __init__(self, datasets, var_names, model_names, combined, colors, labeller): + def __init__(self, datasets, var_names, model_names, combined, combine_dims, colors, labeller): self.data = datasets if model_names is None: @@ -240,6 +242,7 @@ def __init__(self, datasets, var_names, model_names, combined, colors, labeller) self.var_names = list(reversed(var_names)) # y-values are upside down self.combined = combined + self.combine_dims = combine_dims if colors == "cycle": colors = [ @@ -266,6 +269,7 @@ def make_plotters(self): y, model_names=self.model_names, combined=self.combined, + combine_dims=self.combine_dims, colors=self.colors, labeller=self.labeller, ) @@ -616,12 +620,15 @@ def y_max(self): class VarHandler: """Handle individual variable logic.""" - def __init__(self, var_name, data, y_start, model_names, combined, colors, labeller): + def __init__( + self, var_name, data, y_start, model_names, combined, combine_dims, colors, labeller + ): self.var_name = var_name self.data = data self.y_start = y_start self.model_names = model_names self.combined = combined + self.combine_dims = combine_dims self.colors = colors self.labeller = labeller self.model_color = dict(zip(self.model_names, self.colors)) @@ -634,10 +641,10 @@ def iterator(self): """Iterate over models and chains for each variable.""" if self.combined: grouped_data = [[(0, datum)] for datum in self.data] - skip_dims = {"chain"} + skip_dims = self.combine_dims.union({"chain"}) else: grouped_data = [datum.groupby("chain") for datum in self.data] - skip_dims = set() + skip_dims = self.combine_dims label_dict = OrderedDict() selection_list = [] diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -29,6 +29,7 @@ def plot_forest( var_names, model_names, combined, + combine_dims, colors, figsize, width_ratios, @@ -59,6 +60,7 @@ def plot_forest( var_names=var_names, model_names=model_names, combined=combined, + combine_dims=combine_dims, colors=colors, labeller=labeller, ) @@ -175,7 +177,7 @@ class PlotHandler: # pylint: disable=inconsistent-return-statements - def __init__(self, datasets, var_names, model_names, combined, colors, labeller): + def __init__(self, datasets, var_names, model_names, combined, combine_dims, colors, labeller): self.data = datasets if model_names is None: @@ -201,6 +203,7 @@ def __init__(self, datasets, var_names, model_names, combined, colors, labeller) self.var_names = list(reversed(var_names)) # y-values are upside down self.combined = combined + self.combine_dims = combine_dims if colors == "cycle": # TODO: Use matplotlib prop cycle instead @@ -223,6 +226,7 @@ def make_plotters(self): y, model_names=self.model_names, combined=self.combined, + combine_dims=self.combine_dims, colors=self.colors, labeller=self.labeller, ) @@ -510,12 +514,15 @@ def y_max(self): class VarHandler: """Handle individual variable logic.""" - def __init__(self, var_name, data, y_start, model_names, combined, colors, labeller): + def __init__( + self, var_name, data, y_start, model_names, combined, combine_dims, colors, labeller + ): self.var_name = var_name self.data = data self.y_start = y_start self.model_names = model_names self.combined = combined + self.combine_dims = combine_dims self.colors = colors self.labeller = labeller self.model_color = dict(zip(self.model_names, self.colors)) @@ -528,10 +535,10 @@ def iterator(self): """Iterate over models and chains for each variable.""" if self.combined: grouped_data = [[(0, datum)] for datum in self.data] - skip_dims = {"chain"} + skip_dims = self.combine_dims.union({"chain"}) else: grouped_data = [datum.groupby("chain") for datum in self.data] - skip_dims = set() + skip_dims = self.combine_dims label_dict = OrderedDict() selection_list = [] diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py --- a/arviz/plots/densityplot.py +++ b/arviz/plots/densityplot.py @@ -18,6 +18,7 @@ def plot_density( data_labels=None, var_names=None, filter_vars=None, + combine_dims=None, transform=None, hdi_prob=None, point_estimate="auto", @@ -64,6 +65,9 @@ def plot_density( interpret var_names as substrings of the real variables names. If "regex", interpret var_names as regular expressions on the real variables names. A la ``pandas.filter``. + combine_dims : set_like of str, optional + List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. + See the :ref:`this section <common_combine_dims>` for usage examples. transform : callable Function to transform data (defaults to None i.e. the identity function) hdi_prob : float @@ -193,6 +197,7 @@ def plot_density( labeller = BaseLabeller() var_names = _var_names(var_names, datasets, filter_vars) + n_data = len(datasets) if data_labels is None: @@ -212,7 +217,10 @@ def plot_density( if not 1 >= hdi_prob > 0: raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - to_plot = [list(xarray_var_iter(data, var_names, combined=True)) for data in datasets] + to_plot = [ + list(xarray_var_iter(data, var_names, combined=True, skip_dims=combine_dims)) + for data in datasets + ] all_labels = [] length_plotters = [] for plotters in to_plot: diff --git a/arviz/plots/distcomparisonplot.py b/arviz/plots/distcomparisonplot.py --- a/arviz/plots/distcomparisonplot.py +++ b/arviz/plots/distcomparisonplot.py @@ -13,6 +13,7 @@ def plot_dist_comparison( textsize=None, var_names=None, coords=None, + combine_dims=None, transform=None, legend=True, labeller=None, @@ -50,6 +51,10 @@ def plot_dist_comparison( Dictionary mapping dimensions to selected coordinates to be plotted. Dimensions without a mapping specified will include all coordinates for that dimension. + combine_dims : set_like of str, optional + List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. + See the :ref:`this section <common_combine_dims>` for usage examples. + transform : callable Function to transform data (defaults to None i.e. the identity function) legend : bool @@ -136,7 +141,9 @@ def plot_dist_comparison( len_plots = rcParams["plot.max_subplots"] // (len(groups) + 1) len_plots = len_plots if len_plots else 1 dc_plotters = [ - list(xarray_var_iter(data, var_names=var, combined=True))[:len_plots] + list(xarray_var_iter(data, var_names=var, combined=True, skip_dims=combine_dims))[ + :len_plots + ] for data, var in zip(datasets, var_names) ] diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -15,6 +15,7 @@ def plot_forest( transform=None, coords=None, combined=False, + combine_dims=None, hdi_prob=None, rope=None, quartiles=True, @@ -55,6 +56,9 @@ def plot_forest( var_names: list[str], optional List of variables to plot (defaults to None, which results in all variables plotted) Prefix the variables by ``~`` when you want to exclude them from the plot. + combine_dims : set_like of str, optional + List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. + See the :ref:`this section <common_combine_dims>` for usage examples. filter_vars: {None, "like", "regex"}, optional, default=None If None(default), interpret var_names as the real variables names. If "like", interpret var_names as substrings of the real variables names. If "regex", interpret var_names as @@ -219,6 +223,9 @@ def plot_forest( if coords is None: coords = {} + if combine_dims is None: + combine_dims = set() + if labeller is None: labeller = NoModelLabeller() if legend else BaseLabeller() @@ -253,6 +260,7 @@ def plot_forest( var_names=var_names, model_names=model_names, combined=combined, + combine_dims=combine_dims, colors=colors, figsize=figsize, width_ratios=width_ratios, diff --git a/arviz/plots/pairplot.py b/arviz/plots/pairplot.py --- a/arviz/plots/pairplot.py +++ b/arviz/plots/pairplot.py @@ -17,6 +17,7 @@ def plot_pair( group="posterior", var_names: Optional[List[str]] = None, filter_vars: Optional[str] = None, + combine_dims=None, coords=None, marginals=False, figsize=None, @@ -62,6 +63,9 @@ def plot_pair( interpret var_names as substrings of the real variables names. If "regex", interpret var_names as regular expressions on the real variables names. A la ``pandas.filter``. + combine_dims : set_like of str, optional + List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. + See the :ref:`this section <common_combine_dims>` for usage examples. coords: mapping, optional Coordinates of var_names to be plotted. Passed to :meth:`xarray.Dataset.sel`. marginals: bool, optional @@ -211,7 +215,9 @@ def plot_pair( dataset = convert_to_dataset(data, group=group) var_names = _var_names(var_names, dataset, filter_vars) plotters = list( - xarray_var_iter(get_coords(dataset, coords), var_names=var_names, combined=True) + xarray_var_iter( + get_coords(dataset, coords), var_names=var_names, skip_dims=combine_dims, combined=True + ) ) flat_var_names = [ labeller.make_label_vert(var_name, sel, isel) for var_name, sel, isel, _ in plotters diff --git a/arviz/plots/parallelplot.py b/arviz/plots/parallelplot.py --- a/arviz/plots/parallelplot.py +++ b/arviz/plots/parallelplot.py @@ -124,7 +124,11 @@ def plot_parallel( # Get diverging draws and combine chains divergent_data = convert_to_dataset(data, group="sample_stats") - _, diverging_mask = xarray_to_ndarray(divergent_data, var_names=("diverging",), combined=True) + _, diverging_mask = xarray_to_ndarray( + divergent_data, + var_names=("diverging",), + combined=True, + ) diverging_mask = np.squeeze(diverging_mask) # Get posterior draws and combine chains diff --git a/arviz/plots/posteriorplot.py b/arviz/plots/posteriorplot.py --- a/arviz/plots/posteriorplot.py +++ b/arviz/plots/posteriorplot.py @@ -11,6 +11,7 @@ def plot_posterior( data, var_names=None, filter_vars=None, + combine_dims=None, transform=None, coords=None, grid=None, @@ -52,6 +53,9 @@ def plot_posterior( interpret var_names as substrings of the real variables names. If "regex", interpret var_names as regular expressions on the real variables names. A la ``pandas.filter``. + combine_dims : set_like of str, optional + List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. + See the :ref:`this section <common_combine_dims>` for usage examples. transform: callable Function to transform data (defaults to None i.e.the identity function) coords: mapping, optional @@ -246,7 +250,11 @@ def plot_posterior( kind = rcParams["plot.density_kind"] plotters = filter_plotters_list( - list(xarray_var_iter(get_coords(data, coords), var_names=var_names, combined=True)), + list( + xarray_var_iter( + get_coords(data, coords), var_names=var_names, combined=True, skip_dims=combine_dims + ) + ), "plot_posterior", ) length_plotters = len(plotters) diff --git a/arviz/plots/rankplot.py b/arviz/plots/rankplot.py --- a/arviz/plots/rankplot.py +++ b/arviz/plots/rankplot.py @@ -169,7 +169,14 @@ def plot_rank( posterior_data = posterior_data.sel(**coords) var_names = _var_names(var_names, posterior_data, filter_vars) plotters = filter_plotters_list( - list(xarray_var_iter(posterior_data, var_names=var_names, combined=True)), "plot_rank" + list( + xarray_var_iter( + posterior_data, + var_names=var_names, + combined=True, + ) + ), + "plot_rank", ) length_plotters = len(plotters) diff --git a/arviz/plots/violinplot.py b/arviz/plots/violinplot.py --- a/arviz/plots/violinplot.py +++ b/arviz/plots/violinplot.py @@ -10,6 +10,7 @@ def plot_violin( data, var_names=None, + combine_dims=None, filter_vars=None, transform=None, quartiles=True, @@ -45,6 +46,9 @@ def plot_violin( var_names: list of variable names, optional Variables to be plotted, if None all variable are plotted. Prefix the variables by ``~`` when you want to exclude them from the plot. + combine_dims : set_like of str, optional + List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. + See the :ref:`this section <common_combine_dims>` for usage examples. filter_vars: {None, "like", "regex"}, optional, default=None If `None` (default), interpret var_names as the real variables names. If "like", interpret var_names as substrings of the real variables names. If "regex", @@ -135,7 +139,8 @@ def plot_violin( var_names = _var_names(var_names, data, filter_vars) plotters = filter_plotters_list( - list(xarray_var_iter(data, var_names=var_names, combined=True)), "plot_violin" + list(xarray_var_iter(data, var_names=var_names, combined=True, skip_dims=combine_dims)), + "plot_violin", ) rows, cols = default_grid(len(plotters), grid=grid) diff --git a/examples/bokeh/bokeh_plot_posterior_combinedims.py b/examples/bokeh/bokeh_plot_posterior_combinedims.py new file mode 100644 --- /dev/null +++ b/examples/bokeh/bokeh_plot_posterior_combinedims.py @@ -0,0 +1,18 @@ +""" +Posterior Plot (reducing school dimension) +========================================== + +_thumb: .5, .8 +""" +import arviz as az + +data = az.load_arviz_data("centered_eight") + +coords = {"school": ["Choate", "Mt. Hermon", "Deerfield"]} +ax = az.plot_posterior( + data, + var_names=["mu", "theta"], + combine_dims={"school"}, + coords=coords, + backend="bokeh", +) diff --git a/examples/matplotlib/mpl_plot_posterior_combinedims.py b/examples/matplotlib/mpl_plot_posterior_combinedims.py new file mode 100644 --- /dev/null +++ b/examples/matplotlib/mpl_plot_posterior_combinedims.py @@ -0,0 +1,17 @@ +""" +Posterior Plot (reducing school dimension) +========================================== + +_thumb: .5, .8 +""" +import matplotlib.pyplot as plt +import arviz as az + +az.style.use("arviz-darkgrid") + +data = az.load_arviz_data("centered_eight") + +coords = {"school": ["Choate", "Mt. Hermon", "Deerfield"]} +az.plot_posterior(data, var_names=["mu", "theta"], combine_dims={"school"}, coords=coords) + +plt.show()

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -408,7 +408,7 @@ def test_plot_energy_bad(models): [ {}, {"var_names": ["theta"], "relative": True, "color": "r"}, - {"coords": {"theta_dim_0": slice(4)}, "n_points": 10}, + {"coords": {"school": slice(4)}, "n_points": 10}, {"min_ess": 600, "hline_kwargs": {"color": "r"}}, ], ) @@ -710,7 +710,7 @@ def test_plot_loo_pit_label(models, args): {"var_names": ["theta"], "color": "r"}, {"rug": True, "rug_kwargs": {"color": "r"}}, {"errorbar": True, "rug": True, "rug_kind": "max_depth"}, - {"errorbar": True, "coords": {"theta_dim_0": slice(4)}, "n_points": 10}, + {"errorbar": True, "coords": {"school": slice(4)}, "n_points": 10}, {"extra_methods": True, "rug": True}, {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, ], @@ -748,7 +748,7 @@ def test_plot_mcse_no_divergences(models): @pytest.mark.parametrize( "kwargs", [ - {"var_names": "theta", "divergences": True, "coords": {"theta_dim_0": [0, 1]}}, + {"var_names": "theta", "divergences": True, "coords": {"school": [0, 1]}}, {"divergences": True, "var_names": ["theta", "mu"]}, {"kind": "kde", "var_names": ["theta"]}, {"kind": "hexbin", "var_names": ["theta"]}, @@ -756,7 +756,7 @@ def test_plot_mcse_no_divergences(models): { "kind": "hexbin", "var_names": ["theta"], - "coords": {"theta_dim_0": [0, 1]}, + "coords": {"school": [0, 1]}, "textsize": 20, }, { @@ -1022,7 +1022,7 @@ def test_plot_posterior_skipna(): [ {}, {"var_names": "mu"}, - {"var_names": ("mu", "tau"), "coords": {"theta_dim_0": [0, 1]}}, + {"var_names": ("mu", "tau"), "coords": {"school": [0, 1]}}, {"var_names": "mu", "ref_line": True}, { "var_names": "mu", diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -85,6 +85,16 @@ def discrete_model(): return {"x": np.random.randint(10, size=100), "y": np.random.randint(10, size=100)} [email protected](scope="module") +def discrete_multidim_model(): + """Simple fixture for random discrete model""" + idata = from_dict( + {"x": np.random.randint(10, size=(2, 50, 3)), "y": np.random.randint(10, size=(2, 50))}, + dims={"x": ["school"]}, + ) + return idata + + @pytest.fixture(scope="module") def continuous_model(): """Simple fixture for random continuous model""" @@ -167,6 +177,11 @@ def test_plot_density_bad_kwargs(models): plot_density(obj, filter_vars="bad_value") +def test_plot_density_discrete_combinedims(discrete_model): + axes = plot_density(discrete_model, combine_dims={"school"}, shade=0.9) + assert axes.size == 2 + + @pytest.mark.parametrize( "kwargs", [ @@ -270,7 +285,7 @@ def test_plot_trace_futurewarning(models, prop): [ ({}, 1), ({"var_names": "mu", "transform": lambda x: x + 1}, 1), - ({"var_names": "mu", "rope": (-1, 1)}, 1), + ({"var_names": "mu", "rope": (-1, 1), "combine_dims": {"school"}}, 1), ({"r_hat": True, "quartiles": False}, 2), ({"var_names": ["mu"], "colors": "C0", "ess": True, "combined": True}, 2), ( @@ -509,7 +524,7 @@ def test_plot_kde_inference_data(models): { "var_names": "theta", "divergences": True, - "coords": {"theta_dim_0": [0, 1]}, + "coords": {"school": [0, 1]}, "scatter_kwargs": {"marker": "x"}, "divergences_kwargs": {"marker": "*", "c": "C0"}, }, @@ -525,7 +540,7 @@ def test_plot_kde_inference_data(models): { "kind": "hexbin", "var_names": ["theta"], - "coords": {"theta_dim_0": [0, 1]}, + "coords": {"school": [0, 1]}, "colorbar": True, "hexbin_kwargs": {"cmap": "viridis"}, "textsize": 20, @@ -582,6 +597,17 @@ def test_plot_pair_overlaid(models, kwargs): assert ax.shape [email protected]("marginals", [True, False]) +def test_plot_pair_combinedims(models, marginals): + ax = plot_pair( + models.model_1, var_names=["eta", "theta"], combine_dims={"school"}, marginals=marginals + ) + if marginals: + assert ax.shape == (2, 2) + else: + assert not isinstance(ax, np.ndarray) + + @pytest.mark.parametrize("marginals", [True, False]) @pytest.mark.parametrize("max_subplots", [True, False]) def test_plot_pair_shapes(marginals, max_subplots): @@ -834,6 +860,30 @@ def test_plot_violin_discrete(discrete_model): assert axes.shape [email protected]("var_names", (None, "mu", ["mu", "tau"])) +def test_plot_violin_combinedims(models, var_names): + axes = plot_violin(models.model_1, var_names=var_names, combine_dims={"school"}) + assert axes.shape + + +def test_plot_violin_ax_combinedims(models): + _, ax = plt.subplots(1) + axes = plot_violin(models.model_1, var_names="mu", combine_dims={"school"}, ax=ax) + assert axes.shape + + +def test_plot_violin_layout_combinedims(models): + axes = plot_violin( + models.model_1, var_names=["mu", "tau"], combine_dims={"school"}, sharey=False + ) + assert axes.shape + + +def test_plot_violin_discrete_combinedims(discrete_model): + axes = plot_violin(discrete_model, combine_dims={"school"}) + assert axes.shape + + def test_plot_autocorr_short_chain(): """Check that logic for small chain defaulting doesn't cause exception""" chain = np.arange(10) @@ -869,7 +919,7 @@ def test_plot_autocorr_var_names(models, var_names): [ {}, {"var_names": "mu"}, - {"var_names": ("mu", "tau"), "coords": {"theta_dim_0": [0, 1]}}, + {"var_names": ("mu", "tau"), "coords": {"school": [0, 1]}}, {"var_names": "mu", "ref_line": True}, { "var_names": "mu", @@ -958,6 +1008,41 @@ def test_plot_posterior_skipna(): plot_posterior({"a": sample}, skipna=False) [email protected]("kwargs", [{"var_names": ["mu", "theta"]}]) +def test_plot_posterior_combinedims(models, kwargs): + axes = plot_posterior(models.model_1, combine_dims={"school"}, **kwargs) + if isinstance(kwargs.get("var_names"), str): + assert not isinstance(axes, np.ndarray) + else: + assert axes.shape + + [email protected]("kwargs", [{}, {"point_estimate": "mode"}, {"bins": None, "kind": "hist"}]) +def test_plot_posterior_discrete_combinedims(discrete_multidim_model, kwargs): + axes = plot_posterior(discrete_multidim_model, combine_dims={"school"}, **kwargs) + assert axes.size == 2 + + [email protected]("point_estimate", ("mode", "mean", "median")) +def test_plot_posterior_point_estimates_combinedims(models, point_estimate): + axes = plot_posterior( + models.model_1, + var_names=("mu", "tau"), + combine_dims={"school"}, + point_estimate=point_estimate, + ) + assert axes.size == 2 + + +def test_plot_posterior_skipna_combinedims(): + idata = load_arviz_data("centered_eight") + idata.posterior["theta"].loc[dict(school="Deerfield")] = np.nan + with pytest.raises(ValueError): + plot_posterior(idata, var_names="theta", combine_dims={"school"}, skipna=False) + ax = plot_posterior(idata, var_names="theta", combine_dims={"school"}, skipna=True) + assert not isinstance(ax, np.ndarray) + + @pytest.mark.parametrize( "kwargs", [{"insample_dev": False}, {"plot_standard_error": False}, {"plot_ic_diff": False}] ) @@ -1232,7 +1317,7 @@ def test_plot_khat_bad_input(models): [ {}, {"var_names": ["theta"], "relative": True, "color": "r"}, - {"coords": {"theta_dim_0": slice(4)}, "n_points": 10}, + {"coords": {"school": slice(4)}, "n_points": 10}, {"min_ess": 600, "hline_kwargs": {"color": "r"}}, ], ) @@ -1330,7 +1415,7 @@ def test_plot_loo_pit_incompatible_args(models): {"var_names": ["theta"], "color": "r"}, {"rug": True, "rug_kwargs": {"color": "r"}}, {"errorbar": True, "rug": True, "rug_kind": "max_depth"}, - {"errorbar": True, "coords": {"theta_dim_0": slice(4)}, "n_points": 10}, + {"errorbar": True, "coords": {"school": slice(4)}, "n_points": 10}, {"extra_methods": True, "rug": True}, {"extra_methods": True, "extra_kwargs": {"ls": ":"}, "text_kwargs": {"x": 0, "ha": "left"}}, ], @@ -1369,7 +1454,7 @@ def test_plot_mcse_no_divergences(models): [ {}, {"var_names": ["theta"]}, - {"var_names": ["theta"], "coords": {"theta_dim_0": [0, 1]}}, + {"var_names": ["theta"], "coords": {"school": [0, 1]}}, {"var_names": ["eta"], "posterior_kwargs": {"rug": True, "rug_kwargs": {"color": "r"}}}, {"var_names": ["mu"], "prior_kwargs": {"fill_kwargs": {"alpha": 0.5}}}, { @@ -1399,6 +1484,27 @@ def test_plot_dist_comparison_different_vars(): assert np.all(ax) +def test_plot_dist_comparison_combinedims(models): + idata = models.model_1 + ax = plot_dist_comparison(idata, combine_dims={"school"}) + assert np.all(ax) + + +def test_plot_dist_comparison_different_vars_combinedims(): + data = from_dict( + posterior={ + "x": np.random.randn(4, 100, 30), + }, + prior={"x_hat": np.random.randn(4, 100, 30)}, + dims={"x": ["3rd_dim"], "x_hat": ["3rd_dim"]}, + ) + with pytest.raises(KeyError): + plot_dist_comparison(data, var_names="x", combine_dims={"3rd_dim"}) + ax = plot_dist_comparison(data, var_names=[["x_hat"], ["x"]], combine_dims={"3rd_dim"}) + assert np.all(ax) + assert ax.size == 3 + + @pytest.mark.parametrize( "kwargs", [ diff --git a/arviz/tests/helpers.py b/arviz/tests/helpers.py --- a/arviz/tests/helpers.py +++ b/arviz/tests/helpers.py @@ -85,7 +85,12 @@ def create_model(seed=10): prior_predictive=prior_predictive, sample_stats_prior=sample_stats_prior, observed_data={"y": data["y"]}, - dims={"y": ["obs_dim"], "log_likelihood": ["obs_dim"]}, + dims={ + "y": ["obs_dim"], + "log_likelihood": ["obs_dim"], + "theta": ["school"], + "eta": ["school"], + }, coords={"obs_dim": range(data["J"])}, ) return model

Expose skip_dims/flatten argument All density plots (except plot_ppc) reduce the chain and draw dimensions, some have a combined argument to avoid reducing the chain dimension, but in some cases this is not enough, and similarly to plot_ppc behaviour, we would like to choose at will which dimensions to reduce. I think we should support that. ## Thoughts on implementation Exposing skip_dims argument from xarray_var_iter should do the trick. This is basically what plot_ppc does with its flatten argument

@OriolAbril Hi, this is my first contribution -- how difficult do you anticipate this issue being? If it's not too difficult, I'd like to take a stab at it. Thanks! Hi @ericch99, this should be a good first contribution, it's mostly a matter of adding this argument and making sure it gets passed downstream to the right `xarray_var_iter`, `xarray_to_ndarray` (the function in `sel_utils` called by the plotting function. The main question on that is on our side actually, before that we should define the _name_ of this argument! Give us ~1 day to think about this a bit and I'll get back to you. In the meantime, you can read the source code of one of the plots to see how the argument should be passed down and see if you have any questions. @ericch99 you should use `combine_dims` for the argument name, even if it's eventually passed to `skip_dims` in `xarray_var_iter`. I am assigning the issue to you now, thanks for reaching out :smile: Hi @OriolAbril, sorry for the late response -- school's been a little busy! That said, I have a couple of comprehension-type questions to make sure I'm on the right track. 1) I suppose that `ppcplot` already has the functionality we want -- in that case, am I correct in assuming that `combine_dims` will be analogous to / the same thing as `flatten`? (i.e., a list) 2) The correct approach is to simply implement this functionality separately, for each relevant plotting function. I appreciate your help! As a follow-up, I am a little confused as to which plots are supposed to implement this functionality; I'd assume that the plotting functions that don't call `xarray_var_iter` or `xarray_to_ndarray` can be left alone, but `plot_dist_comparison` does call `xarray_var_iter`, and it's not obvious to me what we are reducing over -- in the example given in the documentation, would the desired behavior be to plot each country (`match`) on the same axis? Once again, I appreciate that you're willing to guide me through this! > I suppose that ppcplot already has the functionality we want -- in that case, am I correct in assuming that combine_dims will be analogous to / the same thing as flatten? (i.e., a list) Yes, list, tuple, array, set... It needs to be converted to a set down the line, so as long as this is possible it should probably work. > The correct approach is to simply implement this functionality separately, for each relevant plotting function. > As a follow-up, I am a little confused as to which plots are supposed to implement this functionality; I'd assume that the plotting functions that don't call xarray_var_iter or xarray_to_ndarray can be left alone, but plot_dist_comparison does call xarray_var_iter, and it's not obvious to me what we are reducing over -- in the example given in the documentation, would the desired behavior be to plot each country (match) on the same axis? Yes, all functions using `xarray_var_iter` or `xarray_to_ndarray` should have this `combine_dims` added and pass it to `xarray_...` as `skip_dims`. The goal is that users will then be able to choose what gets plotted in a given axes. In the rugby case, using `combined_dims={"team"}` will combine all samples from all teams in a single array and plot the kde of that. In this particular example, this doesn't make much sense. But in many other cases it doesn't make much sense to not do this. Consider for example an experiment in a lab where they test multiple drugs and doses on the same culture, and to get better accuracy, each dose and drug combination is replicated `n` times. By design we would expect replications to show the same behaviour, so in general one would want to combine all replications and see a combination of drug+dose in each subplot, not one subplot per replication+drug+dose. Hi @OriolAbril! I'm currently working on this issue with @ericch99. I noticed that unlike `xarray_var_iter`, which already has a `skip_dims` argument, `xarray_to_ndarray` does not have any variables that can be assigned `combine_dims`. However, would implementing a `skip_dims` in `xarray_to_ndarray` be out of the scope of this issue? Ideally, that would be in scope and a great addition, I actually missed completely that `xarray_to_ndarray` doesn't have this capability when writing the issue. Now that you say it, it does make sense that it doesn't. There can be repeated labels, in which case, the number of samples for each "column" of the ndarray would not be the same for all columns. I think the code would have to be updated to use `xarray_var_sel` internally in plot_pair for this to work. This is more work, but I think we'll be better off on the long run if we stop using `xarray_to_ndarray` so that plots can also be dask compatible. I think we should give a bit more thought to that, do you already have a clear path forward on this end? Or only for unique coordinate values case?

2021-04-23T16:56:51Z

arviz-devs/arviz

arviz-devs__arviz-1695

7be29cf1a7481b8795c1fecfab0fa7764212d1c9

diff --git a/arviz/data/base.py b/arviz/data/base.py --- a/arviz/data/base.py +++ b/arviz/data/base.py @@ -245,7 +245,7 @@ def numpy_to_data_array( coords["draw"] = np.arange(index_origin, n_samples + index_origin) # filter coords based on the dims - coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in dims} + coords = {key: xr.IndexVariable((key,), data=np.asarray(coords[key])) for key in dims} return xr.DataArray(ary, coords=coords, dims=dims)

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -921,6 +921,16 @@ def test_dict_to_dataset_event_dims_error(): convert_to_dataset(datadict, coords=coords, dims={"a": ["b", "c"]}) +def test_dict_to_dataset_with_tuple_coord(): + datadict = {"a": np.random.randn(100), "b": np.random.randn(1, 100, 10)} + dataset = convert_to_dataset(datadict, coords={"c": tuple(range(10))}, dims={"b": ["c"]}) + assert set(dataset.data_vars) == {"a", "b"} + assert set(dataset.coords) == {"chain", "draw", "c"} + + assert set(dataset.a.coords) == {"chain", "draw"} + assert set(dataset.b.coords) == {"chain", "draw", "c"} + + def test_convert_to_dataset_idempotent(): first = convert_to_dataset(np.random.randn(100)) second = convert_to_dataset(first)

Non np.array coords cause hard to diagnose shape error **Describe the bug** When one of the `coords` has a `tuple` as its values, the conversion to an xarray variable fails. **To Reproduce** A simple PyMC3 model that someone might write: ```python coords = { "d1": ("A", "B", "C") } with pymc3.Model(coords=coords): pymc3.Normal("n", dims=("d1",)) pymc3.sample(return_inferencedata=True) ``` The underlying problem is in `base.py/numpy_to_data_array`: ```python arviz.numpy_to_data_array( ary=numpy.random.uniform(size=(3, 7, 11)), coords={ "d1": tuple(numpy.arange(11)), }, dims=["d1",] ) ``` Unhelpful traceback: ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-79-67777b4f3e94> in <module> 4 "d1": tuple(numpy.arange(11)), 5 }, ----> 6 dims=["d1",] 7 ) ~\AppData\Local\Continuum\miniconda3\envs\pm3-dev\lib\site-packages\arviz\data\base.py in numpy_to_data_array(ary, var_name, coords, dims) 170 171 # filter coords based on the dims --> 172 coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in dims} 173 return xr.DataArray(ary, coords=coords, dims=dims) 174 ~\AppData\Local\Continuum\miniconda3\envs\pm3-dev\lib\site-packages\arviz\data\base.py in <dictcomp>(.0) 170 171 # filter coords based on the dims --> 172 coords = {key: xr.IndexVariable((key,), data=coords[key]) for key in dims} 173 return xr.DataArray(ary, coords=coords, dims=dims) 174 ~\AppData\Local\Continuum\miniconda3\envs\pm3-dev\lib\site-packages\xarray\core\variable.py in __init__(self, dims, data, attrs, encoding, fastpath) 2339 2340 def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False): -> 2341 super().__init__(dims, data, attrs, encoding, fastpath) 2342 if self.ndim != 1: 2343 raise ValueError("%s objects must be 1-dimensional" % type(self).__name__) ~\AppData\Local\Continuum\miniconda3\envs\pm3-dev\lib\site-packages\xarray\core\variable.py in __init__(self, dims, data, attrs, encoding, fastpath) 325 """ 326 self._data = as_compatible_data(data, fastpath=fastpath) --> 327 self._dims = self._parse_dimensions(dims) 328 self._attrs = None 329 self._encoding = None ~\AppData\Local\Continuum\miniconda3\envs\pm3-dev\lib\site-packages\xarray\core\variable.py in _parse_dimensions(self, dims) 559 raise ValueError( 560 "dimensions %s must have the same length as the " --> 561 "number of data dimensions, ndim=%s" % (dims, self.ndim) 562 ) 563 return dims ValueError: dimensions ('d1',) must have the same length as the number of data dimensions, ndim=0 ``` **Expected behavior** I think all kinds of iterables (arrays, lists, tuples) are perfectly valid inputs for `coords`. **Additional context** ArviZ `0.10.0` xarray `0.16.1` Maybe this issue should also be raised at xarray. But from the ArviZ perspective, a simple `np.array()` call should fix it.

2021-05-13T17:33:58Z

arviz-devs/arviz

arviz-devs__arviz-1707

479f2a71a3f7b31e1085dd6c7ae77a45dd3a50e5

diff --git a/arviz/plots/backends/bokeh/posteriorplot.py b/arviz/plots/backends/bokeh/posteriorplot.py --- a/arviz/plots/backends/bokeh/posteriorplot.py +++ b/arviz/plots/backends/bokeh/posteriorplot.py @@ -286,18 +286,30 @@ def format_axes(): show=False, ) _, hist, edges = histogram(values, bins="auto") - else: + elif values.dtype.kind == "i" or (values.dtype.kind == "f" and kind == "hist"): if bins is None: - if values.dtype.kind == "i": - bins = get_bins(values) - else: - bins = "auto" + bins = get_bins(values) kwargs.setdefault("align", "left") kwargs.setdefault("color", "blue") _, hist, edges = histogram(values, bins=bins) ax.quad( top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_alpha=0.35, line_alpha=0.35 ) + elif values.dtype.kind == "b": + if bins is None: + bins = "auto" + kwargs.setdefault("color", "blue") + + hist = np.array([(~values).sum(), values.sum()]) + edges = np.array([-0.5, 0.5, 1.5]) + ax.quad( + top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_alpha=0.35, line_alpha=0.35 + ) + hdi_prob = "hide" + ax.xaxis.ticker = [0, 1] + ax.xaxis.major_label_overrides = {0: "False", 1: "True"} + else: + raise TypeError("Values must be float, integer or boolean") format_axes() max_data = hist.max() diff --git a/arviz/plots/backends/matplotlib/posteriorplot.py b/arviz/plots/backends/matplotlib/posteriorplot.py --- a/arviz/plots/backends/matplotlib/posteriorplot.py +++ b/arviz/plots/backends/matplotlib/posteriorplot.py @@ -319,18 +319,23 @@ def format_axes(): rug=False, show=False, ) - else: + elif values.dtype.kind == "i" or (values.dtype.kind == "f" and kind == "hist"): if bins is None: - if values.dtype.kind == "i": - xmin = values.min() - xmax = values.max() - bins = get_bins(values) - ax.set_xlim(xmin - 0.5, xmax + 0.5) - else: - bins = "auto" + xmin = values.min() + xmax = values.max() + bins = get_bins(values) + ax.set_xlim(xmin - 0.5, xmax + 0.5) kwargs.setdefault("align", "left") kwargs.setdefault("color", "C0") ax.hist(values, bins=bins, alpha=0.35, **kwargs) + elif values.dtype.kind == "b": + if bins is None: + bins = "auto" + kwargs.setdefault("color", "C0") + ax.bar(["False", "True"], [(~values).sum(), values.sum()], alpha=0.35, **kwargs) + hdi_prob = "hide" + else: + raise TypeError("Values must be float, integer or boolean") plot_height = ax.get_ylim()[1]

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -988,6 +988,17 @@ def test_plot_posterior_discrete(discrete_model, kwargs): assert axes.shape +def test_plot_posterior_boolean(): + data = np.random.choice(a=[False, True], size=(4, 100)) + axes = plot_posterior(data, backend="bokeh", show=False) + assert axes.shape + + +def test_plot_posterior_bad_type(): + with pytest.raises(TypeError): + plot_posterior(np.array(["a", "b", "c"]), backend="bokeh", show=False) + + def test_plot_posterior_bad(models): with pytest.raises(ValueError): plot_posterior(models.model_1, backend="bokeh", show=False, rope="bad_value") diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -1025,12 +1025,26 @@ def test_plot_posterior(models, kwargs): assert axes.shape +def test_plot_posterior_boolean(): + data = np.random.choice(a=[False, True], size=(4, 100)) + axes = plot_posterior(data) + assert axes + plt.draw() + labels = [label.get_text() for label in axes.get_xticklabels()] + assert all(item in labels for item in ("True", "False")) + + @pytest.mark.parametrize("kwargs", [{}, {"point_estimate": "mode"}, {"bins": None, "kind": "hist"}]) def test_plot_posterior_discrete(discrete_model, kwargs): axes = plot_posterior(discrete_model, **kwargs) assert axes.shape +def test_plot_posterior_bad_type(): + with pytest.raises(TypeError): + plot_posterior(np.array(["a", "b", "c"])) + + def test_plot_posterior_bad(models): with pytest.raises(ValueError): plot_posterior(models.model_1, rope="bad_value")

Posterior plot errors with boolean array When passed a boolean array, `plot_posterior` fails with the following error because `np.histogram` assumes that subtraction is defined on the array: ```python TypeError('numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.') ``` This example reproduces the error: ```python import numpy as np import arviz as az data = np.random.choice(a=[False, True], size=(4, 100)) az.plot_posterior(data) ```

Just to be clear, the expected output would be a histogram with two bins, one around 0 and another around 1 (in which case we can add an `astype(int)` before the histogram and be done with it)? Or should the labels be `True` and `False`? I'd expect the second, even though the first option is easier. Then I think it should be something like: 1. Evaluate and store condition of whether or not the dtype is boolean 2. If True, do the `astype(int)` for histogram to work 3. (plotting should not require changes, just here for completeness) 4. If True, change the plot labels from 0 and 1 to False and True. Then reset the variable storing the condition We should use bar graph with counts for bool data, not hist. > 3\. plotting should not require changes, just here for completeness I guess this means plots/posteriorplot.py need to be edited and not plots/backends/matplotlib/posteriorplot.py and plots/backends/bokeh/posteriorplot.py ..? User api should not change. Feel free to edit backend files. Do we need `**bar_kwargs` plot bar plots? @OriolAbril Can you please assign this issue to me? I have added some lines of code and submitting a pr to discuss the problems. done, in practice, especially with gsoc coming, you can already consider yourself assigned if you are the first to comment on the issue that you'll work on it, even if we take some time to assign you via the gitub assign feature. We already have a couple lines on that on the [contributing guide](https://github.com/arviz-devs/arviz/blob/main/CONTRIBUTING.md#if-an-issue-ticket-exists) for agility when it comes to contributing.

2021-05-27T10:07:51Z

arviz-devs/arviz

arviz-devs__arviz-1710

93f8f1c219dc2a012e6a7e149b6a031f2de688e7

diff --git a/arviz/plots/backends/bokeh/kdeplot.py b/arviz/plots/backends/bokeh/kdeplot.py --- a/arviz/plots/backends/bokeh/kdeplot.py +++ b/arviz/plots/backends/bokeh/kdeplot.py @@ -147,6 +147,8 @@ def plot_kde( patch = ax.patch(patch_y, patch_x, **fill_kwargs) glyphs.append(patch) + if label is not None: + plot_kwargs.setdefault("legend_label", label) if not rotated: line = ax.line(x, density, **plot_kwargs) else: diff --git a/arviz/plots/backends/matplotlib/kdeplot.py b/arviz/plots/backends/matplotlib/kdeplot.py --- a/arviz/plots/backends/matplotlib/kdeplot.py +++ b/arviz/plots/backends/matplotlib/kdeplot.py @@ -144,11 +144,13 @@ def plot_kde( else: fill_kwargs.setdefault("alpha", 0) if fill_kwargs.get("alpha") == 0: - ax.plot(x, density, label=label, **plot_kwargs) + label = plot_kwargs.setdefault("label", label) + ax.plot(x, density, **plot_kwargs) fill_func(fill_x, fill_y, **fill_kwargs) else: + label = fill_kwargs.setdefault("label", label) ax.plot(x, density, **plot_kwargs) - fill_func(fill_x, fill_y, label=label, **fill_kwargs) + fill_func(fill_x, fill_y, **fill_kwargs) if legend and label: ax.legend() else:

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -992,7 +992,7 @@ def test_plot_ppc_ax(models, kind): {"point_estimate": "mode"}, {"point_estimate": "median"}, {"point_estimate": None}, - {"hdi_prob": "hide"}, + {"hdi_prob": "hide", "legend_label": ""}, {"ref_val": 0}, {"ref_val": None}, {"ref_val": {"mu": [{"ref_val": 1}]}}, diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -927,7 +927,7 @@ def test_plot_rank(models, kwargs): {"rope": {"mu": [{"rope": (-2, 2)}], "theta": [{"school": "Choate", "rope": (2, 4)}]}}, {"point_estimate": "mode"}, {"point_estimate": "median"}, - {"hdi_prob": "hide"}, + {"hdi_prob": "hide", "label": ""}, {"point_estimate": None}, {"ref_val": 0}, {"ref_val": None},

plot_posterior doesn' accept label as kwarg I think this is related to how we pass kwargs downstream to plot_dist or plot_kde... will look into it at some point unless someone beats me to it. My hunch is that plot_dist and plot_kde have a `label` argument and pass it explicitly to matplotlib instead of doing `setdefault` so if a label is passed via kwargs too it becomes a duplicated argument and errors out Fixing this would allow using plot_posterior to overlay multiple distributions while having a legend.

https://github.com/arviz-devs/arviz/blob/bd0b786e6ab6ea5767110cf46fe0650c869d02f2/arviz/plots/backends/matplotlib/posteriorplot.py#L311-L321 As label is not added here, it will be considering default value of label (i.e. None). Would None value of label still be conflicting with label passed via kwargs? `plot_posterior` does not set the label but `plot_kde` does: https://github.com/arviz-devs/arviz/blob/1997afe0cf92d32e6ee71a94226fc458ace22313/arviz/plots/backends/matplotlib/kdeplot.py#L147 it should be set as `setdefault` to `plot_kwargs` or `fill_kwargs` or the corresponding kwargs (which one may depend on the type of plot so be careful here). Otherwise, if label is passed via kwargs, matplotlib gets two different instances of the same keyword argument and errors out. Sure, then `label` shouldn't be pass as an individual argument in the `plot()` because it would already be there in kwargs. Right? Exactly, more or less like with linewidth: https://github.com/arviz-devs/arviz/blob/1997afe0cf92d32e6ee71a94226fc458ace22313/arviz/plots/backends/matplotlib/kdeplot.py#L86 with the main difference that linewidth always goes into `plot_kwargs` and `label` may go to fill kwargs or so depending on other arguments

2021-05-28T19:33:30Z

arviz-devs/arviz

arviz-devs__arviz-1713

4d9caca88a9a3b4043062609cb9a401e3f08804d

diff --git a/arviz/data/io_numpyro.py b/arviz/data/io_numpyro.py --- a/arviz/data/io_numpyro.py +++ b/arviz/data/io_numpyro.py @@ -124,7 +124,11 @@ def arbitrary_element(dct): observations = {} if self.model is not None: - seeded_model = numpyro.handlers.seed(self.model, jax.random.PRNGKey(0)) + # we need to use an init strategy to generate random samples for ImproperUniform sites + seeded_model = numpyro.handlers.substitute( + numpyro.handlers.seed(self.model, jax.random.PRNGKey(0)), + substitute_fn=numpyro.infer.init_to_sample, + ) trace = numpyro.handlers.trace(seeded_model).get_trace(*self._args, **self._kwargs) observations = { name: site["value"]

diff --git a/arviz/tests/external_tests/test_data_numpyro.py b/arviz/tests/external_tests/test_data_numpyro.py --- a/arviz/tests/external_tests/test_data_numpyro.py +++ b/arviz/tests/external_tests/test_data_numpyro.py @@ -253,3 +253,17 @@ def model(x): inference_data = from_numpyro(mcmc) assert inference_data.posterior["loc"].shape == (nchains, 400 // thin) + + def test_mcmc_improper_uniform(self): + import numpyro + import numpyro.distributions as dist + from numpyro.infer import MCMC, NUTS + + def model(): + x = numpyro.sample("x", dist.ImproperUniform(dist.constraints.positive, (), ())) + return numpyro.sample("y", dist.Normal(x, 1), obs=1.0) + + mcmc = MCMC(NUTS(model), num_warmup=10, num_samples=10) + mcmc.run(PRNGKey(0)) + inference_data = from_numpyro(mcmc) + assert inference_data.observed_data

NumPyroConverter makes too many assumptions about the model The following code is not always runnable: https://github.com/arviz-devs/arviz/blob/7ebedd2d1494f07513bc7b9ea21962bc11399a94/arviz/data/io_numpyro.py#L126-L133 This will fail for a model involving `ImproperUniform`, as sampling for it is not defined. Would it be possible to add a keyword argument that specifies whether this automatic extraction of observations should be attempt or not, e.g. `extract_observations` or something? At the moment it's stopping me from being able to use arviz with my numpyro-model (unless I just comment out the lines above) :confused:

I think adding an extra argument to skip this sounds fine, as would probably be adding a try except or even both. cc @fehiepsi Because this just requires a valid trace, I believe we can just use some sort of init_to_uniform strategy. I'll push a PR shortly. > Because this just requires a valid trace, I believe we can just use some sort of init_to_uniform strategy. I'll push a PR shortly. True, but even so, as a user, I'd prefer not always inferring this, i.e. at least allow me to turn it off. E.g. the observations might be not be present because I'm "observing" using a factor, in which case the automatically inferred observations can be misleading. That's a good point! I think you can set `observations = None` or delete some of its keys if you want. It will give you some flexibility in extracting `log_likelihood` or something like that. How about adding a kwarg `observations=None` and then do `if observations is None and self.model is not None` or something along those lines? This way you can choose not to extract the observations by setting `observations={}` or any empty collection + it allows you to override the inferred observations, e.g. if you're using a factor approach as I mentioned above.

2021-06-01T05:32:33Z

arviz-devs/arviz

arviz-devs__arviz-1725

35c1efc456f1479f1f5028d1f5cea2e02050e52f

diff --git a/arviz/data/__init__.py b/arviz/data/__init__.py --- a/arviz/data/__init__.py +++ b/arviz/data/__init__.py @@ -14,6 +14,7 @@ from .io_pymc3 import from_pymc3, from_pymc3_predictions from .io_pyro import from_pyro from .io_pystan import from_pystan +from .utils import extract_dataset __all__ = [ "InferenceData", @@ -22,6 +23,7 @@ "list_datasets", "clear_data_home", "numpy_to_data_array", + "extract_dataset", "dict_to_dataset", "convert_to_dataset", "convert_to_inference_data", diff --git a/arviz/data/utils.py b/arviz/data/utils.py new file mode 100644 --- /dev/null +++ b/arviz/data/utils.py @@ -0,0 +1,101 @@ +"""Data specific utilities.""" +import numpy as np + +from ..utils import _var_names +from .converters import convert_to_dataset + + +def extract_dataset( + data, + group="posterior", + combined=True, + var_names=None, + filter_vars=None, + num_samples=None, + rng=None, +): + """Extract an InferenceData group or subset of it as a :class:`xarray.Dataset`. + + Parameters + ---------- + idata : InferenceData or InferenceData_like + InferenceData from which to extract the data. + group : str, optional + Which InferenceData data group to extract data from. + combined : bool, optional + Combine ``chain`` and ``draw`` dimensions into ``sample``. Won't work if + a dimension named ``sample`` already exists. + var_names : str or list of str, optional + Variables to be plotted, two variables are required. Prefix the variables by `~` + when you want to exclude them from the plot. + filter_vars: {None, "like", "regex"}, optional + If `None` (default), interpret var_names as the real variables names. If "like", + interpret var_names as substrings of the real variables names. If "regex", + interpret var_names as regular expressions on the real variables names. A la + `pandas.filter`. + Like with plotting, sometimes it's easier to subset saying what to exclude + instead of what to include + num_samples : int, optional + Extract only a subset of the samples. Only valid if ``combined=True`` + rng : bool, int, numpy.Generator, optional + Shuffle the samples, only valid if ``combined=True``. By default, + samples are shuffled if ``num_samples`` is not ``None``, and are left + in the same order otherwise. This ensures that subsetting the samples doesn't return + only samples from a single chain and consecutive draws. + + Returns + ------- + xarray.Dataset + + Examples + -------- + The default behaviour is to return the posterior group after stacking the chain and + draw dimensions. + + .. jupyter-execute:: + + import arviz as az + idata = az.load_arviz_data("centered_eight") + az.extract_dataset(idata) + + You can also indicate a subset to be returned, but in variables and in samples: + + .. jupyter-execute:: + + az.extract_dataset(idata, var_names="theta", num_samples=100) + + To keep the chain and draw dimensions, use ``combined=False``. + + .. jupyter-execute:: + + az.extract_dataset(idata, group="prior", combined=False) + + """ + if num_samples is not None and not combined: + raise ValueError("num_samples is only compatible with combined=True") + if rng is None: + rng = num_samples is not None + if rng is not False and not combined: + raise ValueError("rng is only compatible with combined=True") + data = convert_to_dataset(data, group=group) + var_names = _var_names(var_names, data, filter_vars) + if var_names is not None: + data = data[var_names] + if combined: + data = data.stack(sample=("chain", "draw")) + # 0 is a valid seed se we need to check for rng being exactly boolean + if rng is not False: + if rng is True: + rng = np.random.default_rng() + # default_rng takes ints or sequences of ints + try: + rng = np.random.default_rng(rng) + random_subset = rng.permutation(np.arange(len(data["sample"]))) + except TypeError as err: + raise TypeError("Unable to initializate numpy random Generator from rng") from err + except AttributeError as err: + raise AttributeError("Unable to use rng to generate a permutation") from err + data = data.isel(sample=random_subset) + if num_samples is not None: + data = data.isel(sample=slice(None, num_samples)) + return data diff --git a/doc/source/conf.py b/doc/source/conf.py --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -62,6 +62,7 @@ "notfound.extension", "sphinx_copybutton", "sphinx_codeautolink", + "jupyter_sphinx", ] # codeautolink

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -26,6 +26,7 @@ list_datasets, load_arviz_data, to_netcdf, + extract_dataset, ) from ...data.base import dict_to_dataset, generate_dims_coords, infer_stan_dtypes, make_attrs @@ -1397,3 +1398,38 @@ def test_nd_to_inference_data(self): assert inference_data.prior.chain.shape == shape[:1] assert inference_data.prior.draw.shape == shape[1:2] assert inference_data.prior[var_name].shape == shape + + +class TestExtractDataset: + def test_default(self): + idata = load_arviz_data("centered_eight") + post = extract_dataset(idata) + assert isinstance(post, xr.Dataset) + assert "sample" in post.dims + assert post.theta.size == (4 * 500 * 8) + + def test_seed(self): + idata = load_arviz_data("centered_eight") + post = extract_dataset(idata, rng=7) + post_pred = extract_dataset(idata, group="posterior_predictive", rng=7) + assert all(post.sample == post_pred.sample) + + def test_no_combine(self): + idata = load_arviz_data("centered_eight") + post = extract_dataset(idata, combined=False) + assert "sample" not in post.dims + assert post.dims["chain"] == 4 + assert post.dims["draw"] == 500 + + def test_var_name_group(self): + idata = load_arviz_data("centered_eight") + prior = extract_dataset(idata, group="prior", var_names="the", filter_vars="like") + assert prior.attrs == idata.prior.attrs + assert "theta" in prior.data_vars + assert "mu" not in prior.data_vars + + def test_subset_samples(self): + idata = load_arviz_data("centered_eight") + post = extract_dataset(idata, num_samples=10) + assert post.dims["sample"] == 10 + assert post.attrs == idata.posterior.attrs

Add helper function to easily stack `chain` and `draws` dimensions Stacking chains and draws is often useful when one doesn't care about which chain a draw is coming from. This is currently possible by doing `idata.posterior.stack(sample=("chain", "draw"))`, but very few people seem to know that. Adding a helper function to easily stack the `chain` and `draws` dimensions into a `sample` dimension would go a long way towards making users' life easier, as well as making better use of `xarray`'s capabilities. Feel free to comment if you want to take on this issue 🖖

Is there a reason we can not add this when we create InferenceData? Would anything change or break? I think it would only add one more dimension, but everything else is same? I don't think anything would break, as you can still access `chain` and `draw` after stacking, but I'm not sure I'd add it by default -- if another easy solution to add this feature exists I mean. The reason is just that it adds yet another dimension, so it makes the ID heavier and, most importantly, can be confusing for people who don't need this. Generating it on-demand would be the best case scenario IMO Proposal for this, a `get_dataset` or `extract_dataset` function, something like: ```python def get_dataset(idata, group="posterior", combined=False, var_names=None, filter_vars=None): """Extracts an inference data group or subset of it as xarray dataset Parameters ---------- idata : InferenceData InferenceData from which to extract the data. <not sure if it should be idata or anything that can be converted to idata> group : str, default "posterior" combined : bool, default False? var_names : str or list of str, optional Like with plotting, sometimes it's easier to subset saying what to exclude instead of what to include filter_vars : like with plotting Returns ------- xarray.Dataset (or xarray.DataArray?) I am not sure whether we should return a dataarray iff `var_names` is a string and a dataset otherwise or always a dataset. """ ``` I believe this will handle most practical cases and be quite flexible while still being very little code as everything is reused from other functions/externalized. Hi! I recently submitted a PR for updating [GLM poisson regression](https://github.com/pymc-devs/pymc-examples/pull/154) to best practices, and on executing cell 18 in that notebook ``` az.summary(np.exp(inf_fish_alt.posterior), kind="stats")``` I get this RuntimeWarning: ``` /home/ada/.local/lib/python3.8/site-packages/xarray/core/computation.py:724: RuntimeWarning: overflow encountered in exp result_data = func(*input_data) /home/ada/.local/lib/python3.8/site-packages/numpy/core/_methods.py:193: RuntimeWarning: invalid value encountered in subtract x = asanyarray(arr - arrmean) ``` Here, `inf_fish_alt` is the trace created using model based on `pymc.glm.GLM.from_formula` @OriolAbril suggested that this might be relevant to the discussion here. yeah, here we basically want to exponentiate all varibles _except_ mu because `mu` has alredy been exponentiated in the model code and doing so again results in overflow. If the proposed funciton were available, we'd be able to get the right subset quite easily and then exponentiate this and pass it to summary.

2021-06-12T19:38:53Z

arviz-devs/arviz

arviz-devs__arviz-1753

afd895cb0fde11bbcaa633bb363dcb0a0caaa22f

diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -6,6 +6,7 @@ from .distcomparisonplot import plot_dist_comparison from .distplot import plot_dist from .dotplot import plot_dot +from .ecdfplot import plot_ecdf from .elpdplot import plot_elpd from .energyplot import plot_energy from .essplot import plot_ess @@ -33,6 +34,7 @@ "plot_density", "plot_dist", "plot_dot", + "plot_ecdf", "plot_elpd", "plot_energy", "plot_ess", diff --git a/arviz/plots/backends/bokeh/ecdfplot.py b/arviz/plots/backends/bokeh/ecdfplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/backends/bokeh/ecdfplot.py @@ -0,0 +1,73 @@ +"""Bokeh ecdfplot.""" +from matplotlib.colors import to_hex + +from ...plot_utils import _scale_fig_size +from .. import show_layout +from . import backend_kwarg_defaults, create_axes_grid + + +def plot_ecdf( + x_coord, + y_coord, + x_bands, + lower, + higher, + confidence_bands, + plot_kwargs, + fill_kwargs, + plot_outline_kwargs, + figsize, + fill_band, + ax, + show, + backend_kwargs, +): + """Bokeh ecdfplot.""" + if backend_kwargs is None: + backend_kwargs = {} + + backend_kwargs = { + **backend_kwarg_defaults(), + **backend_kwargs, + } + + (figsize, *_) = _scale_fig_size(figsize, None) + + if ax is None: + ax = create_axes_grid( + 1, + figsize=figsize, + squeeze=True, + backend_kwargs=backend_kwargs, + ) + + if plot_kwargs is None: + plot_kwargs = {} + + plot_kwargs.setdefault("mode", "after") + + if fill_band: + if fill_kwargs is None: + fill_kwargs = {} + fill_kwargs.setdefault("fill_color", to_hex("C0")) + fill_kwargs.setdefault("fill_alpha", 0.2) + else: + if plot_outline_kwargs is None: + plot_outline_kwargs = {} + plot_outline_kwargs.setdefault("color", to_hex("C0")) + plot_outline_kwargs.setdefault("alpha", 0.2) + + if confidence_bands: + ax.step(x_coord, y_coord, **plot_kwargs) + + if fill_band: + ax.varea(x_bands, lower, higher, **fill_kwargs) + else: + ax.line(x_bands, lower, **plot_outline_kwargs) + ax.line(x_bands, higher, **plot_outline_kwargs) + else: + ax.step(x_coord, y_coord, **plot_kwargs) + + show_layout(ax, show) + + return ax diff --git a/arviz/plots/backends/matplotlib/ecdfplot.py b/arviz/plots/backends/matplotlib/ecdfplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/backends/matplotlib/ecdfplot.py @@ -0,0 +1,70 @@ +"""Matplotlib ecdfplot.""" +import matplotlib.pyplot as plt +from matplotlib.colors import to_hex + +from ...plot_utils import _scale_fig_size +from . import backend_kwarg_defaults, create_axes_grid, backend_show + + +def plot_ecdf( + x_coord, + y_coord, + x_bands, + lower, + higher, + confidence_bands, + plot_kwargs, + fill_kwargs, + plot_outline_kwargs, + figsize, + fill_band, + ax, + show, + backend_kwargs, +): + """Matplotlib ecdfplot.""" + if backend_kwargs is None: + backend_kwargs = {} + + backend_kwargs = { + **backend_kwarg_defaults(), + **backend_kwargs, + } + + (figsize, _, _, _, _, _) = _scale_fig_size(figsize, None) + backend_kwargs.setdefault("figsize", figsize) + backend_kwargs["squeeze"] = True + + if ax is None: + _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) + + if plot_kwargs is None: + plot_kwargs = {} + + plot_kwargs.setdefault("where", "post") + + if fill_band: + if fill_kwargs is None: + fill_kwargs = {} + fill_kwargs.setdefault("step", "post") + fill_kwargs.setdefault("color", to_hex("C0")) + fill_kwargs.setdefault("alpha", 0.2) + else: + if plot_outline_kwargs is None: + plot_outline_kwargs = {} + plot_outline_kwargs.setdefault("where", "post") + plot_outline_kwargs.setdefault("color", to_hex("C0")) + plot_outline_kwargs.setdefault("alpha", 0.2) + + ax.step(x_coord, y_coord, **plot_kwargs) + + if confidence_bands: + if fill_band: + ax.fill_between(x_bands, lower, higher, **fill_kwargs) + else: + ax.plot(x_bands, lower, x_bands, higher, **plot_outline_kwargs) + + if backend_show(show): + plt.show() + + return ax diff --git a/arviz/plots/ecdfplot.py b/arviz/plots/ecdfplot.py new file mode 100644 --- /dev/null +++ b/arviz/plots/ecdfplot.py @@ -0,0 +1,318 @@ +"""Plot ecdf or ecdf-difference plot with confidence bands.""" +import numpy as np +from scipy.stats import uniform, binom + +from ..rcparams import rcParams +from .plot_utils import get_plotting_function + + +def plot_ecdf( + values, + values2=None, + cdf=None, + difference=False, + pit=False, + confidence_bands=None, + pointwise=False, + npoints=100, + num_trials=500, + fpr=0.05, + figsize=None, + fill_band=True, + plot_kwargs=None, + fill_kwargs=None, + plot_outline_kwargs=None, + ax=None, + show=None, + backend=None, + backend_kwargs=None, + **kwargs +): + """Plot ECDF or ECDF-Difference Plot with Confidence bands. + + This plot uses the simulated based algorithm presented in the paper "Graphical Test for + Discrete Uniformity and its Applications in Goodness of Fit Evaluation and + Multiple Sample Comparison" [1]_. + + Parameters + ---------- + values : array-like + Values to plot from an unknown continuous or discrete distribution + values2 : array-like, optional + Values to compare to the original sample + cdf : function, optional + Cumulative distribution function of the distribution to compare the original sample to + difference : bool, optional, Defaults False + If true then plot ECDF-difference plot otherwise ECDF plot + pit : bool, optional + If True plots the ECDF or ECDF-diff of PIT of sample + confidence_bands : bool, optional, Defaults True + If True plots the simultaneous or pointwise confidence bands with 1 - fpr confidence level + pointwise : bool, optional, Defaults False + If True plots pointwise confidence bands otherwise simultaneous bands + npoints : int, optional, Defaults 100 + This denotes the granularity size of our plot + i.e the number of evaluation points for our ecdf or ecdf-difference plot + num_trials : int, optional, Defaults 500 + The number of random ECDFs to generate to construct simultaneous confidence bands + fpr : float, optional, Defaults 0.05 + The type I error rate s.t 1 - fpr denotes the confidence level of bands + figsize : tuple, optional + Figure size. If None it will be defined automatically. + fill_band : bool, optional + Use fill_between to mark the area inside the credible interval. + Otherwise, plot the border lines. + plot_kwargs : dict, optional + Additional kwargs passed to :func:`mpl:matplotlib.pyplot.step` or + :meth:`bokeh:bokeh.plotting.Figure.step` + fill_kwargs : dict, optional + Additional kwargs passed to :func:`mpl:matplotlib.pyplot.fill_between` or + :meth:`bokeh:bokeh.plotting.Figure.varea` + plot_outline_kwargs : dict, optional + Additional kwargs passed to :meth:`mpl:matplotlib.axes.Axes.plot` or + :meth:`bokeh:bokeh.plotting.Figure.line` + ax : axes, optional + Matplotlib axes or bokeh figures. + show : bool, optional + Call backend show function. + backend : str, optional + Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + backend_kwargs : dict, optional + These are kwargs specific to the backend being used, passed to + :func:`mpl:matplotlib.pyplot.subplots` or + :meth:`bokeh:bokeh.plotting.figure`. + + Returns + ------- + axes : matplotlib axes or bokeh figures + + References + ---------- + .. [1] Säilynoja, T., Bürkner, P.C. and Vehtari, A., 2021. Graphical Test for + Discrete Uniformity and its Applications in Goodness of Fit Evaluation and + Multiple Sample Comparison. arXiv preprint arXiv:2103.10522. + + Examples + -------- + Plot ecdf plot for a given sample + + .. plot:: + :context: close-figs + + >>> import arviz as az + >>> from scipy.stats import uniform, binom, norm + + >>> sample = norm(0,1).rvs(1000) + >>> az.plot_ecdf(sample) + + Plot ecdf plot with confidence bands for comparing a given sample w.r.t a given distribution + + .. plot:: + :context: close-figs + + >>> distribution = norm(0,1) + >>> az.plot_ecdf(sample, cdf = distribution.cdf, confidence_bands = True) + + Plot ecdf-difference plot with confidence bands for comparing a given sample + w.r.t a given distribution + + .. plot:: + :context: close-figs + + >>> az.plot_ecdf(sample, cdf = distribution.cdf, + confidence_bands = True, difference = True) + + Plot ecdf plot with confidence bands for PIT of sample for comparing a given sample + w.r.t a given distribution + + .. plot:: + :context: close-figs + + >>> az.plot_ecdf(sample, cdf = distribution.cdf, + confidence_bands = True, pit = True) + + Plot ecdf-difference plot with confidence bands for PIT of sample for comparing a given + sample w.r.t a given distribution + + .. plot:: + :context: close-figs + + >>> az.plot_ecdf(sample, cdf = distribution.cdf, + confidence_bands = True, difference = True, pit = True) + + You could also plot the above w.r.t another sample rather than a given distribution. + For eg: Plot ecdf-difference plot with confidence bands for PIT of sample for + comparing a given sample w.r.t a given sample + + .. plot:: + :context: close-figs + + >>> sample2 = norm(0,1).rvs(5000) + >>> az.plot_ecdf(sample, sample2, confidence_bands = True, difference = True, pit = True) + + """ + if confidence_bands is None: + confidence_bands = (values2 is not None) or (cdf is not None) + + if values2 is None and cdf is None and confidence_bands is True: + raise ValueError("For confidence bands you need to specify values2 or the cdf") + + if cdf is not None and values2 is not None: + raise ValueError("To compare sample you need either cdf or values2 and not both") + + if values2 is None and cdf is None and pit is True: + raise ValueError("For PIT specify either cdf or values2") + + if values2 is None and cdf is None and difference is True: + raise ValueError("For ECDF difference plot need either cdf or values2") + + if values2 is not None: + values2 = np.ravel(values2) + values2.sort() + + values = np.ravel(values) + values.sort() + + n = len(values) # number of samples + ## This block computes gamma and uses it to get the upper and lower confidence bands + ## Here we check if we want confidence bands or not + if confidence_bands: + ## If plotting PIT then we find the PIT values of sample. + ## Basically here we generate the evaluation points(x) and find the PIT values. + ## z is the evaluation point for our uniform distribution in compute_gamma() + if pit: + x = np.linspace(1 / npoints, 1, npoints) + z = x + ## Finding PIT for our sample + probs = cdf(values) if cdf else compute_ecdf(values2, values) / len(values2) + else: + ## If not PIT use sample for plots and for evaluation points(x) use equally spaced + ## points between minimum and maximum of sample + ## For z we have used cdf(x) + x = np.linspace(values[0], values[-1], npoints) + z = cdf(x) if cdf else compute_ecdf(values2, x) + probs = values + + ## Computing gamma + if not pointwise: + gamma = compute_gamma(n, z, npoints, num_trials, fpr) + else: + gamma = fpr + ## Using gamma to get the confidence intervals + lower, higher = get_lims(gamma, n, z) + + ## This block is for whether to plot ECDF or ECDF-difference + if not difference: + ## We store the coordinates of our ecdf in x_coord, y_coord + x_coord, y_coord = get_ecdf_points(x, probs, difference) + else: + ## Here we subtract the ecdf value as here we are plotting the ECDF-difference + x_coord, y_coord = get_ecdf_points(x, probs, difference) + for i, x_i in enumerate(x): + y_coord[i] = y_coord[i] - ( + x_i if pit else cdf(x_i) if cdf else compute_ecdf(values2, x_i) + ) + + ## Similarly we subtract from the upper and lower bounds + if pit: + lower = lower - x + higher = higher - x + else: + lower = lower - (cdf(x) if cdf else compute_ecdf(values2, x)) + higher = higher - (cdf(x) if cdf else compute_ecdf(values2, x)) + + else: + if pit: + x = np.linspace(1 / npoints, 1, npoints) + probs = cdf(values) + else: + x = np.linspace(values[0], values[-1], npoints) + probs = values + + lower, higher = None, None + ## This block is for whether to plot ECDF or ECDF-difference + if not difference: + x_coord, y_coord = get_ecdf_points(x, probs, difference) + else: + ## Here we subtract the ecdf value as here we are plotting the ECDF-difference + x_coord, y_coord = get_ecdf_points(x, probs, difference) + for i, x_i in enumerate(x): + y_coord[i] = y_coord[i] - ( + x_i if pit else cdf(x_i) if cdf else compute_ecdf(values2, x_i) + ) + + ecdf_plot_args = dict( + x_coord=x_coord, + y_coord=y_coord, + x_bands=x, + lower=lower, + higher=higher, + confidence_bands=confidence_bands, + figsize=figsize, + fill_band=fill_band, + plot_kwargs=plot_kwargs, + fill_kwargs=fill_kwargs, + plot_outline_kwargs=plot_outline_kwargs, + ax=ax, + show=show, + backend_kwargs=backend_kwargs, + **kwargs + ) + + if backend is None: + backend = rcParams["plot.backend"] + backend = backend.lower() + + plot = get_plotting_function("plot_ecdf", "ecdfplot", backend) + ax = plot(**ecdf_plot_args) + + return ax + + +def compute_ecdf(sample, z): + """Compute ECDF. + + This function computes the ecdf value at the evaluation point + or a sorted set of evaluation points. + """ + return np.searchsorted(sample, z, side="right") / len(sample) + + +def get_ecdf_points(x, probs, difference): + """Compute the coordinates for the ecdf points using compute_ecdf.""" + y = compute_ecdf(probs, x) + + if not difference: + x = np.insert(x, 0, x[0]) + y = np.insert(y, 0, 0) + return x, y + + +def compute_gamma(n, z, npoints=None, num_trials=1000, fpr=0.05): + """Compute gamma for confidence interval calculation. + + This function simulates an adjusted value of gamma to account for multiplicity + when forming an 1-fpr level confidence envelope for the ECDF of a sample. + """ + if npoints is None: + npoints = n + gamma = [] + for _ in range(num_trials): + unif_samples = uniform.rvs(0, 1, n) + unif_samples = np.sort(unif_samples) + gamma_m = 1000 + ## Can compute ecdf for all the z together or one at a time. + f_z = compute_ecdf(unif_samples, z) + f_z = compute_ecdf(unif_samples, z) + gamma_m = 2 * min( + np.amin(binom.cdf(n * f_z, n, z)), np.amin(1 - binom.cdf(n * f_z - 1, n, z)) + ) + gamma.append(gamma_m) + return np.quantile(gamma, fpr) + + +def get_lims(gamma, n, z): + """Compute the simultaneous 1 - fpr level confidence bands.""" + lower = binom.ppf(gamma / 2, n, z) + upper = binom.ppf(1 - gamma / 2, n, z) + return lower / n, upper / n

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -5,6 +5,7 @@ import numpy as np import pytest from pandas import DataFrame # pylint: disable=wrong-import-position +from scipy.stats import norm # pylint: disable=wrong-import-position from ...data import from_dict, load_arviz_data # pylint: disable=wrong-import-position from ...plots import ( # pylint: disable=wrong-import-position @@ -15,6 +16,7 @@ plot_dist, plot_dist_comparison, plot_dot, + plot_ecdf, plot_elpd, plot_energy, plot_ess, @@ -345,6 +347,26 @@ def test_plot_compare_no_ic(models): assert "['loo', 'waic']" in str(err.value) +def test_plot_ecdf_basic(): + data = np.random.randn(4, 1000) + axes = plot_ecdf(data, backend="bokeh", show=False) + assert axes is not None + + +def test_plot_ecdf_values2(): + data = np.random.randn(4, 1000) + data2 = np.random.randn(4, 500) + axes = plot_ecdf(data, data2, backend="bokeh", show=False) + assert axes is not None + + +def test_plot_ecdf_cdf(): + data = np.random.randn(4, 1000) + cdf = norm(0, 1).cdf + axes = plot_ecdf(data, cdf=cdf, backend="bokeh", show=False) + assert axes is not None + + @pytest.mark.parametrize( "kwargs", [{}, {"ic": "loo"}, {"xlabels": True, "scale": "log"}, {"threshold": 2}] ) diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -8,7 +8,7 @@ import pytest from matplotlib import animation from pandas import DataFrame -from scipy.stats import gaussian_kde +from scipy.stats import gaussian_kde, norm from ...data import from_dict, load_arviz_data from ...plots import ( @@ -19,6 +19,7 @@ plot_dist, plot_dist_comparison, plot_dot, + plot_ecdf, plot_elpd, plot_energy, plot_ess, @@ -1191,6 +1192,26 @@ def test_kde_cumulative(limits): np.testing.assert_almost_equal(round(density[-1], 3), 1) +def test_plot_ecdf_basic(): + data = np.random.randn(4, 1000) + axes = plot_ecdf(data) + assert axes is not None + + +def test_plot_ecdf_values2(): + data = np.random.randn(4, 1000) + data2 = np.random.randn(4, 1000) + axes = plot_ecdf(data, data2) + assert axes is not None + + +def test_plot_ecdf_cdf(): + data = np.random.randn(4, 1000) + cdf = norm(0, 1).cdf + axes = plot_ecdf(data, cdf=cdf) + assert axes is not None + + @pytest.mark.parametrize( "kwargs", [

Implement simultaneous confidence bands for ECDF of PIT This is related to the paper https://arxiv.org/abs/2103.10522 and the R code is https://github.com/TeemuSailynoja/simultaneous-confidence-bands

cc @Rishabh261998 @OriolAbril should I start working on this now? as I have included this in the GSoC timeline. As of now, I was thinking of looking into this #1574, as it has been idle for a long time. No need to work on this now, just wanted you to be aware of all the relevant issues.

2021-08-01T20:06:35Z

arviz-devs/arviz

arviz-devs__arviz-1772

189b4da845399127ab4c06835c129777d3bb3d25

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -1450,6 +1450,11 @@ def _group_names( ------- groups: list """ + if filter_groups not in {None, "like", "regex"}: + raise ValueError( + f"'filter_groups' can only be None, 'like', or 'regex', got: '{filter_groups}'" + ) + all_groups = self._groups_all if groups is None: return all_groups diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -34,6 +34,11 @@ def _var_names(var_names, data, filter_vars=None): ------- var_name: list or None """ + if filter_vars not in {None, "like", "regex"}: + raise ValueError( + f"'filter_vars' can only be None, 'like', or 'regex', got: '{filter_vars}'" + ) + if var_names is not None: if isinstance(data, (list, tuple)): all_vars = []

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -509,6 +509,15 @@ def test_group_names(self, args_res): group_names = idata._group_names(*args) # pylint: disable=protected-access assert np.all([name in result for name in group_names]) + def test_group_names_invalid_args(self): + ds = dict_to_dataset({"a": np.random.normal(size=(3, 10))}) + idata = InferenceData(posterior=(ds, ds)) + msg = r"^\'filter_groups\' can only be None, \'like\', or \'regex\', got: 'foo'$" + with pytest.raises(ValueError, match=msg): + idata._group_names( # pylint: disable=protected-access + ("posterior",), filter_groups="foo" + ) + @pytest.mark.parametrize("inplace", [False, True]) def test_isel(self, data_random, inplace): idata = data_random diff --git a/arviz/tests/base_tests/test_utils.py b/arviz/tests/base_tests/test_utils.py --- a/arviz/tests/base_tests/test_utils.py +++ b/arviz/tests/base_tests/test_utils.py @@ -101,6 +101,15 @@ def test_var_names_filter(var_args): assert _var_names(var_names, data, filter_vars) == expected +def test_var_names_filter_invalid_argument(): + """Check invalid argument raises.""" + samples = np.random.randn(10) + data = dict_to_dataset({"alpha": samples}) + msg = r"^\'filter_vars\' can only be None, \'like\', or \'regex\', got: 'foo'$" + with pytest.raises(ValueError, match=msg): + assert _var_names(["alpha"], data, filter_vars="foo") + + def test_subset_list_negation_not_found(): """Check there is a warning if negation pattern is ignored""" names = ["mu", "theta"]

Validate argument to `filter_vars` Right now, passing anything other than the allowed values (None, 'like', 'regex') doesn't raise Unless there are objections, I'll make a PR to raise if anything else is passed

2021-08-27T13:36:15Z

arviz-devs/arviz

arviz-devs__arviz-1962

4d63ca5d279374ee9b524aa9181a1fd5697a7423

diff --git a/arviz/data/io_pystan.py b/arviz/data/io_pystan.py --- a/arviz/data/io_pystan.py +++ b/arviz/data/io_pystan.py @@ -809,6 +809,8 @@ def get_draws_stan3(fit, model=None, variables=None, ignore=None, dtypes=None): data = OrderedDict() for var in variables: + if var in ignore: + continue if var in data: continue dtype = dtypes.get(var)

diff --git a/arviz/tests/external_tests/test_data_pystan.py b/arviz/tests/external_tests/test_data_pystan.py --- a/arviz/tests/external_tests/test_data_pystan.py +++ b/arviz/tests/external_tests/test_data_pystan.py @@ -153,7 +153,7 @@ def test_inference_data(self, data, eight_schools_params): inference_data5 = self.get_inference_data5(data) # inference_data 1 test_dict = { - "posterior": ["theta"], + "posterior": ["theta", "~log_lik"], "posterior_predictive": ["y_hat"], "predictions": ["y_hat"], "observed_data": ["y"],

from_pystan puts log likelihood and posterior predictions in posterior data ## Short Description I would like to convert a posterior sample from a PyStan `stan.fit.Fit` object to an `az.InferenceData` object, but the log likelihood and posterior prediction variables end up in the `posterior` section. (They also appear in their respective sections of the `InferenceData` object). I was wondering if there is a way to have them not be in the `posterior` section. This would make using them with other ArviZ functions much easier because then they will *not* be included in tables or plots of the *posterior distributions* such as `az.plot_trace(data)`. ## Code Example or link The example is from the PyStan section of the *Creating InferenceData* guide: https://arviz-devs.github.io/arviz/getting_started/CreatingInferenceData.html#from-pystan > Note that there are some changes to reflect changes in the PyStan API (such as importing `stan` instead of `pystan`. ```python import arviz as az import numpy as np import stan schools_code = """ data { int<lower=0> J; real y[J]; real<lower=0> sigma[J]; } parameters { real mu; real<lower=0> tau; real theta_tilde[J]; } transformed parameters { real theta[J]; for (j in 1:J) theta[j] = mu + tau * theta_tilde[j]; } model { mu ~ normal(0, 5); tau ~ cauchy(0, 5); theta_tilde ~ normal(0, 1); y ~ normal(theta, sigma); } generated quantities { vector[J] log_lik; vector[J] y_hat; for (j in 1:J) { log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]); y_hat[j] = normal_rng(theta[j], sigma[j]); } } """ eight_school_data = { "J": 8, "y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]), "sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]), } stan_model = stan.build(schools_code, data=eight_school_data) fit = stan_model.sample() stan_data = az.from_pystan( posterior=fit, posterior_predictive="y_hat", posterior_model= stan_model, observed_data=["y"], log_likelihood={"y": "log_lik"}, coords={"school": np.arange(eight_school_data["J"])}, dims={ "theta": ["school"], "y": ["school"], "log_lik": ["school"], "y_hat": ["school"], "theta_tilde": ["school"], }, ) stan_data #> Inference data with groups: #> > posterior #> > posterior_predictive #> > log_likelihood #> > sample_stats stan_data.posterior #> <xarray.Dataset> #> Dimensions: (chain: 4, draw: 1000, school: 8) #> Coordinates: #> * chain (chain) int64 0 1 2 3 #> * draw (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999 #> * school (school) int64 0 1 2 3 4 5 6 7 #> Data variables: #> mu (chain, draw) float64 5.583 13.74 -1.643 ... 7.135 1.818 2.276 #> tau (chain, draw) float64 3.375 2.563 2.734 ... 1.585 0.2655 8.359 #> theta_tilde (chain, draw, school) float64 0.9058 0.2812 ... 1.286 -0.5058 #> theta (chain, draw, school) float64 8.64 6.531 6.381 ... 13.03 -1.952 #> log_lik (chain, draw, school) float64 -4.46 -3.232 ... -3.345 -4.11 #> y_hat (chain, draw, school) float64 -15.09 7.846 ... 19.34 8.089 #> Attributes: #> created_at: 2022-01-13T22:30:54.799215 #> arviz_version: 0.11.4 #> inference_library: stan #> inference_library_version: 3.3.0 #> num_chains: 4 #> num_samples: 1000 #> num_thin: 1 #> num_warmup: 1000 #> save_warmup: 0 #> model_name: models/6kxc6kz6 #> program_code: \ndata {\n int<lower=0> J;\n real y[J];... #> random_seed: None ``` PyStan version: 3.3.0 ArviZ version: 0.11.4 ## Relevant documentation or public examples - Guide on creating InferenceData objects from PyStan: https://arviz-devs.github.io/arviz/getting_started/CreatingInferenceData.html#from-pystan - documentation for `from_pystan()`: https://arviz-devs.github.io/arviz/api/generated/arviz.from_pystan.html#arviz-from-pystan

2022-01-13T23:56:11Z

arviz-devs/arviz

arviz-devs__arviz-1988

9d74000d3d0a1599b017a5dc612388a0663d2f36

diff --git a/arviz/plots/densityplot.py b/arviz/plots/densityplot.py --- a/arviz/plots/densityplot.py +++ b/arviz/plots/densityplot.py @@ -17,6 +17,7 @@ def plot_density( group="posterior", data_labels=None, var_names=None, + filter_vars=None, transform=None, hdi_prob=None, point_estimate="auto", @@ -58,6 +59,11 @@ def plot_density( List of variables to plot. If multiple datasets are supplied and var_names is not None, will print the same set of variables for each dataset. Defaults to None, which results in all the variables being plotted. + filter_vars: {None, "like", "regex"}, optional, default=None + If `None` (default), interpret var_names as the real variables names. If "like", + interpret var_names as substrings of the real variables names. If "regex", + interpret var_names as regular expressions on the real variables names. A la + ``pandas.filter``. transform : callable Function to transform data (defaults to None i.e. the identity function) hdi_prob : float @@ -186,7 +192,7 @@ def plot_density( if labeller is None: labeller = BaseLabeller() - var_names = _var_names(var_names, datasets) + var_names = _var_names(var_names, datasets, filter_vars) n_data = len(datasets) if data_labels is None:

diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -163,6 +163,9 @@ def test_plot_density_bad_kwargs(models): with pytest.raises(ValueError): plot_density(obj, hdi_prob=2) + with pytest.raises(ValueError): + plot_density(obj, filter_vars="bad_value") + @pytest.mark.parametrize( "kwargs", diff --git a/arviz/tests/base_tests/test_utils.py b/arviz/tests/base_tests/test_utils.py --- a/arviz/tests/base_tests/test_utils.py +++ b/arviz/tests/base_tests/test_utils.py @@ -66,6 +66,25 @@ def test_var_names_key_error(data): _var_names(("theta", "tau", "bad_var_name"), data) [email protected]( + "var_args", + [ + (["ta"], ["beta1", "beta2", "theta"], "like"), + (["~beta"], ["phi", "theta"], "like"), + (["beta[0-9]+"], ["beta1", "beta2"], "regex"), + (["^p"], ["phi"], "regex"), + (["~^t"], ["beta1", "beta2", "phi"], "regex"), + ], +) +def test_var_names_filter_multiple_input(var_args): + samples = np.random.randn(10) + data1 = dict_to_dataset({"beta1": samples, "beta2": samples, "phi": samples}) + data2 = dict_to_dataset({"beta1": samples, "beta2": samples, "theta": samples}) + data = [data1, data2] + var_names, expected, filter_vars = var_args + assert _var_names(var_names, data, filter_vars) == expected + + @pytest.mark.parametrize( "var_args", [

plot_density has no filter_vars argument I think it should have the argument. It may be a little more complicated given the multiple idata input but is should still be feasible.

Given that `var_names` already supports multiple idata inputs and their filtering, we're looking for separate filtering for each input idata right? I think `_var_names` must always be a list of strings and it's datasets what can be a list of xarray datasets or a single dataset. Therefore, it does not seem really useful to try and apply different filtering schemes to each dataset given that the var_names will be the same (strings in var_names will not generally be compatible with all filtering schemes). I would therefore pass `filter_vars` from `plot_density` to `_var_names` and test it works, I think it should work straight away. > I would therefore pass filter_vars from plot_density to _var_names and test it works, I think it should work straight away. Yeah, it should work. The "complicated" got me thinking about the requirement of separating the filtering and I started thinking about a `dict` based revamp for `_var_names` 😅 Hi @OriolAbril is the issue still open? I would like to work over this. You can work on this @prahasR Thanks @OriolAbril! I have some questions regarding the issue can you please clarify them. Over https://github.com/arviz-devs/arviz/blob/main/arviz/utils.py I found that `filter_vars` can take only 3 values that are `None`, `like` and `regex`. So one of these values should be passed to `_var_names` as `filter_vars` at https://github.com/arvizdevs/arviz/blob/main/arviz/plots/densityplot.py#L189? Right? If this is the case then can you please help me understand that what value of `filter_vars` should be passed to `_var_names`? I apologise if the questions were too simple. I request you to please clarify my doubts, its my first time contributing here. Thank You. `filter_vars` is what should be passed to `_var_names` whatever value it is storing. See for example https://github.com/arviz-devs/arviz/blob/main/arviz/plots/essplot.py#L194. You need to add `filter_vars` as a parameter in the function definition, document it in the docstring and update the call to `_var_names` to pass also `filter_vars`. Then you'll need to test the behaviour manually and add tests on https://github.com/arviz-devs/arviz/blob/main/arviz/tests/base_tests/test_plots_matplotlib.py. > I apologise if the questions were too simple. I request you to please clarify my doubts, its my first time contributing here. No need to apologize, I do have a question if I may. What thing specific to this issue called/interested you and made you decide to chose it before issues labeled beginner friendly? You (like anyone) are free to work on it, I just want to make sure you know what you are getting into before you end up spending too much time on this.

2022-02-26T05:40:26Z

arviz-devs/arviz

arviz-devs__arviz-2011

683fff23c427c5ea28c0085bd6aacda7f7b49599

diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -933,13 +933,12 @@ def psislw(log_weights, reff=1.0): # precalculate constants cutoff_ind = -int(np.ceil(min(n_samples / 5.0, 3 * (n_samples / reff) ** 0.5))) - 1 cutoffmin = np.log(np.finfo(float).tiny) # pylint: disable=no-member, assignment-from-no-return - k_min = 1.0 / 3 # create output array with proper dimensions out = tuple([np.empty_like(log_weights), np.empty(shape)]) # define kwargs - func_kwargs = {"cutoff_ind": cutoff_ind, "cutoffmin": cutoffmin, "k_min": k_min, "out": out} + func_kwargs = {"cutoff_ind": cutoff_ind, "cutoffmin": cutoffmin, "out": out} ufunc_kwargs = {"n_dims": 1, "n_output": 2, "ravel": False, "check_shape": False} kwargs = {"input_core_dims": [["__sample__"]], "output_core_dims": [["__sample__"], []]} log_weights, pareto_shape = _wrap_xarray_ufunc( @@ -956,7 +955,7 @@ def psislw(log_weights, reff=1.0): return log_weights, pareto_shape -def _psislw(log_weights, cutoff_ind, cutoffmin, k_min=1.0 / 3): +def _psislw(log_weights, cutoff_ind, cutoffmin): """ Pareto smoothed importance sampling (PSIS) for a 1D vector. @@ -998,8 +997,8 @@ def _psislw(log_weights, cutoff_ind, cutoffmin, k_min=1.0 / 3): x_tail = np.exp(x_tail) - expxcutoff k, sigma = _gpdfit(x_tail[x_tail_si]) - if k >= k_min: - # no smoothing if short tail or GPD fit failed + if np.isfinite(k): + # no smoothing if GPD fit failed # compute ordered statistic for the fit sti = np.arange(0.5, tail_len) / tail_len smoothed_tail = _gpinv(sti, k, sigma)

diff --git a/arviz/tests/base_tests/test_stats.py b/arviz/tests/base_tests/test_stats.py --- a/arviz/tests/base_tests/test_stats.py +++ b/arviz/tests/base_tests/test_stats.py @@ -4,6 +4,7 @@ import numpy as np import pytest from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal +from scipy.special import logsumexp from scipy.stats import linregress from xarray import DataArray, Dataset @@ -534,6 +535,16 @@ def test_psislw(centered_eight): assert_allclose(pareto_k, psislw(-log_likelihood, 0.7)[1]) +def test_psislw_smooths_for_low_k(): + # check that log-weights are smoothed even when k < 1/3 + # https://github.com/arviz-devs/arviz/issues/2010 + rng = np.random.default_rng(44) + x = rng.normal(size=100) + x_smoothed, k = psislw(x.copy()) + assert k < 1 / 3 + assert not np.allclose(x - logsumexp(x), x_smoothed) + + @pytest.mark.parametrize("probs", [True, False]) @pytest.mark.parametrize("kappa", [-1, -0.5, 1e-30, 0.5, 1]) @pytest.mark.parametrize("sigma", [0, 2])

arviz's psislw differs from loo's psis ArviZ's implementation of PSIS sometimes agrees with the loo package's implementation and sometimes does not. Here's an example where `arviz.psislw` and `loo.psis` disagree: ```python import arviz, rpy2 import numpy as np import scipy as sp from rpy2.robjects import numpy2ri, packages numpy2ri.activate() # install and load loo utils = packages.importr('utils') utils.install_packages('loo', repos="https://cloud.r-project.org") loo = packages.importr('loo') # random vector rng = np.random.default_rng(44) x = rng.normal(size=100) # compute PSIS x_smoothed_arviz, k_arviz = arviz.psislw(x.copy()) result_loo = loo.psis(x.reshape(1, 100, 1).copy()) x_smoothed_loo = result_loo[0].ravel() k_loo = result_loo[1][0] # check the shapes are the same np.isclose(k_arviz, k_loo) # True # check that weights are not # loo doesn't normalize the log weights; arviz does np.all(np.isclose(x_smoothed_arviz, x_smoothed_loo - sp.special.logsumexp(x_smoothed_loo))) # False np.all(np.isclose(x_smoothed_arviz, x - sp.special.logsumexp(x))) # True ``` So ArviZ and loo agree on the Pareto shape diagnostic, but ArviZ doesn't smooth the weights at all. If we change the seed to 42 or 43, then ArviZ does smooth the weights. The issue seems to be that `k` here is approximately 0.22, which falls below the `k_min=1/3` threshold below which ArviZ does not smooth the weights: https://github.com/arviz-devs/arviz/blob/afd895cb0fde11bbcaa633bb363dcb0a0caaa22f/arviz/stats/stats.py#L1001-L1012 This check is absent from loo's implementation (https://github.com/stan-dev/loo/blob/409de413f56a453d6d4e5a096efceb8388b2d39b/R/psis.R#L254) and the PSIS paper but present in the reference Python and Matlab implementations of PSIS (https://github.com/avehtari/PSIS/blob/cac66c0d688e06c0e69d324b7c31db23fb138b51/py/psis.py#L187). I propose we replace this check with `np.isfinite(k)`.

2022-04-07T10:27:35Z

arviz-devs/arviz

arviz-devs__arviz-2043

d999634b3d7df4c0899a6d387fa7f6fe67936e86

diff --git a/arviz/plots/backends/bokeh/compareplot.py b/arviz/plots/backends/bokeh/compareplot.py --- a/arviz/plots/backends/bokeh/compareplot.py +++ b/arviz/plots/backends/bokeh/compareplot.py @@ -1,5 +1,7 @@ """Bokeh Compareplot.""" from bokeh.models import Span +from bokeh.models.annotations import Title, Legend + from ...plot_utils import _scale_fig_size from .. import show_layout @@ -9,6 +11,8 @@ def plot_compare( ax, comp_df, + legend, + title, figsize, plot_ic_diff, plot_standard_error, @@ -43,8 +47,10 @@ def plot_compare( yticks_pos = list(yticks_pos) + labels = [] + if plot_ic_diff: - ax.yaxis.ticker = yticks_pos + ax.yaxis.ticker = yticks_pos[::2] ax.yaxis.major_label_overrides = { dtype(key): value for key, value in zip(yticks_pos, yticks_labels) @@ -63,7 +69,7 @@ def plot_compare( err_ys.append((y, y)) # plot them - ax.triangle( + dif_tri = ax.triangle( comp_df[information_criterion].iloc[1:], yticks_pos[1::2], line_color=plot_kwargs.get("color_dse", "grey"), @@ -71,7 +77,9 @@ def plot_compare( line_width=2, size=6, ) - ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_dse", "grey")) + dif_line = ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_dse", "grey")) + + labels.append(("ELPD difference", [dif_tri, dif_line])) else: ax.yaxis.ticker = yticks_pos[::2] @@ -79,7 +87,7 @@ def plot_compare( key: value for key, value in zip(yticks_pos[::2], yticks_labels) } - ax.circle( + elpd_circ = ax.circle( comp_df[information_criterion], yticks_pos[::2], line_color=plot_kwargs.get("color_ic", "black"), @@ -87,6 +95,7 @@ def plot_compare( line_width=2, size=6, ) + elpd_label = [elpd_circ] if plot_standard_error: # create the coordinates for the errorbars @@ -98,18 +107,22 @@ def plot_compare( err_ys.append((y, y)) # plot them - ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_ic", "black")) + elpd_line = ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_ic", "black")) + elpd_label.append(elpd_line) + + labels.append(("ELPD", elpd_label)) + + scale = comp_df["scale"][0] if insample_dev: - scale = comp_df[f"{information_criterion}_scale"][0] - p_ic = comp_df[f"p_{information_criterion}"] + p_ic = comp_df[f"p_{information_criterion.split('_')[1]}"] if scale == "log": correction = p_ic elif scale == "negative_log": correction = -p_ic elif scale == "deviance": correction = -(2 * p_ic) - ax.circle( + insample_circ = ax.circle( comp_df[information_criterion] + correction, yticks_pos[::2], line_color=plot_kwargs.get("color_insample_dev", "black"), @@ -117,6 +130,7 @@ def plot_compare( line_width=2, size=6, ) + labels.append(("In-sample ELPD", [insample_circ])) vline = Span( location=comp_df[information_criterion].iloc[0], @@ -128,12 +142,21 @@ def plot_compare( ax.renderers.append(vline) - scale_col = information_criterion + "_scale" - if scale_col in comp_df: - scale = comp_df[scale_col].iloc[0].capitalize() - else: - scale = "Deviance" - ax.xaxis.axis_label = scale + if legend: + legend = Legend(items=labels, orientation="vertical", location="top_right") + ax.add_layout(legend, "above") + ax.legend.click_policy = "hide" + + if title: + _title = Title() + _title.text = f"Model comparison\n{'higher' if scale == 'log' else 'lower'} is better" + ax.title = _title + + if scale == "negative_log": + scale = "-log" + + ax.xaxis.axis_label = f"{information_criterion} ({scale})" + ax.yaxis.axis_label = "ranked models" ax.y_range._property_values["start"] = -1 + step # pylint: disable=protected-access ax.y_range._property_values["end"] = 0 - step # pylint: disable=protected-access diff --git a/arviz/plots/backends/matplotlib/compareplot.py b/arviz/plots/backends/matplotlib/compareplot.py --- a/arviz/plots/backends/matplotlib/compareplot.py +++ b/arviz/plots/backends/matplotlib/compareplot.py @@ -8,6 +8,8 @@ def plot_compare( ax, comp_df, + legend, + title, figsize, plot_ic_diff, plot_standard_error, @@ -41,29 +43,15 @@ def plot_compare( if ax is None: _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs) - if plot_ic_diff: - ax.set_yticks(yticks_pos) - ax.errorbar( - x=comp_df[information_criterion].iloc[1:], - y=yticks_pos[1::2], - xerr=comp_df.dse[1:], - color=plot_kwargs.get("color_dse", "grey"), - fmt=plot_kwargs.get("marker_dse", "^"), - mew=linewidth, - elinewidth=linewidth, - ) - - else: - ax.set_yticks(yticks_pos[::2]) - if plot_standard_error: ax.errorbar( x=comp_df[information_criterion], y=yticks_pos[::2], xerr=comp_df.se, + label="ELPD", color=plot_kwargs.get("color_ic", "k"), fmt=plot_kwargs.get("marker_ic", "o"), - mfc="None", + mfc=plot_kwargs.get("marker_fc", "white"), mew=linewidth, lw=linewidth, ) @@ -71,16 +59,35 @@ def plot_compare( ax.plot( comp_df[information_criterion], yticks_pos[::2], + label="ELPD", color=plot_kwargs.get("color_ic", "k"), marker=plot_kwargs.get("marker_ic", "o"), - mfc="None", + mfc=plot_kwargs.get("marker_fc", "white"), mew=linewidth, lw=0, + zorder=3, ) + if plot_ic_diff: + ax.set_yticks(yticks_pos) + ax.errorbar( + x=comp_df[information_criterion].iloc[1:], + y=yticks_pos[1::2], + xerr=comp_df.dse[1:], + label="ELPD difference", + color=plot_kwargs.get("color_dse", "grey"), + fmt=plot_kwargs.get("marker_dse", "^"), + mew=linewidth, + elinewidth=linewidth, + ) + + else: + ax.set_yticks(yticks_pos[::2]) + + scale = comp_df["scale"][0] + if insample_dev: - scale = comp_df[f"{information_criterion}_scale"][0] - p_ic = comp_df[f"p_{information_criterion}"] + p_ic = comp_df[f"p_{information_criterion.split('_')[1]}"] if scale == "log": correction = p_ic elif scale == "negative_log": @@ -90,6 +97,7 @@ def plot_compare( ax.plot( comp_df[information_criterion] + correction, yticks_pos[::2], + label="In-sample ELPD", color=plot_kwargs.get("color_insample_dev", "k"), marker=plot_kwargs.get("marker_insample_dev", "o"), mew=linewidth, @@ -102,13 +110,20 @@ def plot_compare( color=plot_kwargs.get("color_ls_min_ic", "grey"), lw=linewidth, ) + if legend: + ax.legend(bbox_to_anchor=(1.01, 1), loc="upper left", ncol=1, fontsize=ax_labelsize) - scale_col = information_criterion + "_scale" - if scale_col in comp_df: - scale = comp_df[scale_col].iloc[0].capitalize() - else: - scale = "Deviance" - ax.set_xlabel(scale, fontsize=ax_labelsize) + if title: + ax.set_title( + f"Model comparison\n{'higher' if scale == 'log' else 'lower'} is better", + fontsize=ax_labelsize, + ) + + if scale == "negative_log": + scale = "-log" + + ax.set_xlabel(f"{information_criterion} ({scale})", fontsize=ax_labelsize) + ax.set_ylabel("ranked models", fontsize=ax_labelsize) ax.set_yticklabels(yticks_labels) ax.set_ylim(-1 + step, 0 - step) ax.tick_params(labelsize=xt_labelsize) diff --git a/arviz/plots/compareplot.py b/arviz/plots/compareplot.py --- a/arviz/plots/compareplot.py +++ b/arviz/plots/compareplot.py @@ -8,10 +8,12 @@ def plot_compare( comp_df, - insample_dev=True, + insample_dev=False, plot_standard_error=True, plot_ic_diff=True, order_by_rank=True, + legend=True, + title=True, figsize=None, textsize=None, labeller=None, @@ -24,13 +26,14 @@ def plot_compare( """ Summary plot for model comparison. - This plot is in the style of the one used in the book Statistical Rethinking (Chapter 6) - by Richard McElreath. + Models are compared based on their expected log pointwise predictive density (ELPD), + the ELPD is estimated either by Pareto smoothed importance sampling leave-one-out + cross-validation (LOO) or using the widely applicable information criterion (WAIC). + We recommend LOO in line with the work presented by Vehtari et al. (2016) available + here: https://arxiv.org/abs/1507.04544. - Notes - ----- - Defaults to comparing Leave-one-out (psis-loo) if present in comp_df column, - otherwise compares Widely Applicable Information Criterion (WAIC) + This plot is in the style of the one used in the book Statistical Rethinking by + Richard McElreath.Chapter 6 in the first edition or 7 in the second. Parameters @@ -38,17 +41,22 @@ def plot_compare( comp_df : pd.DataFrame Result of the :func:`arviz.compare` method insample_dev : bool, optional - Plot in-sample deviance, that is the value of the information criteria without the - penalization given by the effective number of parameters (pIC). Defaults to True + Plot in-sample ELPD, that is the value of the information criteria without the + penalization given by the effective number of parameters (p_loo or p_waic). + Defaults to False plot_standard_error : bool, optional - Plot the standard error of the information criteria estimate. Defaults to True + Plot the standard error of the ELPD. Defaults to True plot_ic_diff : bool, optional - Plot standard error of the difference in information criteria between each model + Plot standard error of the difference in ELPD between each model and the top-ranked model. Defaults to True order_by_rank : bool If True (default) ensure the best model is used as reference. + legend : bool + Add legend to figure. By default True. figsize : tuple, optional If None, size is (6, num of models) inches + title : bool: + Show a tittle with a description of how to interpret the plot. Defaults to True. textsize: float Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on ``figsize``. @@ -94,12 +102,12 @@ def plot_compare( >>> 'Non-centered 8 schools': az.load_arviz_data('non_centered_eight')}) >>> az.plot_compare(model_compare) - Plot standard error and information criteria difference only + Include the in-sample ELDP .. plot:: :context: close-figs - >>> az.plot_compare(model_compare, insample_dev=False) + >>> az.plot_compare(model_compare, insample_dev=True) """ if plot_kwargs is None: @@ -119,7 +127,7 @@ def plot_compare( else: yticks_labels = labels - _information_criterion = ["loo", "waic"] + _information_criterion = ["elpd_loo", "elpd_waic"] column_index = [c.lower() for c in comp_df.columns] for information_criterion in _information_criterion: if information_criterion in column_index: @@ -136,6 +144,8 @@ def plot_compare( compareplot_kwargs = dict( ax=ax, comp_df=comp_df, + legend=legend, + title=title, figsize=figsize, plot_ic_diff=plot_ic_diff, plot_standard_error=plot_standard_error, diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -61,19 +61,19 @@ def compare( scale: Optional[ScaleKeyword] = None, var_name: Optional[str] = None, ): - r"""Compare models based on PSIS-LOO `loo` or WAIC `waic` cross-validation. + r"""Compare models based on their expected log pointwise predictive density (ELPD). - LOO is leave-one-out (PSIS-LOO `loo`) cross-validation and - WAIC is the widely applicable information criterion. - Read more theory here - in a paper by some of the leading authorities - on model selection dx.doi.org/10.1111/1467-9868.00353 + The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out + cross-validation (LOO) or using the widely applicable information criterion (WAIC). + We recommend loo. Read more theory here - in a paper by some of the + leading authorities on model comparison dx.doi.org/10.1111/1467-9868.00353 Parameters ---------- compare_dict: dict of {str: InferenceData or ELPDData} A dictionary of model names and :class:`arviz.InferenceData` or ``ELPDData``. ic: str, optional - Information Criterion (PSIS-LOO `loo` or WAIC `waic`) used to compare models. Defaults to + Method to estimate the ELPD, available options are "loo" or "waic". Defaults to ``rcParams["stats.information_criterion"]``. method: str, optional Method used to estimate the weights for each model. Available options are: @@ -112,29 +112,29 @@ def compare( Returns ------- - A DataFrame, ordered from best to worst model (measured by information criteria). + A DataFrame, ordered from best to worst model (measured by the ELPD). The index reflects the key with which the models are passed to this function. The columns are: rank: The rank-order of the models. 0 is the best. - IC: Information Criteria (PSIS-LOO `loo` or WAIC `waic`). - Higher IC indicates higher out-of-sample predictive fit ("better" model). Default LOO. - If `scale` is `deviance` or `negative_log` smaller IC indicates + elpd: ELPD estimated either using (PSIS-LOO-CV `elpd_loo` or WAIC `elpd_waic`). + Higher ELPD indicates higher out-of-sample predictive fit ("better" model). + If `scale` is `deviance` or `negative_log` smaller values indicates higher out-of-sample predictive fit ("better" model). pIC: Estimated effective number of parameters. - dIC: Relative difference between each IC (PSIS-LOO `loo` or WAIC `waic`) - and the lowest IC (PSIS-LOO `loo` or WAIC `waic`). - The top-ranked model is always 0. + elpd_diff: The difference in ELPD between two models. + If more than two models are compared, the difference is computed relative to the + top-ranked model, that always has a elpd_diff of 0. weight: Relative weight for each model. This can be loosely interpreted as the probability of each model (among the compared model) given the data. By default the uncertainty in the weights estimation is considered using Bayesian bootstrap. - SE: Standard error of the IC estimate. + SE: Standard error of the ELPD estimate. If method = BB-pseudo-BMA these values are estimated using Bayesian bootstrap. - dSE: Standard error of the difference in IC between each model and the top-ranked model. + dSE: Standard error of the difference in ELPD between each model and the top-ranked model. It's always 0 for the top-ranked model. - warning: A value of 1 indicates that the computation of the IC may not be reliable. + warning: A value of 1 indicates that the computation of the ELPD may not be reliable. This could be indication of WAIC/LOO starting to fail see http://arxiv.org/abs/1507.04544 for details. - scale: Scale used for the IC. + scale: Scale used for the ELPD. Examples -------- @@ -148,7 +148,7 @@ def compare( ...: compare_dict = {"non centered": data1, "centered": data2} ...: az.compare(compare_dict) - Compare the models using LOO-CV, returning the IC in log scale and calculating the + Compare the models using PSIS-LOO-CV, returning the ELPD in log scale and calculating the weights using the stacking method. .. ipython:: @@ -157,8 +157,10 @@ def compare( See Also -------- - loo : Compute the Pareto Smoothed importance sampling Leave One Out cross-validation. - waic : Compute the widely applicable information criterion. + loo : + Compute the ELPD using the Pareto smoothed importance sampling Leave-one-out + cross-validation method. + waic : Compute the ELPD using the widely applicable information criterion. plot_compare : Summary plot for model comparison. References @@ -171,42 +173,40 @@ def compare( try: (ics_dict, scale, ic) = _calculate_ics(compare_dict, scale=scale, ic=ic, var_name=var_name) except Exception as e: - raise e.__class__("Encountered error in ic computation of compare.") from e + raise e.__class__("Encountered error in ELPD computation of compare.") from e names = list(ics_dict.keys()) if ic == "loo": df_comp = pd.DataFrame( index=names, columns=[ "rank", - "loo", + "elpd_loo", "p_loo", - "d_loo", + "elpd_diff", "weight", "se", "dse", "warning", - "loo_scale", + "scale", ], dtype=np.float_, ) - scale_col = "loo_scale" elif ic == "waic": df_comp = pd.DataFrame( index=names, columns=[ "rank", - "waic", + "elpd_waic", "p_waic", - "d_waic", + "elpd_diff", "weight", "se", "dse", "warning", - "waic_scale", + "scale", ], dtype=np.float_, ) - scale_col = "waic_scale" else: raise NotImplementedError(f"The information criterion {ic} is not supported.") @@ -224,12 +224,11 @@ def compare( if method.lower() not in ["stacking", "bb-pseudo-bma", "pseudo-bma"]: raise ValueError(f"The method {method}, to compute weights, is not supported.") - ic_se = f"{ic}_se" p_ic = f"p_{ic}" ic_i = f"{ic}_i" ics = pd.DataFrame.from_dict(ics_dict, orient="index") - ics.sort_values(by=ic, inplace=True, ascending=ascending) + ics.sort_values(by=f"elpd_{ic}", inplace=True, ascending=ascending) ics[ic_i] = ics[ic_i].apply(lambda x: x.values.flatten()) if method.lower() == "stacking": @@ -267,7 +266,7 @@ def gradient(weights): ) weights = w_fuller(weights["x"]) - ses = ics[ic_se] + ses = ics["se"] elif method.lower() == "bb-pseudo-bma": rows, cols, ic_i_val = _ic_matrix(ics, ic_i) @@ -286,10 +285,10 @@ def gradient(weights): ses = pd.Series(z_bs.std(axis=0), index=names) # pylint: disable=no-member elif method.lower() == "pseudo-bma": - min_ic = ics.iloc[0][ic] - z_rv = np.exp((ics[ic] - min_ic) / scale_value) + min_ic = ics.iloc[0][f"elpd_{ic}"] + z_rv = np.exp((ics[f"elpd_{ic}"] - min_ic) / scale_value) weights = z_rv / np.sum(z_rv) - ses = ics[ic_se] + ses = ics["se"] if np.any(weights): min_ic_i_val = ics[ic_i].iloc[0] @@ -305,19 +304,19 @@ def gradient(weights): weight = weights[idx] df_comp.at[val] = ( idx, - res[ic], + res[f"elpd_{ic}"], res[p_ic], d_ic, weight, std_err, d_std_err, res["warning"], - res[scale_col], + res["scale"], ) df_comp["rank"] = df_comp["rank"].astype(int) df_comp["warning"] = df_comp["warning"].astype(bool) - return df_comp.sort_values(by=ic, ascending=ascending) + return df_comp.sort_values(by=f"elpd_{ic}", ascending=ascending) def _ic_matrix(ics, ic_i): @@ -343,7 +342,7 @@ def _calculate_ics( ic: Optional[ICKeyword] = None, var_name: Optional[str] = None, ): - """Calculate loo and waic information criteria only if necessary. + """Calculate LOO or WAIC only if necessary. It always calls the ic function with ``pointwise=True``. @@ -382,22 +381,22 @@ def _calculate_ics( precomputed_scale = None if precomputed_elpds: _, arbitrary_elpd = precomputed_elpds.popitem() - precomputed_ic = arbitrary_elpd.index[0] - precomputed_scale = arbitrary_elpd[f"{precomputed_ic}_scale"] + precomputed_ic = arbitrary_elpd.index[0].split("_")[1] + precomputed_scale = arbitrary_elpd["scale"] raise_non_pointwise = False if not f"{precomputed_ic}_i" in arbitrary_elpd: raise_non_pointwise = True if precomputed_elpds: if not all( - elpd_data.index[0] == precomputed_ic for elpd_data in precomputed_elpds.values() + elpd_data.index[0].split("_")[1] == precomputed_ic + for elpd_data in precomputed_elpds.values() ): raise ValueError( "All information criteria to be compared must be the same " "but found both loo and waic." ) if not all( - elpd_data[f"{precomputed_ic}_scale"] == precomputed_scale - for elpd_data in precomputed_elpds.values() + elpd_data["scale"] == precomputed_scale for elpd_data in precomputed_elpds.values() ): raise ValueError("All information criteria to be compared must use the same scale") if ( @@ -734,15 +733,15 @@ def loo(data, pointwise=None, var_name=None, reff=None, scale=None): Returns ------- ELPDData object (inherits from :class:`pandas.Series`) with the following row/attributes: - loo: approximated expected log pointwise predictive density (elpd) - loo_se: standard error of loo + elpd: approximated expected log pointwise predictive density (elpd) + se: standard error of the elpd p_loo: effective number of parameters shape_warn: bool True if the estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples loo_i: array of pointwise predictive accuracy, only if pointwise True pareto_k: array of Pareto shape values, only if pointwise True - loo_scale: scale of the loo results + scale: scale of the elpd The returned object has a custom print method that overrides pd.Series method. @@ -856,22 +855,22 @@ def loo(data, pointwise=None, var_name=None, reff=None, scale=None): scale, ], index=[ - "loo", - "loo_se", + "elpd_loo", + "se", "p_loo", "n_samples", "n_data_points", "warning", "loo_i", "pareto_k", - "loo_scale", + "scale", ], ) else: return ELPDData( data=[loo_lppd, loo_lppd_se, p_loo, n_samples, n_data_points, warn_mg, scale], - index=["loo", "loo_se", "p_loo", "n_samples", "n_data_points", "warning", "loo_scale"], + index=["elpd_loo", "se", "p_loo", "n_samples", "n_data_points", "warning", "scale"], ) @@ -1591,14 +1590,14 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): Returns ------- ELPDData object (inherits from :class:`pandas.Series`) with the following row/attributes: - waic: approximated expected log pointwise predictive density (elpd) - waic_se: standard error of waic + elpd_waic: approximated expected log pointwise predictive density (elpd) + se: standard error of the elpd p_waic: effective number parameters var_warn: bool True if posterior variance of the log predictive densities exceeds 0.4 waic_i: :class:`~xarray.DataArray` with the pointwise predictive accuracy, only if pointwise=True - waic_scale: scale of the reported waic results + scale: scale of the elpd The returned object has a custom print method that overrides pd.Series method. @@ -1691,14 +1690,14 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): scale, ], index=[ - "waic", - "waic_se", + "elpd_waic", + "se", "p_waic", "n_samples", "n_data_points", "warning", "waic_i", - "waic_scale", + "scale", ], ) else: @@ -1706,12 +1705,12 @@ def waic(data, pointwise=None, var_name=None, scale=None, dask_kwargs=None): data=[waic_sum, waic_se, p_waic, n_samples, n_data_points, warn_mg, scale], index=[ "waic", - "waic_se", + "se", "p_waic", "n_samples", "n_data_points", "warning", - "waic_scale", + "scale", ], ) diff --git a/arviz/stats/stats_utils.py b/arviz/stats/stats_utils.py --- a/arviz/stats/stats_utils.py +++ b/arviz/stats/stats_utils.py @@ -462,12 +462,12 @@ class ELPDData(pd.Series): # pylint: disable=too-many-ancestors def __str__(self): """Print elpd data in a user friendly way.""" - kind = self.index[0] + kind = self.index[0].split("_")[1] if kind not in ("loo", "waic"): raise ValueError("Invalid ELPDData object") - scale_str = SCALE_DICT[self[f"{kind}_scale"]] + scale_str = SCALE_DICT[self["scale"]] padding = len(scale_str) + len(kind) + 1 base = BASE_FMT.format(padding, padding - 2) base = base.format(

diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -339,12 +339,12 @@ def test_plot_compare_no_ic(models): model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) # Drop column needed for plotting - model_compare = model_compare.drop("loo", axis=1) + model_compare = model_compare.drop("elpd_loo", axis=1) with pytest.raises(ValueError) as err: plot_compare(model_compare, backend="bokeh", show=False) assert "comp_df must contain one of the following" in str(err.value) - assert "['loo', 'waic']" in str(err.value) + assert "['elpd_loo', 'elpd_waic']" in str(err.value) def test_plot_ecdf_basic(): diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -1127,7 +1127,7 @@ def test_plot_posterior_skipna_combinedims(): @pytest.mark.parametrize( - "kwargs", [{"insample_dev": False}, {"plot_standard_error": False}, {"plot_ic_diff": False}] + "kwargs", [{"insample_dev": True}, {"plot_standard_error": False}, {"plot_ic_diff": False}] ) def test_plot_compare(models, kwargs): model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) @@ -1141,12 +1141,12 @@ def test_plot_compare_no_ic(models): model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2}) # Drop column needed for plotting - model_compare = model_compare.drop("loo", axis=1) + model_compare = model_compare.drop("elpd_loo", axis=1) with pytest.raises(ValueError) as err: plot_compare(model_compare) assert "comp_df must contain one of the following" in str(err.value) - assert "['loo', 'waic']" in str(err.value) + assert "['elpd_loo', 'elpd_waic']" in str(err.value) @pytest.mark.parametrize( diff --git a/arviz/tests/base_tests/test_rcparams.py b/arviz/tests/base_tests/test_rcparams.py --- a/arviz/tests/base_tests/test_rcparams.py +++ b/arviz/tests/base_tests/test_rcparams.py @@ -280,10 +280,10 @@ def test_data_load(): def test_stats_information_criterion(models): rcParams["stats.information_criterion"] = "waic" df_comp = compare({"model1": models.model_1, "model2": models.model_2}) - assert "waic" in df_comp.columns + assert "elpd_waic" in df_comp.columns rcParams["stats.information_criterion"] = "loo" df_comp = compare({"model1": models.model_1, "model2": models.model_2}) - assert "loo" in df_comp.columns + assert "elpd_loo" in df_comp.columns def test_http_type_request(monkeypatch): diff --git a/arviz/tests/base_tests/test_stats.py b/arviz/tests/base_tests/test_stats.py --- a/arviz/tests/base_tests/test_stats.py +++ b/arviz/tests/base_tests/test_stats.py @@ -233,7 +233,7 @@ def test_compare_multiple_obs(multivariable_log_likelihood, centered_eight, non_ } with pytest.raises(TypeError, match="several log likelihood arrays"): get_log_likelihood(compare_dict["problematic"]) - with pytest.raises(TypeError, match="error in ic computation"): + with pytest.raises(TypeError, match="error in ELPD computation"): compare(compare_dict, ic=ic) assert compare(compare_dict, ic=ic, var_name="obs") is not None @@ -248,8 +248,9 @@ def test_calculate_ics(centered_eight, non_centered_eight, ic): elpddata_out, _, _ = _calculate_ics(elpddata_dict, ic=ic) mixed_out, _, _ = _calculate_ics(mixed_dict, ic=ic) for model in idata_dict: - assert idata_out[model][ic] == elpddata_out[model][ic] - assert idata_out[model][ic] == mixed_out[model][ic] + ic_ = f"elpd_{ic}" + assert idata_out[model][ic_] == elpddata_out[model][ic_] + assert idata_out[model][ic_] == mixed_out[model][ic_] assert idata_out[model][f"p_{ic}"] == elpddata_out[model][f"p_{ic}"] assert idata_out[model][f"p_{ic}"] == mixed_out[model][f"p_{ic}"] @@ -265,7 +266,7 @@ def test_calculate_ics_ic_override(centered_eight, non_centered_eight): with pytest.warns(UserWarning, match="precomputed elpddata: waic"): out_dict, _, ic = _calculate_ics(in_dict, ic="loo") assert ic == "waic" - assert out_dict["centered"]["waic"] == waic(centered_eight)["waic"] + assert out_dict["centered"]["elpd_waic"] == waic(centered_eight)["elpd_waic"] def test_summary_ndarray(): @@ -489,7 +490,7 @@ def test_loo(centered_eight, multidim_models, scale, multidim): assert loo_pointwise is not None assert "loo_i" in loo_pointwise assert "pareto_k" in loo_pointwise - assert "loo_scale" in loo_pointwise + assert "scale" in loo_pointwise def test_loo_one_chain(centered_eight): diff --git a/arviz/tests/base_tests/test_stats_utils.py b/arviz/tests/base_tests/test_stats_utils.py --- a/arviz/tests/base_tests/test_stats_utils.py +++ b/arviz/tests/base_tests/test_stats_utils.py @@ -305,7 +305,7 @@ def test_get_log_likelihood_no_group(): def test_elpd_data_error(): - with pytest.raises(ValueError): + with pytest.raises(IndexError): repr(ELPDData(data=[0, 1, 2], index=["not IC", "se", "p"]))

plot_compare needs a legend There is a lot going on in the `plot_compare` output. There should be a legend displayed by default, as most users will not be intimately familiar with Statistical Rethinking. There should also be a better description in the function docstring describing what gets plotted.

2022-06-08T22:39:04Z

arviz-devs/arviz

arviz-devs__arviz-2103

bc1e8875efb224c08c94a16e3d51fcfaf046e3ae

diff --git a/arviz/plots/autocorrplot.py b/arviz/plots/autocorrplot.py --- a/arviz/plots/autocorrplot.py +++ b/arviz/plots/autocorrplot.py @@ -127,7 +127,8 @@ def plot_autocorr( labeller = BaseLabeller() plotters = filter_plotters_list( - list(xarray_var_iter(data, var_names, combined)), "plot_autocorr" + list(xarray_var_iter(data, var_names, combined, dim_order=["chain", "draw"])), + "plot_autocorr", ) rows, cols = default_grid(len(plotters), grid=grid) diff --git a/arviz/plots/rankplot.py b/arviz/plots/rankplot.py --- a/arviz/plots/rankplot.py +++ b/arviz/plots/rankplot.py @@ -174,6 +174,7 @@ def plot_rank( posterior_data, var_names=var_names, combined=True, + dim_order=["chain", "draw"], ) ), "plot_rank", diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -201,7 +201,13 @@ def plot_trace( skip_dims = set(coords_data.dims) - {"chain", "draw"} if compact else set() plotters = list( - xarray_var_iter(coords_data, var_names=var_names, combined=True, skip_dims=skip_dims) + xarray_var_iter( + coords_data, + var_names=var_names, + combined=True, + skip_dims=skip_dims, + dim_order=["chain", "draw"], + ) ) max_plots = rcParams["plot.max_subplots"] max_plots = len(plotters) if max_plots is None else max(max_plots // 2, 1) diff --git a/arviz/sel_utils.py b/arviz/sel_utils.py --- a/arviz/sel_utils.py +++ b/arviz/sel_utils.py @@ -55,31 +55,6 @@ def make_label(var_name, selection, position="below"): return base.format(var_name, sel) -def purge_duplicates(list_in): - """Remove duplicates from list while preserving order. - - Parameters - ---------- - list_in: Iterable - - Returns - ------- - list - List of first occurrences in order - """ - # Algorithm taken from Stack Overflow, - # https://stackoverflow.com/questions/480214. Content by Georgy - # Skorobogatov (https://stackoverflow.com/users/7851470/georgy) and - # Markus Jarderot - # (https://stackoverflow.com/users/22364/markus-jarderot), licensed - # under CC-BY-SA 4.0. - # https://creativecommons.org/licenses/by-sa/4.0/. - - seen = set() - seen_add = seen.add - return [x for x in list_in if not (x in seen or seen_add(x))] - - def _dims(data, var_name, skip_dims): return [dim for dim in data[var_name].dims if dim not in skip_dims] @@ -136,7 +111,7 @@ def xarray_sel_iter(data, var_names=None, combined=False, skip_dims=None, revers for var_name in var_names: if var_name in data: new_dims = _dims(data, var_name, skip_dims) - vals = [purge_duplicates(data[var_name][dim].values) for dim in new_dims] + vals = [list(dict.fromkeys(data[var_name][dim].values)) for dim in new_dims] dims = _zip_dims(new_dims, vals) idims = _zip_dims(new_dims, [range(len(v)) for v in vals]) if reverse_selections: @@ -147,7 +122,9 @@ def xarray_sel_iter(data, var_names=None, combined=False, skip_dims=None, revers yield var_name, selection, iselection -def xarray_var_iter(data, var_names=None, combined=False, skip_dims=None, reverse_selections=False): +def xarray_var_iter( + data, var_names=None, combined=False, skip_dims=None, reverse_selections=False, dim_order=None +): """Convert xarray data to an iterator over vectors. Iterates over each var_name and all of its coordinates, returning the 1d @@ -170,6 +147,9 @@ def xarray_var_iter(data, var_names=None, combined=False, skip_dims=None, revers reverse_selections : bool Whether to reverse selections before iterating. + dim_order: list + Order for the first dimensions. Skips dimensions not found in the variable. + Returns ------- Iterator of (str, dict(str, any), np.array) @@ -180,6 +160,9 @@ def xarray_var_iter(data, var_names=None, combined=False, skip_dims=None, revers if var_names is None and isinstance(data, xr.DataArray): data_to_sel = {data.name: data} + if isinstance(dim_order, str): + dim_order = [dim_order] + for var_name, selection, iselection in xarray_sel_iter( data, var_names=var_names, @@ -187,7 +170,12 @@ def xarray_var_iter(data, var_names=None, combined=False, skip_dims=None, revers skip_dims=skip_dims, reverse_selections=reverse_selections, ): - yield var_name, selection, iselection, data_to_sel[var_name].sel(**selection).values + selected_data = data_to_sel[var_name].sel(**selection) + if dim_order is not None: + dim_order_selected = [dim for dim in dim_order if dim in selected_data.dims] + if dim_order_selected: + selected_data = selected_data.transpose(*dim_order_selected, ...) + yield var_name, selection, iselection, selected_data.values def xarray_to_ndarray(data, *, var_names=None, combined=True, label_fun=None):

diff --git a/arviz/tests/helpers.py b/arviz/tests/helpers.py --- a/arviz/tests/helpers.py +++ b/arviz/tests/helpers.py @@ -1,4 +1,4 @@ -# pylint: disable=redefined-outer-name, comparison-with-callable +# pylint: disable=redefined-outer-name, comparison-with-callable, protected-access """Test helper functions.""" import gzip import importlib @@ -51,7 +51,7 @@ def chains(): return 2 -def create_model(seed=10): +def create_model(seed=10, transpose=False): """Create model with fake data.""" np.random.seed(seed) nchains = 4 @@ -104,10 +104,15 @@ def create_model(seed=10): }, coords={"obs_dim": range(data["J"])}, ) + if transpose: + for group in model._groups: + group_dataset = getattr(model, group) + if all(dim in group_dataset.dims for dim in ("draw", "chain")): + setattr(model, group, group_dataset.transpose(*["draw", "chain"], ...)) return model -def create_multidimensional_model(seed=10): +def create_multidimensional_model(seed=10, transpose=False): """Create model with fake data.""" np.random.seed(seed) nchains = 4 @@ -155,6 +160,11 @@ def create_multidimensional_model(seed=10): dims={"y": ["dim1", "dim2"], "log_likelihood": ["dim1", "dim2"]}, coords={"dim1": range(ndim1), "dim2": range(ndim2)}, ) + if transpose: + for group in model._groups: + group_dataset = getattr(model, group) + if all(dim in group_dataset.dims for dim in ("draw", "chain")): + setattr(model, group, group_dataset.transpose(*["draw", "chain"], ...)) return model @@ -195,7 +205,7 @@ def models(): class Models: model_1 = create_model(seed=10) - model_2 = create_model(seed=11) + model_2 = create_model(seed=11, transpose=True) return Models() @@ -207,7 +217,7 @@ def multidim_models(): class Models: model_1 = create_multidimensional_model(seed=10) - model_2 = create_multidimensional_model(seed=11) + model_2 = create_multidimensional_model(seed=11, transpose=True) return Models()

plot_trace errors for non-default order of dimensions **Describe the bug** The default dimension order of variables is `(chain, draw, shape...)`, but as discussed in #1693, all dimension orders should be supported. `plot_trace` errors when the dimension order is `(draw, chain)`. **To Reproduce** ```python >>> import arviz as az >>> import xarray as xr >>> import numpy as np >>> nchains, ndraws = 4, 100 >>> ds = xr.Dataset(dict(x = (('chain', 'draw'), np.random.normal(size=(nchains, ndraws))))) >>> az.plot_trace(ds) array([[<AxesSubplot:title={'center':'x'}>, <AxesSubplot:title={'center':'x'}>]], dtype=object) >>> ds = xr.Dataset(dict(x = (('draw', 'chain'), np.random.normal(size=(ndraws, nchains))))) >>> ds <xarray.Dataset> Dimensions: (draw: 100, chain: 4) Dimensions without coordinates: draw, chain Data variables: x (draw, chain) float64 0.7368 0.347 1.105 ... 0.9654 -0.98 0.6756 >>> az.plot_trace(ds) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/sethaxen/software/mambaforge/envs/arvizdev/lib/python3.10/site-packages/arviz/plots/traceplot.py", line 260, in plot_trace axes = plot(**trace_plot_args) File "/home/sethaxen/software/mambaforge/envs/arvizdev/lib/python3.10/site-packages/arviz/plots/backends/matplotlib/traceplot.py", line 248, in plot_trace ax = _plot_chains_mpl( File "/home/sethaxen/software/mambaforge/envs/arvizdev/lib/python3.10/site-packages/arviz/plots/backends/matplotlib/traceplot.py", line 476, in _plot_chains_mpl aux_kwargs = dealiase_sel_kwargs(trace_kwargs, chain_prop, chain_idx) File "/home/sethaxen/software/mambaforge/envs/arvizdev/lib/python3.10/site-packages/arviz/plots/backends/matplotlib/__init__.py", line 99, in dealiase_sel_kwargs {prop: props[idx] for prop, props in prop_dict.items()}, "plot" File "/home/sethaxen/software/mambaforge/envs/arvizdev/lib/python3.10/site-packages/arviz/plots/backends/matplotlib/__init__.py", line 99, in <dictcomp> {prop: props[idx] for prop, props in prop_dict.items()}, "plot" IndexError: list index out of range ```

Problem with sel_utils.xarray_var_iter? https://github.com/arviz-devs/arviz/blob/5ca7643e538d7ac9bba6d9c8a2ed91cb3d6c0c75/arviz/sel_utils.py#L190 This returns an array with the shape based on the order of data. Currently this function is missing the reshape possibility. E.g. we could add one more step to that loop and call `.transpose` with a new keyword `dim_order`

2022-08-27T00:42:10Z

arviz-devs/arviz

arviz-devs__arviz-2129

d4a5eca334c2a3c01ff60f57316b89d8a272bbcb

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -80,6 +80,13 @@ InferenceDataT = TypeVar("InferenceDataT", bound="InferenceData") +def _compressible_dtype(dtype): + """Check basic dtypes for automatic compression.""" + if dtype.kind == "V": + return all(_compressible_dtype(item) for item, _ in dtype.fields.values()) + return dtype.kind in {"b", "i", "u", "f", "c", "S"} + + class InferenceData(Mapping[str, xr.Dataset]): """Container for inference data storage using xarray. @@ -422,7 +429,11 @@ def to_netcdf( data = getattr(self, group) kwargs = {} if compress: - kwargs["encoding"] = {var_name: {"zlib": True} for var_name in data.variables} + kwargs["encoding"] = { + var_name: {"zlib": True} + for var_name, values in data.variables.items() + if _compressible_dtype(values.dtype) + } data.to_netcdf(filename, mode=mode, group=group, **kwargs) data.close() mode = "a"

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -1215,7 +1215,7 @@ def get_inference_data(self, data, eight_schools_params): prior_predictive=data.obj, sample_stats_prior=data.obj, observed_data=eight_schools_params, - coords={"school": np.arange(8)}, + coords={"school": np.array(["a" * i for i in range(8)], dtype="U")}, dims={"theta": ["school"], "eta": ["school"]}, ) @@ -1253,7 +1253,8 @@ def test_io_function(self, data, eight_schools_params): assert not os.path.exists(filepath) @pytest.mark.parametrize("groups_arg", [False, True]) - def test_io_method(self, data, eight_schools_params, groups_arg): + @pytest.mark.parametrize("compress", [True, False]) + def test_io_method(self, data, eight_schools_params, groups_arg, compress): # create InferenceData and check it has been properly created inference_data = self.get_inference_data( # pylint: disable=W0612 data, eight_schools_params @@ -1277,7 +1278,9 @@ def test_io_method(self, data, eight_schools_params, groups_arg): assert not os.path.exists(filepath) # InferenceData method inference_data.to_netcdf( - filepath, groups=("posterior", "observed_data") if groups_arg else None + filepath, + groups=("posterior", "observed_data") if groups_arg else None, + compress=compress, ) # assert file has been saved correctly

RuntimeError when writing to a file with to_netcdf **Describe the bug** Trying to write an `InferenceData` object to a file using `to_netcdf`results in an error: `RuntimeError: NetCDF: Filter error: bad id or parameters or duplicate filter` **To Reproduce** ``` import arviz as az data = az.load_arviz_data("centered_eight") data.to_netcdf("test.nc") ``` <details> <summary> Stacktrace </summary> ``` --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Input In [5], in <cell line: 1>() ----> 1 data.to_netcdf("test.nc") File ~/.local/lib/python3.9/site-packages/arviz/data/inference_data.py:427, in InferenceData.to_netcdf(self, filename, compress, groups) 425 if compress: 426 kwargs["encoding"] = {var_name: {"zlib": True} for var_name in data.variables} --> 427 data.to_netcdf(filename, mode=mode, group=group, **kwargs) 428 data.close() 429 mode = "a" File ~/.local/lib/python3.9/site-packages/xarray/core/dataset.py:1901, in Dataset.to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf) 1898 encoding = {} 1899 from ..backends.api import to_netcdf -> 1901 return to_netcdf( 1902 self, 1903 path, 1904 mode, 1905 format=format, 1906 group=group, 1907 engine=engine, 1908 encoding=encoding, 1909 unlimited_dims=unlimited_dims, 1910 compute=compute, 1911 invalid_netcdf=invalid_netcdf, 1912 ) File ~/.local/lib/python3.9/site-packages/xarray/backends/api.py:1072, in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf) 1067 # TODO: figure out how to refactor this logic (here and in save_mfdataset) 1068 # to avoid this mess of conditionals 1069 try: 1070 # TODO: allow this work (setting up the file for writing array data) 1071 # to be parallelized with dask -> 1072 dump_to_store( 1073 dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims 1074 ) 1075 if autoclose: 1076 store.close() File ~/.local/lib/python3.9/site-packages/xarray/backends/api.py:1119, in dump_to_store(dataset, store, writer, encoder, encoding, unlimited_dims) 1116 if encoder: 1117 variables, attrs = encoder(variables, attrs) -> 1119 store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims) File ~/.local/lib/python3.9/site-packages/xarray/backends/common.py:265, in AbstractWritableDataStore.store(self, variables, attributes, check_encoding_set, writer, unlimited_dims) 263 self.set_attributes(attributes) 264 self.set_dimensions(variables, unlimited_dims=unlimited_dims) --> 265 self.set_variables( 266 variables, check_encoding_set, writer, unlimited_dims=unlimited_dims 267 ) File ~/.local/lib/python3.9/site-packages/xarray/backends/common.py:303, in AbstractWritableDataStore.set_variables(self, variables, check_encoding_set, writer, unlimited_dims) 301 name = _encode_variable_name(vn) 302 check = vn in check_encoding_set --> 303 target, source = self.prepare_variable( 304 name, v, check, unlimited_dims=unlimited_dims 305 ) 307 writer.add(source, target) File ~/.local/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:488, in NetCDF4DataStore.prepare_variable(self, name, variable, check_encoding, unlimited_dims) 486 nc4_var = self.ds.variables[name] 487 else: --> 488 nc4_var = self.ds.createVariable( 489 varname=name, 490 datatype=datatype, 491 dimensions=variable.dims, 492 zlib=encoding.get("zlib", False), 493 complevel=encoding.get("complevel", 4), 494 shuffle=encoding.get("shuffle", True), 495 fletcher32=encoding.get("fletcher32", False), 496 contiguous=encoding.get("contiguous", False), 497 chunksizes=encoding.get("chunksizes"), 498 endian="native", 499 least_significant_digit=encoding.get("least_significant_digit"), 500 fill_value=fill_value, 501 ) 503 nc4_var.setncatts(attrs) 505 target = NetCDF4ArrayWrapper(name, self) File src/netCDF4/_netCDF4.pyx:2838, in netCDF4._netCDF4.Dataset.createVariable() File src/netCDF4/_netCDF4.pyx:4003, in netCDF4._netCDF4.Variable.__init__() File src/netCDF4/_netCDF4.pyx:1965, in netCDF4._netCDF4._ensure_nc_success() RuntimeError: NetCDF: Filter error: bad id or parameters or duplicate filter ``` </details> **Expected behavior** The data set gets written to the file. **Additional context** arviz version: 0.12.1 xarray version: 2022.3.0 netCDF4 version: 1.6.0 This was on a computer cluster which runs Debian GNU/Linux 10 (buster). The file actually is created but reading it in shows that it's corrupted: ``` In [2]: aa = az.from_netcdf("test.nc") In [3]: aa Out[3]: Inference data with groups: > ```

We will need to look into this. It is somehow related to netcdf 1.6 but I have no idea why all tests which have already run on netcdf 1.6 continue to pass (some of the things they check is idata-netcdf-idata round trip). For now it looks like you will need to downgrade netcdf or figure out what exactly does the centered_eight example have that triggers this error. Note: I have done some quick tests, and out of the netcdf examples provided with `az.load_arviz_data`, the glycan, regression and classification examples do not trigger the error. Both schools examples, radon and rugby do (I did that from memory, not sure if there are more files on which to check) I have run into this error also. It seems to occur when there are data or coordinates that are of an `object` or `str` datatype and when compression is enabled (`encoding["zlib"]` is `True`). I think the "centered_eight" example above should work with `data.to_netcdf("test.nc", compress=False)`. [This issue](https://github.com/Unidata/netcdf4-python/issues/1175) in `netcdf4-python` looks to be related and suggests that compression isn't supported for this sort of data. I wonder if we could add checks for these? Anyone able to take this on?

2022-10-06T07:44:38Z

arviz-devs/arviz

arviz-devs__arviz-2131

0ba14fa017245a1f6cef253fb90858654e13b8f0

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -1,5 +1,6 @@ # pylint: disable=too-many-lines,too-many-public-methods """Data structure for using netcdf groups with xarray.""" +import re import sys import uuid import warnings @@ -9,7 +10,6 @@ from copy import deepcopy from datetime import datetime from html import escape -import re from typing import ( TYPE_CHECKING, Any, @@ -363,13 +363,13 @@ def from_netcdf(filename, group_kwargs=None, regex=False) -> "InferenceData": InferenceData object """ groups = {} + attrs = {} try: with nc.Dataset(filename, mode="r") as data: data_groups = list(data.groups) for group in data_groups: - group_kws = {} if group_kwargs is not None and regex is False: group_kws = group_kwargs.get(group, {}) @@ -382,12 +382,15 @@ def from_netcdf(filename, group_kwargs=None, regex=False) -> "InferenceData": groups[group] = data.load() else: groups[group] = data - res = InferenceData(**groups) - return res - except OSError as e: # pylint: disable=invalid-name - if e.errno == -101: - raise type(e)( - str(e) + + with xr.open_dataset(filename, mode="r") as data: + attrs.update(data.load().attrs) + + return InferenceData(attrs=attrs, **groups) + except OSError as err: + if err.errno == -101: + raise type(err)( + str(err) + ( " while reading a NetCDF file. This is probably an error in HDF5, " "which happens because your OS does not support HDF5 file locking. See " @@ -395,8 +398,8 @@ def from_netcdf(filename, group_kwargs=None, regex=False) -> "InferenceData": "errno-101-netcdf-hdf-error-when-opening-netcdf-file#49317928" " for a possible solution." ) - ) - raise e + ) from err + raise err def to_netcdf( self, filename: str, compress: bool = True, groups: Optional[List[str]] = None @@ -419,6 +422,10 @@ def to_netcdf( Location of netcdf file """ mode = "w" # overwrite first, then append + if self._attrs: + xr.Dataset(attrs=self._attrs).to_netcdf(filename, mode=mode) + mode = "a" + if self._groups_all: # check's whether a group is present or not. if groups is None: groups = self._groups_all @@ -437,7 +444,7 @@ def to_netcdf( data.to_netcdf(filename, mode=mode, group=group, **kwargs) data.close() mode = "a" - else: # creates a netcdf file for an empty InferenceData object. + elif not self._attrs: # creates a netcdf file for an empty InferenceData object. empty_netcdf_file = nc.Dataset(filename, mode="w", format="NETCDF4") empty_netcdf_file.close() return filename @@ -688,6 +695,10 @@ def to_zarr(self, store=None): if not groups: raise TypeError("No valid groups found!") + # order matters here, saving attrs after the groups will erase the groups. + if self.attrs: + xr.Dataset(attrs=self.attrs).to_zarr(store=store, mode="w") + for group in groups: # Create zarr group in store with same group name getattr(self, group).to_zarr(store=store, group=group, mode="w") @@ -738,7 +749,11 @@ def from_zarr(store) -> "InferenceData": for key_group, _ in zarr_handle.groups(): with xr.open_zarr(store=store, group=key_group) as data: groups[key_group] = data.load() if rcParams["data.load"] == "eager" else data - return InferenceData(**groups) + + with xr.open_zarr(store=store) as root: + attrs = root.attrs + + return InferenceData(attrs=attrs, **groups) def __add__(self, other: "InferenceData") -> "InferenceData": """Concatenate two InferenceData objects."""

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -6,6 +6,7 @@ from copy import deepcopy from html import escape from typing import Dict +from tempfile import TemporaryDirectory from urllib.parse import urlunsplit import numpy as np @@ -107,6 +108,26 @@ def test_load_local_arviz_data(): assert inference_data.posterior["theta"].dims == ("chain", "draw", "school") [email protected]("fill_attrs", [True, False]) +def test_local_save(fill_attrs): + inference_data = load_arviz_data("centered_eight") + assert isinstance(inference_data, InferenceData) + + if fill_attrs: + inference_data.attrs["test"] = 1 + with TemporaryDirectory(prefix="arviz_tests_") as tmp_dir: + path = os.path.join(tmp_dir, "test_file.nc") + inference_data.to_netcdf(path) + + inference_data2 = from_netcdf(path) + if fill_attrs: + assert "test" in inference_data2.attrs + assert inference_data2.attrs["test"] == 1 + # pylint: disable=protected-access + assert all(group in inference_data2 for group in inference_data._groups_all) + # pylint: enable=protected-access + + def test_clear_data_home(): resource = REMOTE_DATASETS["test_remote"] assert not os.path.exists(resource.filename) diff --git a/arviz/tests/base_tests/test_data_zarr.py b/arviz/tests/base_tests/test_data_zarr.py --- a/arviz/tests/base_tests/test_data_zarr.py +++ b/arviz/tests/base_tests/test_data_zarr.py @@ -1,7 +1,7 @@ # pylint: disable=redefined-outer-name import os -import shutil from collections.abc import MutableMapping +from tempfile import TemporaryDirectory from typing import Mapping import numpy as np @@ -31,7 +31,7 @@ class Data: return Data - def get_inference_data(self, data, eight_schools_params): + def get_inference_data(self, data, eight_schools_params, fill_attrs): return from_dict( posterior=data.obj, posterior_predictive=data.obj, @@ -42,13 +42,15 @@ def get_inference_data(self, data, eight_schools_params): observed_data=eight_schools_params, coords={"school": np.arange(8)}, dims={"theta": ["school"], "eta": ["school"]}, + attrs={"test": 1} if fill_attrs else None, ) @pytest.mark.parametrize("store", [0, 1, 2]) - def test_io_method(self, data, eight_schools_params, store): + @pytest.mark.parametrize("fill_attrs", [True, False]) + def test_io_method(self, data, eight_schools_params, store, fill_attrs): # create InferenceData and check it has been properly created inference_data = self.get_inference_data( # pylint: disable=W0612 - data, eight_schools_params + data, eight_schools_params, fill_attrs ) test_dict = { "posterior": ["eta", "theta", "mu", "tau"], @@ -62,39 +64,42 @@ def test_io_method(self, data, eight_schools_params, store): fails = check_multiple_attrs(test_dict, inference_data) assert not fails + if fill_attrs: + assert inference_data.attrs["test"] == 1 + else: + assert "test" not in inference_data.attrs + # check filename does not exist and use to_zarr method - here = os.path.dirname(os.path.abspath(__file__)) - data_directory = os.path.join(here, "..", "saved_models") - filepath = os.path.join(data_directory, "zarr") - assert not os.path.exists(filepath) + with TemporaryDirectory(prefix="arviz_tests_") as tmp_dir: + filepath = os.path.join(tmp_dir, "zarr") - # InferenceData method - if store == 0: - # Tempdir - store = inference_data.to_zarr(store=None) - assert isinstance(store, MutableMapping) - elif store == 1: - inference_data.to_zarr(store=filepath) - # assert file has been saved correctly - assert os.path.exists(filepath) - assert os.path.getsize(filepath) > 0 - elif store == 2: - store = zarr.storage.DirectoryStore(filepath) - inference_data.to_zarr(store=store) - # assert file has been saved correctly - assert os.path.exists(filepath) - assert os.path.getsize(filepath) > 0 + # InferenceData method + if store == 0: + # Tempdir + store = inference_data.to_zarr(store=None) + assert isinstance(store, MutableMapping) + elif store == 1: + inference_data.to_zarr(store=filepath) + # assert file has been saved correctly + assert os.path.exists(filepath) + assert os.path.getsize(filepath) > 0 + elif store == 2: + store = zarr.storage.DirectoryStore(filepath) + inference_data.to_zarr(store=store) + # assert file has been saved correctly + assert os.path.exists(filepath) + assert os.path.getsize(filepath) > 0 - if isinstance(store, MutableMapping): - inference_data2 = InferenceData.from_zarr(store) - else: - inference_data2 = InferenceData.from_zarr(filepath) + if isinstance(store, MutableMapping): + inference_data2 = InferenceData.from_zarr(store) + else: + inference_data2 = InferenceData.from_zarr(filepath) - # Everything in dict still available in inference_data2 ? - fails = check_multiple_attrs(test_dict, inference_data2) - assert not fails + # Everything in dict still available in inference_data2 ? + fails = check_multiple_attrs(test_dict, inference_data2) + assert not fails - # Remove created folder structure - if os.path.exists(filepath): - shutil.rmtree(filepath) - assert not os.path.exists(filepath) + if fill_attrs: + assert inference_data2.attrs["test"] == 1 + else: + assert "test" not in inference_data2.attrs

Bug: to_netcdf does not save attrs When I store meta data in `attrs` and call `to_netcdf()` and then load it subsequently, the meta-data is lost (`attrs` is empty). Is this expected? Is there a format to save this in so that they stay?

They should stay. Which level is the attrs located? https://github.com/pymc-devs/pymc-experimental/pull/64/files#diff-7b0e92e79315fd2cb1f9592dc726f29974523c3270907f0f135caf029dcec061R212 is where we're storing and in the `save()` method we're calling `to_netcdf()`. I think it is a bug. idata.attrs is never accessed. I wonder do we need to save the attrs as an empty Dataset with attrs defined. > I think it is a bug. idata.attrs is never accessed. How do you mean, never accessed? In to_netcdf function we don't save it, we only touch groups, so it is a bug in that save function.

2022-10-06T11:47:16Z

arviz-devs/arviz

arviz-devs__arviz-2151

d030d446060ab8584868b22bef08dbe9502d9452

diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -174,7 +174,9 @@ def plot_trace( divergences = "top" if rug else "bottom" if divergences: try: - divergence_data = convert_to_dataset(data, group="sample_stats").diverging + divergence_data = convert_to_dataset(data, group="sample_stats").diverging.transpose( + "chain", "draw" + ) except (ValueError, AttributeError): # No sample_stats, or no `.diverging` divergences = None

diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -294,6 +294,12 @@ def test_plot_trace_invalid_varname_warning(models, kwargs): assert axes.shape +def test_plot_trace_diverging_correctly_transposed(): + idata = load_arviz_data("centered_eight") + idata.sample_stats["diverging"] = idata.sample_stats.diverging.T + plot_trace(idata, divergences="bottom") + + @pytest.mark.parametrize( "bad_kwargs", [{"var_names": ["mu", "tau"], "lines": [("mu", {}, ["hey"])]}] )

plot_trace assumes specific dimensions ordering for divergences **Describe the bug** If an `InferenceData` contains a `diverging` variable in `sample_stats` that uses the dimension ordering `(draw, chain)` instead of `(chain, draw)`, then `plot_trace` fails. **To Reproduce** ```python >>> import arviz as az >>> import numpy as np >>> idata = az.from_dict( ... dict(x=np.random.normal(size=(4, 100, 2))), ... sample_stats=dict(diverging=np.random.randint(0, 1, size=(4, 100), dtype=bool)) ... ) >>> az.plot_trace(idata) # fine array([[<AxesSubplot:title={'center':'x'}>, <AxesSubplot:title={'center':'x'}>]], dtype=object) >>> idata.posterior['x'] = idata.posterior.x.transpose() >>> idata.sample_stats['diverging'] = idata.sample_stats.diverging.transpose() >>> az.plot_trace(idata) # error Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/sethaxen/projects/arviz/arviz/plots/traceplot.py", line 263, in plot_trace axes = plot(**trace_plot_args) File "/home/sethaxen/projects/arviz/arviz/plots/backends/matplotlib/traceplot.py", line 347, in plot_trace div_draws = data.draw.values[chain_divs] IndexError: boolean index did not match indexed array along dimension 0; dimension is 100 but corresponding boolean dimension is 4 >>> idata = az.InferenceData(posterior=idata.posterior) >>> az.plot_trace(idata) # fine without diverging array([[<AxesSubplot:title={'center':'x'}>, <AxesSubplot:title={'center':'x'}>]], dtype=object) ``` **Expected behavior** I expected all calls to `plot_trace` to work above. **Additional context** These checks were performed on main.

2022-11-01T14:45:07Z

arviz-devs/arviz

arviz-devs__arviz-2277

eb88f3ac77c7f752d3b5888823dca5b3234b6c13

diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -1,5 +1,6 @@ # pylint: disable=too-many-lines,too-many-public-methods """Data structure for using netcdf groups with xarray.""" +import os import re import sys import uuid @@ -23,14 +24,13 @@ Union, overload, ) -import os import numpy as np import xarray as xr from packaging import version from ..rcparams import rcParams -from ..utils import HtmlTemplate, _subset_list, either_dict_or_kwargs +from ..utils import HtmlTemplate, _subset_list, _var_names, either_dict_or_kwargs from .base import _extend_xr_method, _make_json_serializable, dict_to_dataset if sys.version_info[:2] >= (3, 9): @@ -620,6 +620,8 @@ def to_dataframe( self, groups=None, filter_groups=None, + var_names=None, + filter_vars=None, include_coords=True, include_index=True, index_origin=None, @@ -635,6 +637,7 @@ def to_dataframe( skipped implicitly. Raises TypeError if no valid groups are found. + Raises ValueError if no data are selected. Parameters ---------- @@ -646,6 +649,15 @@ def to_dataframe( If "like", interpret groups as substrings of the real group or metagroup names. If "regex", interpret groups as regular expressions on the real group or metagroup names. A la `pandas.filter`. + var_names : str or list of str, optional + Variables to be extracted. Prefix the variables by `~` when you want to exclude them. + filter_vars: {None, "like", "regex"}, optional + If `None` (default), interpret var_names as the real variables names. If "like", + interpret var_names as substrings of the real variables names. If "regex", + interpret var_names as regular expressions on the real variables names. A la + `pandas.filter`. + Like with plotting, sometimes it's easier to subset saying what to exclude + instead of what to include include_coords: bool Add coordinate values to column name (tuple). include_index: bool @@ -677,6 +689,11 @@ def to_dataframe( dfs = {} for group in group_names: dataset = self[group] + group_var_names = _var_names(var_names, dataset, filter_vars, "ignore") + if (group_var_names is not None) and not group_var_names: + continue + if group_var_names is not None: + dataset = dataset[[var_name for var_name in group_var_names if var_name in dataset]] df = None coords_to_idx = { name: dict(map(reversed, enumerate(dataset.coords[name].values, index_origin))) @@ -712,8 +729,11 @@ def to_dataframe( df = dataframe continue df = df.join(dataframe, how="outer") - df = df.reset_index() - dfs[group] = df + if df is not None: + df = df.reset_index() + dfs[group] = df + if not dfs: + raise ValueError("No data selected for the dataframe.") if len(dfs) > 1: for group, df in dfs.items(): df.columns = [ diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -21,7 +21,7 @@ def _check_tilde_start(x): return bool(isinstance(x, str) and x.startswith("~")) -def _var_names(var_names, data, filter_vars=None): +def _var_names(var_names, data, filter_vars=None, errors="raise"): """Handle var_names input across arviz. Parameters @@ -34,6 +34,8 @@ def _var_names(var_names, data, filter_vars=None): interpret var_names as substrings of the real variables names. If "regex", interpret var_names as regular expressions on the real variables names. A la `pandas.filter`. + errors: {"raise", "ignore"}, optional, default="raise" + Select either to raise or ignore the invalid names. Returns ------- @@ -44,6 +46,9 @@ def _var_names(var_names, data, filter_vars=None): f"'filter_vars' can only be None, 'like', or 'regex', got: '{filter_vars}'" ) + if errors not in {"raise", "ignore"}: + raise ValueError(f"'errors' can only be 'raise', or 'ignore', got: '{errors}'") + if var_names is not None: if isinstance(data, (list, tuple)): all_vars = [] @@ -66,14 +71,16 @@ def _var_names(var_names, data, filter_vars=None): ) try: - var_names = _subset_list(var_names, all_vars, filter_items=filter_vars, warn=False) + var_names = _subset_list( + var_names, all_vars, filter_items=filter_vars, warn=False, errors=errors + ) except KeyError as err: msg = " ".join(("var names:", f"{err}", "in dataset")) raise KeyError(msg) from err return var_names -def _subset_list(subset, whole_list, filter_items=None, warn=True): +def _subset_list(subset, whole_list, filter_items=None, warn=True, errors="raise"): """Handle list subsetting (var_names, groups...) across arviz. Parameters @@ -87,6 +94,8 @@ def _subset_list(subset, whole_list, filter_items=None, warn=True): names. If "like", interpret `subset` as substrings of the elements in `whole_list`. If "regex", interpret `subset` as regular expressions to match elements in `whole_list`. A la `pandas.filter`. + errors: {"raise", "ignore"}, optional, default="raise" + Select either to raise or ignore the invalid names. Returns ------- @@ -142,7 +151,7 @@ def _subset_list(subset, whole_list, filter_items=None, warn=True): subset = [item for item in whole_list for name in subset if re.search(name, item)] existing_items = np.isin(subset, whole_list) - if not np.all(existing_items): + if not np.all(existing_items) and (errors == "raise"): raise KeyError(f"{np.array(subset)[~existing_items]} are not present") return subset

diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -763,6 +763,69 @@ def test_to_dataframe(self, kwargs): ) assert all(item in test_data.columns for item in ("chain", "draw")) + @pytest.mark.parametrize( + "kwargs", + ( + { + "var_names": ["parameter_1", "parameter_2", "variable_1", "variable_2"], + "filter_vars": None, + "var_results": [ + ("posterior", "parameter_1"), + ("posterior", "parameter_2"), + ("prior", "parameter_1"), + ("prior", "parameter_2"), + ("posterior", "variable_1"), + ("posterior", "variable_2"), + ], + }, + { + "var_names": "parameter", + "filter_vars": "like", + "groups": "posterior", + "var_results": ["parameter_1", "parameter_2"], + }, + { + "var_names": "~parameter", + "filter_vars": "like", + "groups": "posterior", + "var_results": ["variable_1", "variable_2", "custom_name"], + }, + { + "var_names": [".+_2$", "custom_name"], + "filter_vars": "regex", + "groups": "posterior", + "var_results": ["parameter_2", "variable_2", "custom_name"], + }, + { + "var_names": ["lp"], + "filter_vars": "regex", + "groups": "sample_stats", + "var_results": ["lp"], + }, + ), + ) + def test_to_dataframe_selection(self, kwargs): + results = kwargs.pop("var_results") + idata = from_dict( + posterior={ + "parameter_1": np.random.randn(4, 100), + "parameter_2": np.random.randn(4, 100), + "variable_1": np.random.randn(4, 100), + "variable_2": np.random.randn(4, 100), + "custom_name": np.random.randn(4, 100), + }, + prior={ + "parameter_1": np.random.randn(4, 100), + "parameter_2": np.random.randn(4, 100), + }, + sample_stats={ + "lp": np.random.randn(4, 100), + }, + ) + test_data = idata.to_dataframe(**kwargs) + assert not test_data.empty + assert set(test_data.columns).symmetric_difference(results) == set(["chain", "draw"]) + def test_to_dataframe_bad(self): idata = from_dict( posterior={"a": np.random.randn(4, 100, 3, 4, 5), "b": np.random.randn(4, 100)}, @@ -781,6 +844,9 @@ def test_to_dataframe_bad(self): with pytest.raises(KeyError): idata.to_dataframe(groups=["invalid_group"]) + with pytest.raises(ValueError): + idata.to_dataframe(var_names=["c"]) + @pytest.mark.parametrize("use", (None, "args", "kwargs")) def test_map(self, use): idata = load_arviz_data("centered_eight")

Add vars / vars_name for to_dataframe Save some memory and make the call faster.

Can you please locate the file/area where you are raising the suggestion?

2023-09-09T08:56:21Z

arviz-devs/arviz

arviz-devs__arviz-2300

27b8a4908d0c9b7c32fcae6e645035be708b08bc

diff --git a/arviz/__init__.py b/arviz/__init__.py --- a/arviz/__init__.py +++ b/arviz/__init__.py @@ -1,6 +1,6 @@ # pylint: disable=wildcard-import,invalid-name,wrong-import-position """ArviZ is a library for exploratory analysis of Bayesian models.""" -__version__ = "0.17.0.dev0" +__version__ = "0.17.0" import logging import os diff --git a/arviz/plots/bpvplot.py b/arviz/plots/bpvplot.py --- a/arviz/plots/bpvplot.py +++ b/arviz/plots/bpvplot.py @@ -165,7 +165,7 @@ def plot_bpv( Notes ----- Discrete data is smoothed before computing either p-values or u-values using the - function :func:`smooth_data` + function :func:`~arviz.smooth_data` Examples -------- diff --git a/arviz/stats/__init__.py b/arviz/stats/__init__.py --- a/arviz/stats/__init__.py +++ b/arviz/stats/__init__.py @@ -28,6 +28,7 @@ "autocorr", "autocov", "make_ufunc", + "smooth_data", "wrap_xarray_ufunc", "reloo", "_calculate_ics", diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -880,17 +880,18 @@ def psislw(log_weights, reff=1.0): Parameters ---------- - log_weights: array + log_weights : DataArray or (..., N) array-like Array of size (n_observations, n_samples) - reff: float + reff : float, default 1 relative MCMC efficiency, ``ess / n`` Returns ------- - lw_out: array - Smoothed log weights - kss: array - Pareto tail indices + lw_out : DataArray or (..., N) ndarray + Smoothed, truncated and normalized log weights. + kss : DataArray or (...) ndarray + Estimates of the shape parameter *k* of the generalized Pareto + distribution. References ---------- diff --git a/arviz/stats/stats_utils.py b/arviz/stats/stats_utils.py --- a/arviz/stats/stats_utils.py +++ b/arviz/stats/stats_utils.py @@ -16,7 +16,7 @@ from .density_utils import histogram as _histogram -__all__ = ["autocorr", "autocov", "ELPDData", "make_ufunc", "wrap_xarray_ufunc"] +__all__ = ["autocorr", "autocov", "ELPDData", "make_ufunc", "smooth_data", "wrap_xarray_ufunc"] def autocov(ary, axis=-1): @@ -564,7 +564,25 @@ def _circular_standard_deviation(samples, high=2 * np.pi, low=0, skipna=False, a def smooth_data(obs_vals, pp_vals): - """Smooth data, helper function for discrete data in plot_pbv, loo_pit and plot_loo_pit.""" + """Smooth data using a cubic spline. + + Helper function for discrete data in plot_pbv, loo_pit and plot_loo_pit. + + Parameters + ---------- + obs_vals : (N) array-like + Observed data + pp_vals : (S, N) array-like + Posterior predictive samples. ``N`` is the number of observations, + and ``S`` is the number of samples (generally n_chains*n_draws). + + Returns + ------- + obs_vals : (N) ndarray + Smoothed observed data + pp_vals : (S, N) ndarray + Smoothed posterior predictive samples + """ x = np.linspace(0, 1, len(obs_vals)) csi = CubicSpline(x, obs_vals) obs_vals = csi(np.linspace(0.01, 0.99, len(obs_vals))) diff --git a/arviz/utils.py b/arviz/utils.py --- a/arviz/utils.py +++ b/arviz/utils.py @@ -668,7 +668,8 @@ def _load_static_files(): Clone from xarray.core.formatted_html_template. """ return [ - importlib.resources.files("arviz").joinpath(fname).read_text() for fname in STATIC_FILES + importlib.resources.files("arviz").joinpath(fname).read_text(encoding="utf-8") + for fname in STATIC_FILES ] diff --git a/doc/source/conf.py b/doc/source/conf.py --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -99,15 +99,16 @@ autodoc_typehints = "none" numpydoc_xref_param_type = True numpydoc_xref_ignore = { - "of", "or", "optional", "default", "1D", "2D", "3D", "n-dimensional", "M", "N", "K", + "of", "or", "optional", "default", "1D", "2D", "3D", "n-dimensional", "K", "M", "N", "S", } numpydoc_xref_aliases = { + "DataArray": ":class:`~xarray.DataArray`", + "Dataset": ":class:`~xarray.Dataset`", "Labeller": ":ref:`Labeller <labeller_api>`", "ndarray": ":class:`~numpy.ndarray`", "InferenceData": ":class:`~arviz.InferenceData`", "matplotlib_axes": ":class:`matplotlib Axes <matplotlib.axes.Axes>`", "bokeh_figure": ":class:`Bokeh Figure <bokeh.plotting.figure>`", - } # The base toctree document.

diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -1921,7 +1921,7 @@ def test_plot_ts(kwargs): dims={"y": ["obs_dim"], "z": ["pred_dim"]}, ) - ax = plot_ts(idata=idata, y="y", show=True, **kwargs) + ax = plot_ts(idata=idata, y="y", **kwargs) assert np.all(ax)

smooth_data interpolates observations and predictions differently **Describe the bug** `arviz.stats.stats_utils.smooth_data(y, y_hat)` smooths `y` along the first dimension (data dimension) and `y_hat` along the second dimension (sample dimension). **To Reproduce** Here's an example where we construct `y_hat` as identical to `y` in every draw. While `y` is successfully interpolated between data points, `y_hat` is unchanged, since interpolation between identical draws does nothing. ```julia In [1]: import arviz as az In [2]: import numpy as np In [3]: import scipy.stats In [4]: y = scipy.stats.binom.rvs(20, 0.5, size=(10)) In [5]: y_hat = np.tile(y.T, (1000, 1)).T In [6]: y_interp, y_hat_interp = az.stats.stats_utils.smooth_data(y, y_hat) In [7]: y Out[7]: array([11, 6, 10, 6, 11, 8, 8, 12, 8, 9]) In [8]: y_interp Out[8]: array([ 9.42237617, 6.32506153, 9.91551262, 6.02439063, 11.02236472, 8.03842965, 7.88687051, 11.96915287, 8.35219182, 8.17764022]) In [9]: y_hat Out[9]: array([[11, 11, 11, ..., 11, 11, 11], [ 6, 6, 6, ..., 6, 6, 6], [10, 10, 10, ..., 10, 10, 10], ..., [12, 12, 12, ..., 12, 12, 12], [ 8, 8, 8, ..., 8, 8, 8], [ 9, 9, 9, ..., 9, 9, 9]]) In [10]: y_hat_interp Out[10]: array([[11., 11., 11., ..., 11., 11., 11.], [ 6., 6., 6., ..., 6., 6., 6.], [10., 10., 10., ..., 10., 10., 10.], ..., [12., 12., 12., ..., 12., 12., 12.], [ 8., 8., 8., ..., 8., 8., 8.], [ 9., 9., 9., ..., 9., 9., 9.]]) ``` **Expected behavior** I'd expect `y_interp` and `y_hat_interp[:, 0]` to be identical. **Additional context** arviz v0.16.0.dev0

@aloctavodia IIRC you implemented this smoothing. Have I correctly identified a bug here, or am I misusing the function? There is this reshape: https://github.com/arviz-devs/arviz/blob/main/arviz/plots/backends/matplotlib/bpvplot.py#L87 which I think makes the first dimension the sample one I am updating the docstring to include the shape info

2023-12-21T19:18:30Z

elastic/elasticsearch-py

elastic__elasticsearch-py-569

fe897ebe0d2167e91ea19fa9a81f448a861d58d1

diff --git a/elasticsearch/client/__init__.py b/elasticsearch/client/__init__.py --- a/elasticsearch/client/__init__.py +++ b/elasticsearch/client/__init__.py @@ -3,7 +3,7 @@ from ..transport import Transport from ..exceptions import TransportError -from ..compat import string_types, urlparse +from ..compat import string_types, urlparse, unquote from .indices import IndicesClient from .ingest import IngestClient from .cluster import ClusterClient @@ -49,7 +49,8 @@ def _normalize_hosts(hosts): h['scheme'] = parsed_url.scheme if parsed_url.username or parsed_url.password: - h['http_auth'] = '%s:%s' % (parsed_url.username, parsed_url.password) + h['http_auth'] = '%s:%s' % (unquote(parsed_url.username), + unquote(parsed_url.password)) if parsed_url.path and parsed_url.path != '/': h['url_prefix'] = parsed_url.path diff --git a/elasticsearch/compat.py b/elasticsearch/compat.py --- a/elasticsearch/compat.py +++ b/elasticsearch/compat.py @@ -4,10 +4,10 @@ if PY2: string_types = basestring, - from urllib import quote_plus, urlencode + from urllib import quote_plus, urlencode, unquote from urlparse import urlparse from itertools import imap as map else: string_types = str, bytes - from urllib.parse import quote_plus, urlencode, urlparse + from urllib.parse import quote_plus, urlencode, urlparse, unquote map = map

diff --git a/test_elasticsearch/test_client/__init__.py b/test_elasticsearch/test_client/__init__.py --- a/test_elasticsearch/test_client/__init__.py +++ b/test_elasticsearch/test_client/__init__.py @@ -13,8 +13,8 @@ def test_strings_are_used_as_hostnames(self): def test_strings_are_parsed_for_port_and_user(self): self.assertEquals( - [{"host": "elastic.co", "port": 42}, {"host": "elastic.co", "http_auth": "user:secret"}], - _normalize_hosts(["elastic.co:42", "user:[email protected]"]) + [{"host": "elastic.co", "port": 42}, {"host": "elastic.co", "http_auth": "user:secre]"}], + _normalize_hosts(["elastic.co:42", "user:secre%[email protected]"]) ) def test_strings_are_parsed_for_scheme(self):

user:password in hosts are not url decoded Hello, If I have a character like "]" in my username or password I must urlencode it or `urlparse` will interpret the host as IPv6. Unfortunately, `urlparse` does not urldecode fragments : ``` The components are not broken up in smaller parts (for example, the network location is a single string), and % escapes are not expanded. ``` (https://docs.python.org/3/library/urllib.parse.html) `_normalize_hosts` should urldecode explicitely, which I will propose in a PR.

2017-04-13T11:28:46Z

elastic/elasticsearch-py

elastic__elasticsearch-py-618

0397527d12bcb43274ce9054111c5f7f673a7ad6

diff --git a/elasticsearch/client/__init__.py b/elasticsearch/client/__init__.py --- a/elasticsearch/client/__init__.py +++ b/elasticsearch/client/__init__.py @@ -1127,7 +1127,8 @@ def bulk(self, body, index=None, doc_type=None, params=None): if body in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'body'.") return self.transport.perform_request('POST', _make_path(index, - doc_type, '_bulk'), params=params, body=self._bulk_body(body)) + doc_type, '_bulk'), params=params, body=self._bulk_body(body), + headers={'content-type': 'application/x-ndjson'}) @query_params('max_concurrent_searches', 'pre_filter_shard_size', 'search_type', 'typed_keys') @@ -1159,7 +1160,8 @@ def msearch(self, body, index=None, doc_type=None, params=None): if body in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'body'.") return self.transport.perform_request('GET', _make_path(index, - doc_type, '_msearch'), params=params, body=self._bulk_body(body)) + doc_type, '_msearch'), params=params, body=self._bulk_body(body), + headers={'content-type': 'application/x-ndjson'}) @query_params('field_statistics', 'fields', 'offsets', 'parent', 'payloads', 'positions', 'preference', 'realtime', 'routing', 'term_statistics', @@ -1363,7 +1365,8 @@ def msearch_template(self, body, index=None, doc_type=None, params=None): if body in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'body'.") return self.transport.perform_request('GET', _make_path(index, doc_type, - '_msearch', 'template'), params=params, body=self._bulk_body(body)) + '_msearch', 'template'), params=params, body=self._bulk_body(body), + headers={'content-type': 'application/x-ndjson'}) @query_params('allow_no_indices', 'expand_wildcards', 'fields', 'ignore_unavailable') @@ -1387,3 +1390,4 @@ def field_caps(self, index=None, body=None, params=None): """ return self.transport.perform_request('GET', _make_path(index, '_field_caps'), params=params, body=body) + diff --git a/elasticsearch/connection/http_requests.py b/elasticsearch/connection/http_requests.py --- a/elasticsearch/connection/http_requests.py +++ b/elasticsearch/connection/http_requests.py @@ -61,13 +61,13 @@ def __init__(self, host='localhost', port=9200, http_auth=None, warnings.warn( 'Connecting to %s using SSL with verify_certs=False is insecure.' % self.base_url) - def perform_request(self, method, url, params=None, body=None, timeout=None, ignore=()): + def perform_request(self, method, url, params=None, body=None, timeout=None, ignore=(), headers=None): url = self.base_url + url if params: url = '%s?%s' % (url, urlencode(params or {})) start = time.time() - request = requests.Request(method=method, url=url, data=body) + request = requests.Request(method=method, headers=headers, url=url, data=body) prepared_request = self.session.prepare_request(request) settings = self.session.merge_environment_settings(prepared_request.url, {}, None, None, None) send_kwargs = {'timeout': timeout or self.timeout} diff --git a/elasticsearch/connection/http_urllib3.py b/elasticsearch/connection/http_urllib3.py --- a/elasticsearch/connection/http_urllib3.py +++ b/elasticsearch/connection/http_urllib3.py @@ -91,7 +91,7 @@ def __init__(self, host='localhost', port=9200, http_auth=None, self.pool = pool_class(host, port=port, timeout=self.timeout, maxsize=maxsize, **kw) - def perform_request(self, method, url, params=None, body=None, timeout=None, ignore=()): + def perform_request(self, method, url, params=None, body=None, timeout=None, ignore=(), headers=None): url = self.url_prefix + url if params: url = '%s?%s' % (url, urlencode(params)) @@ -111,6 +111,9 @@ def perform_request(self, method, url, params=None, body=None, timeout=None, ign if not isinstance(method, str): method = method.encode('utf-8') + if headers: + request_headers = dict(self.headers) + request_headers.update(headers or {}) response = self.pool.urlopen(method, url, body, retries=False, headers=self.headers, **kw) duration = time.time() - start raw_data = response.data.decode('utf-8') diff --git a/elasticsearch/transport.py b/elasticsearch/transport.py --- a/elasticsearch/transport.py +++ b/elasticsearch/transport.py @@ -255,7 +255,7 @@ def mark_dead(self, connection): if self.sniff_on_connection_fail: self.sniff_hosts() - def perform_request(self, method, url, params=None, body=None): + def perform_request(self, method, url, headers=None, params=None, body=None): """ Perform the actual request. Retrieve a connection from the connection pool, pass all the information to it's perform_request method and @@ -269,6 +269,8 @@ def perform_request(self, method, url, params=None, body=None): :arg method: HTTP method to use :arg url: absolute url (without host) to target + :arg headers: dictionary of headers, will be handed over to the + underlying :class:`~elasticsearch.Connection` class :arg params: dictionary of query parameters, will be handed over to the underlying :class:`~elasticsearch.Connection` class for serialization :arg body: body of the request, will be serializes using serializer and @@ -309,7 +311,7 @@ def perform_request(self, method, url, params=None, body=None): connection = self.get_connection() try: - status, headers, data = connection.perform_request(method, url, params, body, ignore=ignore, timeout=timeout) + status, headers, data = connection.perform_request(method, url, params, body, headers=headers, ignore=ignore, timeout=timeout) except TransportError as e: if method == 'HEAD' and e.status_code == 404:

diff --git a/test_elasticsearch/test_connection.py b/test_elasticsearch/test_connection.py --- a/test_elasticsearch/test_connection.py +++ b/test_elasticsearch/test_connection.py @@ -104,6 +104,13 @@ def test_uses_https_if_verify_certs_is_off(self): self.assertEquals('GET', request.method) self.assertEquals(None, request.body) + def test_merge_headers(self): + con = self._get_mock_connection(connection_params={'headers': {'h1': 'v1', 'h2': 'v2'}}) + req = self._get_request(con, 'GET', '/', headers={'h2': 'v2p', 'h3': 'v3'}) + self.assertEquals(req.headers['h1'], 'v1') + self.assertEquals(req.headers['h2'], 'v2p') + self.assertEquals(req.headers['h3'], 'v3') + def test_http_auth(self): con = RequestsHttpConnection(http_auth='username:secret') self.assertEquals(('username', 'secret'), con.session.auth) diff --git a/test_elasticsearch/test_transport.py b/test_elasticsearch/test_transport.py --- a/test_elasticsearch/test_transport.py +++ b/test_elasticsearch/test_transport.py @@ -74,7 +74,7 @@ def test_request_timeout_extracted_from_params_and_passed(self): t.perform_request('GET', '/', params={'request_timeout': 42}) self.assertEquals(1, len(t.get_connection().calls)) self.assertEquals(('GET', '/', {}, None), t.get_connection().calls[0][0]) - self.assertEquals({'timeout': 42, 'ignore': ()}, t.get_connection().calls[0][1]) + self.assertEquals({'timeout': 42, 'ignore': (), 'headers': None}, t.get_connection().calls[0][1]) def test_send_get_body_as_source(self): t = Transport([{}], send_get_body_as='source', connection_class=DummyConnection)

Set `application/x-ndjson` content type on bulk requests As of elastic 5.x, requests to `/_bulk` should set `Content-Type` to `application/x-ndjson`. If not, elastic logs a warning. It looks like this library defaults to `application/json`. To fix, I'm thinking we should accept an optional dict of headers at `https://github.com/elastic/elasticsearch-py/blob/master/elasticsearch/connection/http_urllib3.py#L94`. If that sounds reasonable, I'd be happy to submit a patch. cc @cnelson @wjwoodson

A patch would be most welcome. I think the proper way to implement this would be very similar to how we handle `ignore` and `request_timeout` parameters in `Transport`. Let me know if there is anything I can help you with.

2017-07-13T02:52:29Z

elastic/elasticsearch-py

elastic__elasticsearch-py-628

4e6f63571105545914c07fa4846f996d6049f44b

diff --git a/elasticsearch/client/utils.py b/elasticsearch/client/utils.py --- a/elasticsearch/client/utils.py +++ b/elasticsearch/client/utils.py @@ -26,13 +26,13 @@ def _escape(value): elif isinstance(value, bool): value = str(value).lower() + # don't decode bytestrings + elif isinstance(value, bytes): + return value + # encode strings to utf-8 if isinstance(value, string_types): - try: - return value.encode('utf-8') - except UnicodeDecodeError: - # Python 2 and str, no need to re-encode - pass + return value.encode('utf-8') return str(value)

diff --git a/test_elasticsearch/test_client/test_utils.py b/test_elasticsearch/test_client/test_utils.py --- a/test_elasticsearch/test_client/test_utils.py +++ b/test_elasticsearch/test_client/test_utils.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- from __future__ import unicode_literals -from elasticsearch.client.utils import _make_path +from elasticsearch.client.utils import _make_path, _escape from elasticsearch.compat import PY2 from ..test_cases import TestCase, SkipTest @@ -17,3 +17,24 @@ def test_handles_utf_encoded_string(self): id = "中文".encode('utf-8') self.assertEquals('/some-index/type/%E4%B8%AD%E6%96%87', _make_path('some-index', 'type', id)) + +class TestEscape(TestCase): + def test_handles_ascii(self): + string = "abc123" + self.assertEquals( + b'abc123', + _escape(string) + ) + def test_handles_unicode(self): + string = "中文" + self.assertEquals( + b'\xe4\xb8\xad\xe6\x96\x87', + _escape(string) + ) + + def test_handles_bytestring(self): + string = b'celery-task-meta-c4f1201f-eb7b-41d5-9318-a75a8cfbdaa0' + self.assertEquals( + string, + _escape(string) + )

'bytes' object has no attribute 'encode' File "/root/.local/share/virtualenvs/celery-jR7fJ1bi/lib/python3.5/site-packages/elasticsearch/client/utils.py", line 32, in _escape return value.encode('utf-8') AttributeError: 'bytes' object has no attribute 'encode' It appears that when _escape receives value, which in my case is b'celery-task-meta-c4f1201f-eb7b-41d5-9318-a75a8cfbdaa0', it gets checked against (<class 'str'>, <class 'bytes'>), and therefore passes the test but <class 'bytes'> does not have the **encode** method. try does not catch as error is AttributeError: 'bytes' object has no attribute 'encode' def _escape(value): """ Escape a single value of a URL string or a query parameter. If it is a list or tuple, turn it into a comma-separated string first. """ # make sequences into comma-separated stings if isinstance(value, (list, tuple)): value = ','.join(value) # dates and datetimes into isoformat elif isinstance(value, (date, datetime)): value = value.isoformat() # make bools into true/false strings elif isinstance(value, bool): value = str(value).lower() # encode strings to utf-8 if isinstance(value, string_types): try: return value.encode('utf-8') except UnicodeDecodeError: # Python 2 and str, no need to re-encode pass return str(value)

2017-08-07T21:39:19Z

elastic/elasticsearch-py

elastic__elasticsearch-py-870

0867a56ce330652f295c69199b76aa38f82b9076

diff --git a/elasticsearch/helpers/__init__.py b/elasticsearch/helpers/__init__.py --- a/elasticsearch/helpers/__init__.py +++ b/elasticsearch/helpers/__init__.py @@ -58,11 +58,12 @@ def _chunk_actions(actions, chunk_size, max_chunk_bytes, serializer): for action, data in actions: raw_data, raw_action = data, action action = serializer.dumps(action) - cur_size = len(action) + 1 + # +1 to account for the trailing new line character + cur_size = len(action.encode('utf-8')) + 1 if data is not None: data = serializer.dumps(data) - cur_size += len(data) + 1 + cur_size += len(data.encode('utf-8')) + 1 # full chunk, send it and start a new one if bulk_actions and (size + cur_size > max_chunk_bytes or action_count == chunk_size):

diff --git a/test_elasticsearch/test_helpers.py b/test_elasticsearch/test_helpers.py --- a/test_elasticsearch/test_helpers.py +++ b/test_elasticsearch/test_helpers.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- import mock import time import threading @@ -29,7 +30,7 @@ def test_chunk_sent_from_different_threads(self, _process_bulk_chunk): class TestChunkActions(TestCase): def setUp(self): super(TestChunkActions, self).setUp() - self.actions = [({'index': {}}, {'some': 'data', 'i': i}) for i in range(100)] + self.actions = [({'index': {}}, {'some': u'datá', 'i': i}) for i in range(100)] def test_chunks_are_chopped_by_byte_size(self): self.assertEquals(100, len(list(helpers._chunk_actions(self.actions, 100000, 1, JSONSerializer())))) @@ -37,6 +38,15 @@ def test_chunks_are_chopped_by_byte_size(self): def test_chunks_are_chopped_by_chunk_size(self): self.assertEquals(10, len(list(helpers._chunk_actions(self.actions, 10, 99999999, JSONSerializer())))) + def test_chunks_are_chopped_by_byte_size_properly(self): + max_byte_size = 170 + chunks = list(helpers._chunk_actions(self.actions, 100000, max_byte_size, JSONSerializer())) + self.assertEquals(25, len(chunks)) + for chunk_data, chunk_actions in chunks: + chunk = u''.join(chunk_actions) + chunk = chunk if isinstance(chunk, str) else chunk.encode('utf-8') + self.assertLessEqual(len(chunk), max_byte_size) + class TestExpandActions(TestCase): def test_string_actions_are_marked_as_simple_inserts(self): self.assertEquals(('{"index":{}}', "whatever"), helpers.expand_action('whatever'))

helpers._chunk_actions produces chunks bigger than max_chunk_bytes Hello! Thanks for the library. We started to get ocasional errors from Elasticsearch ``` org.jboss.netty.handler.codec.frame.TooLongFrameException: HTTP content length exceeded 104857600 bytes. ``` So I investigated and found an issue in `helpers._chunk_actions` There are two cases 1. If `max_chunk_bytes` is pretty low, `_chunk_actions` will split actions one by one, but each chunk will be bigger than `max_chunk_bytes`. It's actually used in test https://github.com/elastic/elasticsearch-py/blob/64202273e02ab5f0b99271771d4e8143b3599c3e/test_elasticsearch/test_helpers.py#L35 Maybe it makes sense to raise a warning in this case. 2. **Most important** `_chunk_actions` count characters instead of bytes to check for chunk size. If user passes actions with unicode values it is possible that output chunk size will exceed `max_chunk_bytes`. This test failes ```python class TestChunkActions(TestCase): def setUp(self): super(TestChunkActions, self).setUp() self.actions = [({'index': {}}, {'some': u'€€€€€€€€', 'i': i}) for i in range(100)] def test_chunks_are_chopped_by_byte_size_properly(self): chunks = list(helpers._chunk_actions(self.actions, 100000, 90, JSONSerializer())) self.assertEquals(100, len(chunks)) for chunk in chunks: chunk = u''.join(chunk) chunk = chunk if isinstance(chunk, str) else chunk.encode('utf-8') self.assertLessEqual(len(chunk), 90) ``` `_chunk_actions` should produce 100 chunks (each less than 90 bytes), but produces 50 chunks (each ~114 bytes)

2018-11-16T23:30:17Z

elastic/elasticsearch-py

elastic__elasticsearch-py-889

da2d5c460e7a1277baae688a8ae577e24521d35b

diff --git a/elasticsearch5/exceptions.py b/elasticsearch5/exceptions.py --- a/elasticsearch5/exceptions.py +++ b/elasticsearch5/exceptions.py @@ -51,8 +51,11 @@ def info(self): def __str__(self): cause = '' try: - if self.info: - cause = ', %r' % self.info['error']['root_cause'][0]['reason'] + if self.info and 'error' in self.info: + if isinstance(self.info['error'], dict): + cause = ', %r' % self.info['error']['root_cause'][0]['reason'] + else: + cause = ', %r' % self.info['error'] except LookupError: pass return 'TransportError(%s, %r%s)' % (self.status_code, self.error, cause)

diff --git a/test_elasticsearch/test_exceptions.py b/test_elasticsearch/test_exceptions.py new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_exceptions.py @@ -0,0 +1,23 @@ +from elasticsearch.exceptions import TransportError + +from .test_cases import TestCase + + +class TestTransformError(TestCase): + def test_transform_error_parse_with_error_reason(self): + e = TransportError(500, 'InternalServerError', { + 'error': { + 'root_cause': [ + {"type": "error", "reason": "error reason"} + ] + } + }) + + self.assertEqual(str(e), "TransportError(500, 'InternalServerError', 'error reason')") + + def test_transform_error_parse_with_error_string(self): + e = TransportError(500, 'InternalServerError', { + 'error': 'something error message' + }) + + self.assertEqual(str(e), "TransportError(500, 'InternalServerError', 'something error message')")

TypeError: string indices must be integers in TransportError When the error message returned from the server has the structure as below, TransportError can not handle the error content correctly. example error response: ```json {"error":"Content-Type header [application/octet-stream] is not supported","status":406} ``` error logs: ``` Traceback (most recent call last): File "/google-cloud-sdk/platform/google_appengine/google/appengine/api/app_logging.py", line 80, in emit message = self._AppLogsMessage(record) File "/google-cloud-sdk/platform/google_appengine/google/appengine/api/app_logging.py", line 96, in _AppLogsMessage message = self.format(record).replace("\r\n", NEWLINE_REPLACEMENT) File "/usr/lib/python2.7/logging/__init__.py", line 734, in format return fmt.format(record) File "/usr/lib/python2.7/logging/__init__.py", line 465, in format record.message = record.getMessage() File "/usr/lib/python2.7/logging/__init__.py", line 325, in getMessage msg = str(self.msg) File "/usr/src/app/app/lib/elasticsearch/exceptions.py", line 58, in __str__ cause = ', %r' % self.info['error']['root_cause'][0]['reason'] TypeError: string indices must be integers ```

2019-01-08T09:18:59Z

elastic/elasticsearch-py

elastic__elasticsearch-py-949

206f5e2754b01789facc0d50dac5177444afd2f6

diff --git a/elasticsearch/client/__init__.py b/elasticsearch/client/__init__.py --- a/elasticsearch/client/__init__.py +++ b/elasticsearch/client/__init__.py @@ -329,6 +329,11 @@ def index(self, index, body, doc_type="_doc", id=None, params=None): :arg index: The name of the index :arg body: The document :arg id: Document ID + :arg if_primary_term: only perform the index operation if the last + operation that has changed the document has the specified primary + term + :arg if_seq_no: only perform the index operation if the last operation + that has changed the document has the specified sequence number :arg doc_type: Document type, defaults to `_doc`. Not used on ES 7 clusters. :arg op_type: Explicit operation type, default 'index', valid choices are: 'index', 'create' @@ -356,10 +361,7 @@ def index(self, index, body, doc_type="_doc", id=None, params=None): if param in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument.") return self.transport.perform_request( - "POST" if id in SKIP_IN_PATH else "PUT", - _make_path(index, doc_type, id), - params=params, - body=body, + "POST", _make_path(index, doc_type, id), params=params, body=body ) @query_params( @@ -672,6 +674,7 @@ def update(self, index, id, doc_type="_doc", body=None, params=None): "analyze_wildcard", "analyzer", "batched_reduce_size", + "ccs_minimize_roundtrips", "default_operator", "df", "docvalue_fields", @@ -736,6 +739,9 @@ def search(self, index=None, body=None, params=None): as a protection mechanism to reduce the memory overhead per search request if the potential number of shards in the request can be large., default 512 + :arg ccs_minimize_roundtrips: Indicates whether network round-trips + should be minimized as part of cross-cluster search requests + execution, default 'true' :arg default_operator: The default operator for query string query (AND or OR), default 'OR', valid choices are: 'AND', 'OR' :arg df: The field to use as default where no field prefix is given in @@ -935,10 +941,28 @@ def update_by_query(self, index, body=None, params=None): "POST", _make_path(index, "_update_by_query"), params=params, body=body ) + @query_params("requests_per_second") + def update_by_query_rethrottle(self, task_id, params=None): + """ + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-update-by-query.html>`_ + + :arg task_id: The task id to rethrottle + :arg requests_per_second: The throttle to set on this request in + floating sub-requests per second. -1 means set no throttle. + """ + if task_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for a required argument 'task_id'.") + return self.transport.perform_request( + "POST", + _make_path("_update_by_query", task_id, "_rethrottle"), + params=params, + ) + @query_params( "refresh", "requests_per_second", "slices", + "scroll", "timeout", "wait_for_active_shards", "wait_for_completion", @@ -956,6 +980,8 @@ def reindex(self, body, params=None): :arg slices: The number of slices this task should be divided into. Defaults to 1 meaning the task isn't sliced into subtasks., default 1 + :arg scroll: Control how long to keep the search context alive, default + '5m' :arg timeout: Time each individual bulk request should wait for shards that are unavailable., default '1m' :arg wait_for_active_shards: Sets the number of shard copies that must @@ -1104,6 +1130,23 @@ def delete_by_query(self, index, body, params=None): "POST", _make_path(index, "_delete_by_query"), params=params, body=body ) + @query_params("requests_per_second") + def delete_by_query_rethrottle(self, task_id, params=None): + """ + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-delete-by-query.html>`_ + + :arg task_id: The task id to rethrottle + :arg requests_per_second: The throttle to set on this request in + floating sub-requests per second. -1 means set no throttle. + """ + if task_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for a required argument 'task_id'.") + return self.transport.perform_request( + "POST", + _make_path("_delete_by_query", task_id, "_rethrottle"), + params=params, + ) + @query_params( "allow_no_indices", "expand_wildcards", @@ -1142,12 +1185,14 @@ def search_shards(self, index=None, params=None): @query_params( "allow_no_indices", + "ccs_minimize_roundtrips", "expand_wildcards", "explain", "ignore_unavailable", "preference", "profile", "routing", + "rest_total_hits_as_int", "scroll", "search_type", "typed_keys", @@ -1165,6 +1210,9 @@ def search_template(self, index=None, body=None, params=None): :arg allow_no_indices: Whether to ignore if a wildcard indices expression resolves into no concrete indices. (This includes `_all` string or when no indices have been specified) + :arg ccs_minimize_roundtrips: Indicates whether network round-trips + should be minimized as part of cross-cluster search requests + execution, default 'true' :arg expand_wildcards: Whether to expand wildcard expression to concrete indices that are open, closed or both., default 'open', valid choices are: 'open', 'closed', 'none', 'all' @@ -1176,6 +1224,9 @@ def search_template(self, index=None, body=None, params=None): performed on (default: random) :arg profile: Specify whether to profile the query execution :arg routing: A comma-separated list of specific routing values + :arg rest_total_hits_as_int: Indicates whether hits.total should be + rendered as an integer or an object in the rest search response, + default False :arg scroll: Specify how long a consistent view of the index should be maintained for scrolled search :arg search_type: Search operation type, valid choices are: @@ -1244,8 +1295,8 @@ def explain(self, index, id, doc_type="_doc", body=None, params=None): "GET", _make_path(index, doc_type, id, "_explain"), params=params, body=body ) - @query_params("scroll", "rest_total_hits_as_int") - def scroll(self, scroll_id=None, body=None, params=None): + @query_params("scroll", "rest_total_hits_as_int", "scroll_id") + def scroll(self, body=None, params=None): """ Scroll a search request created by specifying the scroll parameter. `<http://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-scroll.html>`_ @@ -1258,12 +1309,6 @@ def scroll(self, scroll_id=None, body=None, params=None): in the response. This param is added version 6.x to handle mixed cluster queries where nodes are in multiple versions (7.0 and 6.latest) """ - if scroll_id in SKIP_IN_PATH and body in SKIP_IN_PATH: - raise ValueError("You need to supply scroll_id or body.") - elif scroll_id and not body: - body = {"scroll_id": scroll_id} - elif scroll_id: - params["scroll_id"] = scroll_id return self.transport.perform_request( "GET", "/_search/scroll", params=params, body=body @@ -1309,6 +1354,11 @@ def delete(self, index, id, doc_type="_doc", params=None): :arg index: The name of the index :arg id: The document ID + :arg if_primary_term: only perform the delete operation if the last + operation that has changed the document has the specified primary + term + :arg if_seq_no: only perform the delete operation if the last operation + that has changed the document has the specified sequence number :arg parent: ID of parent document :arg refresh: If `true` then refresh the effected shards to make this operation visible to search, if `wait_for` then wait for a refresh @@ -1341,6 +1391,7 @@ def delete(self, index, id, doc_type="_doc", params=None): "df", "expand_wildcards", "ignore_unavailable", + "ignore_throttled", "lenient", "min_score", "preference", @@ -1372,6 +1423,8 @@ def count(self, doc_type=None, index=None, body=None, params=None): choices are: 'open', 'closed', 'none', 'all' :arg ignore_unavailable: Whether specified concrete indices should be ignored when unavailable (missing or closed) + :arg ignore_throttled: Whether specified concrete, expanded or aliased + indices should be ignored when throttled :arg lenient: Specify whether format-based query failures (such as providing text to a numeric field) should be ignored :arg min_score: Include only documents with a specific `_score` value in @@ -1446,6 +1499,7 @@ def bulk(self, body, doc_type=None, index=None, params=None): ) @query_params( + "ccs_minimize_roundtrips", "max_concurrent_searches", "max_concurrent_shard_requests", "pre_filter_shard_size", @@ -1462,6 +1516,9 @@ def msearch(self, body, index=None, params=None): pairs), separated by newlines :arg index: A list of index names, or a string containing a comma-separated list of index names, to use as the default + :arg ccs_minimize_roundtrips: Indicates whether network round-trips + should be minimized as part of cross-cluster search requests + execution, default 'true' :arg max_concurrent_searches: Controls the maximum number of concurrent searches the multi search api will execute :arg pre_filter_shard_size: A threshold that enforces a pre-filter @@ -1614,7 +1671,7 @@ def mtermvectors(self, doc_type=None, index=None, body=None, params=None): body=body, ) - @query_params() + @query_params("master_timeout", "timeout") def put_script(self, id, body, context=None, params=None): """ Create a script in given language with specified ID. @@ -1622,7 +1679,9 @@ def put_script(self, id, body, context=None, params=None): :arg id: Script ID :arg body: The document - """ + :arg master_timeout: Specify timeout for connection to master + :arg timeout: Explicit operation timeout + """ for param in (id, body): if param in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument.") @@ -1630,13 +1689,38 @@ def put_script(self, id, body, context=None, params=None): "PUT", _make_path("_scripts", id, context), params=params, body=body ) - @query_params() + @query_params("allow_no_indices", "expand_wildcards", "ignore_unavailable") + def rank_eval(self, body, index=None, params=None): + """ + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/search-rank-eval.html>`_ + + :arg body: The ranking evaluation search definition, including search + requests, document ratings and ranking metric definition. + :arg index: A comma-separated list of index names to search; use `_all` + or empty string to perform the operation on all indices + :arg allow_no_indices: Whether to ignore if a wildcard indices + expression resolves into no concrete indices. (This includes `_all` + string or when no indices have been specified) + :arg expand_wildcards: Whether to expand wildcard expression to concrete + indices that are open, closed or both., default 'open', valid + choices are: 'open', 'closed', 'none', 'all' + :arg ignore_unavailable: Whether specified concrete indices should be + ignored when unavailable (missing or closed) + """ + if body in SKIP_IN_PATH: + raise ValueError("Empty value passed for a required argument 'body'.") + return self.transport.perform_request( + "GET", _make_path(index, "_rank_eval"), params=params, body=body + ) + + @query_params("master_timeout") def get_script(self, id, params=None): """ Retrieve a script from the API. `<http://www.elastic.co/guide/en/elasticsearch/reference/master/modules-scripting.html>`_ :arg id: Script ID + :arg master_timeout: Specify timeout for connection to master """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'id'.") @@ -1644,14 +1728,15 @@ def get_script(self, id, params=None): "GET", _make_path("_scripts", id), params=params ) - @query_params() + @query_params("master_timeout", "timeout") def delete_script(self, id, params=None): """ Remove a stored script from elasticsearch. `<http://www.elastic.co/guide/en/elasticsearch/reference/master/modules-scripting.html>`_ :arg id: Script ID - """ + :arg master_timeout: Specify timeout for connection to master + :arg timeout: Explicit operation timeout """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'id'.") return self.transport.perform_request( @@ -1670,7 +1755,35 @@ def render_search_template(self, id=None, body=None, params=None): "GET", _make_path("_render", "template", id), params=params, body=body ) - @query_params("max_concurrent_searches", "search_type", "typed_keys") + @query_params("context") + def scripts_painless_context(self, params=None): + """ + `<>`_ + + :arg context: Select a specific context to retrieve API information + about + """ + return self.transport.perform_request( + "GET", "/_scripts/painless/_context", params=params + ) + + @query_params() + def scripts_painless_execute(self, body=None, params=None): + """ + `<https://www.elastic.co/guide/en/elasticsearch/painless/master/painless-execute-api.html>`_ + + :arg body: The script to execute + """ + return self.transport.perform_request( + "GET", "/_scripts/painless/_execute", params=params, body=body + ) + + @query_params( + "ccs_minimize_roundtrips", + "max_concurrent_searches", + "search_type", + "typed_keys", + ) def msearch_template(self, body, index=None, params=None): """ The /_search/template endpoint allows to use the mustache language to @@ -1682,6 +1795,9 @@ def msearch_template(self, body, index=None, params=None): pairs), separated by newlines :arg index: A list of index names, or a string containing a comma-separated list of index names, to use as the default + :arg ccs_minimize_roundtrips: Indicates whether network round-trips + should be minimized as part of cross-cluster search requests + execution, default 'true' :arg max_concurrent_searches: Controls the maximum number of concurrent searches the multi search api will execute :arg search_type: Search operation type, valid choices are: @@ -1701,7 +1817,11 @@ def msearch_template(self, body, index=None, params=None): ) @query_params( - "allow_no_indices", "expand_wildcards", "fields", "ignore_unavailable" + "allow_no_indices", + "expand_wildcards", + "fields", + "ignore_unavailable", + "include_unmapped", ) def field_caps(self, index=None, body=None, params=None): """ @@ -1721,6 +1841,8 @@ def field_caps(self, index=None, body=None, params=None): :arg fields: A comma-separated list of field names :arg ignore_unavailable: Whether specified concrete indices should be ignored when unavailable (missing or closed) + :arg include_unmapped: Indicates whether unmapped fields should be + included in the response., default False """ return self.transport.perform_request( "GET", _make_path(index, "_field_caps"), params=params, body=body diff --git a/elasticsearch/client/cat.py b/elasticsearch/client/cat.py --- a/elasticsearch/client/cat.py +++ b/elasticsearch/client/cat.py @@ -24,9 +24,7 @@ def aliases(self, name=None, params=None): "GET", _make_path("_cat", "aliases", name), params=params ) - @query_params( - "bytes", "size", "format", "h", "help", "local", "master_timeout", "s", "v" - ) + @query_params("bytes", "format", "h", "help", "local", "master_timeout", "s", "v") def allocation(self, node_id=None, params=None): """ Allocation provides a snapshot of how shards have located around the @@ -52,7 +50,7 @@ def allocation(self, node_id=None, params=None): "GET", _make_path("_cat", "allocation", node_id), params=params ) - @query_params("size", "format", "h", "help", "local", "master_timeout", "s", "v") + @query_params("format", "h", "help", "local", "master_timeout", "s", "v") def count(self, index=None, params=None): """ Count provides quick access to the document count of the entire cluster, @@ -111,7 +109,6 @@ def help(self, params=None): @query_params( "bytes", - "time", "size", "format", "h", @@ -218,16 +215,7 @@ def recovery(self, index=None, params=None): ) @query_params( - "bytes", - "time", - "size", - "format", - "h", - "help", - "local", - "master_timeout", - "s", - "v", + "bytes", "size", "format", "h", "help", "local", "master_timeout", "s", "v" ) def shards(self, index=None, params=None): """ @@ -442,6 +430,7 @@ def snapshots(self, repository, params=None): "h", "help", "nodes", + "node_id", "parent_task_id", "s", "v", @@ -458,6 +447,10 @@ def tasks(self, params=None): :arg h: Comma-separated list of column names to display :arg help: Return help information, default False :arg nodes: A comma-separated list of node IDs or names to limit the + returned information; use `_local` to return information from the + node you're connecting to, leave empty to get information from all + nodes (used for older version of Elasticsearch) + :arg node_id: A comma-separated list of node IDs or names to limit the returned information; use `_local` to return information from the node you're connecting to, leave empty to get information from all nodes diff --git a/elasticsearch/client/cluster.py b/elasticsearch/client/cluster.py --- a/elasticsearch/client/cluster.py +++ b/elasticsearch/client/cluster.py @@ -3,16 +3,17 @@ class ClusterClient(NamespacedClient): @query_params( + "expand_wildcards", "level", "local", "master_timeout", "timeout", "wait_for_active_shards", "wait_for_events", + "wait_for_no_initializing_shards", "wait_for_no_relocating_shards", "wait_for_nodes", "wait_for_status", - "wait_for_no_initializing_shards", ) def health(self, index=None, params=None): """ @@ -20,6 +21,9 @@ def health(self, index=None, params=None): `<http://www.elastic.co/guide/en/elasticsearch/reference/current/cluster-health.html>`_ :arg index: Limit the information returned to a specific index + :arg expand_wildcards: Whether to expand wildcard expression to concrete + indices that are open, closed or both., default 'all', valid choices + are: 'open', 'closed', 'none', 'all' :arg level: Specify the level of detail for returned information, default 'cluster', valid choices are: 'cluster', 'indices', 'shards' :arg local: Return local information, do not retrieve the state from @@ -66,6 +70,8 @@ def pending_tasks(self, params=None): "ignore_unavailable", "local", "master_timeout", + "wait_for_metadata_version", + "wait_for_timeout", ) def state(self, metric=None, index=None, params=None): """ @@ -87,6 +93,10 @@ def state(self, metric=None, index=None, params=None): :arg local: Return local information, do not retrieve the state from master node (default: false) :arg master_timeout: Specify timeout for connection to master + :arg wait_for_metadata_version: Wait for the metadata version to be + equal or greater than the specified metadata version + :arg wait_for_timeout: The maximum time to wait for + wait_for_metadata_version before timing out """ if index and not metric: metric = "_all" @@ -174,6 +184,13 @@ def put_settings(self, body=None, params=None): "PUT", "/_cluster/settings", params=params, body=body ) + @query_params() + def remote_info(self, params=None): + """ + `<http://www.elastic.co/guide/en/elasticsearch/reference/master/cluster-remote-info.html>`_ + """ + return self.transport.perform_request("GET", "/_remote/info", params=params) + @query_params("include_disk_info", "include_yes_decisions") def allocation_explain(self, body=None, params=None): """ diff --git a/elasticsearch/client/indices.py b/elasticsearch/client/indices.py --- a/elasticsearch/client/indices.py +++ b/elasticsearch/client/indices.py @@ -80,7 +80,7 @@ def flush(self, index=None, params=None): @query_params( "master_timeout", - "request_timeout", + "timeout" "request_timeout", "wait_for_active_shards", "include_type_name", ) @@ -92,6 +92,7 @@ def create(self, index, body=None, params=None): :arg index: The name of the index :arg body: The configuration for the index (`settings` and `mappings`) :arg master_timeout: Specify timeout for connection to master + :arg timeout: Explicit operation timeout :arg request_timeout: Explicit operation timeout :arg wait_for_active_shards: Set the number of active shards to wait for before the operation returns. @@ -112,6 +113,7 @@ def create(self, index, body=None, params=None): "include_defaults", "local", "include_type_name", + "master_timeout", ) def get(self, index, feature=None, params=None): """ @@ -132,6 +134,7 @@ def get(self, index, feature=None, params=None): master node (default: false) :arg include_type_name: Specify whether requests and responses should include a type name (default: depends on Elasticsearch version). + :arg master_timeout: Specify timeout for connection to master """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'index'.") @@ -144,7 +147,9 @@ def get(self, index, feature=None, params=None): "expand_wildcards", "ignore_unavailable", "master_timeout", + "timeout", "request_timeout", + "wait_for_active_shards", ) def open(self, index, params=None): """ @@ -161,7 +166,10 @@ def open(self, index, params=None): :arg ignore_unavailable: Whether specified concrete indices should be ignored when unavailable (missing or closed) :arg master_timeout: Specify timeout for connection to master + :arg timeout: Explicit operation timeout :arg request_timeout: Explicit operation timeout + :arg wait_for_active_shards: Sets the number of active shards to wait + for before the operation returns. """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'index'.") @@ -175,6 +183,7 @@ def open(self, index, params=None): "ignore_unavailable", "master_timeout", "request_timeout", + "wait_for_active_shards", ) def close(self, index, params=None): """ @@ -193,6 +202,8 @@ def close(self, index, params=None): ignored when unavailable (missing or closed) :arg master_timeout: Specify timeout for connection to master :arg request_timeout: Explicit operation timeout + :arg wait_for_active_shards: Sets the number of active shards to wait + for before the operation returns. """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'index'.") @@ -204,6 +215,7 @@ def close(self, index, params=None): "allow_no_indices", "expand_wildcards", "ignore_unavailable", + "timeout", "master_timeout", "request_timeout", ) @@ -221,6 +233,7 @@ def delete(self, index, params=None): choices are: 'open', 'closed', 'none', 'all' :arg ignore_unavailable: Ignore unavailable indexes (default: false) :arg master_timeout: Specify timeout for connection to master + :arg timeout: Explicit operation timeout :arg request_timeout: Explicit operation timeout """ if index in SKIP_IN_PATH: @@ -291,6 +304,7 @@ def exists_type(self, index, doc_type, params=None): "expand_wildcards", "ignore_unavailable", "master_timeout", + "timeout", "request_timeout", "include_type_name", ) @@ -313,7 +327,8 @@ def put_mapping(self, body, doc_type=None, index=None, params=None): :arg ignore_unavailable: Whether specified concrete indices should be ignored when unavailable (missing or closed) :arg master_timeout: Specify timeout for connection to master - :arg request_timeout: Explicit operation timeout + :arg timeout: Explicit operation timeout + :arg request_timeout: Explicit operation timeout (For pre 7.x ES Clusters) :arg include_type_name: Specify whether requests and responses should include a type name (default: depends on Elasticsearch version). """ @@ -330,6 +345,7 @@ def put_mapping(self, body, doc_type=None, index=None, params=None): "ignore_unavailable", "local", "include_type_name", + "master_timeout", ) def get_mapping(self, index=None, doc_type=None, params=None): """ @@ -350,6 +366,7 @@ def get_mapping(self, index=None, doc_type=None, params=None): master node (default: false) :arg include_type_name: Specify whether requests and responses should include a type name (default: depends on Elasticsearch version). + :arg master_timeout: Specify timeout for connection to master """ return self.transport.perform_request( "GET", _make_path(index, "_mapping", doc_type), params=params @@ -461,7 +478,7 @@ def get_alias(self, index=None, name=None, params=None): "GET", _make_path(index, "_alias", name), params=params ) - @query_params("master_timeout", "request_timeout") + @query_params("master_timeout", "request_timeout", "timeout") def update_aliases(self, body, params=None): """ Update specified aliases. @@ -469,7 +486,8 @@ def update_aliases(self, body, params=None): :arg body: The definition of `actions` to perform :arg master_timeout: Specify timeout for connection to master - :arg request_timeout: Request timeout + :arg request_timeout: Request timeout (For pre 7.x ES Clusters) + :arg timeout: Request timeout """ if body in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'body'.") @@ -477,7 +495,7 @@ def update_aliases(self, body, params=None): "POST", "/_aliases", params=params, body=body ) - @query_params("master_timeout", "request_timeout") + @query_params("master_timeout", "request_timeout", "timeout") def delete_alias(self, index, name, params=None): """ Delete specific alias. @@ -489,7 +507,8 @@ def delete_alias(self, index, name, params=None): wildcards); use `_all` to delete all aliases for the specified indices. :arg master_timeout: Specify timeout for connection to master - :arg request_timeout: Explicit timeout for the operation + :arg request_timeout: Explicit timeout for the operation (for pre 7.x ES clusters) + :arg timeout: Explicit timeout for the operation """ for param in (index, name): if param in SKIP_IN_PATH: @@ -504,6 +523,7 @@ def delete_alias(self, index, name, params=None): "master_timeout", "order", "request_timeout", + "timeout", "include_type_name", ) def put_template(self, name, body, params=None): @@ -520,7 +540,8 @@ def put_template(self, name, body, params=None): :arg master_timeout: Specify timeout for connection to master :arg order: The order for this template when merging multiple matching ones (higher numbers are merged later, overriding the lower numbers) - :arg request_timeout: Explicit operation timeout + :arg request_timeout: Explicit operation timeout (For pre ES 6 clusters) + :arg timeout: Explicit operation timeout :arg include_type_name: Specify whether requests and responses should include a type name (default: depends on Elasticsearch version). """ @@ -569,7 +590,7 @@ def get_template(self, name=None, params=None): "GET", _make_path("_template", name), params=params ) - @query_params("master_timeout", "request_timeout") + @query_params("master_timeout", "request_timeout", "timeout") def delete_template(self, name, params=None): """ Delete an index template by its name. @@ -577,7 +598,8 @@ def delete_template(self, name, params=None): :arg name: The name of the template :arg master_timeout: Specify timeout for connection to master - :arg request_timeout: Explicit operation timeout + :arg request_timeout: Explicit operation timeout (for pre 7.x clusters) + :arg timeout: Explicit operation timeout """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'name'.") @@ -592,6 +614,7 @@ def delete_template(self, name, params=None): "ignore_unavailable", "include_defaults", "local", + "master_timeout", ) def get_settings(self, index=None, name=None, params=None): """ @@ -614,6 +637,7 @@ def get_settings(self, index=None, name=None, params=None): the indices., default False :arg local: Return local information, do not retrieve the state from master node (default: false) + :arg master_timeout: Specify timeout for connection to master """ return self.transport.perform_request( "GET", _make_path(index, "_settings", name), params=params @@ -625,6 +649,7 @@ def get_settings(self, index=None, name=None, params=None): "flat_settings", "ignore_unavailable", "master_timeout", + "timeout", "preserve_existing", ) def put_settings(self, body, index=None, params=None): @@ -648,6 +673,7 @@ def put_settings(self, body, index=None, params=None): :arg preserve_existing: Whether to update existing settings. If set to `true` existing settings on an index remain unchanged, the default is `false` + :arg timeout: Explicit operation timeout """ if body in SKIP_IN_PATH: raise ValueError("Empty value passed for a required argument 'body'.") @@ -657,10 +683,13 @@ def put_settings(self, body, index=None, params=None): @query_params( "completion_fields", + "expand_wildcards", "fielddata_fields", "fields", + "forbid_closed_indices", "groups", "include_segment_file_sizes", + "include_unloaded_segments", "level", "types", ) @@ -674,15 +703,24 @@ def stats(self, index=None, metric=None, params=None): :arg metric: Limit the information returned the specific metrics. :arg completion_fields: A comma-separated list of fields for `fielddata` and `suggest` index metric (supports wildcards) + :arg expand_wildcards: Whether to expand wildcard expression to concrete + indices that are open, closed or both., default 'open', valid + choices are: 'open', 'closed', 'none', 'all' :arg fielddata_fields: A comma-separated list of fields for `fielddata` index metric (supports wildcards) :arg fields: A comma-separated list of fields for `fielddata` and `completion` index metric (supports wildcards) + :arg forbid_closed_indices: If set to false stats will also collected + from closed indices if explicitly specified or if expand_wildcards + expands to closed indices, default True :arg groups: A comma-separated list of search groups for `search` index metric :arg include_segment_file_sizes: Whether to report the aggregated disk usage of each one of the Lucene index files (only applies if segment stats are requested), default False + :arg include_unloaded_segments: If set to true segment stats will + include stats for segments that are not currently loaded into + memory, default False :arg level: Return stats aggregated at cluster, index or shard level, default 'indices', valid choices are: 'cluster', 'indices', 'shards' :arg types: A comma-separated list of document types for the `indexing` @@ -980,14 +1018,15 @@ def forcemerge(self, index=None, params=None): :arg max_num_segments: The number of segments the index should be merged into (default: dynamic) :arg only_expunge_deletes: Specify whether the operation should only - expunge deleted documents - :arg operation_threading: TODO: ? + expunge deleted documents (for pre 7.x ES clusters) """ return self.transport.perform_request( "POST", _make_path(index, "_forcemerge"), params=params ) - @query_params("master_timeout", "request_timeout", "wait_for_active_shards") + @query_params( + "master_timeout", "timeout", "request_timeout", "wait_for_active_shards" + ) def shrink(self, index, target, body=None, params=None): """ The shrink index API allows you to shrink an existing index into a new @@ -1006,7 +1045,8 @@ def shrink(self, index, target, body=None, params=None): :arg body: The configuration for the target index (`settings` and `aliases`) :arg master_timeout: Specify timeout for connection to master - :arg request_timeout: Explicit operation timeout + :arg request_timeout: Explicit operation timeout (For pre 7.x ES clusters) + :arg timeout: Explicit operation timeout :arg wait_for_active_shards: Set the number of active shards to wait for on the shrunken index before the operation returns. """ @@ -1017,10 +1057,32 @@ def shrink(self, index, target, body=None, params=None): "PUT", _make_path(index, "_shrink", target), params=params, body=body ) + @query_params("master_timeout", "timeout", "wait_for_active_shards") + def split(self, index, target, body=None, params=None): + """ + `<http://www.elastic.co/guide/en/elasticsearch/reference/master/indices-split-index.html>`_ + + :arg index: The name of the source index to split + :arg target: The name of the target index to split into + :arg body: The configuration for the target index (`settings` and + `aliases`) + :arg master_timeout: Specify timeout for connection to master + :arg timeout: Explicit operation timeout + :arg wait_for_active_shards: Set the number of active shards to wait for + on the shrunken index before the operation returns. + """ + for param in (index, target): + if param in SKIP_IN_PATH: + raise ValueError("Empty value passed for a required argument.") + return self.transport.perform_request( + "PUT", _make_path(index, "_split", target), params=params, body=body + ) + @query_params( "dry_run", "master_timeout", "request_timeout", + "timeout", "wait_for_active_shards", "include_type_name", ) @@ -1042,7 +1104,8 @@ def rollover(self, alias, new_index=None, body=None, params=None): but not actually performed even if a condition matches. The default is false :arg master_timeout: Specify timeout for connection to master - :arg request_timeout: Explicit operation timeout + :arg request_timeout: Explicit operation timeout (for pre 7.x ES clusters) + :arg timeout: Explicit operation timeout :arg wait_for_active_shards: Set the number of active shards to wait for on the newly created rollover index before the operation returns. :arg include_type_name: Specify whether requests and responses should include a diff --git a/elasticsearch/client/ingest.py b/elasticsearch/client/ingest.py --- a/elasticsearch/client/ingest.py +++ b/elasticsearch/client/ingest.py @@ -67,3 +67,12 @@ def simulate(self, body, id=None, params=None): params=params, body=body, ) + + @query_params() + def processor_grok(self, params=None): + """ + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/grok-processor.html#grok-processor-rest-get>`_ + """ + return self.transport.perform_request( + "GET", "/_ingest/processor/grok", params=params + ) diff --git a/elasticsearch/client/nodes.py b/elasticsearch/client/nodes.py --- a/elasticsearch/client/nodes.py +++ b/elasticsearch/client/nodes.py @@ -2,12 +2,16 @@ class NodesClient(NamespacedClient): - @query_params() - def reload_secure_settings(self, params=None): + @query_params("timeout") + def reload_secure_settings(self, node_id=None, params=None): """ Reload any settings that have been marked as "reloadable" `<https://www.elastic.co/guide/en/elasticsearch/reference/current/secure-settings.html#reloadable-secure-settings>`_ + :arg node_id: A comma-separated list of node IDs to span the + reload/reinit call. Should stay empty because reloading usually + involves all cluster nodes. + :arg timeout: Explicit operation timeout """ return self.transport.perform_request( "POST", _make_path("_nodes", "reload_secure_settings"), params=params @@ -82,7 +86,7 @@ def stats(self, node_id=None, metric=None, index_metric=None, params=None): ) @query_params( - "type", "ignore_idle_threads", "interval", "snapshots", "threads", "timeout" + "doc_type", "ignore_idle_threads", "interval", "snapshots", "threads", "timeout" ) def hot_threads(self, node_id=None, params=None): """ diff --git a/elasticsearch/client/xpack/__init__.py b/elasticsearch/client/xpack/__init__.py --- a/elasticsearch/client/xpack/__init__.py +++ b/elasticsearch/client/xpack/__init__.py @@ -1,13 +1,20 @@ from ..utils import NamespacedClient, query_params +from .ccr import CcrClient +from .data_frame import Data_FrameClient +from .deprecation import DeprecationClient from .graph import GraphClient +from .ilm import IlmClient +from .indices import IndicesClient from .license import LicenseClient +from .migration import MigrationClient +from .ml import MlClient from .monitoring import MonitoringClient +from .rollup import RollupClient from .security import SecurityClient +from .sql import SqlClient +from .ssl import SslClient from .watcher import WatcherClient -from .ml import MlClient -from .migration import MigrationClient -from .deprecation import DeprecationClient class XPackClient(NamespacedClient): @@ -15,14 +22,21 @@ class XPackClient(NamespacedClient): def __init__(self, *args, **kwargs): super(XPackClient, self).__init__(*args, **kwargs) + self.ccr = CcrClient(self.client) + self.data_frame = Data_FrameClient(self.client) + self.deprecation = DeprecationClient(self.client) self.graph = GraphClient(self.client) + self.ilm = IlmClient(self.client) + self.indices = IndicesClient(self.client) self.license = LicenseClient(self.client) + self.migration = MigrationClient(self.client) + self.ml = MlClient(self.client) self.monitoring = MonitoringClient(self.client) + self.rollup = RollupClient(self.client) self.security = SecurityClient(self.client) + self.sql = SqlClient(self.client) + self.ssl = SslClient(self.client) self.watcher = WatcherClient(self.client) - self.ml = MlClient(self.client) - self.migration = MigrationClient(self.client) - self.deprecation = DeprecationClient(self.client) @query_params("categories", "human") def info(self, params=None): diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -454,13 +454,8 @@ def scan( "Scroll request has only succeeded on %d shards out of %d." % (resp["_shards"]["successful"], resp["_shards"]["total"]), ) - - resp = client.scroll( - scroll_id, - scroll=scroll, - request_timeout=request_timeout, - **scroll_kwargs - ) + scroll_kwargs.update({"scroll_id": scroll_id, "scroll": scroll}) + resp = client.scroll(**scroll_kwargs) scroll_id = resp.get("_scroll_id") finally:

diff --git a/test_elasticsearch/test_client/__init__.py b/test_elasticsearch/test_client/__init__.py --- a/test_elasticsearch/test_client/__init__.py +++ b/test_elasticsearch/test_client/__init__.py @@ -96,4 +96,4 @@ def test_index_uses_post_if_id_is_empty(self): def test_index_uses_put_if_id_is_not_empty(self): self.client.index(index="my-index", id=0, body={}) - self.assert_url_called("PUT", "/my-index/_doc/0") + self.assert_url_called("POST", "/my-index/_doc/0")

.xpack.sql not wired in the `SqlClient` is not wired into the `xpack` namespace. It is also missing from the docs.

2019-05-10T20:43:48Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1048

fc78c992ef6ceca157f369186760c8c550fb3bc4

diff --git a/elasticsearch/connection/base.py b/elasticsearch/connection/base.py --- a/elasticsearch/connection/base.py +++ b/elasticsearch/connection/base.py @@ -65,7 +65,7 @@ def __eq__(self, other): raise TypeError( "Unsupported equality check for %s and %s" % (self, other) ) - return True + return self.__hash__() == other.__hash__() def __hash__(self): return id(self) diff --git a/elasticsearch/connection_pool.py b/elasticsearch/connection_pool.py --- a/elasticsearch/connection_pool.py +++ b/elasticsearch/connection_pool.py @@ -150,6 +150,7 @@ def mark_dead(self, connection, now=None): try: self.connections.remove(connection) except ValueError: + logger.info("Attempted to remove %r, but it does not exist in the connection pool.", connection) # connection not alive or another thread marked it already, ignore return else:

diff --git a/test_elasticsearch/test_connection_pool.py b/test_elasticsearch/test_connection_pool.py --- a/test_elasticsearch/test_connection_pool.py +++ b/test_elasticsearch/test_connection_pool.py @@ -5,6 +5,7 @@ RoundRobinSelector, DummyConnectionPool, ) +from elasticsearch.connection import Connection from elasticsearch.exceptions import ImproperlyConfigured from .test_cases import TestCase @@ -72,6 +73,17 @@ def test_connection_is_skipped_when_dead(self): [pool.get_connection(), pool.get_connection(), pool.get_connection()], ) + def test_new_connection_is_not_marked_dead(self): + # Create 10 connections + pool = ConnectionPool([(Connection(), {}) for _ in range(10)]) + + # Pass in a new connection that is not in the pool to mark as dead + new_connection = Connection() + pool.mark_dead(new_connection) + + # Nothing should be marked dead + self.assertEquals(0, len(pool.dead_count)) + def test_connection_is_forcibly_resurrected_when_no_live_ones_are_availible(self): pool = ConnectionPool([(x, {}) for x in range(2)]) pool.dead_count[0] = 1

mark_dead() in ConnectionPool always removes the first connection in the pool `ConnectionPool`'s `mark_dead()` method is supposed to remove the dead `connection` from the `connections` list: `self.connections.remove(connection)` However the equality comparison implementation `__eq__` in the `Connection` class is incorrect. It always returns `True` as long as both objects are `Connection` instances: ``` def __eq__(self, other): if not isinstance(other, Connection): raise TypeError( "Unsupported equality check for %s and %s" % (self, other) ) return True ``` This defect manifests itself by removing all good connections until the connections is empty. One dead connection will bring down the whole connection pool. Proposed solution: removing `__eq__` from `Connection`. Thoughts?

That is definitely the correct solution, would you be open to sending a PR? Thank you!

2019-10-29T15:47:45Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1117

1a1ab99d9a2b0825f5c23db6aedcce75d4cbf794

diff --git a/elasticsearch/connection/http_urllib3.py b/elasticsearch/connection/http_urllib3.py --- a/elasticsearch/connection/http_urllib3.py +++ b/elasticsearch/connection/http_urllib3.py @@ -7,10 +7,11 @@ import gzip from base64 import decodestring -# sentinal value for `verify_certs`. -# This is used to detect if a user is passing in a value for `verify_certs` -# so we can raise a warning if using SSL kwargs AND SSLContext. -VERIFY_CERTS_DEFAULT = None +# sentinel value for `verify_certs` and `ssl_show_warn`. +# This is used to detect if a user is passing in a value +# for SSL kwargs if also using an SSLContext. +VERIFY_CERTS_DEFAULT = object() +SSL_SHOW_WARN_DEFAULT = object() CA_CERTS = None @@ -85,7 +86,7 @@ def __init__( http_auth=None, use_ssl=False, verify_certs=VERIFY_CERTS_DEFAULT, - ssl_show_warn=True, + ssl_show_warn=SSL_SHOW_WARN_DEFAULT, ca_certs=None, client_cert=None, client_key=None, @@ -138,11 +139,11 @@ def __init__( # if providing an SSL context, raise error if any other SSL related flag is used if ssl_context and ( (verify_certs is not VERIFY_CERTS_DEFAULT) + or (ssl_show_warn is not SSL_SHOW_WARN_DEFAULT) or ca_certs or client_cert or client_key or ssl_version - or ssl_show_warn ): warnings.warn( "When using `ssl_context`, all other SSL related kwargs are ignored" @@ -168,9 +169,12 @@ def __init__( } ) - # If `verify_certs` is sentinal value, default `verify_certs` to `True` + # Convert all sentinel values to their actual default + # values if not using an SSLContext. if verify_certs is VERIFY_CERTS_DEFAULT: verify_certs = True + if ssl_show_warn is SSL_SHOW_WARN_DEFAULT: + ssl_show_warn = True ca_certs = CA_CERTS if ca_certs is None else ca_certs if verify_certs:

diff --git a/test_elasticsearch/test_connection.py b/test_elasticsearch/test_connection.py --- a/test_elasticsearch/test_connection.py +++ b/test_elasticsearch/test_connection.py @@ -143,6 +143,34 @@ def test_doesnt_use_https_if_not_specified(self): con = Urllib3HttpConnection() self.assertIsInstance(con.pool, urllib3.HTTPConnectionPool) + def test_no_warning_when_using_ssl_context(self): + ctx = ssl.create_default_context() + with warnings.catch_warnings(record=True) as w: + Urllib3HttpConnection(ssl_context=ctx) + self.assertEquals(0, len(w)) + + def test_warns_if_using_non_default_ssl_kwargs_with_ssl_context(self): + for kwargs in ( + {"ssl_show_warn": False}, + {"ssl_show_warn": True}, + {"verify_certs": True}, + {"verify_certs": False}, + {"ca_certs": "/path/to/certs"}, + {"ssl_show_warn": True, "ca_certs": "/path/to/certs"}, + ): + kwargs["ssl_context"] = ssl.create_default_context() + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + + Urllib3HttpConnection(**kwargs) + + self.assertEquals(1, len(w)) + self.assertEquals( + "When using `ssl_context`, all other SSL related kwargs are ignored", + str(w[0].message), + ) + class TestRequestsConnection(TestCase): def _get_mock_connection(

A wrong warning is shown when creating a secured elasticsearch ssl (since 7.5.0) **Elasticsearch version** not relevant: **`elasticsearch-py` version 7.5.1**: **When creating a secured elasticsearch ssl connection a warning is shown**: ``` http_urllib3.py:148: UserWarning: When using `ssl_context`, all other SSL related kwargs are ignored "When using `ssl_context`, all other SSL related kwargs are ignored" ``` This happens because the new `ssh_show_warn` parameter defaults to `True` but also is in the or statement that shows the above error. The warning is wrong, because we are only setting the ssl_context parameter and nothing else.

Yep this looks incorrect, I'll make a PR to fix the issue.

2020-02-25T20:35:56Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1143

5611445203ebabb1a450b17c2c93cd3546a12071

diff --git a/elasticsearch/client/cluster.py b/elasticsearch/client/cluster.py --- a/elasticsearch/client/cluster.py +++ b/elasticsearch/client/cluster.py @@ -102,6 +102,9 @@ def state(self, metric=None, index=None, params=None): :arg wait_for_timeout: The maximum time to wait for wait_for_metadata_version before timing out """ + if index and metric in SKIP_IN_PATH: + metric = "_all" + return self.transport.perform_request( "GET", _make_path("_cluster", "state", metric, index), params=params )

diff --git a/test_elasticsearch/test_client/test_cluster.py b/test_elasticsearch/test_client/test_cluster.py new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_client/test_cluster.py @@ -0,0 +1,27 @@ +from test_elasticsearch.test_cases import ElasticsearchTestCase + + +class TestCluster(ElasticsearchTestCase): + def test_stats_without_node_id(self): + self.client.cluster.stats() + self.assert_url_called("GET", "/_cluster/stats") + + def test_stats_with_node_id(self): + self.client.cluster.stats("node-1") + self.assert_url_called("GET", "/_cluster/stats/nodes/node-1") + + self.client.cluster.stats(node_id="node-2") + self.assert_url_called("GET", "/_cluster/stats/nodes/node-2") + + def test_state_with_index_without_metric_defaults_to_all(self): + self.client.cluster.state() + self.assert_url_called("GET", "/_cluster/state") + + self.client.cluster.state(metric="cluster_name") + self.assert_url_called("GET", "/_cluster/state/cluster_name") + + self.client.cluster.state(index="index-1") + self.assert_url_called("GET", "/_cluster/state/_all/index-1") + + self.client.cluster.state(index="index-1", metric="cluster_name") + self.assert_url_called("GET", "/_cluster/state/cluster_name/index-1") diff --git a/test_elasticsearch/test_client/test_indices.py b/test_elasticsearch/test_client/test_indices.py --- a/test_elasticsearch/test_client/test_indices.py +++ b/test_elasticsearch/test_client/test_indices.py @@ -18,3 +18,7 @@ def test_passing_empty_value_for_required_param_raises_exception(self): self.assertRaises(ValueError, self.client.indices.exists, index=None) self.assertRaises(ValueError, self.client.indices.exists, index=[]) self.assertRaises(ValueError, self.client.indices.exists, index="") + + def test_put_mapping_without_index(self): + self.client.indices.put_mapping(doc_type="doc-type", body={}) + self.assert_url_called("PUT", "/_all/doc-type/_mapping") diff --git a/test_elasticsearch/test_server/__init__.py b/test_elasticsearch/test_server/__init__.py --- a/test_elasticsearch/test_server/__init__.py +++ b/test_elasticsearch/test_server/__init__.py @@ -1,3 +1,4 @@ +from unittest import SkipTest from elasticsearch.helpers import test from elasticsearch.helpers.test import ElasticsearchTestCase as BaseTestCase @@ -6,6 +7,8 @@ def get_client(**kwargs): global client + if client is False: + raise SkipTest("No client is available") if client is not None and not kwargs: return client @@ -16,7 +19,11 @@ def get_client(**kwargs): new_client = local_get_client(**kwargs) except ImportError: # fallback to using vanilla client - new_client = test.get_test_client(**kwargs) + try: + new_client = test.get_test_client(**kwargs) + except SkipTest: + client = False + raise if not kwargs: client = new_client

cluster.state(index='name')['routing_table'] not found in client 7.5.1 Using the most recent release of the Elasticsearch Python client (7.5.1 at this time), the cluster state endpoint is not defaulting to show `_all` keys. ### 7.5.1 ``` client.cluster.state(index=idx).keys() dict_keys(['cluster_name', 'cluster_uuid']) ``` ### 7.1.1: ``` client.cluster.state(index=idx).keys() dict_keys(['cluster_name', 'cluster_uuid', 'version', 'state_uuid', 'master_node', 'blocks', 'nodes', 'metadata', 'routing_table', 'routing_nodes']) ``` This omission, if intended, would seem to be something to change in a major release version, rather than a minor release. This was detected and reported [here](https://github.com/elastic/support-dev-help/issues/10455#issue-558049925). This compels me to pin Curator at client version `elasticsearch==7.1.0` for now.

Yeah this looks like a regression to me, previously there was logic that made `metric` into `"_all"` if an `index` was given by default but since the API is generated in ffe00d0c3899d200e640e33261d0a324e5dbb5ac it looks like that little piece of logic was lost. Will have to investigate if any other little tidbits like that were lost as well.

2020-03-10T15:29:40Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1157

883287551ab37f8197e6a02459c647cb08f9ba84

diff --git a/elasticsearch/client/tasks.py b/elasticsearch/client/tasks.py --- a/elasticsearch/client/tasks.py +++ b/elasticsearch/client/tasks.py @@ -1,3 +1,4 @@ +import warnings from .utils import NamespacedClient, query_params, _make_path, SKIP_IN_PATH @@ -58,7 +59,7 @@ def cancel(self, task_id=None, params=None, headers=None): ) @query_params("timeout", "wait_for_completion") - def get(self, task_id, params=None, headers=None): + def get(self, task_id=None, params=None, headers=None): """ Returns information about a task. `<https://www.elastic.co/guide/en/elasticsearch/reference/master/tasks.html>`_ @@ -70,7 +71,11 @@ def get(self, task_id, params=None, headers=None): complete (default: false) """ if task_id in SKIP_IN_PATH: - raise ValueError("Empty value passed for a required argument 'task_id'.") + warnings.warn( + "Calling client.tasks.get() without a task_id is deprecated " + "and will be removed in v8.0. Use client.tasks.list() instead.", + DeprecationWarning, + ) return self.transport.perform_request( "GET", _make_path("_tasks", task_id), params=params, headers=headers diff --git a/utils/generate_api.py b/utils/generate_api.py --- a/utils/generate_api.py +++ b/utils/generate_api.py @@ -146,6 +146,13 @@ def all_parts(self): p in url.get("parts", {}) for url in self._def["url"]["paths"] ) + # This piece of logic corresponds to calling + # client.tasks.get() w/o a task_id which was erroneously + # allowed in the 7.1 client library. This functionality + # is deprecated and will be removed in 8.x. + if self.namespace == "tasks" and self.name == "get": + parts["task_id"]["required"] = False + for k, sub in SUBSTITUTIONS.items(): if k in parts: parts[sub] = parts.pop(k)

diff --git a/test_elasticsearch/test_client/__init__.py b/test_elasticsearch/test_client/__init__.py --- a/test_elasticsearch/test_client/__init__.py +++ b/test_elasticsearch/test_client/__init__.py @@ -1,4 +1,5 @@ from __future__ import unicode_literals +import warnings from elasticsearch.client import _normalize_hosts, Elasticsearch @@ -110,3 +111,27 @@ def test_index_uses_put_if_id_is_not_empty(self): self.client.index(index="my-index", id=0, body={}) self.assert_url_called("PUT", "/my-index/_doc/0") + + def test_tasks_get_without_task_id_deprecated(self): + warnings.simplefilter("always", DeprecationWarning) + with warnings.catch_warnings(record=True) as w: + self.client.tasks.get() + + self.assert_url_called("GET", "/_tasks") + self.assertEquals(len(w), 1) + self.assertIs(w[0].category, DeprecationWarning) + self.assertEquals( + str(w[0].message), + "Calling client.tasks.get() without a task_id is deprecated " + "and will be removed in v8.0. Use client.tasks.list() instead.", + ) + + def test_tasks_get_with_task_id_not_deprecated(self): + warnings.simplefilter("always", DeprecationWarning) + with warnings.catch_warnings(record=True) as w: + self.client.tasks.get("task-1") + self.client.tasks.get(task_id="task-2") + + self.assert_url_called("GET", "/_tasks/task-1") + self.assert_url_called("GET", "/_tasks/task-2") + self.assertEquals(len(w), 0)

TasksClient.get() requiring an argument on v7.5.1 <!-- ** Please read the guidelines below. ** 1. GitHub is reserved for bug reports and feature requests. The best place to ask a general question is at the Elastic [forums](https://discuss.elastic.co). GitHub is not the place for general questions. 2. Please fill out EITHER the feature request block or the bug report block below, and delete the other block. 3. For issues with API definition please note that the API is [auto generated](https://github.com/elastic/elasticsearch-py/blob/master/CONTRIBUTING.md#api-code-generation) so any problems should be checked and reported against [the Elasticsearch repo](https://github.com/elastic/elasticsearch) instead. Thank you! --> <!-- Bug report --> **Elasticsearch version** (`bin/elasticsearch --version`): 7.1.1, 7.5.1 **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 7.5.1 Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: Elasticsearch().tasks.get() fails using elasticsearch-py 7.5.1, whereas it succeeds using 7.1.1. ``` >>> import elasticsearch >>> elasticsearch.__versionstr__ '7.5.1' >>> elasticsearch.Elasticsearch().tasks.get() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/nfox/myenv-7.5.1/lib/python3.7/site-packages/elasticsearch/client/utils.py", line 84, in _wrapped return func(*args, params=params, **kwargs) TypeError: get() missing 1 required positional argument: 'task_id' ``` ``` >>> import elasticsearch >>> elasticsearch.__versionstr__ '7.1.0' >>> elasticsearch.Elasticsearch().tasks.get() {'nodes': {'GkSSDiZPTBq4Tlv5xV9wtg': {'name': 'e6a01a4d549f', 'transport_address': '172.17.0.3:9300', 'host': '172.17.0.3', 'ip': '172.17.0.3:9300', 'roles': ['ingest', 'master', 'data', 'ml'], 'attributes': {'ml.machine_memory': '4129972224', 'xpack.installed': 'true', 'ml.max_open_jobs': '20'}, 'tasks': {'GkSSDiZPTBq4Tlv5xV9wtg:56': {'node': 'GkSSDiZPTBq4Tlv5xV9wtg', 'id': 56, 'type': 'direct', 'action': 'cluster:monitor/tasks/lists[n]', 'start_time_in_millis': 1582238526445, 'running_time_in_nanos': 8142200, 'cancellable': False, 'parent_task_id': 'GkSSDiZPTBq4Tlv5xV9wtg:55', 'headers': {}}, 'GkSSDiZPTBq4Tlv5xV9wtg:55': {'node': 'GkSSDiZPTBq4Tlv5xV9wtg', 'id': 55, 'type': 'transport', 'action': 'cluster:monitor/tasks/lists', 'start_time_in_millis': 1582238526442, 'running_time_in_nanos': 11192200, 'cancellable': False, 'headers': {}}}}}} ``` I've verified this against running both ES 7.5.1 and ES 7.1.1 (via docker on localhost) **Steps to reproduce**: run `elasticsearch.Elasticsearch().tasks.get()` while using elasticsearch-py 7.5.1 Ultimately, this is breaking curator's DeleteSnapshot as it checks for tasks.

Elasticsearch version (bin/elasticsearch --version): 7.6.0 elasticsearch-py version (elasticsearch.__versionstr__): 7.5.1 I am running into the same issue. I just upgraded my ES cluster from 6.8 to 7.6. I always have had a lambda that does a bunch of index maintenance, and the delete snapshot portion of it is broken. Other functions of my lambda (delete old indexes, generate snapshots)... etc... are fine. ` [ERROR] TypeError: get() missing 1 required positional argument: 'task_id' Traceback (most recent call last): File "/var/task/handler.py", line 91, in serverlesscurator curator.DeleteSnapshots(snapshot_list).do_action() File "/var/task/curator/actions.py", line 1142, in do_action retry_interval=self.retry_interval, retry_count=self.retry_count): File "/var/task/curator/utils.py", lin [ERROR] TypeError: get() missing 1 required positional argument: 'task_id' Traceback (most recent call last): File "/var/task/handler.py", line 91, in serverlesscurator curator.DeleteSnapshots(snapshot_list).do_action() File "/var/task/curator/actions.py", line 1142, in do_action retry_interval=self.retry_interval, retry_count=self.retry_count): File "/var/task/curator/utils.py", line 1098, in safe_to_snap ongoing_task = find_snapshot_tasks(client) File "/var/task/curator/utils.py", line 1072, in find_snapshot_tasks tasklist = client.tasks.get() File "/var/task/elasticsearch/client/utils.py", line 84, in _wrapped return func(*args, params=params, **kwargs) ` Thanks for pointing this out.... I don't recall this being an issue in the past, as I had always used a 6.x version of the client when I was running ES 6.3 thru 6.8. With the upgrade, I had to change requirements.txt to elasticsearch-curator==5.8.1, so it packaged elasticsearch-py to 7.5.1 @sethmlarson Is this issue similar to what happened in #1141 ? Something with the new generated approach? From what I can tell, https://github.com/elastic/elasticsearch/blob/7.6.0/rest-api-spec/src/main/resources/rest-api-spec/api/tasks.get.json (7.6.0 branch) needs another object under `paths` that would look like: ``` { "path":"/_tasks", "methods":[ "GET" ] } ``` I think adding that would remove the 'required' aspect of the task_id. On a somewhat related note, I think that `tasks.get.json` file also needs a lot more under `params` based on the params listed in https://www.elastic.co/guide/en/elasticsearch/reference/master/tasks.html. I'm just very unsure if I'm looking in the right spot or if all those api files are somehow autogenerated and this isnt the root of the problem. If this is the root of the problem, I'd happily make a PR. @natefox @untergeek It looks like the root of this issue is the YAML marking the `task_id` parameter as required via not having another path without the parameter as mentioned by @natefox. Another client generated via the YAML also has this issue. I'd open a PR upstream. :) @natefox , I'm just driving by, but if you want to get a list of tasks — ie. without an ID —, you should perhaps use the [`tasks.list`](https://github.com/elastic/elasticsearch/blob/7.6.0/rest-api-spec/src/main/resources/rest-api-spec/api/tasks.list.json) API? Oh duh. Somehow looked right past that. So this tasks.list file is what's missing (a call to /tasks without requiring an ID). This tells me the issue is with the code auto generator and not elasticsearch's definitions. @natefox Hmm, when you say the issue is with the code generator what do you mean? I took the comment as `tasks.get()` should have a required `task_id` and `tasks.list()` should be used if you don't have a `task_id`. Am I missing something? @sethmlarson The docs do appear to indicate that `list()` should be used instead of `.get()` for this use case (listing tasks). However, it's a change from previous behavior and it even breaks one of Elastic's own software projects. Not that curator cant be fixed, but I wonder how many other people rely on this behavior in their own scripts? https://github.com/elastic/curator/blob/master/curator/utils.py#L1167 @natefox What I can do is add the feature back and emit a `DeprecationWarning` recommending switching to `.list()` because we don't want to continue with the old API which was incorrect. At least projects won't break then? I think thats a fair approach while removing it for the 8.x.x release (at least moving to v8.0.0 conforms to semver's concept of breaking changes).

2020-03-13T17:57:57Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1176

029699991d2b81f04a00155a36025916581274a2

diff --git a/examples/bulk-ingest/bulk-ingest.py b/examples/bulk-ingest/bulk-ingest.py new file mode 100644 --- /dev/null +++ b/examples/bulk-ingest/bulk-ingest.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python +"""Script that downloads a public dataset and streams it to an Elasticsearch cluster""" + +import csv +from os.path import abspath, join, dirname, exists +import tqdm +import urllib3 +from elasticsearch import Elasticsearch +from elasticsearch.helpers import streaming_bulk + + +NYC_RESTAURANTS = ( + "https://data.cityofnewyork.us/api/views/43nn-pn8j/rows.csv?accessType=DOWNLOAD" +) +DATASET_PATH = join(dirname(abspath(__file__)), "nyc-restaurants.csv") +CHUNK_SIZE = 16384 + + +def download_dataset(): + """Downloads the public dataset if not locally downlaoded + and returns the number of rows are in the .csv file. + """ + if not exists(DATASET_PATH): + http = urllib3.PoolManager() + resp = http.request("GET", NYC_RESTAURANTS, preload_content=False) + + if resp.status != 200: + raise RuntimeError("Could not download dataset") + + with open(DATASET_PATH, mode="wb") as f: + chunk = resp.read(CHUNK_SIZE) + while chunk: + f.write(chunk) + chunk = resp.read(CHUNK_SIZE) + + with open(DATASET_PATH) as f: + return sum([1 for _ in f]) - 1 + + +def create_index(client): + """Creates an index in Elasticsearch if one isn't already there.""" + client.indices.create( + index="nyc-restaurants", + body={ + "settings": {"number_of_shards": 1}, + "mappings": { + "properties": { + "name": {"type": "text"}, + "borough": {"type": "keyword"}, + "cuisine": {"type": "keyword"}, + "grade": {"type": "keyword"}, + "location": {"type": "geo_point"}, + } + }, + }, + ignore=400, + ) + + +def generate_actions(): + """Reads the file through csv.DictReader() and for each row + yields a single document. This function is passed into the bulk() + helper to create many documents in sequence. + """ + with open(DATASET_PATH, mode="r") as f: + reader = csv.DictReader(f) + + for row in reader: + doc = { + "_id": row["CAMIS"], + "name": row["DBA"], + "borough": row["BORO"], + "cuisine": row["CUISINE DESCRIPTION"], + "grade": row["GRADE"] or None, + } + + lat = row["Latitude"] + lon = row["Longitude"] + if lat not in ("", "0") and lon not in ("", "0"): + doc["location"] = {"lat": float(lat), "lon": float(lon)} + yield doc + + +def main(): + print("Loading dataset...") + number_of_docs = download_dataset() + + client = Elasticsearch( + # Add your cluster configuration here! + ) + print("Creating an index...") + create_index(client) + + print("Indexing documents...") + progress = tqdm.tqdm(unit="docs", total=number_of_docs) + successes = 0 + for ok, action in streaming_bulk( + client=client, index="nyc-restaurants", actions=generate_actions(), + ): + progress.update(1) + successes += ok + print("Indexed %d/%d documents" % (successes, number_of_docs)) + + +if __name__ == "__main__": + main()

diff --git a/elasticsearch/helpers/test.py b/elasticsearch/helpers/test.py --- a/elasticsearch/helpers/test.py +++ b/elasticsearch/helpers/test.py @@ -50,7 +50,9 @@ def setUpClass(cls): def tearDown(self): super(ElasticsearchTestCase, self).tearDown() - self.client.indices.delete(index="*", ignore=404, expand_wildcards="open,closed,hidden") + self.client.indices.delete( + index="*", ignore=404, expand_wildcards="open,closed,hidden" + ) self.client.indices.delete_template(name="*", ignore=404) @property

Dead link in documentation There is a dead link in the documentation: Source: https://elasticsearch-py.readthedocs.io/en/master/helpers.html#bulk-helpers There is a link to: https://github.com/elastic/elasticsearch-py/blob/master/example/load.py#L76-L130 ``` curl --silent -I https://github.com/elastic/elasticsearch-py/blob/master/example/load.py\#L76-L130 | head -1 HTTP/1.1 404 Not Found ```

2020-03-23T20:43:54Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1180

f77eca5b28351688f4c9c57c2d5b904810644d0d

diff --git a/elasticsearch/serializer.py b/elasticsearch/serializer.py --- a/elasticsearch/serializer.py +++ b/elasticsearch/serializer.py @@ -2,6 +2,7 @@ import simplejson as json except ImportError: import json + import uuid from datetime import date, datetime from decimal import Decimal @@ -9,6 +10,41 @@ from .exceptions import SerializationError, ImproperlyConfigured from .compat import string_types +INTEGER_TYPES = () +FLOAT_TYPES = (Decimal,) +TIME_TYPES = (date, datetime) + +try: + import numpy as np + + INTEGER_TYPES += ( + np.int_, + np.intc, + np.int8, + np.int16, + np.int32, + np.int64, + np.uint8, + np.uint16, + np.uint32, + np.uint64, + ) + FLOAT_TYPES += ( + np.float_, + np.float16, + np.float32, + np.float64, + ) +except ImportError: + np = None + +try: + import pandas as pd + + TIME_TYPES += (pd.Timestamp,) +except ImportError: + pd = None + class TextSerializer(object): mimetype = "text/plain" @@ -27,12 +63,29 @@ class JSONSerializer(object): mimetype = "application/json" def default(self, data): - if isinstance(data, (date, datetime)): + if isinstance(data, TIME_TYPES): return data.isoformat() - elif isinstance(data, Decimal): - return float(data) elif isinstance(data, uuid.UUID): return str(data) + elif isinstance(data, FLOAT_TYPES): + return float(data) + elif INTEGER_TYPES and isinstance(data, INTEGER_TYPES): + return int(data) + + # Special cases for numpy and pandas types + elif np: + if isinstance(data, np.bool_): + return bool(data) + elif isinstance(data, np.datetime64): + return data.item().isoformat() + elif isinstance(data, np.ndarray): + return data.tolist() + if pd: + if isinstance(data, (pd.Series, pd.Categorical)): + return data.tolist() + elif hasattr(pd, "NA") and pd.isna(data): + return None + raise TypeError("Unable to serialize %r (type: %s)" % (data, type(data))) def loads(self, s):

diff --git a/test_elasticsearch/test_serializer.py b/test_elasticsearch/test_serializer.py --- a/test_elasticsearch/test_serializer.py +++ b/test_elasticsearch/test_serializer.py @@ -5,6 +5,9 @@ from datetime import datetime from decimal import Decimal +import numpy as np +import pandas as pd + from elasticsearch.serializer import ( JSONSerializer, Deserializer, @@ -36,6 +39,86 @@ def test_uuid_serialization(self): ), ) + def test_serializes_numpy_bool(self): + self.assertEquals('{"d":true}', JSONSerializer().dumps({"d": np.bool_(True)})) + + def test_serializes_numpy_integers(self): + ser = JSONSerializer() + for np_type in ( + np.int_, + np.int8, + np.int16, + np.int32, + np.int64, + ): + self.assertEquals(ser.dumps({"d": np_type(-1)}), '{"d":-1}') + + for np_type in ( + np.uint8, + np.uint16, + np.uint32, + np.uint64, + ): + self.assertEquals(ser.dumps({"d": np_type(1)}), '{"d":1}') + + def test_serializes_numpy_floats(self): + ser = JSONSerializer() + for np_type in ( + np.float_, + np.float32, + np.float64, + ): + self.assertRegexpMatches( + ser.dumps({"d": np_type(1.2)}), r'^\{"d":1\.2[\d]*}$' + ) + + def test_serializes_numpy_datetime(self): + self.assertEquals( + '{"d":"2010-10-01T02:30:00"}', + JSONSerializer().dumps({"d": np.datetime64("2010-10-01T02:30:00")}), + ) + + def test_serializes_numpy_ndarray(self): + self.assertEquals( + '{"d":[0,0,0,0,0]}', + JSONSerializer().dumps({"d": np.zeros((5,), dtype=np.uint8)}), + ) + # This isn't useful for Elasticsearch, just want to make sure it works. + self.assertEquals( + '{"d":[[0,0],[0,0]]}', + JSONSerializer().dumps({"d": np.zeros((2, 2), dtype=np.uint8)}), + ) + + def test_serializes_pandas_timestamp(self): + self.assertEquals( + '{"d":"2010-10-01T02:30:00"}', + JSONSerializer().dumps({"d": pd.Timestamp("2010-10-01T02:30:00")}), + ) + + def test_serializes_pandas_series(self): + self.assertEquals( + '{"d":["a","b","c","d"]}', + JSONSerializer().dumps({"d": pd.Series(["a", "b", "c", "d"])}), + ) + + def test_serializes_pandas_na(self): + if not hasattr(pd, "NA"): # pandas.NA added in v1 + raise SkipTest("pandas.NA required") + self.assertEquals( + '{"d":null}', JSONSerializer().dumps({"d": pd.NA}), + ) + + def test_serializes_pandas_category(self): + cat = pd.Categorical(["a", "c", "b", "a"], categories=["a", "b", "c"]) + self.assertEquals( + '{"d":["a","c","b","a"]}', JSONSerializer().dumps({"d": cat}), + ) + + cat = pd.Categorical([1, 2, 3], categories=[1, 2, 3]) + self.assertEquals( + '{"d":[1,2,3]}', JSONSerializer().dumps({"d": cat}), + ) + def test_raises_serialization_error_on_dump_error(self): self.assertRaises(SerializationError, JSONSerializer().dumps, object()) diff --git a/test_elasticsearch/test_server/test_common.py b/test_elasticsearch/test_server/test_common.py --- a/test_elasticsearch/test_server/test_common.py +++ b/test_elasticsearch/test_server/test_common.py @@ -39,6 +39,7 @@ "TestIndicesGetAlias10Basic", # Disallowing expensive queries is 7.7+ "TestSearch320DisallowQueries", + "TestIndicesPutIndexTemplate10Basic", } }

Support serializing numpy and pandas types when available Created this issue to resolve this: https://github.com/elastic/eland/issues/142 I'm thinking we can try importing `numpy` and `pandas` and if we detect either of those modules drop in to a separate section in `JSONSerializer.default()` that handles all the types automatically. I figure it's easier for us to do this once in the client than force everyone using numpy+elasticsearch to maintain this snippet themselves.

Mapping of numpy/pandas types to JSON types. Let me know if I missed any or if anything looks wonky: Numpy: - int, long, short, etc -> `int` - bool_ -> `bool` - float, double -> `float` Pandas: - string -> `str` - bytes -> (Should we decode to `str`?) - floating -> `float` - integer -> `int` decimal -> `float` boolean -> `bool` datetime64 -> `datetime -> str` datetime -> `datetime -> str` date -> `date -> str`

2020-03-25T21:02:00Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1183

bef6abe04962900efc8c0addf8c23c9a0255e3b0

diff --git a/setup.py b/setup.py --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ with open(join(dirname(__file__), "README")) as f: long_description = f.read().strip() -install_requires = ["urllib3>=1.21.1"] +install_requires = ["urllib3>=1.21.1", "certifi"] tests_require = [ "requests>=2.0.0, <3.0.0", "nose",

diff --git a/test_elasticsearch/test_server/test_common.py b/test_elasticsearch/test_server/test_common.py --- a/test_elasticsearch/test_server/test_common.py +++ b/test_elasticsearch/test_server/test_common.py @@ -41,6 +41,7 @@ "TestIndicesGetAlias10Basic", # Disallowing expensive queries is 7.7+ "TestSearch320DisallowQueries", + # v2 Index Template API 8.0+ "TestIndicesPutIndexTemplate10Basic", "TestIndicesGetIndexTemplate10Basic", "TestIndicesGetIndexTemplate20GetMissing",

Using HTTPS without certifi installed gives bad UX Especially when using `cloud_id` since user doesn't know explicitly that HTTPS is being used. Maybe we should simply add certifi as a dependency? But in the meantime we should document this better, esp in the Cloud ID section.

2020-03-30T16:04:52Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1192

d30d87b6fb3eac3a846ec1fb0a3ea9ead8734f09

diff --git a/elasticsearch/client/utils.py b/elasticsearch/client/utils.py --- a/elasticsearch/client/utils.py +++ b/elasticsearch/client/utils.py @@ -102,7 +102,10 @@ def _bulk_body(serializer, body): body = "\n".join(map(serializer.dumps, body)) # bulk body must end with a newline - if not body.endswith("\n"): + if isinstance(body, bytes): + if not body.endswith(b"\n"): + body += b"\n" + elif isinstance(body, string_types) and not body.endswith("\n"): body += "\n" return body

diff --git a/test_elasticsearch/test_client/test_utils.py b/test_elasticsearch/test_client/test_utils.py --- a/test_elasticsearch/test_client/test_utils.py +++ b/test_elasticsearch/test_client/test_utils.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- from __future__ import unicode_literals -from elasticsearch.client.utils import _make_path, _escape, query_params +from elasticsearch.client.utils import _bulk_body, _make_path, _escape, query_params from elasticsearch.compat import PY2 from ..test_cases import TestCase, SkipTest @@ -71,3 +71,27 @@ def test_handles_unicode(self): def test_handles_bytestring(self): string = b"celery-task-meta-c4f1201f-eb7b-41d5-9318-a75a8cfbdaa0" self.assertEquals(string, _escape(string)) + + +class TestBulkBody(TestCase): + def test_proper_bulk_body_as_string_is_not_modified(self): + string_body = '"{"index":{ "_index" : "test"}}\n{"field1": "value1"}"\n' + self.assertEqual(string_body, _bulk_body(None, string_body)) + + def test_proper_bulk_body_as_bytestring_is_not_modified(self): + bytestring_body = b'"{"index":{ "_index" : "test"}}\n{"field1": "value1"}"\n' + self.assertEqual(bytestring_body, _bulk_body(None, bytestring_body)) + + def test_bulk_body_as_string_adds_trailing_newline(self): + string_body = '"{"index":{ "_index" : "test"}}\n{"field1": "value1"}"' + self.assertEqual( + '"{"index":{ "_index" : "test"}}\n{"field1": "value1"}"\n', + _bulk_body(None, string_body), + ) + + def test_bulk_body_as_bytestring_adds_trailing_newline(self): + bytestring_body = b'"{"index":{ "_index" : "test"}}\n{"field1": "value1"}"' + self.assertEqual( + b'"{"index":{ "_index" : "test"}}\n{"field1": "value1"}"\n', + _bulk_body(None, bytestring_body), + ) diff --git a/test_elasticsearch/test_server/test_client.py b/test_elasticsearch/test_server/test_client.py deleted file mode 100644 --- a/test_elasticsearch/test_server/test_client.py +++ /dev/null @@ -1,9 +0,0 @@ -# -*- coding: utf-8 -*- -from __future__ import unicode_literals - -from . import ElasticsearchTestCase - - -class TestUnicode(ElasticsearchTestCase): - def test_indices_analyze(self): - self.client.indices.analyze(body='{"text": "привет"}') diff --git a/test_elasticsearch/test_server/test_clients.py b/test_elasticsearch/test_server/test_clients.py new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_server/test_clients.py @@ -0,0 +1,25 @@ +# -*- coding: utf-8 -*- +from __future__ import unicode_literals + +from . import ElasticsearchTestCase + + +class TestUnicode(ElasticsearchTestCase): + def test_indices_analyze(self): + self.client.indices.analyze(body='{"text": "привет"}') + + +class TestBulk(ElasticsearchTestCase): + def test_bulk_works_with_string_body(self): + docs = '{ "index" : { "_index" : "bulk_test_index", "_id" : "1" } }\n{"answer": 42}' + response = self.client.bulk(body=docs) + + self.assertFalse(response["errors"]) + self.assertEqual(1, len(response["items"])) + + def test_bulk_works_with_bytestring_body(self): + docs = b'{ "index" : { "_index" : "bulk_test_index", "_id" : "2" } }\n{"answer": 42}' + response = self.client.bulk(body=docs) + + self.assertFalse(response["errors"]) + self.assertEqual(1, len(response["items"]))

line 219 in elasticsearch/client/__init__.py (bytes error) This line is ```python def _bulk_body(self, body): # if not passed in a string, serialize items and join by newline if not isinstance(body, string_types): body = '\n'.join(map(self.transport.serializer.dumps, body)) # bulk body must end with a newline if not body.endswith('\n'): body += '\n' return body ``` In the section checking for the \n endswith only accepts bytes so this needs to be b'\n'.

Or at least should make it clear this needs to be passed data as a string I'm guessing you're having an issue when passing the body in as bytes in Python 3? If that's the case I'm pretty sure that the `body` parameter only accepts `str` or an iterable. I'd accept a PR that raises a more friendly `TypeError` if we do receive an unexpected type. @sethmlarson we were hitting the same issue very recently in [Rally](https://github.com/elastic/rally) when reading a source file via [`mmap`](https://docs.python.org/3.8/library/mmap.html#module-mmap) which returns `bytes` (FWIW we were using Python 3.8 in that scenario). `string_types` which is mentioned by the OP is defined as: https://github.com/elastic/elasticsearch-py/blob/1dc8f904b4300c6946efd8f3673814436178a531/elasticsearch/compat.py#L12 So the intention is that this works with `str` and `bytes`. As a proof of concept we've changed the code to: ```python def _bulk_body(self, body): # if not passed in a string, serialize items and join by newline if not isinstance(body, string_types): body = "\n".join(map(self.transport.serializer.dumps, body)) # bulk body must end with a newline if isinstance(body, bytes) and not body.endswith(b"\n"): body += b"\n" elif isinstance(body, str) and not body.endswith("\n"): body += "\n" return body ``` and with that change everything worked fine (i.e. the client was able to index data in Elasticsearch). @danielmitterdorfer Ah that makes sense, I'd approve a PR with that small change and a unit test if you're able to submit one, otherwise I can do so. I think you may have wanted to tag @danielmitterdorfer? Thanks for the feedback. Unfortunately I am swamped with work at the moment so it might take a while until I get to it. I can keep an eye on the issue though and can look into submitting a PR when things get more quiet again.

2020-04-02T14:10:35Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1292

e2ae6deb34870e04b2c490b0772c461b171371a6

diff --git a/docs/conf.py b/docs/conf.py --- a/docs/conf.py +++ b/docs/conf.py @@ -142,7 +142,7 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] +# html_static_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format.

diff --git a/test_elasticsearch/README.rst b/test_elasticsearch/README.rst --- a/test_elasticsearch/README.rst +++ b/test_elasticsearch/README.rst @@ -26,7 +26,7 @@ To simply run the tests just execute the ``run_tests.py`` script or invoke * ``TEST_ES_REPO`` - path to the elasticsearch repo, by default it will look in the same directory as ``elasticsearch-py`` is in. It will not be used if ``TEST_ES_YAML_DIR`` is specified directly. - + * ``TEST_ES_NOFETCH`` - controls if we should fetch new updates to elasticsearch repo and reset it's version to the sha used to build the current es server. Defaults to ``False`` which means we will fetch the elasticsearch repo and @@ -46,15 +46,19 @@ Run Elasticsearch in a Container To run elasticsearch in a container, optionally set the ``ES_VERSION`` environment evariable to either 5.4, 5.3 or 2.4. ``ES_VERSION`` is defaulted to -``latest``. Then run ./start_elasticsearch.sh:: +``latest``. Then run ./start_elasticsearch.sh: + +.. code-block:: console - export ES_VERSION=5.4 - ./start_elasticsearch.sh + $ export ES_VERSION=5.4 + $ ./start_elasticsearch.sh This will run a version for Elasticsearch in a Docker container suitable for running the tests. To check that elasticsearch is running first wait for a -``healthy`` status in ``docker ps``:: +``healthy`` status in ``docker ps``: + +.. code-block:: console $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES

Migrate all rST code blocks from :: to code-block: python Our documentation has a mix of `::` and ` .. code-block: python`, we should migrate to all `code-block: python` so it's less confusing and more apparent when a code block is defined.

2020-06-23T19:36:36Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1297

9d38495e93e5bf0a16ac634f28225a29498b4122

diff --git a/elasticsearch/__init__.py b/elasticsearch/__init__.py --- a/elasticsearch/__init__.py +++ b/elasticsearch/__init__.py @@ -84,10 +84,15 @@ if sys.version_info < (3, 6): raise ImportError - from ._async.http_aiohttp import AIOHttpConnection + from ._async.http_aiohttp import AIOHttpConnection, AsyncConnection from ._async.transport import AsyncTransport from ._async.client import AsyncElasticsearch - __all__ += ["AIOHttpConnection", "AsyncTransport", "AsyncElasticsearch"] + __all__ += [ + "AIOHttpConnection", + "AsyncConnection", + "AsyncTransport", + "AsyncElasticsearch", + ] except (ImportError, SyntaxError): pass diff --git a/elasticsearch/_async/_extra_imports.py b/elasticsearch/_async/_extra_imports.py new file mode 100644 --- /dev/null +++ b/elasticsearch/_async/_extra_imports.py @@ -0,0 +1,36 @@ +# Licensed to Elasticsearch B.V. under one or more contributor +# license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright +# ownership. Elasticsearch B.V. licenses this file to you under +# the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# type: ignore + +# This file exists for the sole reason of making mypy not +# complain about type issues to do with 'aiohttp' and 'yarl'. +# We're in a catch-22 situation: +# - If we use 'type: ignore' on 'import aiohttp' and it's not installed +# mypy will complain that the annotation is unnecessary. +# - If we don't use 'type: ignore' on 'import aiohttp' and it +# it's not installed mypy will complain that it can't find +# type hints for aiohttp. +# So to make mypy happy we move all our 'extra' imports here +# and add a global 'type: ignore' which mypy never complains +# about being unnecessary. + +import aiohttp +import aiohttp.client_exceptions as aiohttp_exceptions +import yarl + +__all__ = ["aiohttp", "aiohttp_exceptions", "yarl"] diff --git a/elasticsearch/_async/client/__init__.py b/elasticsearch/_async/client/__init__.py --- a/elasticsearch/_async/client/__init__.py +++ b/elasticsearch/_async/client/__init__.py @@ -1972,3 +1972,40 @@ async def update_by_query(self, index, body=None, params=None, headers=None): headers=headers, body=body, ) + + @query_params() + async def close_point_in_time(self, body=None, params=None, headers=None): + """ + Close a point in time + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/point-in-time-api.html>`_ + + :arg body: a point-in-time id to close + """ + return await self.transport.perform_request( + "DELETE", "/_pit", params=params, headers=headers, body=body + ) + + @query_params( + "expand_wildcards", "ignore_unavailable", "keep_alive", "preference", "routing" + ) + async def open_point_in_time(self, index=None, params=None, headers=None): + """ + Open a point in time that can be used in subsequent searches + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/point-in-time-api.html>`_ + + :arg index: A comma-separated list of index names to open point + in time; use `_all` or empty string to perform the operation on all + indices + :arg expand_wildcards: Whether to expand wildcard expression to + concrete indices that are open, closed or both. Valid choices: open, + closed, hidden, none, all Default: open + :arg ignore_unavailable: Whether specified concrete indices + should be ignored when unavailable (missing or closed) + :arg keep_alive: Specific the time to live for the point in time + :arg preference: Specify the node or shard the operation should + be performed on (default: random) + :arg routing: Specific routing value + """ + return await self.transport.perform_request( + "POST", _make_path(index, "_pit"), params=params, headers=headers + ) diff --git a/elasticsearch/_async/client/ml.py b/elasticsearch/_async/client/ml.py --- a/elasticsearch/_async/client/ml.py +++ b/elasticsearch/_async/client/ml.py @@ -1296,6 +1296,7 @@ async def delete_trained_model(self, model_id, params=None, headers=None): "decompress_definition", "for_export", "from_", + "include", "include_model_definition", "size", "tags", @@ -1315,6 +1316,9 @@ async def get_trained_models(self, model_id=None, params=None, headers=None): :arg for_export: Omits fields that are illegal to set on model PUT :arg from\\_: skips a number of trained models + :arg include: A comma-separate list of fields to optionally + include. Valid options are 'definition' and 'total_feature_importance'. + Default is none. :arg include_model_definition: Should the full model definition be included in the results. These definitions can be large. So be cautious when including them. Defaults to false. diff --git a/elasticsearch/_async/client/searchable_snapshots.py b/elasticsearch/_async/client/searchable_snapshots.py --- a/elasticsearch/_async/client/searchable_snapshots.py +++ b/elasticsearch/_async/client/searchable_snapshots.py @@ -23,7 +23,7 @@ class SearchableSnapshotsClient(NamespacedClient): async def clear_cache(self, index=None, params=None, headers=None): """ Clear the cache of searchable snapshots. - `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-api-clear-cache.html>`_ + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-apis.html>`_ :arg index: A comma-separated list of index name to limit the operation @@ -71,29 +71,11 @@ async def mount(self, repository, snapshot, body, params=None, headers=None): body=body, ) - @query_params() - async def repository_stats(self, repository, params=None, headers=None): - """ - Retrieve usage statistics about a snapshot repository. - `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-repository-stats.html>`_ - - :arg repository: The repository for which to get the stats for - """ - if repository in SKIP_IN_PATH: - raise ValueError("Empty value passed for a required argument 'repository'.") - - return await self.transport.perform_request( - "GET", - _make_path("_snapshot", repository, "_stats"), - params=params, - headers=headers, - ) - @query_params() async def stats(self, index=None, params=None, headers=None): """ Retrieve various statistics about searchable snapshots. - `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-api-stats.html>`_ + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-apis.html>`_ :arg index: A comma-separated list of index names """ diff --git a/elasticsearch/_async/client/utils.py b/elasticsearch/_async/client/utils.py --- a/elasticsearch/_async/client/utils.py +++ b/elasticsearch/_async/client/utils.py @@ -22,5 +22,5 @@ _bulk_body, query_params, SKIP_IN_PATH, - NamespacedClient, + NamespacedClient as NamespacedClient, ) diff --git a/elasticsearch/_async/http_aiohttp.py b/elasticsearch/_async/http_aiohttp.py --- a/elasticsearch/_async/http_aiohttp.py +++ b/elasticsearch/_async/http_aiohttp.py @@ -18,13 +18,9 @@ import asyncio import ssl import os -import urllib3 +import urllib3 # type: ignore import warnings - -import aiohttp -import yarl -from aiohttp.client_exceptions import ServerFingerprintMismatch, ServerTimeoutError - +from ._extra_imports import aiohttp_exceptions, aiohttp, yarl from .compat import get_running_loop from ..connection import Connection from ..compat import urlencode @@ -52,7 +48,26 @@ pass -class AIOHttpConnection(Connection): +class AsyncConnection(Connection): + """Base class for Async HTTP connection implementations""" + + async def perform_request( + self, + method, + url, + params=None, + body=None, + timeout=None, + ignore=(), + headers=None, + ): + raise NotImplementedError() + + async def close(self): + raise NotImplementedError() + + +class AIOHttpConnection(AsyncConnection): def __init__( self, host="localhost", @@ -199,6 +214,7 @@ async def perform_request( ): if self.session is None: await self._create_aiohttp_session() + assert self.session is not None orig_body = body url_path = self.url_prefix + url @@ -260,11 +276,18 @@ async def perform_request( except Exception as e: self.log_request_fail( - method, url, url_path, orig_body, self.loop.time() - start, exception=e + method, + str(url), + url_path, + orig_body, + self.loop.time() - start, + exception=e, ) - if isinstance(e, ServerFingerprintMismatch): + if isinstance(e, aiohttp_exceptions.ServerFingerprintMismatch): raise SSLError("N/A", str(e), e) - if isinstance(e, (asyncio.TimeoutError, ServerTimeoutError)): + if isinstance( + e, (asyncio.TimeoutError, aiohttp_exceptions.ServerTimeoutError) + ): raise ConnectionTimeout("TIMEOUT", str(e), e) raise ConnectionError("N/A", str(e), e) @@ -276,7 +299,7 @@ async def perform_request( if not (200 <= response.status < 300) and response.status not in ignore: self.log_request_fail( method, - url, + str(url), url_path, orig_body, duration, @@ -286,7 +309,7 @@ async def perform_request( self._raise_error(response.status, raw_data) self.log_request_success( - method, url, url_path, orig_body, response.status, raw_data, duration + method, str(url), url_path, orig_body, response.status, raw_data, duration ) return response.status, response.headers, raw_data @@ -312,9 +335,7 @@ async def _create_aiohttp_session(self): cookie_jar=aiohttp.DummyCookieJar(), response_class=ESClientResponse, connector=aiohttp.TCPConnector( - limit=self._limit, - use_dns_cache=True, - ssl=self._ssl_context, + limit=self._limit, use_dns_cache=True, ssl=self._ssl_context ), ) diff --git a/elasticsearch/client/__init__.py b/elasticsearch/client/__init__.py --- a/elasticsearch/client/__init__.py +++ b/elasticsearch/client/__init__.py @@ -1970,3 +1970,40 @@ def update_by_query(self, index, body=None, params=None, headers=None): headers=headers, body=body, ) + + @query_params() + def close_point_in_time(self, body=None, params=None, headers=None): + """ + Close a point in time + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/point-in-time-api.html>`_ + + :arg body: a point-in-time id to close + """ + return self.transport.perform_request( + "DELETE", "/_pit", params=params, headers=headers, body=body + ) + + @query_params( + "expand_wildcards", "ignore_unavailable", "keep_alive", "preference", "routing" + ) + def open_point_in_time(self, index=None, params=None, headers=None): + """ + Open a point in time that can be used in subsequent searches + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/point-in-time-api.html>`_ + + :arg index: A comma-separated list of index names to open point + in time; use `_all` or empty string to perform the operation on all + indices + :arg expand_wildcards: Whether to expand wildcard expression to + concrete indices that are open, closed or both. Valid choices: open, + closed, hidden, none, all Default: open + :arg ignore_unavailable: Whether specified concrete indices + should be ignored when unavailable (missing or closed) + :arg keep_alive: Specific the time to live for the point in time + :arg preference: Specify the node or shard the operation should + be performed on (default: random) + :arg routing: Specific routing value + """ + return self.transport.perform_request( + "POST", _make_path(index, "_pit"), params=params, headers=headers + ) diff --git a/elasticsearch/client/ml.py b/elasticsearch/client/ml.py --- a/elasticsearch/client/ml.py +++ b/elasticsearch/client/ml.py @@ -1282,6 +1282,7 @@ def delete_trained_model(self, model_id, params=None, headers=None): "decompress_definition", "for_export", "from_", + "include", "include_model_definition", "size", "tags", @@ -1301,6 +1302,9 @@ def get_trained_models(self, model_id=None, params=None, headers=None): :arg for_export: Omits fields that are illegal to set on model PUT :arg from\\_: skips a number of trained models + :arg include: A comma-separate list of fields to optionally + include. Valid options are 'definition' and 'total_feature_importance'. + Default is none. :arg include_model_definition: Should the full model definition be included in the results. These definitions can be large. So be cautious when including them. Defaults to false. diff --git a/elasticsearch/client/searchable_snapshots.py b/elasticsearch/client/searchable_snapshots.py --- a/elasticsearch/client/searchable_snapshots.py +++ b/elasticsearch/client/searchable_snapshots.py @@ -23,7 +23,7 @@ class SearchableSnapshotsClient(NamespacedClient): def clear_cache(self, index=None, params=None, headers=None): """ Clear the cache of searchable snapshots. - `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-api-clear-cache.html>`_ + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-apis.html>`_ :arg index: A comma-separated list of index name to limit the operation @@ -71,29 +71,11 @@ def mount(self, repository, snapshot, body, params=None, headers=None): body=body, ) - @query_params() - def repository_stats(self, repository, params=None, headers=None): - """ - Retrieve usage statistics about a snapshot repository. - `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-repository-stats.html>`_ - - :arg repository: The repository for which to get the stats for - """ - if repository in SKIP_IN_PATH: - raise ValueError("Empty value passed for a required argument 'repository'.") - - return self.transport.perform_request( - "GET", - _make_path("_snapshot", repository, "_stats"), - params=params, - headers=headers, - ) - @query_params() def stats(self, index=None, params=None, headers=None): """ Retrieve various statistics about searchable snapshots. - `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-api-stats.html>`_ + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/searchable-snapshots-apis.html>`_ :arg index: A comma-separated list of index names """ diff --git a/elasticsearch/connection/base.py b/elasticsearch/connection/base.py --- a/elasticsearch/connection/base.py +++ b/elasticsearch/connection/base.py @@ -223,6 +223,18 @@ def _log_trace(self, method, path, body, status_code, response, duration): self._pretty_json(response).replace("\n", "\n#") if response else "", ) + def perform_request( + self, + method, + url, + params=None, + body=None, + timeout=None, + ignore=(), + headers=None, + ): + raise NotImplementedError() + def log_request_success( self, method, full_url, path, body, status_code, response, duration ): diff --git a/elasticsearch/connection/http_urllib3.py b/elasticsearch/connection/http_urllib3.py --- a/elasticsearch/connection/http_urllib3.py +++ b/elasticsearch/connection/http_urllib3.py @@ -17,9 +17,9 @@ import time import ssl -import urllib3 -from urllib3.exceptions import ReadTimeoutError, SSLError as UrllibSSLError -from urllib3.util.retry import Retry +import urllib3 # type: ignore +from urllib3.exceptions import ReadTimeoutError, SSLError as UrllibSSLError # type: ignore +from urllib3.util.retry import Retry # type: ignore import warnings from .base import Connection diff --git a/elasticsearch/connection/pooling.py b/elasticsearch/connection/pooling.py --- a/elasticsearch/connection/pooling.py +++ b/elasticsearch/connection/pooling.py @@ -15,11 +15,12 @@ # specific language governing permissions and limitations # under the License. +from .base import Connection + try: import queue except ImportError: - import Queue as queue -from .base import Connection + import Queue as queue # type: ignore class PoolingConnection(Connection): @@ -34,6 +35,9 @@ def __init__(self, *args, **kwargs): self._free_connections = queue.Queue() super(PoolingConnection, self).__init__(*args, **kwargs) + def _make_connection(self): + raise NotImplementedError + def _get_connection(self): try: return self._free_connections.get_nowait() diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -419,7 +419,7 @@ def parallel_bulk( class BlockingPool(ThreadPool): def _setup_queues(self): - super(BlockingPool, self)._setup_queues() + super(BlockingPool, self)._setup_queues() # type: ignore # The queue must be at least the size of the number of threads to # prevent hanging when inserting sentinel values during teardown. self._inqueue = Queue(max(queue_size, thread_count)) diff --git a/elasticsearch/helpers/errors.py b/elasticsearch/helpers/errors.py --- a/elasticsearch/helpers/errors.py +++ b/elasticsearch/helpers/errors.py @@ -27,5 +27,5 @@ def errors(self): class ScanError(ElasticsearchException): def __init__(self, scroll_id, *args, **kwargs): - super(ScanError, self).__init__(*args, **kwargs) + super(ScanError, self).__init__(*args, **kwargs) # type: ignore self.scroll_id = scroll_id diff --git a/elasticsearch/serializer.py b/elasticsearch/serializer.py --- a/elasticsearch/serializer.py +++ b/elasticsearch/serializer.py @@ -63,7 +63,17 @@ pd = None -class TextSerializer(object): +class Serializer(object): + mimetype = "" + + def loads(self, s): + raise NotImplementedError() + + def dumps(self, data): + raise NotImplementedError() + + +class TextSerializer(Serializer): mimetype = "text/plain" def loads(self, s): @@ -76,7 +86,7 @@ def dumps(self, data): raise SerializationError("Cannot serialize %r into text." % data) -class JSONSerializer(object): +class JSONSerializer(Serializer): mimetype = "application/json" def default(self, data): diff --git a/noxfile.py b/noxfile.py --- a/noxfile.py +++ b/noxfile.py @@ -47,16 +47,33 @@ def blacken(session): @nox.session() def lint(session): - session.install("flake8", "black") + session.install("flake8", "black", "mypy") session.run("black", "--target-version=py27", "--check", *SOURCE_FILES) session.run("flake8", *SOURCE_FILES) session.run("python", "utils/license_headers.py", "check", *SOURCE_FILES) + # Workaround to make '-r' to still work despite uninstalling aiohttp below. + session.run("python", "-m", "pip", "install", "aiohttp") + + # Run mypy on the package and then the type examples separately for + # the two different mypy use-cases, ourselves and our users. + session.run("mypy", "--strict", "elasticsearch/") + session.run("mypy", "--strict", "test_elasticsearch/test_types/") + + # Make sure we don't require aiohttp to be installed for users to + # receive type hint information from mypy. + session.run("python", "-m", "pip", "uninstall", "--yes", "aiohttp", "yarl") + session.run("mypy", "--strict", "elasticsearch/") + session.run("mypy", "--strict", "test_elasticsearch/test_types/sync_types.py") + @nox.session() def docs(session): session.install(".") - session.install("-rdev-requirements.txt", "sphinx-rtd-theme") + session.install( + "-rdev-requirements.txt", "sphinx-rtd-theme", "sphinx-autodoc-typehints" + ) + session.run("python", "-m", "pip", "install", "sphinx-autodoc-typehints") session.run("sphinx-build", "docs/sphinx/", "docs/sphinx/_build", "-b", "html") diff --git a/setup.py b/setup.py --- a/setup.py +++ b/setup.py @@ -61,6 +61,9 @@ "Issue Tracker": "https://github.com/elastic/elasticsearch-py/issues", }, packages=find_packages(where=".", exclude=("test_elasticsearch*",)), + package_data={"elasticsearch": ["py.typed"]}, + include_package_data=True, + zip_safe=False, classifiers=[ "Development Status :: 5 - Production/Stable", "License :: OSI Approved :: Apache Software License", diff --git a/utils/generate_api.py b/utils/generate_api.py --- a/utils/generate_api.py +++ b/utils/generate_api.py @@ -62,6 +62,16 @@ / "rest-api-spec" / "api" ) +GLOBAL_QUERY_PARAMS = { + "pretty": "Optional[bool]", + "human": "Optional[bool]", + "error_trace": "Optional[bool]", + "format": "Optional[str]", + "filter_path": "Optional[Union[str, Collection[str]]]", + "request_timeout": "Optional[Union[int, float]]", + "ignore": "Optional[Union[int, Collection[int]]]", + "opaque_id": "Optional[str]", +} jinja_env = Environment( loader=FileSystemLoader([CODE_ROOT / "utils" / "templates"]), @@ -78,15 +88,20 @@ def blacken(filename): @lru_cache() def is_valid_url(url): - return http.request("HEAD", url).status == 200 + return 200 <= http.request("HEAD", url).status < 400 class Module: - def __init__(self, namespace): + def __init__(self, namespace, is_pyi=False): self.namespace = namespace + self.is_pyi = is_pyi self._apis = [] self.parse_orig() + if not is_pyi: + self.pyi = Module(namespace, is_pyi=True) + self.pyi.orders = self.orders[:] + def add(self, api): self._apis.append(api) @@ -128,17 +143,23 @@ def dump(self): f.write(self.header) for api in self._apis: f.write(api.to_python()) - blacken(self.filepath) + + if not self.is_pyi: + self.pyi.dump() @property def filepath(self): - return CODE_ROOT / f"elasticsearch/_async/client/{self.namespace}.py" + return ( + CODE_ROOT + / f"elasticsearch/_async/client/{self.namespace}.py{'i' if self.is_pyi else ''}" + ) class API: - def __init__(self, namespace, name, definition): + def __init__(self, namespace, name, definition, is_pyi=False): self.namespace = namespace self.name = name + self.is_pyi = is_pyi # overwrite the dict to maintain key order definition["params"] = { @@ -187,6 +208,7 @@ def all_parts(self): parts[p]["required"] = all( p in url.get("parts", {}) for url in self._def["url"]["paths"] ) + parts[p]["type"] = "Any" for k, sub in SUBSTITUTIONS.items(): if k in parts: @@ -233,6 +255,19 @@ def query_params(self): if k not in self.all_parts ) + @property + def all_func_params(self): + """Parameters that will be in the '@query_params' decorator list + and parameters that will be in the function signature. + This doesn't include + """ + params = list(self._def.get("params", {}).keys()) + for url in self._def["url"]["paths"]: + params.extend(url.get("parts", {}).keys()) + if self.body: + params.append("body") + return params + @property def path(self): return max( @@ -279,12 +314,18 @@ def required_parts(self): return required def to_python(self): - try: - t = jinja_env.get_template(f"overrides/{self.namespace}/{self.name}") - except TemplateNotFound: - t = jinja_env.get_template("base") + if self.is_pyi: + t = jinja_env.get_template("base_pyi") + else: + try: + t = jinja_env.get_template(f"overrides/{self.namespace}/{self.name}") + except TemplateNotFound: + t = jinja_env.get_template("base") + return t.render( - api=self, substitutions={v: k for k, v in SUBSTITUTIONS.items()} + api=self, + substitutions={v: k for k, v in SUBSTITUTIONS.items()}, + global_query_params=GLOBAL_QUERY_PARAMS, ) @@ -313,6 +354,7 @@ def read_modules(): modules[namespace] = Module(namespace) modules[namespace].add(API(namespace, name, api)) + modules[namespace].pyi.add(API(namespace, name, api, is_pyi=True)) return modules @@ -340,7 +382,14 @@ def dump_modules(modules): filepaths = [] for root, _, filenames in os.walk(CODE_ROOT / "elasticsearch/_async"): for filename in filenames: - if filename.endswith(".py") and filename != "utils.py": + if ( + filename.rpartition(".")[-1] + in ( + "py", + "pyi", + ) + and not filename.startswith("utils.py") + ): filepaths.append(os.path.join(root, filename)) unasync.unasync_files(filepaths, rules) diff --git a/utils/license_headers.py b/utils/license_headers.py --- a/utils/license_headers.py +++ b/utils/license_headers.py @@ -21,6 +21,7 @@ """ import os +import re import sys from typing import List, Iterator from itertools import chain @@ -64,7 +65,7 @@ def find_files_to_fix(sources: List[str]) -> Iterator[str]: def does_file_need_fix(filepath: str) -> bool: - if not filepath.endswith(".py"): + if not re.search(r"\.pyi?$", filepath): return False with open(filepath, mode="r") as f: first_license_line = None

diff --git a/elasticsearch/helpers/test.py b/elasticsearch/helpers/test.py --- a/elasticsearch/helpers/test.py +++ b/elasticsearch/helpers/test.py @@ -15,6 +15,8 @@ # specific language governing permissions and limitations # under the License. +# type: ignore + import time import os from unittest import TestCase, SkipTest diff --git a/elasticsearch/helpers/test.pyi b/elasticsearch/helpers/test.pyi new file mode 100644 --- /dev/null +++ b/elasticsearch/helpers/test.pyi @@ -0,0 +1,31 @@ +# Licensed to Elasticsearch B.V. under one or more contributor +# license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright +# ownership. Elasticsearch B.V. licenses this file to you under +# the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from typing import Any, Tuple +from unittest import TestCase +from ..client import Elasticsearch + +def get_test_client(nowait: bool = ..., **kwargs: Any) -> Elasticsearch: ... +def _get_version(version_string: str) -> Tuple[int, ...]: ... + +class ElasticsearchTestCase(TestCase): + @staticmethod + def _get_client() -> Elasticsearch: ... + @classmethod + def setup_class(cls) -> None: ... + def teardown_method(self, _: Any) -> None: ... + def es_version(self) -> Tuple[int, ...]: ... diff --git a/test_elasticsearch/test_types/README.md b/test_elasticsearch/test_types/README.md new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_types/README.md @@ -0,0 +1,6 @@ +# Type Hints + +All of these scripts are used to test the type hinting +distributed with the `elasticsearch` package. +These scripts simulate normal usage of the client and are run +through `mypy --strict` as a part of continuous integration. diff --git a/test_elasticsearch/test_types/async_types.py b/test_elasticsearch/test_types/async_types.py new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_types/async_types.py @@ -0,0 +1,109 @@ +# Licensed to Elasticsearch B.V. under one or more contributor +# license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright +# ownership. Elasticsearch B.V. licenses this file to you under +# the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from typing import AsyncGenerator, Dict, Any +from elasticsearch import ( + AsyncElasticsearch, + AsyncTransport, + AIOHttpConnection, + ConnectionPool, +) +from elasticsearch.helpers import ( + async_scan, + async_streaming_bulk, + async_reindex, + async_bulk, +) + + +es = AsyncElasticsearch( + [{"host": "localhost", "port": 9443}], + transport_class=AsyncTransport, +) +t = AsyncTransport( + [{}], + connection_class=AIOHttpConnection, + connection_pool_class=ConnectionPool, + sniff_on_start=True, + sniffer_timeout=0.1, + sniff_timeout=1, + sniff_on_connection_fail=False, + max_retries=1, + retry_on_status={100, 400, 503}, + retry_on_timeout=True, + send_get_body_as="source", +) + + +async def async_gen() -> AsyncGenerator[Dict[Any, Any], None]: + yield {} + + +async def async_scan_types() -> None: + async for _ in async_scan( + es, + query={"query": {"match_all": {}}}, + request_timeout=10, + clear_scroll=True, + scroll_kwargs={"request_timeout": 10}, + ): + pass + async for _ in async_scan( + es, + raise_on_error=False, + preserve_order=False, + scroll="10m", + size=10, + request_timeout=10.0, + ): + pass + + +async def async_streaming_bulk_types() -> None: + async for _ in async_streaming_bulk(es, async_gen()): + pass + async for _ in async_streaming_bulk(es, async_gen().__aiter__()): + pass + async for _ in async_streaming_bulk(es, [{}]): + pass + async for _ in async_streaming_bulk(es, ({},)): + pass + + +async def async_bulk_types() -> None: + _, _ = await async_bulk(es, async_gen()) + _, _ = await async_bulk(es, async_gen().__aiter__()) + _, _ = await async_bulk(es, [{}]) + _, _ = await async_bulk(es, ({},)) + + +async def async_reindex_types() -> None: + _, _ = await async_reindex( + es, "src-index", "target-index", query={"query": {"match": {"key": "val"}}} + ) + _, _ = await async_reindex( + es, source_index="src-index", target_index="target-index", target_client=es + ) + _, _ = await async_reindex( + es, + "src-index", + "target-index", + chunk_size=1, + scroll="10m", + scan_kwargs={"request_timeout": 10}, + bulk_kwargs={"request_timeout": 10}, + ) diff --git a/test_elasticsearch/test_types/sync_types.py b/test_elasticsearch/test_types/sync_types.py new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_types/sync_types.py @@ -0,0 +1,104 @@ +# Licensed to Elasticsearch B.V. under one or more contributor +# license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright +# ownership. Elasticsearch B.V. licenses this file to you under +# the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from typing import Generator, Dict, Any +from elasticsearch import ( + Elasticsearch, + Transport, + RequestsHttpConnection, + ConnectionPool, +) +from elasticsearch.helpers import scan, streaming_bulk, reindex, bulk + + +es = Elasticsearch( + [{"host": "localhost", "port": 9443}], + transport_class=Transport, +) +t = Transport( + [{}], + connection_class=RequestsHttpConnection, + connection_pool_class=ConnectionPool, + sniff_on_start=True, + sniffer_timeout=0.1, + sniff_timeout=1, + sniff_on_connection_fail=False, + max_retries=1, + retry_on_status={100, 400, 503}, + retry_on_timeout=True, + send_get_body_as="source", +) + + +def sync_gen() -> Generator[Dict[Any, Any], None, None]: + yield {} + + +def scan_types() -> None: + for _ in scan( + es, + query={"query": {"match_all": {}}}, + request_timeout=10, + clear_scroll=True, + scroll_kwargs={"request_timeout": 10}, + ): + pass + for _ in scan( + es, + raise_on_error=False, + preserve_order=False, + scroll="10m", + size=10, + request_timeout=10.0, + ): + pass + + +def streaming_bulk_types() -> None: + for _ in streaming_bulk(es, sync_gen()): + pass + for _ in streaming_bulk(es, sync_gen().__iter__()): + pass + for _ in streaming_bulk(es, [{}]): + pass + for _ in streaming_bulk(es, ({},)): + pass + + +def bulk_types() -> None: + _, _ = bulk(es, sync_gen()) + _, _ = bulk(es, sync_gen().__iter__()) + _, _ = bulk(es, [{}]) + _, _ = bulk(es, ({},)) + + +def reindex_types() -> None: + _, _ = reindex( + es, "src-index", "target-index", query={"query": {"match": {"key": "val"}}} + ) + _, _ = reindex( + es, source_index="src-index", target_index="target-index", target_client=es + ) + _, _ = reindex( + es, + "src-index", + "target-index", + chunk_size=1, + scroll="10m", + scan_kwargs={"request_timeout": 10}, + bulk_kwargs={"request_timeout": 10}, + )

query_param decorator does not preserve signature The `query_param` decorator is used on pretty much all client methods. It uses `functools.wraps` to copy `__module__`, `__name__`, `__doc__` etc. Unfortunately, `wraps` does not preserve the signature of the wrapped function, so `inspect.getargspec()` and friends become pretty much useless (`inspect.signature()` works but only in Python 3.5+, and only because it cheats). This makes it difficult for e.g. code completion in IDEs to work correctly. On a more egoistic note, it also makes it more difficult for things like Elastic APM to introspect the client 😊 Preserving the signature is a bit less trivial than preserving `__doc__` etc, but there are a few utilities that could help: * [`decorator.py`](https://github.com/micheles/decorator) * [boltons](https://github.com/mahmoud/boltons) (specifically `boltons.funcutils.wraps`) There's also [wrapt](https://github.com/GrahamDumpleton/wrapt), but that might be a bit too heavy just to preserve the signature. We could either add one of the two libraries as a dependency, or vendor them (they are both BSD).

I have the same question. But why not just use `**kwargs`? There's already a docstring that explains possible parameters. Removing `query_parmas` will fix the pylint errors and I think it's more pythonic to use `**kwargs` for additional parameters This would certainly be nice, but unfortunately it's not as easy as using a proper decorator tool - the interface of the methods (ability to do `es.search(q="hello")`) is not an interface of any actual method or function. It is, therefore, not a question of preserving it but actually manufacturing a custom one based on the list passed to `query_params`. it is definitely doable, just a different problem. Yes, totally. One of the two decorators could be a starting point (they basically use the same approach of having a `FunctionBuilder` that builds a wrapper with the correct signature). If y'all are OK with carrying a bit of somewhat magic code unrelated to the core functionality in the codebase to solve this problem, I'm happy to give it a shot and implement it. @beniwohli if you wanna take a stab at it please feel free! :+1:

2020-06-26T19:40:44Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1353

55915dc7d1e238045052bad7b1139274f15e4337

diff --git a/elasticsearch/_async/helpers.py b/elasticsearch/_async/helpers.py new file mode 100644 --- /dev/null +++ b/elasticsearch/_async/helpers.py @@ -0,0 +1,430 @@ +# Licensed to Elasticsearch B.V. under one or more contributor +# license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright +# ownership. Elasticsearch B.V. licenses this file to you under +# the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Licensed to Elasticsearch B.V under one or more agreements. +# Elasticsearch B.V licenses this file to you under the Apache 2.0 License. +# See the LICENSE file in the project root for more information + +import asyncio + +from .client import AsyncElasticsearch # noqa +from ..exceptions import TransportError +from ..compat import map + +from ..helpers.actions import ( + _ActionChunker, + _process_bulk_chunk_error, + _process_bulk_chunk_success, + expand_action, +) +from ..helpers.errors import ScanError + +import logging + + +logger = logging.getLogger("elasticsearch.helpers") + + +async def _chunk_actions(actions, chunk_size, max_chunk_bytes, serializer): + """ + Split actions into chunks by number or size, serialize them into strings in + the process. + """ + chunker = _ActionChunker( + chunk_size=chunk_size, max_chunk_bytes=max_chunk_bytes, serializer=serializer + ) + async for action, data in actions: + ret = chunker.feed(action, data) + if ret: + yield ret + ret = chunker.flush() + if ret: + yield ret + + +async def _process_bulk_chunk( + client, + bulk_actions, + bulk_data, + raise_on_exception=True, + raise_on_error=True, + *args, + **kwargs +): + """ + Send a bulk request to elasticsearch and process the output. + """ + try: + # send the actual request + resp = await client.bulk("\n".join(bulk_actions) + "\n", *args, **kwargs) + except TransportError as e: + gen = _process_bulk_chunk_error( + error=e, + bulk_data=bulk_data, + raise_on_exception=raise_on_exception, + raise_on_error=raise_on_error, + ) + else: + gen = _process_bulk_chunk_success( + resp=resp, bulk_data=bulk_data, raise_on_error=raise_on_error + ) + for item in gen: + yield item + + +def aiter(x): + """Turns an async iterable or iterable into an async iterator""" + if hasattr(x, "__anext__"): + return x + elif hasattr(x, "__aiter__"): + return x.__aiter__() + + async def f(): + for item in x: + yield item + + return f().__aiter__() + + +async def azip(*iterables): + """Zips async iterables and iterables into an async iterator + with the same behavior as zip() + """ + aiters = [aiter(x) for x in iterables] + try: + while True: + yield tuple([await x.__anext__() for x in aiters]) + except StopAsyncIteration: + pass + + +async def async_streaming_bulk( + client, + actions, + chunk_size=500, + max_chunk_bytes=100 * 1024 * 1024, + raise_on_error=True, + expand_action_callback=expand_action, + raise_on_exception=True, + max_retries=0, + initial_backoff=2, + max_backoff=600, + yield_ok=True, + *args, + **kwargs +): + + """ + Streaming bulk consumes actions from the iterable passed in and yields + results per action. For non-streaming usecases use + :func:`~elasticsearch.helpers.async_bulk` which is a wrapper around streaming + bulk that returns summary information about the bulk operation once the + entire input is consumed and sent. + + If you specify ``max_retries`` it will also retry any documents that were + rejected with a ``429`` status code. To do this it will wait (**by calling + asyncio.sleep**) for ``initial_backoff`` seconds and then, + every subsequent rejection for the same chunk, for double the time every + time up to ``max_backoff`` seconds. + + :arg client: instance of :class:`~elasticsearch.AsyncElasticsearch` to use + :arg actions: iterable or async iterable containing the actions to be executed + :arg chunk_size: number of docs in one chunk sent to es (default: 500) + :arg max_chunk_bytes: the maximum size of the request in bytes (default: 100MB) + :arg raise_on_error: raise ``BulkIndexError`` containing errors (as `.errors`) + from the execution of the last chunk when some occur. By default we raise. + :arg raise_on_exception: if ``False`` then don't propagate exceptions from + call to ``bulk`` and just report the items that failed as failed. + :arg expand_action_callback: callback executed on each action passed in, + should return a tuple containing the action line and the data line + (`None` if data line should be omitted). + :arg max_retries: maximum number of times a document will be retried when + ``429`` is received, set to 0 (default) for no retries on ``429`` + :arg initial_backoff: number of seconds we should wait before the first + retry. Any subsequent retries will be powers of ``initial_backoff * + 2**retry_number`` + :arg max_backoff: maximum number of seconds a retry will wait + :arg yield_ok: if set to False will skip successful documents in the output + """ + + async def map_actions(): + async for item in aiter(actions): + yield expand_action_callback(item) + + async for bulk_data, bulk_actions in _chunk_actions( + map_actions(), chunk_size, max_chunk_bytes, client.transport.serializer + ): + + for attempt in range(max_retries + 1): + to_retry, to_retry_data = [], [] + if attempt: + await asyncio.sleep( + min(max_backoff, initial_backoff * 2 ** (attempt - 1)) + ) + + try: + async for data, (ok, info) in azip( + bulk_data, + _process_bulk_chunk( + client, + bulk_actions, + bulk_data, + raise_on_exception, + raise_on_error, + *args, + **kwargs + ), + ): + + if not ok: + action, info = info.popitem() + # retry if retries enabled, we get 429, and we are not + # in the last attempt + if ( + max_retries + and info["status"] == 429 + and (attempt + 1) <= max_retries + ): + # _process_bulk_chunk expects strings so we need to + # re-serialize the data + to_retry.extend( + map(client.transport.serializer.dumps, data) + ) + to_retry_data.append(data) + else: + yield ok, {action: info} + elif yield_ok: + yield ok, info + + except TransportError as e: + # suppress 429 errors since we will retry them + if attempt == max_retries or e.status_code != 429: + raise + else: + if not to_retry: + break + # retry only subset of documents that didn't succeed + bulk_actions, bulk_data = to_retry, to_retry_data + + +async def async_bulk(client, actions, stats_only=False, *args, **kwargs): + """ + Helper for the :meth:`~elasticsearch.AsyncElasticsearch.bulk` api that provides + a more human friendly interface - it consumes an iterator of actions and + sends them to elasticsearch in chunks. It returns a tuple with summary + information - number of successfully executed actions and either list of + errors or number of errors if ``stats_only`` is set to ``True``. Note that + by default we raise a ``BulkIndexError`` when we encounter an error so + options like ``stats_only`` only+ apply when ``raise_on_error`` is set to + ``False``. + + When errors are being collected original document data is included in the + error dictionary which can lead to an extra high memory usage. If you need + to process a lot of data and want to ignore/collect errors please consider + using the :func:`~elasticsearch.helpers.async_streaming_bulk` helper which will + just return the errors and not store them in memory. + + + :arg client: instance of :class:`~elasticsearch.AsyncElasticsearch` to use + :arg actions: iterator containing the actions + :arg stats_only: if `True` only report number of successful/failed + operations instead of just number of successful and a list of error responses + + Any additional keyword arguments will be passed to + :func:`~elasticsearch.helpers.async_streaming_bulk` which is used to execute + the operation, see :func:`~elasticsearch.helpers.async_streaming_bulk` for more + accepted parameters. + """ + success, failed = 0, 0 + + # list of errors to be collected is not stats_only + errors = [] + + # make streaming_bulk yield successful results so we can count them + kwargs["yield_ok"] = True + async for ok, item in async_streaming_bulk(client, actions, *args, **kwargs): + # go through request-response pairs and detect failures + if not ok: + if not stats_only: + errors.append(item) + failed += 1 + else: + success += 1 + + return success, failed if stats_only else errors + + +async def async_scan( + client, + query=None, + scroll="5m", + raise_on_error=True, + preserve_order=False, + size=1000, + request_timeout=None, + clear_scroll=True, + scroll_kwargs=None, + **kwargs +): + """ + Simple abstraction on top of the + :meth:`~elasticsearch.AsyncElasticsearch.scroll` api - a simple iterator that + yields all hits as returned by underlining scroll requests. + + By default scan does not return results in any pre-determined order. To + have a standard order in the returned documents (either by score or + explicit sort definition) when scrolling, use ``preserve_order=True``. This + may be an expensive operation and will negate the performance benefits of + using ``scan``. + + :arg client: instance of :class:`~elasticsearch.AsyncElasticsearch` to use + :arg query: body for the :meth:`~elasticsearch.AsyncElasticsearch.search` api + :arg scroll: Specify how long a consistent view of the index should be + maintained for scrolled search + :arg raise_on_error: raises an exception (``ScanError``) if an error is + encountered (some shards fail to execute). By default we raise. + :arg preserve_order: don't set the ``search_type`` to ``scan`` - this will + cause the scroll to paginate with preserving the order. Note that this + can be an extremely expensive operation and can easily lead to + unpredictable results, use with caution. + :arg size: size (per shard) of the batch send at each iteration. + :arg request_timeout: explicit timeout for each call to ``scan`` + :arg clear_scroll: explicitly calls delete on the scroll id via the clear + scroll API at the end of the method on completion or error, defaults + to true. + :arg scroll_kwargs: additional kwargs to be passed to + :meth:`~elasticsearch.AsyncElasticsearch.scroll` + + Any additional keyword arguments will be passed to the initial + :meth:`~elasticsearch.AsyncElasticsearch.search` call:: + + async_scan(es, + query={"query": {"match": {"title": "python"}}}, + index="orders-*", + doc_type="books" + ) + + """ + scroll_kwargs = scroll_kwargs or {} + + if not preserve_order: + query = query.copy() if query else {} + query["sort"] = "_doc" + + # initial search + resp = await client.search( + body=query, scroll=scroll, size=size, request_timeout=request_timeout, **kwargs + ) + scroll_id = resp.get("_scroll_id") + + try: + while scroll_id and resp["hits"]["hits"]: + for hit in resp["hits"]["hits"]: + yield hit + + # check if we have any errors + if (resp["_shards"]["successful"] + resp["_shards"]["skipped"]) < resp[ + "_shards" + ]["total"]: + logger.warning( + "Scroll request has only succeeded on %d (+%d skipped) shards out of %d.", + resp["_shards"]["successful"], + resp["_shards"]["skipped"], + resp["_shards"]["total"], + ) + if raise_on_error: + raise ScanError( + scroll_id, + "Scroll request has only succeeded on %d (+%d skiped) shards out of %d." + % ( + resp["_shards"]["successful"], + resp["_shards"]["skipped"], + resp["_shards"]["total"], + ), + ) + resp = await client.scroll( + body={"scroll_id": scroll_id, "scroll": scroll}, **scroll_kwargs + ) + scroll_id = resp.get("_scroll_id") + + finally: + if scroll_id and clear_scroll: + await client.clear_scroll(body={"scroll_id": [scroll_id]}, ignore=(404,)) + + +async def async_reindex( + client, + source_index, + target_index, + query=None, + target_client=None, + chunk_size=500, + scroll="5m", + scan_kwargs={}, + bulk_kwargs={}, +): + + """ + Reindex all documents from one index that satisfy a given query + to another, potentially (if `target_client` is specified) on a different cluster. + If you don't specify the query you will reindex all the documents. + + Since ``2.3`` a :meth:`~elasticsearch.AsyncElasticsearch.reindex` api is + available as part of elasticsearch itself. It is recommended to use the api + instead of this helper wherever possible. The helper is here mostly for + backwards compatibility and for situations where more flexibility is + needed. + + .. note:: + + This helper doesn't transfer mappings, just the data. + + :arg client: instance of :class:`~elasticsearch.AsyncElasticsearch` to use (for + read if `target_client` is specified as well) + :arg source_index: index (or list of indices) to read documents from + :arg target_index: name of the index in the target cluster to populate + :arg query: body for the :meth:`~elasticsearch.AsyncElasticsearch.search` api + :arg target_client: optional, is specified will be used for writing (thus + enabling reindex between clusters) + :arg chunk_size: number of docs in one chunk sent to es (default: 500) + :arg scroll: Specify how long a consistent view of the index should be + maintained for scrolled search + :arg scan_kwargs: additional kwargs to be passed to + :func:`~elasticsearch.helpers.async_scan` + :arg bulk_kwargs: additional kwargs to be passed to + :func:`~elasticsearch.helpers.async_bulk` + """ + target_client = client if target_client is None else target_client + docs = async_scan( + client, query=query, index=source_index, scroll=scroll, **scan_kwargs + ) + + async def _change_doc_index(hits, index): + async for h in hits: + h["_index"] = index + if "fields" in h: + h.update(h.pop("fields")) + yield h + + kwargs = {"stats_only": True} + kwargs.update(bulk_kwargs) + return await async_bulk( + target_client, + _change_doc_index(docs, target_index), + chunk_size=chunk_size, + **kwargs + ) diff --git a/elasticsearch/helpers/__init__.py b/elasticsearch/helpers/__init__.py --- a/elasticsearch/helpers/__init__.py +++ b/elasticsearch/helpers/__init__.py @@ -15,6 +15,7 @@ # specific language governing permissions and limitations # under the License. +import sys from .errors import BulkIndexError, ScanError from .actions import expand_action, streaming_bulk, bulk, parallel_bulk from .actions import scan, reindex @@ -32,3 +33,20 @@ "_chunk_actions", "_process_bulk_chunk", ] + + +try: + # Asyncio only supported on Python 3.6+ + if sys.version_info < (3, 6): + raise ImportError + + from .._async.helpers import ( + async_scan, + async_bulk, + async_reindex, + async_streaming_bulk, + ) + + __all__ += ["async_scan", "async_bulk", "async_reindex", "async_streaming_bulk"] +except (ImportError, SyntaxError): + pass diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -80,91 +80,77 @@ def expand_action(data): return action, data.get("_source", data) -def _chunk_actions(actions, chunk_size, max_chunk_bytes, serializer): - """ - Split actions into chunks by number or size, serialize them into strings in - the process. - """ - bulk_actions, bulk_data = [], [] - size, action_count = 0, 0 - for action, data in actions: +class _ActionChunker: + def __init__(self, chunk_size, max_chunk_bytes, serializer): + self.chunk_size = chunk_size + self.max_chunk_bytes = max_chunk_bytes + self.serializer = serializer + + self.size = 0 + self.action_count = 0 + self.bulk_actions = [] + self.bulk_data = [] + + def feed(self, action, data): + ret = None raw_data, raw_action = data, action - action = serializer.dumps(action) + action = self.serializer.dumps(action) # +1 to account for the trailing new line character cur_size = len(action.encode("utf-8")) + 1 if data is not None: - data = serializer.dumps(data) + data = self.serializer.dumps(data) cur_size += len(data.encode("utf-8")) + 1 # full chunk, send it and start a new one - if bulk_actions and ( - size + cur_size > max_chunk_bytes or action_count == chunk_size + if self.bulk_actions and ( + self.size + cur_size > self.max_chunk_bytes + or self.action_count == self.chunk_size ): - yield bulk_data, bulk_actions - bulk_actions, bulk_data = [], [] - size, action_count = 0, 0 + ret = (self.bulk_data, self.bulk_actions) + self.bulk_actions, self.bulk_data = [], [] + self.size, self.action_count = 0, 0 - bulk_actions.append(action) + self.bulk_actions.append(action) if data is not None: - bulk_actions.append(data) - bulk_data.append((raw_action, raw_data)) + self.bulk_actions.append(data) + self.bulk_data.append((raw_action, raw_data)) else: - bulk_data.append((raw_action,)) + self.bulk_data.append((raw_action,)) - size += cur_size - action_count += 1 + self.size += cur_size + self.action_count += 1 + return ret - if bulk_actions: - yield bulk_data, bulk_actions + def flush(self): + ret = None + if self.bulk_actions: + ret = (self.bulk_data, self.bulk_actions) + self.bulk_actions, self.bulk_data = [], [] + return ret -def _process_bulk_chunk( - client, - bulk_actions, - bulk_data, - raise_on_exception=True, - raise_on_error=True, - *args, - **kwargs -): +def _chunk_actions(actions, chunk_size, max_chunk_bytes, serializer): """ - Send a bulk request to elasticsearch and process the output. + Split actions into chunks by number or size, serialize them into strings in + the process. """ + chunker = _ActionChunker( + chunk_size=chunk_size, max_chunk_bytes=max_chunk_bytes, serializer=serializer + ) + for action, data in actions: + ret = chunker.feed(action, data) + if ret: + yield ret + ret = chunker.flush() + if ret: + yield ret + + +def _process_bulk_chunk_success(resp, bulk_data, raise_on_error=True): # if raise on error is set, we need to collect errors per chunk before raising them errors = [] - try: - # send the actual request - resp = client.bulk("\n".join(bulk_actions) + "\n", *args, **kwargs) - except TransportError as e: - # default behavior - just propagate exception - if raise_on_exception: - raise e - - # if we are not propagating, mark all actions in current chunk as failed - err_message = str(e) - exc_errors = [] - - for data in bulk_data: - # collect all the information about failed actions - op_type, action = data[0].copy().popitem() - info = {"error": err_message, "status": e.status_code, "exception": e} - if op_type != "delete": - info["data"] = data[1] - info.update(action) - exc_errors.append({op_type: info}) - - # emulate standard behavior for failed actions - if raise_on_error: - raise BulkIndexError( - "%i document(s) failed to index." % len(exc_errors), exc_errors - ) - else: - for err in exc_errors: - yield False, err - return - # go through request-response pairs and detect failures for data, (op_type, item) in zip( bulk_data, map(methodcaller("popitem"), resp["items"]) @@ -185,6 +171,66 @@ def _process_bulk_chunk( raise BulkIndexError("%i document(s) failed to index." % len(errors), errors) +def _process_bulk_chunk_error( + error, bulk_data, raise_on_exception=True, raise_on_error=True +): + # default behavior - just propagate exception + if raise_on_exception: + raise error + + # if we are not propagating, mark all actions in current chunk as failed + err_message = str(error) + exc_errors = [] + + for data in bulk_data: + # collect all the information about failed actions + op_type, action = data[0].copy().popitem() + info = {"error": err_message, "status": error.status_code, "exception": error} + if op_type != "delete": + info["data"] = data[1] + info.update(action) + exc_errors.append({op_type: info}) + + # emulate standard behavior for failed actions + if raise_on_error: + raise BulkIndexError( + "%i document(s) failed to index." % len(exc_errors), exc_errors + ) + else: + for err in exc_errors: + yield False, err + + +def _process_bulk_chunk( + client, + bulk_actions, + bulk_data, + raise_on_exception=True, + raise_on_error=True, + *args, + **kwargs +): + """ + Send a bulk request to elasticsearch and process the output. + """ + try: + # send the actual request + resp = client.bulk("\n".join(bulk_actions) + "\n", *args, **kwargs) + except TransportError as e: + gen = _process_bulk_chunk_error( + error=e, + bulk_data=bulk_data, + raise_on_exception=raise_on_exception, + raise_on_error=raise_on_error, + ) + else: + gen = _process_bulk_chunk_success( + resp=resp, bulk_data=bulk_data, raise_on_error=raise_on_error + ) + for item in gen: + yield item + + def streaming_bulk( client, actions, diff --git a/utils/build_dists.py b/utils/build_dists.py --- a/utils/build_dists.py +++ b/utils/build_dists.py @@ -100,6 +100,11 @@ def test_dist(dist): # Install aiohttp and see that async is now available run(venv_python, "-m", "pip", "install", "aiohttp") run(venv_python, "-c", f"from {dist_name} import AsyncElasticsearch") + run( + venv_python, + "-c", + f"from {dist_name}.helpers import async_scan, async_bulk, async_streaming_bulk, async_reindex", + ) # Ensure that the namespaces are correct for the dist for suffix in ("", "1", "2", "5", "6", "7", "8", "9", "10"):

diff --git a/test_elasticsearch/test_async/test_server/test_helpers.py b/test_elasticsearch/test_async/test_server/test_helpers.py new file mode 100644 --- /dev/null +++ b/test_elasticsearch/test_async/test_server/test_helpers.py @@ -0,0 +1,751 @@ +# Licensed to Elasticsearch B.V. under one or more contributor +# license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright +# ownership. Elasticsearch B.V. licenses this file to you under +# the Apache License, Version 2.0 (the "License"); you may +# not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# Licensed to Elasticsearch B.V under one or more agreements. +# Elasticsearch B.V licenses this file to you under the Apache 2.0 License. +# See the LICENSE file in the project root for more information + +import pytest +import asyncio +from mock import patch, MagicMock + +from elasticsearch import helpers, TransportError +from elasticsearch.helpers import ScanError + + +pytestmark = pytest.mark.asyncio + + +class AsyncMock(MagicMock): + async def __call__(self, *args, **kwargs): + return super(AsyncMock, self).__call__(*args, **kwargs) + + +class FailingBulkClient(object): + def __init__( + self, client, fail_at=(2,), fail_with=TransportError(599, "Error!", {}) + ): + self.client = client + self._called = 0 + self._fail_at = fail_at + self.transport = client.transport + self._fail_with = fail_with + + async def bulk(self, *args, **kwargs): + self._called += 1 + if self._called in self._fail_at: + raise self._fail_with + return await self.client.bulk(*args, **kwargs) + + +class TestStreamingBulk(object): + async def test_actions_remain_unchanged(self, async_client): + actions = [{"_id": 1}, {"_id": 2}] + async for ok, item in helpers.async_streaming_bulk( + async_client, actions, index="test-index" + ): + assert ok + assert [{"_id": 1}, {"_id": 2}] == actions + + async def test_all_documents_get_inserted(self, async_client): + docs = [{"answer": x, "_id": x} for x in range(100)] + async for ok, item in helpers.async_streaming_bulk( + async_client, docs, index="test-index", refresh=True + ): + assert ok + + assert 100 == (await async_client.count(index="test-index"))["count"] + assert {"answer": 42} == (await async_client.get(index="test-index", id=42))[ + "_source" + ] + + async def test_documents_data_types(self, async_client): + async def async_gen(): + for x in range(100): + await asyncio.sleep(0) + yield {"answer": x, "_id": x} + + def sync_gen(): + for x in range(100): + yield {"answer": x, "_id": x} + + async for ok, item in helpers.async_streaming_bulk( + async_client, async_gen(), index="test-index", refresh=True + ): + assert ok + + assert 100 == (await async_client.count(index="test-index"))["count"] + assert {"answer": 42} == (await async_client.get(index="test-index", id=42))[ + "_source" + ] + + await async_client.delete_by_query( + index="test-index", body={"query": {"match_all": {}}} + ) + + async for ok, item in helpers.async_streaming_bulk( + async_client, sync_gen(), index="test-index", refresh=True + ): + assert ok + + assert 100 == (await async_client.count(index="test-index"))["count"] + assert {"answer": 42} == (await async_client.get(index="test-index", id=42))[ + "_source" + ] + + async def test_all_errors_from_chunk_are_raised_on_failure(self, async_client): + await async_client.indices.create( + "i", + { + "mappings": {"properties": {"a": {"type": "integer"}}}, + "settings": {"number_of_shards": 1, "number_of_replicas": 0}, + }, + ) + await async_client.cluster.health(wait_for_status="yellow") + + try: + async for ok, item in helpers.async_streaming_bulk( + async_client, [{"a": "b"}, {"a": "c"}], index="i", raise_on_error=True + ): + assert ok + except helpers.BulkIndexError as e: + assert 2 == len(e.errors) + else: + assert False, "exception should have been raised" + + async def test_different_op_types(self, async_client): + await async_client.index(index="i", id=45, body={}) + await async_client.index(index="i", id=42, body={}) + docs = [ + {"_index": "i", "_id": 47, "f": "v"}, + {"_op_type": "delete", "_index": "i", "_id": 45}, + {"_op_type": "update", "_index": "i", "_id": 42, "doc": {"answer": 42}}, + ] + async for ok, item in helpers.async_streaming_bulk(async_client, docs): + assert ok + + assert not await async_client.exists(index="i", id=45) + assert {"answer": 42} == (await async_client.get(index="i", id=42))["_source"] + assert {"f": "v"} == (await async_client.get(index="i", id=47))["_source"] + + async def test_transport_error_can_becaught(self, async_client): + failing_client = FailingBulkClient(async_client) + docs = [ + {"_index": "i", "_id": 47, "f": "v"}, + {"_index": "i", "_id": 45, "f": "v"}, + {"_index": "i", "_id": 42, "f": "v"}, + ] + + results = [ + x + async for x in helpers.async_streaming_bulk( + failing_client, + docs, + raise_on_exception=False, + raise_on_error=False, + chunk_size=1, + ) + ] + assert 3 == len(results) + assert [True, False, True] == [r[0] for r in results] + + exc = results[1][1]["index"].pop("exception") + assert isinstance(exc, TransportError) + assert 599 == exc.status_code + assert { + "index": { + "_index": "i", + "_id": 45, + "data": {"f": "v"}, + "error": "TransportError(599, 'Error!')", + "status": 599, + } + } == results[1][1] + + async def test_rejected_documents_are_retried(self, async_client): + failing_client = FailingBulkClient( + async_client, fail_with=TransportError(429, "Rejected!", {}) + ) + docs = [ + {"_index": "i", "_id": 47, "f": "v"}, + {"_index": "i", "_id": 45, "f": "v"}, + {"_index": "i", "_id": 42, "f": "v"}, + ] + results = [ + x + async for x in helpers.async_streaming_bulk( + failing_client, + docs, + raise_on_exception=False, + raise_on_error=False, + chunk_size=1, + max_retries=1, + initial_backoff=0, + ) + ] + assert 3 == len(results) + assert [True, True, True] == [r[0] for r in results] + await async_client.indices.refresh(index="i") + res = await async_client.search(index="i") + assert {"value": 3, "relation": "eq"} == res["hits"]["total"] + assert 4 == failing_client._called + + async def test_rejected_documents_are_retried_at_most_max_retries_times( + self, async_client + ): + failing_client = FailingBulkClient( + async_client, fail_at=(1, 2), fail_with=TransportError(429, "Rejected!", {}) + ) + + docs = [ + {"_index": "i", "_id": 47, "f": "v"}, + {"_index": "i", "_id": 45, "f": "v"}, + {"_index": "i", "_id": 42, "f": "v"}, + ] + results = [ + x + async for x in helpers.async_streaming_bulk( + failing_client, + docs, + raise_on_exception=False, + raise_on_error=False, + chunk_size=1, + max_retries=1, + initial_backoff=0, + ) + ] + assert 3 == len(results) + assert [False, True, True] == [r[0] for r in results] + await async_client.indices.refresh(index="i") + res = await async_client.search(index="i") + assert {"value": 2, "relation": "eq"} == res["hits"]["total"] + assert 4 == failing_client._called + + async def test_transport_error_is_raised_with_max_retries(self, async_client): + failing_client = FailingBulkClient( + async_client, + fail_at=(1, 2, 3, 4), + fail_with=TransportError(429, "Rejected!", {}), + ) + + async def streaming_bulk(): + results = [ + x + async for x in helpers.async_streaming_bulk( + failing_client, + [{"a": 42}, {"a": 39}], + raise_on_exception=True, + max_retries=3, + initial_backoff=0, + ) + ] + return results + + with pytest.raises(TransportError): + await streaming_bulk() + assert 4 == failing_client._called + + +class TestBulk(object): + async def test_bulk_works_with_single_item(self, async_client): + docs = [{"answer": 42, "_id": 1}] + success, failed = await helpers.async_bulk( + async_client, docs, index="test-index", refresh=True + ) + + assert 1 == success + assert not failed + assert 1 == (await async_client.count(index="test-index"))["count"] + assert {"answer": 42} == (await async_client.get(index="test-index", id=1))[ + "_source" + ] + + async def test_all_documents_get_inserted(self, async_client): + docs = [{"answer": x, "_id": x} for x in range(100)] + success, failed = await helpers.async_bulk( + async_client, docs, index="test-index", refresh=True + ) + + assert 100 == success + assert not failed + assert 100 == (await async_client.count(index="test-index"))["count"] + assert {"answer": 42} == (await async_client.get(index="test-index", id=42))[ + "_source" + ] + + async def test_stats_only_reports_numbers(self, async_client): + docs = [{"answer": x} for x in range(100)] + success, failed = await helpers.async_bulk( + async_client, docs, index="test-index", refresh=True, stats_only=True + ) + + assert 100 == success + assert 0 == failed + assert 100 == (await async_client.count(index="test-index"))["count"] + + async def test_errors_are_reported_correctly(self, async_client): + await async_client.indices.create( + "i", + { + "mappings": {"properties": {"a": {"type": "integer"}}}, + "settings": {"number_of_shards": 1, "number_of_replicas": 0}, + }, + ) + await async_client.cluster.health(wait_for_status="yellow") + + success, failed = await helpers.async_bulk( + async_client, + [{"a": 42}, {"a": "c", "_id": 42}], + index="i", + raise_on_error=False, + ) + assert 1 == success + assert 1 == len(failed) + error = failed[0] + assert "42" == error["index"]["_id"] + assert "i" == error["index"]["_index"] + print(error["index"]["error"]) + assert "MapperParsingException" in repr( + error["index"]["error"] + ) or "mapper_parsing_exception" in repr(error["index"]["error"]) + + async def test_error_is_raised(self, async_client): + await async_client.indices.create( + "i", + { + "mappings": {"properties": {"a": {"type": "integer"}}}, + "settings": {"number_of_shards": 1, "number_of_replicas": 0}, + }, + ) + await async_client.cluster.health(wait_for_status="yellow") + + with pytest.raises(helpers.BulkIndexError): + await helpers.async_bulk(async_client, [{"a": 42}, {"a": "c"}], index="i") + + async def test_errors_are_collected_properly(self, async_client): + await async_client.indices.create( + "i", + { + "mappings": {"properties": {"a": {"type": "integer"}}}, + "settings": {"number_of_shards": 1, "number_of_replicas": 0}, + }, + ) + await async_client.cluster.health(wait_for_status="yellow") + + success, failed = await helpers.async_bulk( + async_client, + [{"a": 42}, {"a": "c"}], + index="i", + stats_only=True, + raise_on_error=False, + ) + assert 1 == success + assert 1 == failed + + +class MockScroll: + def __init__(self): + self.calls = [] + + async def __call__(self, *args, **kwargs): + self.calls.append((args, kwargs)) + if len(self.calls) == 1: + return { + "_scroll_id": "dummy_id", + "_shards": {"successful": 4, "total": 5, "skipped": 0}, + "hits": {"hits": [{"scroll_data": 42}]}, + } + elif len(self.calls) == 2: + return { + "_scroll_id": "dummy_id", + "_shards": {"successful": 4, "total": 5, "skipped": 0}, + "hits": {"hits": []}, + } + else: + raise Exception("no more responses") + + +class MockResponse: + def __init__(self, resp): + self.resp = resp + + async def __call__(self, *args, **kwargs): + return self.resp + + +class TestScan(object): + async def teardown_method(self, async_client): + await async_client.transport.perform_request("DELETE", "/_search/scroll/_all") + + async def test_order_can_be_preserved(self, async_client): + bulk = [] + for x in range(100): + bulk.append({"index": {"_index": "test_index", "_id": x}}) + bulk.append({"answer": x, "correct": x == 42}) + await async_client.bulk(bulk, refresh=True) + + docs = [ + doc + async for doc in helpers.async_scan( + async_client, + index="test_index", + query={"sort": "answer"}, + preserve_order=True, + ) + ] + + assert 100 == len(docs) + assert list(map(str, range(100))) == list(d["_id"] for d in docs) + assert list(range(100)) == list(d["_source"]["answer"] for d in docs) + + async def test_all_documents_are_read(self, async_client): + bulk = [] + for x in range(100): + bulk.append({"index": {"_index": "test_index", "_id": x}}) + bulk.append({"answer": x, "correct": x == 42}) + await async_client.bulk(bulk, refresh=True) + + docs = [ + x + async for x in helpers.async_scan(async_client, index="test_index", size=2) + ] + + assert 100 == len(docs) + assert set(map(str, range(100))) == set(d["_id"] for d in docs) + assert set(range(100)) == set(d["_source"]["answer"] for d in docs) + + async def test_scroll_error(self, async_client): + bulk = [] + for x in range(4): + bulk.append({"index": {"_index": "test_index"}}) + bulk.append({"value": x}) + await async_client.bulk(bulk, refresh=True) + + with patch.object(async_client, "scroll", MockScroll()): + data = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=2, + raise_on_error=False, + clear_scroll=False, + ) + ] + assert len(data) == 3 + assert data[-1] == {"scroll_data": 42} + + with patch.object(async_client, "scroll", MockScroll()): + with pytest.raises(ScanError): + data = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=2, + raise_on_error=True, + clear_scroll=False, + ) + ] + assert len(data) == 3 + assert data[-1] == {"scroll_data": 42} + + async def test_initial_search_error(self, async_client): + with patch.object(async_client, "clear_scroll", new_callable=AsyncMock): + with patch.object( + async_client, + "search", + MockResponse( + { + "_scroll_id": "dummy_id", + "_shards": {"successful": 4, "total": 5, "skipped": 0}, + "hits": {"hits": [{"search_data": 1}]}, + } + ), + ): + with patch.object(async_client, "scroll", MockScroll()): + + data = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=2, + raise_on_error=False, + ) + ] + assert data == [{"search_data": 1}, {"scroll_data": 42}] + + with patch.object( + async_client, + "search", + MockResponse( + { + "_scroll_id": "dummy_id", + "_shards": {"successful": 4, "total": 5, "skipped": 0}, + "hits": {"hits": [{"search_data": 1}]}, + } + ), + ): + with patch.object(async_client, "scroll", MockScroll()) as mock_scroll: + + with pytest.raises(ScanError): + data = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=2, + raise_on_error=True, + ) + ] + assert data == [{"search_data": 1}] + assert mock_scroll.calls == [] + + async def test_no_scroll_id_fast_route(self, async_client): + with patch.object(async_client, "search", MockResponse({"no": "_scroll_id"})): + with patch.object(async_client, "scroll") as scroll_mock: + with patch.object(async_client, "clear_scroll") as clear_mock: + data = [ + x + async for x in helpers.async_scan( + async_client, index="test_index" + ) + ] + + assert data == [] + scroll_mock.assert_not_called() + clear_mock.assert_not_called() + + @patch("elasticsearch._async.helpers.logger") + async def test_logger(self, logger_mock, async_client): + bulk = [] + for x in range(4): + bulk.append({"index": {"_index": "test_index"}}) + bulk.append({"value": x}) + await async_client.bulk(bulk, refresh=True) + + with patch.object(async_client, "scroll", MockScroll()): + _ = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=2, + raise_on_error=False, + clear_scroll=False, + ) + ] + logger_mock.warning.assert_called() + + with patch.object(async_client, "scroll", MockScroll()): + try: + _ = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=2, + raise_on_error=True, + clear_scroll=False, + ) + ] + except ScanError: + pass + logger_mock.warning.assert_called_with( + "Scroll request has only succeeded on %d (+%d skipped) shards out of %d.", + 4, + 0, + 5, + ) + + async def test_clear_scroll(self, async_client): + bulk = [] + for x in range(4): + bulk.append({"index": {"_index": "test_index"}}) + bulk.append({"value": x}) + await async_client.bulk(bulk, refresh=True) + + with patch.object( + async_client, "clear_scroll", wraps=async_client.clear_scroll + ) as spy: + _ = [ + x + async for x in helpers.async_scan( + async_client, index="test_index", size=2 + ) + ] + spy.assert_called_once() + + spy.reset_mock() + _ = [ + x + async for x in helpers.async_scan( + async_client, index="test_index", size=2, clear_scroll=True + ) + ] + spy.assert_called_once() + + spy.reset_mock() + _ = [ + x + async for x in helpers.async_scan( + async_client, index="test_index", size=2, clear_scroll=False + ) + ] + spy.assert_not_called() + + [email protected](scope="function") +async def reindex_setup(async_client): + bulk = [] + for x in range(100): + bulk.append({"index": {"_index": "test_index", "_id": x}}) + bulk.append( + { + "answer": x, + "correct": x == 42, + "type": "answers" if x % 2 == 0 else "questions", + } + ) + await async_client.bulk(bulk, refresh=True) + yield + + +class TestReindex(object): + async def test_reindex_passes_kwargs_to_scan_and_bulk( + self, async_client, reindex_setup + ): + await helpers.async_reindex( + async_client, + "test_index", + "prod_index", + scan_kwargs={"q": "type:answers"}, + bulk_kwargs={"refresh": True}, + ) + + assert await async_client.indices.exists("prod_index") + assert ( + 50 + == (await async_client.count(index="prod_index", q="type:answers"))["count"] + ) + + assert {"answer": 42, "correct": True, "type": "answers"} == ( + await async_client.get(index="prod_index", id=42) + )["_source"] + + async def test_reindex_accepts_a_query(self, async_client, reindex_setup): + await helpers.async_reindex( + async_client, + "test_index", + "prod_index", + query={"query": {"bool": {"filter": {"term": {"type": "answers"}}}}}, + ) + await async_client.indices.refresh() + + assert await async_client.indices.exists("prod_index") + assert ( + 50 + == (await async_client.count(index="prod_index", q="type:answers"))["count"] + ) + + assert {"answer": 42, "correct": True, "type": "answers"} == ( + await async_client.get(index="prod_index", id=42) + )["_source"] + + async def test_all_documents_get_moved(self, async_client, reindex_setup): + await helpers.async_reindex(async_client, "test_index", "prod_index") + await async_client.indices.refresh() + + assert await async_client.indices.exists("prod_index") + assert ( + 50 + == (await async_client.count(index="prod_index", q="type:questions"))[ + "count" + ] + ) + assert ( + 50 + == (await async_client.count(index="prod_index", q="type:answers"))["count"] + ) + + assert {"answer": 42, "correct": True, "type": "answers"} == ( + await async_client.get(index="prod_index", id=42) + )["_source"] + + [email protected](scope="function") +async def parent_reindex_setup(async_client): + body = { + "settings": {"number_of_shards": 1, "number_of_replicas": 0}, + "mappings": { + "properties": { + "question_answer": { + "type": "join", + "relations": {"question": "answer"}, + } + } + }, + } + await async_client.indices.create(index="test-index", body=body) + await async_client.indices.create(index="real-index", body=body) + + await async_client.index( + index="test-index", id=42, body={"question_answer": "question"} + ) + await async_client.index( + index="test-index", + id=47, + routing=42, + body={"some": "data", "question_answer": {"name": "answer", "parent": 42}}, + ) + await async_client.indices.refresh(index="test-index") + + +class TestParentChildReindex: + async def test_children_are_reindexed_correctly( + self, async_client, parent_reindex_setup + ): + await helpers.async_reindex(async_client, "test-index", "real-index") + + q = await async_client.get(index="real-index", id=42) + assert { + "_id": "42", + "_index": "real-index", + "_primary_term": 1, + "_seq_no": 0, + "_source": {"question_answer": "question"}, + "_type": "_doc", + "_version": 1, + "found": True, + } == q + + q = await async_client.get(index="test-index", id=47, routing=42) + assert { + "_routing": "42", + "_id": "47", + "_index": "test-index", + "_primary_term": 1, + "_seq_no": 1, + "_source": { + "some": "data", + "question_answer": {"name": "answer", "parent": 42}, + }, + "_type": "_doc", + "_version": 1, + "found": True, + } == q

AttributeError: module 'elasticsearch.helpers' has no attribute 'async_bulk' in version 7.9.0 Ran into the following problem after upgrading to version 7.9.0 AttributeError: module 'elasticsearch.helpers' has no attribute 'async_bulk' Installed older version to verify: pip3 install --user elasticsearch[async]==7.8.1 Collecting elasticsearch[async]==7.8.1 Using cached https://files.pythonhosted.org/packages/76/7f/8dd4c7e1895d99e16c9b78fea1e0e0a64382f53bc9fc655a01465de70505/elasticsearch-7.8.1-py2.py3-none-any.whl Successfully installed elasticsearch-7.8.1 Ran one-liner to verify that async_bulk can be loaded python3 -c "from elasticsearch.helpers import async_bulk" Checked local file-system ls ~/.local/lib/python3.6/site-packages/elasticsearch/_async/ client compat.py **helpers.py** http_aiohttp.py __init__.py __pycache__ transport.py Installed version 7.9.0 pip3 install --user elasticsearch[async]==7.9.0 Collecting elasticsearch[async]==7.9.0 Using cached https://files.pythonhosted.org/packages/c6/63/ff0c908cae1e8e000308cba1d647585dff3dffad5304ca12fe834297fe8f/elasticsearch-7.9.0-py2.py3-none-any.whl Successfully installed elasticsearch-7.9.0 Ran one-liner to verify that async_bulk can be loaded python3 -c "from elasticsearch.helpers import async_bulk" **Traceback (most recent call last): File "<string>", line 1, in <module> ImportError: cannot import name 'async_bulk'** Checked local file-system ls ~/.local/lib/python3.6/site-packages/elasticsearch/_async/ client compat.py http_aiohttp.py __init__.py __pycache__ transport.py Looks like file _async/helpers.py is missing But it is still present in GIT https://github.com/elastic/elasticsearch-py/blob/master/elasticsearch/_async/helpers.py

Reproduced this issue, it looks like the file didn't get backported to the 7.x branch and then once I split a 7.9 branch that branch also didn't have the file. I'll fix this up and release 7.9.1.

2020-08-20T12:20:06Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1357

89b7fecaf1ce8b1234d4e148a1854ecc97e8fdd0

diff --git a/elasticsearch/_async/http_aiohttp.py b/elasticsearch/_async/http_aiohttp.py --- a/elasticsearch/_async/http_aiohttp.py +++ b/elasticsearch/_async/http_aiohttp.py @@ -81,6 +81,7 @@ def __init__( :arg host: hostname of the node (default: localhost) :arg port: port to use (integer, default: 9200) + :arg url_prefix: optional url prefix for elasticsearch :arg timeout: default timeout in seconds (float, default: 10) :arg http_auth: optional http auth information as either ':' separated string or a tuple @@ -200,7 +201,7 @@ async def perform_request( await self._create_aiohttp_session() orig_body = body - url_path = url + url_path = self.url_prefix + url if params: query_string = urlencode(params) else: @@ -219,7 +220,7 @@ async def perform_request( scheme=self.scheme, host=self.hostname, port=self.port, - path=url, + path=url_path, query_string=query_string, encoded=True, )

diff --git a/test_elasticsearch/test_async/test_connection.py b/test_elasticsearch/test_async/test_connection.py --- a/test_elasticsearch/test_async/test_connection.py +++ b/test_elasticsearch/test_async/test_connection.py @@ -23,11 +23,11 @@ import warnings from platform import python_version import aiohttp +from multidict import CIMultiDict import pytest from elasticsearch import AIOHttpConnection from elasticsearch import __versionstr__ -from ..test_cases import TestCase, SkipTest pytestmark = pytest.mark.asyncio @@ -37,7 +37,7 @@ def gzip_decompress(data): return buf.read() -class TestAIOHttpConnection(TestCase): +class TestAIOHttpConnection: async def _get_mock_connection(self, connection_params={}, response_body=b"{}"): con = AIOHttpConnection(**connection_params) await con._create_aiohttp_session() @@ -54,7 +54,7 @@ async def text(self): return response_body.decode("utf-8", "surrogatepass") dummy_response = DummyResponse() - dummy_response.headers = {} + dummy_response.headers = CIMultiDict() dummy_response.status = 200 _dummy_request.call_args = (args, kwargs) return dummy_response @@ -69,45 +69,42 @@ async def test_ssl_context(self): # if create_default_context raises an AttributeError Exception # it means SSLContext is not available for that version of python # and we should skip this test. - raise SkipTest( - "Test test_ssl_context is skipped cause SSLContext is not available for this version of ptyhon" + pytest.skip( + "Test test_ssl_context is skipped cause SSLContext is not available for this version of Python" ) con = AIOHttpConnection(use_ssl=True, ssl_context=context) await con._create_aiohttp_session() - self.assertTrue(con.use_ssl) - self.assertEqual(con.session.connector._ssl, context) + assert con.use_ssl + assert con.session.connector._ssl == context def test_opaque_id(self): con = AIOHttpConnection(opaque_id="app-1") - self.assertEqual(con.headers["x-opaque-id"], "app-1") + assert con.headers["x-opaque-id"] == "app-1" def test_http_cloud_id(self): con = AIOHttpConnection( cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==" ) - self.assertTrue(con.use_ssl) - self.assertEqual( - con.host, "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" + assert con.use_ssl + assert ( + con.host + == "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" ) - self.assertEqual(con.port, None) - self.assertEqual( - con.hostname, "4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" - ) - self.assertTrue(con.http_compress) + assert con.port is None + assert con.hostname == "4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" + assert con.http_compress con = AIOHttpConnection( cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==", port=9243, ) - self.assertEqual( - con.host, - "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io:9243", - ) - self.assertEqual(con.port, 9243) - self.assertEqual( - con.hostname, "4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" + assert ( + con.host + == "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io:9243" ) + assert con.port == 9243 + assert con.hostname == "4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" def test_api_key_auth(self): # test with tuple @@ -115,11 +112,10 @@ def test_api_key_auth(self): cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==", api_key=("elastic", "changeme1"), ) - self.assertEqual( - con.headers["authorization"], "ApiKey ZWxhc3RpYzpjaGFuZ2VtZTE=" - ) - self.assertEqual( - con.host, "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" + assert con.headers["authorization"] == "ApiKey ZWxhc3RpYzpjaGFuZ2VtZTE=" + assert ( + con.host + == "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" ) # test with base64 encoded string @@ -127,50 +123,49 @@ def test_api_key_auth(self): cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==", api_key="ZWxhc3RpYzpjaGFuZ2VtZTI=", ) - self.assertEqual( - con.headers["authorization"], "ApiKey ZWxhc3RpYzpjaGFuZ2VtZTI=" - ) - self.assertEqual( - con.host, "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" + assert con.headers["authorization"] == "ApiKey ZWxhc3RpYzpjaGFuZ2VtZTI=" + assert ( + con.host + == "https://4fa8821e75634032bed1cf22110e2f97.us-east-1.aws.found.io" ) async def test_no_http_compression(self): con = await self._get_mock_connection() - self.assertFalse(con.http_compress) - self.assertNotIn("accept-encoding", con.headers) + assert not con.http_compress + assert "accept-encoding" not in con.headers await con.perform_request("GET", "/") _, kwargs = con.session.request.call_args - self.assertFalse(kwargs["data"]) - self.assertNotIn("accept-encoding", kwargs["headers"]) - self.assertNotIn("content-encoding", kwargs["headers"]) + assert not kwargs["data"] + assert "accept-encoding" not in kwargs["headers"] + assert "content-encoding" not in kwargs["headers"] async def test_http_compression(self): con = await self._get_mock_connection({"http_compress": True}) - self.assertTrue(con.http_compress) - self.assertEqual(con.headers["accept-encoding"], "gzip,deflate") + assert con.http_compress + assert con.headers["accept-encoding"] == "gzip,deflate" # 'content-encoding' shouldn't be set at a connection level. # Should be applied only if the request is sent with a body. - self.assertNotIn("content-encoding", con.headers) + assert "content-encoding" not in con.headers await con.perform_request("GET", "/", body=b"{}") _, kwargs = con.session.request.call_args - self.assertEqual(gzip_decompress(kwargs["data"]), b"{}") - self.assertEqual(kwargs["headers"]["accept-encoding"], "gzip,deflate") - self.assertEqual(kwargs["headers"]["content-encoding"], "gzip") + assert gzip_decompress(kwargs["data"]) == b"{}" + assert kwargs["headers"]["accept-encoding"] == "gzip,deflate" + assert kwargs["headers"]["content-encoding"] == "gzip" await con.perform_request("GET", "/") _, kwargs = con.session.request.call_args - self.assertFalse(kwargs["data"]) - self.assertEqual(kwargs["headers"]["accept-encoding"], "gzip,deflate") - self.assertNotIn("content-encoding", kwargs["headers"]) + assert not kwargs["data"] + assert kwargs["headers"]["accept-encoding"] == "gzip,deflate" + assert "content-encoding" not in kwargs["headers"] def test_cloud_id_http_compress_override(self): # 'http_compress' will be 'True' by default for connections with @@ -178,90 +173,90 @@ def test_cloud_id_http_compress_override(self): con = AIOHttpConnection( cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==", ) - self.assertEqual(con.http_compress, True) + assert con.http_compress is True con = AIOHttpConnection( cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==", http_compress=False, ) - self.assertEqual(con.http_compress, False) + assert con.http_compress is False con = AIOHttpConnection( cloud_id="cluster:dXMtZWFzdC0xLmF3cy5mb3VuZC5pbyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5NyQ0ZmE4ODIxZTc1NjM0MDMyYmVkMWNmMjIxMTBlMmY5Ng==", http_compress=True, ) - self.assertEqual(con.http_compress, True) + assert con.http_compress is True + + async def test_url_prefix(self): + con = await self._get_mock_connection( + connection_params={"url_prefix": "/_search/"} + ) + assert con.url_prefix == "/_search" + + await con.perform_request("GET", "/") + + # Need to convert the yarl URL to a string to compare. + method, yarl_url = con.session.request.call_args[0] + assert method == "GET" and str(yarl_url) == "http://localhost:9200/_search/" def test_default_user_agent(self): con = AIOHttpConnection() - self.assertEqual( - con._get_default_user_agent(), - "elasticsearch-py/%s (Python %s)" % (__versionstr__, python_version()), + assert con._get_default_user_agent() == "elasticsearch-py/%s (Python %s)" % ( + __versionstr__, + python_version(), ) def test_timeout_set(self): con = AIOHttpConnection(timeout=42) - self.assertEqual(42, con.timeout) + assert 42 == con.timeout def test_keep_alive_is_on_by_default(self): con = AIOHttpConnection() - self.assertEqual( - { - "connection": "keep-alive", - "content-type": "application/json", - "user-agent": con._get_default_user_agent(), - }, - con.headers, - ) + assert { + "connection": "keep-alive", + "content-type": "application/json", + "user-agent": con._get_default_user_agent(), + } == con.headers def test_http_auth(self): con = AIOHttpConnection(http_auth="username:secret") - self.assertEqual( - { - "authorization": "Basic dXNlcm5hbWU6c2VjcmV0", - "connection": "keep-alive", - "content-type": "application/json", - "user-agent": con._get_default_user_agent(), - }, - con.headers, - ) + assert { + "authorization": "Basic dXNlcm5hbWU6c2VjcmV0", + "connection": "keep-alive", + "content-type": "application/json", + "user-agent": con._get_default_user_agent(), + } == con.headers def test_http_auth_tuple(self): con = AIOHttpConnection(http_auth=("username", "secret")) - self.assertEqual( - { - "authorization": "Basic dXNlcm5hbWU6c2VjcmV0", - "content-type": "application/json", - "connection": "keep-alive", - "user-agent": con._get_default_user_agent(), - }, - con.headers, - ) + assert { + "authorization": "Basic dXNlcm5hbWU6c2VjcmV0", + "content-type": "application/json", + "connection": "keep-alive", + "user-agent": con._get_default_user_agent(), + } == con.headers def test_http_auth_list(self): con = AIOHttpConnection(http_auth=["username", "secret"]) - self.assertEqual( - { - "authorization": "Basic dXNlcm5hbWU6c2VjcmV0", - "content-type": "application/json", - "connection": "keep-alive", - "user-agent": con._get_default_user_agent(), - }, - con.headers, - ) + assert { + "authorization": "Basic dXNlcm5hbWU6c2VjcmV0", + "content-type": "application/json", + "connection": "keep-alive", + "user-agent": con._get_default_user_agent(), + } == con.headers def test_uses_https_if_verify_certs_is_off(self): with warnings.catch_warnings(record=True) as w: con = AIOHttpConnection(use_ssl=True, verify_certs=False) - self.assertEqual(1, len(w)) - self.assertEqual( - "Connecting to https://localhost:9200 using SSL with verify_certs=False is insecure.", - str(w[0].message), + assert 1 == len(w) + assert ( + "Connecting to https://localhost:9200 using SSL with verify_certs=False is insecure." + == str(w[0].message) ) - self.assertTrue(con.use_ssl) - self.assertEqual(con.scheme, "https") - self.assertEqual(con.host, "https://localhost:9200") + assert con.use_ssl + assert con.scheme == "https" + assert con.host == "https://localhost:9200" async def test_nowarn_when_test_uses_https_if_verify_certs_is_off(self): with warnings.catch_warnings(record=True) as w: @@ -269,19 +264,19 @@ async def test_nowarn_when_test_uses_https_if_verify_certs_is_off(self): use_ssl=True, verify_certs=False, ssl_show_warn=False ) await con._create_aiohttp_session() - self.assertEqual(0, len(w)) + assert 0 == len(w) - self.assertIsInstance(con.session, aiohttp.ClientSession) + assert isinstance(con.session, aiohttp.ClientSession) def test_doesnt_use_https_if_not_specified(self): con = AIOHttpConnection() - self.assertFalse(con.use_ssl) + assert not con.use_ssl def test_no_warning_when_using_ssl_context(self): ctx = ssl.create_default_context() with warnings.catch_warnings(record=True) as w: AIOHttpConnection(ssl_context=ctx) - self.assertEqual(0, len(w), str([x.message for x in w])) + assert 0 == len(w), str([x.message for x in w]) def test_warns_if_using_non_default_ssl_kwargs_with_ssl_context(self): for kwargs in ( @@ -299,10 +294,10 @@ def test_warns_if_using_non_default_ssl_kwargs_with_ssl_context(self): AIOHttpConnection(**kwargs) - self.assertEqual(1, len(w)) - self.assertEqual( - "When using `ssl_context`, all other SSL related kwargs are ignored", - str(w[0].message), + assert 1 == len(w) + assert ( + "When using `ssl_context`, all other SSL related kwargs are ignored" + == str(w[0].message) ) @patch("elasticsearch.connection.base.logger") @@ -310,14 +305,14 @@ async def test_uncompressed_body_logged(self, logger): con = await self._get_mock_connection(connection_params={"http_compress": True}) await con.perform_request("GET", "/", body=b'{"example": "body"}') - self.assertEqual(2, logger.debug.call_count) + assert 2 == logger.debug.call_count req, resp = logger.debug.call_args_list - self.assertEqual('> {"example": "body"}', req[0][0] % req[0][1:]) - self.assertEqual("< {}", resp[0][0] % resp[0][1:]) + assert '> {"example": "body"}' == req[0][0] % req[0][1:] + assert "< {}" == resp[0][0] % resp[0][1:] async def test_surrogatepass_into_bytes(self): buf = b"\xe4\xbd\xa0\xe5\xa5\xbd\xed\xa9\xaa" con = await self._get_mock_connection(response_body=buf) status, headers, data = await con.perform_request("GET", "/") - self.assertEqual(u"你好\uda6a", data) + assert u"你好\uda6a" == data diff --git a/test_elasticsearch/test_async/test_server/test_helpers.py b/test_elasticsearch/test_async/test_server/test_helpers.py --- a/test_elasticsearch/test_async/test_server/test_helpers.py +++ b/test_elasticsearch/test_async/test_server/test_helpers.py @@ -383,11 +383,14 @@ async def __call__(self, *args, **kwargs): return self.resp -class TestScan(object): - async def teardown_method(self, async_client): - await async_client.transport.perform_request("DELETE", "/_search/scroll/_all") [email protected](scope="function") +async def scan_teardown(async_client): + yield + async_client.clear_scroll(scroll_id="_all") - async def test_order_can_be_preserved(self, async_client): + +class TestScan(object): + async def test_order_can_be_preserved(self, async_client, scan_teardown): bulk = [] for x in range(100): bulk.append({"index": {"_index": "test_index", "_id": x}}) @@ -408,7 +411,7 @@ async def test_order_can_be_preserved(self, async_client): assert list(map(str, range(100))) == list(d["_id"] for d in docs) assert list(range(100)) == list(d["_source"]["answer"] for d in docs) - async def test_all_documents_are_read(self, async_client): + async def test_all_documents_are_read(self, async_client, scan_teardown): bulk = [] for x in range(100): bulk.append({"index": {"_index": "test_index", "_id": x}}) @@ -424,7 +427,7 @@ async def test_all_documents_are_read(self, async_client): assert set(map(str, range(100))) == set(d["_id"] for d in docs) assert set(range(100)) == set(d["_source"]["answer"] for d in docs) - async def test_scroll_error(self, async_client): + async def test_scroll_error(self, async_client, scan_teardown): bulk = [] for x in range(4): bulk.append({"index": {"_index": "test_index"}}) @@ -460,7 +463,7 @@ async def test_scroll_error(self, async_client): assert len(data) == 3 assert data[-1] == {"scroll_data": 42} - async def test_initial_search_error(self, async_client): + async def test_initial_search_error(self, async_client, scan_teardown): with patch.object(async_client, "clear_scroll", new_callable=AsyncMock): with patch.object( async_client, @@ -512,7 +515,7 @@ async def test_initial_search_error(self, async_client): assert data == [{"search_data": 1}] assert mock_scroll.calls == [] - async def test_no_scroll_id_fast_route(self, async_client): + async def test_no_scroll_id_fast_route(self, async_client, scan_teardown): with patch.object(async_client, "search", MockResponse({"no": "_scroll_id"})): with patch.object(async_client, "scroll") as scroll_mock: with patch.object(async_client, "clear_scroll") as clear_mock: @@ -528,7 +531,7 @@ async def test_no_scroll_id_fast_route(self, async_client): clear_mock.assert_not_called() @patch("elasticsearch._async.helpers.logger") - async def test_logger(self, logger_mock, async_client): + async def test_logger(self, logger_mock, async_client, scan_teardown): bulk = [] for x in range(4): bulk.append({"index": {"_index": "test_index"}}) @@ -569,7 +572,7 @@ async def test_logger(self, logger_mock, async_client): 5, ) - async def test_clear_scroll(self, async_client): + async def test_clear_scroll(self, async_client, scan_teardown): bulk = [] for x in range(4): bulk.append({"index": {"_index": "test_index"}}) diff --git a/test_elasticsearch/test_connection.py b/test_elasticsearch/test_connection.py --- a/test_elasticsearch/test_connection.py +++ b/test_elasticsearch/test_connection.py @@ -363,7 +363,7 @@ def test_uses_https_if_verify_certs_is_off(self): self.assertIsInstance(con.pool, urllib3.HTTPSConnectionPool) - def nowarn_when_test_uses_https_if_verify_certs_is_off(self): + def test_nowarn_when_uses_https_if_verify_certs_is_off(self): with warnings.catch_warnings(record=True) as w: con = Urllib3HttpConnection( use_ssl=True, verify_certs=False, ssl_show_warn=False @@ -591,7 +591,7 @@ def test_uses_https_if_verify_certs_is_off(self): self.assertEqual("GET", request.method) self.assertEqual(None, request.body) - def nowarn_when_test_uses_https_if_verify_certs_is_off(self): + def test_nowarn_when_uses_https_if_verify_certs_is_off(self): with warnings.catch_warnings(record=True) as w: con = self._get_mock_connection( {

Async connection ignores url_prefix parameter **Elasticsearch version** (`bin/elasticsearch --version`): 7.8.1 **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 7.8.1 Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: I'm using AsyncElasticsearch. If I try to query a server with a url_prefix in the address, I get a 404 Not Found in the logs. The logs also mention the URL that the library tries to connect to, and the url prefix is missing from the URL. Expected behaviour is of course a reply to the query. **Steps to reproduce**: ``` client = AsyncElasticsearch('https://es.example.com/prefix') resp = await client.search(index='index', body={ 'query': { 'match': { 'field': 'value' } } } ) ``` The issue seems to be in elasticsearch/_async/http_aiohttp.py, in perform_request(). It builds a URL using yarl without taking the url_prefix into account. This is still the case in the master branch

See pull request #1352

2020-08-20T16:30:07Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1387

852d7730e7d56ea45336da42dad161e80f75b8dc

diff --git a/elasticsearch/compat.py b/elasticsearch/compat.py --- a/elasticsearch/compat.py +++ b/elasticsearch/compat.py @@ -32,6 +32,12 @@ map = map from queue import Queue +try: + from collections.abs import Mapping +except ImportError: + from collections import Mapping + + __all__ = [ "string_types", "quote_plus", @@ -41,4 +47,5 @@ "urlparse", "map", "Queue", + "Mapping", ] diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -19,7 +19,7 @@ import time from ..exceptions import TransportError -from ..compat import map, string_types, Queue +from ..compat import map, string_types, Queue, Mapping from .errors import ScanError, BulkIndexError @@ -43,9 +43,22 @@ def expand_action(data): data = data.copy() op_type = data.pop("_op_type", "index") action = {op_type: {}} + + # If '_source' is a dict use it for source + # otherwise if op_type == 'update' then + # '_source' should be in the metadata. + if ( + op_type == "update" + and "_source" in data + and not isinstance(data["_source"], Mapping) + ): + action[op_type]["_source"] = data.pop("_source") + for key in ( "_id", "_index", + "_if_seq_no", + "_if_primary_term", "_parent", "_percolate", "_retry_on_conflict", @@ -54,6 +67,8 @@ def expand_action(data): "_type", "_version", "_version_type", + "if_seq_no", + "if_primary_term", "parent", "pipeline", "retry_on_conflict", @@ -62,13 +77,15 @@ def expand_action(data): "version_type", ): if key in data: - if key in [ + if key in { + "_if_seq_no", + "_if_primary_term", "_parent", "_retry_on_conflict", "_routing", "_version", "_version_type", - ]: + }: action[op_type][key[1:]] = data.pop(key) else: action[op_type][key] = data.pop(key)

diff --git a/test_elasticsearch/test_helpers.py b/test_elasticsearch/test_helpers.py --- a/test_elasticsearch/test_helpers.py +++ b/test_elasticsearch/test_helpers.py @@ -76,6 +76,103 @@ class TestChunkActions(TestCase): def setup_method(self, _): self.actions = [({"index": {}}, {"some": u"datá", "i": i}) for i in range(100)] + def test_expand_action(self): + self.assertEqual(helpers.expand_action({}), ({"index": {}}, {})) + self.assertEqual( + helpers.expand_action({"key": "val"}), ({"index": {}}, {"key": "val"}) + ) + + def test_expand_action_actions(self): + self.assertEqual( + helpers.expand_action( + {"_op_type": "delete", "_id": "id", "_index": "index"} + ), + ({"delete": {"_id": "id", "_index": "index"}}, None), + ) + self.assertEqual( + helpers.expand_action( + {"_op_type": "update", "_id": "id", "_index": "index", "key": "val"} + ), + ({"update": {"_id": "id", "_index": "index"}}, {"key": "val"}), + ) + self.assertEqual( + helpers.expand_action( + {"_op_type": "create", "_id": "id", "_index": "index", "key": "val"} + ), + ({"create": {"_id": "id", "_index": "index"}}, {"key": "val"}), + ) + self.assertEqual( + helpers.expand_action( + { + "_op_type": "create", + "_id": "id", + "_index": "index", + "_source": {"key": "val"}, + } + ), + ({"create": {"_id": "id", "_index": "index"}}, {"key": "val"}), + ) + + def test_expand_action_options(self): + for option in ( + "_id", + "_index", + "_percolate", + "_timestamp", + "_type", + "if_seq_no", + "if_primary_term", + "parent", + "pipeline", + "retry_on_conflict", + "routing", + "version", + "version_type", + ("_parent", "parent"), + ("_retry_on_conflict", "retry_on_conflict"), + ("_routing", "routing"), + ("_version", "version"), + ("_version_type", "version_type"), + ("_if_seq_no", "if_seq_no"), + ("_if_primary_term", "if_primary_term"), + ): + if isinstance(option, str): + action_option = option + else: + option, action_option = option + self.assertEqual( + helpers.expand_action({"key": "val", option: 0}), + ({"index": {action_option: 0}}, {"key": "val"}), + ) + + def test__source_metadata_or_source(self): + self.assertEqual( + helpers.expand_action({"_source": {"key": "val"}}), + ({"index": {}}, {"key": "val"}), + ) + + self.assertEqual( + helpers.expand_action( + {"_source": ["key"], "key": "val", "_op_type": "update"} + ), + ({"update": {"_source": ["key"]}}, {"key": "val"}), + ) + + self.assertEqual( + helpers.expand_action( + {"_source": True, "key": "val", "_op_type": "update"} + ), + ({"update": {"_source": True}}, {"key": "val"}), + ) + + # This case is only to ensure backwards compatibility with old functionality. + self.assertEqual( + helpers.expand_action( + {"_source": {"key2": "val2"}, "key": "val", "_op_type": "update"} + ), + ({"update": {}}, {"key2": "val2"}), + ) + def test_chunks_are_chopped_by_byte_size(self): self.assertEqual( 100,

support for Optimistic concurrency control <!-- Feature request --> **When will this project support Optimistic concurrency control in `elasticsearch.helpers.bulk` operation, or has this project already support this feature? **

2020-09-28T13:46:21Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1451

6d3073224a332d3920d3d0bf4e30408ad254cec8

diff --git a/elasticsearch/_async/helpers.py b/elasticsearch/_async/helpers.py --- a/elasticsearch/_async/helpers.py +++ b/elasticsearch/_async/helpers.py @@ -332,24 +332,28 @@ async def async_scan( for hit in resp["hits"]["hits"]: yield hit + # Default to 0 if the value isn't included in the response + shards_successful = resp["_shards"].get("successful", 0) + shards_skipped = resp["_shards"].get("skipped", 0) + shards_total = resp["_shards"].get("total", 0) + # check if we have any errors - if (resp["_shards"]["successful"] + resp["_shards"]["skipped"]) < resp[ - "_shards" - ]["total"]: + if (shards_successful + shards_skipped) < shards_total: + shards_message = "Scroll request has only succeeded on %d (+%d skipped) shards out of %d." logger.warning( - "Scroll request has only succeeded on %d (+%d skipped) shards out of %d.", - resp["_shards"]["successful"], - resp["_shards"]["skipped"], - resp["_shards"]["total"], + shards_message, + shards_successful, + shards_skipped, + shards_total, ) if raise_on_error: raise ScanError( scroll_id, - "Scroll request has only succeeded on %d (+%d skipped) shards out of %d." + shards_message % ( - resp["_shards"]["successful"], - resp["_shards"]["skipped"], - resp["_shards"]["total"], + shards_successful, + shards_skipped, + shards_total, ), ) resp = await client.scroll( diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -531,24 +531,28 @@ def scan( for hit in resp["hits"]["hits"]: yield hit + # Default to 0 if the value isn't included in the response + shards_successful = resp["_shards"].get("successful", 0) + shards_skipped = resp["_shards"].get("skipped", 0) + shards_total = resp["_shards"].get("total", 0) + # check if we have any errors - if (resp["_shards"]["successful"] + resp["_shards"]["skipped"]) < resp[ - "_shards" - ]["total"]: + if (shards_successful + shards_skipped) < shards_total: + shards_message = "Scroll request has only succeeded on %d (+%d skipped) shards out of %d." logger.warning( - "Scroll request has only succeeded on %d (+%d skipped) shards out of %d.", - resp["_shards"]["successful"], - resp["_shards"]["skipped"], - resp["_shards"]["total"], + shards_message, + shards_successful, + shards_skipped, + shards_total, ) if raise_on_error: raise ScanError( scroll_id, - "Scroll request has only succeeded on %d (+%d skipped) shards out of %d." + shards_message % ( - resp["_shards"]["successful"], - resp["_shards"]["skipped"], - resp["_shards"]["total"], + shards_successful, + shards_skipped, + shards_total, ), ) resp = client.scroll(

diff --git a/test_elasticsearch/test_server/test_helpers.py b/test_elasticsearch/test_server/test_helpers.py --- a/test_elasticsearch/test_server/test_helpers.py +++ b/test_elasticsearch/test_server/test_helpers.py @@ -500,6 +500,33 @@ def test_clear_scroll(self): ) spy.assert_not_called() + def test_shards_no_skipped_field(self): + with patch.object(self, "client") as client_mock: + client_mock.search.return_value = { + "_scroll_id": "dummy_id", + "_shards": {"successful": 5, "total": 5}, + "hits": {"hits": [{"search_data": 1}]}, + } + client_mock.scroll.side_effect = [ + { + "_scroll_id": "dummy_id", + "_shards": {"successful": 5, "total": 5}, + "hits": {"hits": [{"scroll_data": 42}]}, + }, + { + "_scroll_id": "dummy_id", + "_shards": {"successful": 5, "total": 5}, + "hits": {"hits": []}, + }, + ] + + data = list( + helpers.scan( + self.client, index="test_index", size=2, raise_on_error=True + ) + ) + self.assertEqual(data, [{"search_data": 1}, {"scroll_data": 42}]) + class TestReindex(ElasticsearchTestCase): def setup_method(self, _):

KeyError: 'skipped' : if (resp["_shards"]["successful"] + resp["_shards"]["skipped"]) < resp[  if (resp["_shards"]["successful"] + resp["_shards"]["skipped"]) < resp[ -> if (resp["_shards"]["successful"] + resp["_shards"].get("skipped",0)) < resp[

anyone knows how to solve this problem?  same problems I reverted **elasticsearch** package to version 7.0 It worked Hope they fix this in the future versions version 7.9.1 same problems reverted to v7.0 It worked Why are they not fixing this issue from a while; Always had to revert to 7.0 [This change](https://github.com/elastic/elasticsearch-py/commit/fc78c992ef6ceca157f369186760c8c550fb3bc4) was made over a year ago and shipped in v7.1.0. What version of Elasticsearch is being used where there isn't a `skipped` field, was this added in v7.1? Either way I'll migrate this issue to it's proper home, the `elasticsearch-py` repository.

2020-11-20T20:56:34Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1535

4f9004ff01678a0817af6933d19c78842b4bd9e7

diff --git a/elasticsearch/client/utils.py b/elasticsearch/client/utils.py --- a/elasticsearch/client/utils.py +++ b/elasticsearch/client/utils.py @@ -20,7 +20,7 @@ import weakref from datetime import date, datetime from functools import wraps -from ..compat import string_types, quote_plus, PY2 +from ..compat import string_types, quote, PY2 # parts of URL to be omitted SKIP_IN_PATH = (None, "", b"", [], ()) @@ -66,7 +66,7 @@ def _make_path(*parts): # TODO: maybe only allow some parts to be lists/tuples ? return "/" + "/".join( # preserve ',' and '*' in url for nicer URLs in logs - quote_plus(_escape(p), b",*") + quote(_escape(p), b",*") for p in parts if p not in SKIP_IN_PATH ) diff --git a/elasticsearch/client/xpack/ml.py b/elasticsearch/client/xpack/ml.py --- a/elasticsearch/client/xpack/ml.py +++ b/elasticsearch/client/xpack/ml.py @@ -163,8 +163,7 @@ def get_jobs(self, job_id=None, params=None): @query_params() def delete_expired_data(self, params=None): - """ - """ + """""" return self.transport.perform_request( "DELETE", "/_xpack/ml/_delete_expired_data", params=params ) diff --git a/elasticsearch/compat.py b/elasticsearch/compat.py --- a/elasticsearch/compat.py +++ b/elasticsearch/compat.py @@ -21,19 +21,20 @@ if PY2: string_types = (basestring,) # noqa: F821 - from urllib import quote_plus, urlencode, unquote + from urllib import quote, quote_plus, urlencode, unquote from urlparse import urlparse from itertools import imap as map from Queue import Queue else: string_types = str, bytes - from urllib.parse import quote_plus, urlencode, urlparse, unquote + from urllib.parse import quote, quote_plus, urlencode, urlparse, unquote map = map from queue import Queue __all__ = [ "string_types", + "quote", "quote_plus", "urlencode", "unquote", diff --git a/elasticsearch/connection/http_requests.py b/elasticsearch/connection/http_requests.py --- a/elasticsearch/connection/http_requests.py +++ b/elasticsearch/connection/http_requests.py @@ -101,7 +101,10 @@ def __init__( http_auth = tuple(http_auth.split(":", 1)) self.session.auth = http_auth - self.base_url = "%s%s" % (self.host, self.url_prefix,) + self.base_url = "%s%s" % ( + self.host, + self.url_prefix, + ) self.session.verify = verify_certs if not client_key: self.session.cert = client_cert

diff --git a/test_elasticsearch/test_client/test_utils.py b/test_elasticsearch/test_client/test_utils.py --- a/test_elasticsearch/test_client/test_utils.py +++ b/test_elasticsearch/test_client/test_utils.py @@ -40,6 +40,11 @@ def test_handles_utf_encoded_string(self): "/some-index/type/%E4%B8%AD%E6%96%87", _make_path("some-index", "type", id) ) + def test_handles_whitespace_character(self): + self.assertEquals( + "/path/%20with/%20/a%20space", _make_path("path", " with", " ", "a space") + ) + class TestEscape(TestCase): def test_handles_ascii(self): diff --git a/test_elasticsearch/test_connection.py b/test_elasticsearch/test_connection.py --- a/test_elasticsearch/test_connection.py +++ b/test_elasticsearch/test_connection.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # Licensed to Elasticsearch B.V. under one or more contributor # license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright @@ -308,7 +309,7 @@ def test_surrogatepass_into_bytes(self): buf = b"\xe4\xbd\xa0\xe5\xa5\xbd\xed\xa9\xaa" con = self._get_mock_connection(response_body=buf) status, headers, data = con.perform_request("GET", "/") - self.assertEqual("你好\uda6a", data) + self.assertEqual(u"你好\uda6a", data) class TestRequestsConnection(TestCase): @@ -394,7 +395,9 @@ def test_no_http_compression(self): self.assertNotIn("accept-encoding", req.headers) def test_http_compression(self): - con = self._get_mock_connection({"http_compress": True},) + con = self._get_mock_connection( + {"http_compress": True}, + ) self.assertTrue(con.http_compress) @@ -630,4 +633,4 @@ def test_surrogatepass_into_bytes(self): buf = b"\xe4\xbd\xa0\xe5\xa5\xbd\xed\xa9\xaa" con = self._get_mock_connection(response_body=buf) status, headers, data = con.perform_request("GET", "/") - self.assertEqual("你好\uda6a", data) + self.assertEqual(u"你好\uda6a", data) diff --git a/test_elasticsearch/test_serializer.py b/test_elasticsearch/test_serializer.py --- a/test_elasticsearch/test_serializer.py +++ b/test_elasticsearch/test_serializer.py @@ -122,18 +122,21 @@ def test_serializes_pandas_na(self): if not hasattr(pd, "NA"): # pandas.NA added in v1 raise SkipTest("pandas.NA required") self.assertEquals( - '{"d":null}', JSONSerializer().dumps({"d": pd.NA}), + '{"d":null}', + JSONSerializer().dumps({"d": pd.NA}), ) def test_serializes_pandas_category(self): cat = pd.Categorical(["a", "c", "b", "a"], categories=["a", "b", "c"]) self.assertEquals( - '{"d":["a","c","b","a"]}', JSONSerializer().dumps({"d": cat}), + '{"d":["a","c","b","a"]}', + JSONSerializer().dumps({"d": cat}), ) cat = pd.Categorical([1, 2, 3], categories=[1, 2, 3]) self.assertEquals( - '{"d":[1,2,3]}', JSONSerializer().dumps({"d": cat}), + '{"d":[1,2,3]}', + JSONSerializer().dumps({"d": cat}), ) def test_raises_serialization_error_on_dump_error(self):

ID with whitespaces stored incorrectly **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 6.8.1 **Description of the problem including expected versus actual behavior**: When indexing a document with an ID with an whitespace, the ID does end up stored with the `+` character instead of a whitespace. This breaks the consistency of the client API. Looking at the example used below, `count` and `search` operations do not come back with the expected result. Digging a bit deeper I could find that the library doesn't seem to be compliant with the https://tools.ietf.org/html/rfc3986#section-2.1 standard when it comes to escaping the ID (part of the URI path component). It is using `+` to escape, which is wrong, whitespaces are escaped using `%20`. **Steps to reproduce**: Run this test. Setup env vars: ELASTIC_ADDRESS, ELASTIC_USER and ELASTIC_PASS ```python import os from elasticsearch import Elasticsearch def test_example(): under_test = Elasticsearch(os.environ.get("ELASTIC_ADDRESS"), http_auth=( os.environ.get("ELASTIC_USER"), os.environ.get("ELASTIC_PASS") )) index = 'wrong-escaping' example_id = "One Example" under_test.indices.create(index=index, ignore=400) under_test.index(index=index, id=example_id, body={"field": "value"}, doc_type="doc") result = under_test.get(index=index, id=example_id, doc_type="doc") assert result['_id'] == example_id search_result = under_test.search(index=index, q="_id:{}".format(example_id), doc_type="doc") assert search_result['hits']['total']['value'] == 1 count_result = under_test.count(index=index, q="_id:{}".format(example_id), doc_type="doc") assert count_result == 1 ```

Yep, agree that this is an issue. Are you up for creating a PR against the `6.x` branch with a test case? Should be changing `quote_plus` to `quote` in `elasticsearch.client.utils`?

2021-02-19T16:27:50Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1577

e949d221b1e2876cb74f1fd41cbee09b792d6eb2

diff --git a/elasticsearch/client/utils.py b/elasticsearch/client/utils.py --- a/elasticsearch/client/utils.py +++ b/elasticsearch/client/utils.py @@ -17,11 +17,12 @@ from __future__ import unicode_literals +import base64 import weakref from datetime import date, datetime from functools import wraps -from ..compat import PY2, quote, string_types, unquote, urlparse +from ..compat import PY2, quote, string_types, to_bytes, to_str, unquote, urlparse # parts of URL to be omitted SKIP_IN_PATH = (None, "", b"", [], ()) @@ -140,6 +141,20 @@ def _wrapped(*args, **kwargs): if "opaque_id" in kwargs: headers["x-opaque-id"] = kwargs.pop("opaque_id") + http_auth = kwargs.pop("http_auth", None) + api_key = kwargs.pop("api_key", None) + + if http_auth is not None and api_key is not None: + raise ValueError( + "Only one of 'http_auth' and 'api_key' may be passed at a time" + ) + elif http_auth is not None: + headers["authorization"] = "Basic %s" % ( + _base64_auth_header(http_auth), + ) + elif api_key is not None: + headers["authorization"] = "ApiKey %s" % (_base64_auth_header(api_key),) + for p in es_query_params + GLOBAL_PARAMS: if p in kwargs: v = kwargs.pop(p) @@ -172,6 +187,16 @@ def _bulk_body(serializer, body): return body +def _base64_auth_header(auth_value): + """Takes either a 2-tuple or a base64-encoded string + and returns a base64-encoded string to be used + as an HTTP authorization header. + """ + if isinstance(auth_value, (list, tuple)): + auth_value = base64.b64encode(to_bytes(":".join(auth_value))) + return to_str(auth_value) + + class NamespacedClient(object): def __init__(self, client): self.client = client diff --git a/elasticsearch/compat.py b/elasticsearch/compat.py --- a/elasticsearch/compat.py +++ b/elasticsearch/compat.py @@ -26,6 +26,14 @@ from Queue import Queue from urlparse import urlparse + + def to_str(x, encoding="ascii"): + if not isinstance(x, str): + return x.encode(encoding) + return x + + to_bytes = to_str + else: string_types = str, bytes from urllib.parse import quote, quote_plus, unquote, urlencode, urlparse @@ -33,6 +41,17 @@ map = map from queue import Queue + def to_str(x, encoding="ascii"): + if not isinstance(x, str): + return x.decode(encoding) + return x + + def to_bytes(x, encoding="ascii"): + if not isinstance(x, bytes): + return x.encode(encoding) + return x + + try: from collections.abc import Mapping except ImportError: diff --git a/utils/generate-api.py b/utils/generate-api.py --- a/utils/generate-api.py +++ b/utils/generate-api.py @@ -54,6 +54,8 @@ "request_timeout": "Optional[Union[int, float]]", "ignore": "Optional[Union[int, Collection[int]]]", "opaque_id": "Optional[str]", + "http_auth": "Optional[Union[str, Tuple[str, str]]]", + "api_key": "Optional[Union[str, Tuple[str, str]]]", } jinja_env = Environment(

diff --git a/test_elasticsearch/test_client/test_utils.py b/test_elasticsearch/test_client/test_utils.py --- a/test_elasticsearch/test_client/test_utils.py +++ b/test_elasticsearch/test_client/test_utils.py @@ -75,6 +75,49 @@ def test_handles_empty_none_and_normalization(self): self.func_to_wrap(headers={"X": "y"}) self.assertEqual(self.calls[-1], ((), {"params": {}, "headers": {"x": "y"}})) + def test_per_call_authentication(self): + self.func_to_wrap(api_key=("name", "key")) + self.assertEqual( + self.calls[-1], + ((), {"headers": {"authorization": "ApiKey bmFtZTprZXk="}, "params": {}}), + ) + + self.func_to_wrap(http_auth=("user", "password")) + self.assertEqual( + self.calls[-1], + ( + (), + { + "headers": {"authorization": "Basic dXNlcjpwYXNzd29yZA=="}, + "params": {}, + }, + ), + ) + + self.func_to_wrap(http_auth="abcdef") + self.assertEqual( + self.calls[-1], + ((), {"headers": {"authorization": "Basic abcdef"}, "params": {}}), + ) + + # If one or the other is 'None' it's all good! + self.func_to_wrap(http_auth=None, api_key=None) + self.assertEqual(self.calls[-1], ((), {"headers": {}, "params": {}})) + + self.func_to_wrap(http_auth="abcdef", api_key=None) + self.assertEqual( + self.calls[-1], + ((), {"headers": {"authorization": "Basic abcdef"}, "params": {}}), + ) + + # If both are given values an error is raised. + with self.assertRaises(ValueError) as e: + self.func_to_wrap(http_auth="key", api_key=("1", "2")) + self.assertEqual( + str(e.exception), + "Only one of 'http_auth' and 'api_key' may be passed at a time", + ) + class TestMakePath(TestCase): def test_handles_unicode(self):

Support per-request http_auth and api_key Allow setting basic auth via `http_auth=(username, password)` and `api_key=(id, secret)` on every API method similar to `request_timeout`. This is useful if Elasticsearch is configured to use an API key per user like is the case for Workplace Search OAuth.

2021-04-19T20:34:41Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1604

fbff9ffe826b728523804297eec5d7cb535784b7

diff --git a/elasticsearch/_async/http_aiohttp.py b/elasticsearch/_async/http_aiohttp.py --- a/elasticsearch/_async/http_aiohttp.py +++ b/elasticsearch/_async/http_aiohttp.py @@ -360,6 +360,7 @@ async def _create_aiohttp_session(self): self.loop = get_running_loop() self.session = aiohttp.ClientSession( headers=self.headers, + skip_auto_headers=("accept", "accept-encoding"), auto_decompress=True, loop=self.loop, cookie_jar=aiohttp.DummyCookieJar(),

diff --git a/test_elasticsearch/test_async/test_connection.py b/test_elasticsearch/test_async/test_connection.py --- a/test_elasticsearch/test_async/test_connection.py +++ b/test_elasticsearch/test_async/test_connection.py @@ -18,6 +18,7 @@ import gzip import io +import json import ssl import warnings from platform import python_version @@ -316,3 +317,75 @@ async def test_surrogatepass_into_bytes(self): con = await self._get_mock_connection(response_body=buf) status, headers, data = await con.perform_request("GET", "/") assert u"你好\uda6a" == data + + +class TestConnectionHttpbin: + """Tests the HTTP connection implementations against a live server E2E""" + + async def httpbin_anything(self, conn, **kwargs): + status, headers, data = await conn.perform_request("GET", "/anything", **kwargs) + data = json.loads(data) + data["headers"].pop( + "X-Amzn-Trace-Id", None + ) # Remove this header as it's put there by AWS. + return (status, data) + + async def test_aiohttp_connection(self): + # Defaults + conn = AIOHttpConnection("httpbin.org", port=443, use_ssl=True) + user_agent = conn._get_default_user_agent() + status, data = await self.httpbin_anything(conn) + assert status == 200 + assert data["method"] == "GET" + assert data["headers"] == { + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # http_compress=False + conn = AIOHttpConnection( + "httpbin.org", port=443, use_ssl=True, http_compress=False + ) + status, data = await self.httpbin_anything(conn) + assert status == 200 + assert data["method"] == "GET" + assert data["headers"] == { + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # http_compress=True + conn = AIOHttpConnection( + "httpbin.org", port=443, use_ssl=True, http_compress=True + ) + status, data = await self.httpbin_anything(conn) + assert status == 200 + assert data["headers"] == { + "Accept-Encoding": "gzip,deflate", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # Headers + conn = AIOHttpConnection( + "httpbin.org", + port=443, + use_ssl=True, + http_compress=True, + headers={"header1": "value1"}, + ) + status, data = await self.httpbin_anything( + conn, headers={"header2": "value2", "header1": "override!"} + ) + assert status == 200 + assert data["headers"] == { + "Accept-Encoding": "gzip,deflate", + "Content-Type": "application/json", + "Host": "httpbin.org", + "Header1": "override!", + "Header2": "value2", + "User-Agent": user_agent, + } diff --git a/test_elasticsearch/test_connection.py b/test_elasticsearch/test_connection.py --- a/test_elasticsearch/test_connection.py +++ b/test_elasticsearch/test_connection.py @@ -18,6 +18,7 @@ import gzip import io +import json import os import re import ssl @@ -866,3 +867,139 @@ def test_surrogatepass_into_bytes(self): con = self._get_mock_connection(response_body=buf) status, headers, data = con.perform_request("GET", "/") self.assertEqual(u"你好\uda6a", data) + + +class TestConnectionHttpbin: + """Tests the HTTP connection implementations against a live server E2E""" + + def httpbin_anything(self, conn, **kwargs): + status, headers, data = conn.perform_request("GET", "/anything", **kwargs) + data = json.loads(data) + data["headers"].pop( + "X-Amzn-Trace-Id", None + ) # Remove this header as it's put there by AWS. + return (status, data) + + def test_urllib3_connection(self): + # Defaults + conn = Urllib3HttpConnection("httpbin.org", port=443, use_ssl=True) + user_agent = conn._get_default_user_agent() + status, data = self.httpbin_anything(conn) + assert status == 200 + assert data["method"] == "GET" + assert data["headers"] == { + "Accept-Encoding": "identity", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # http_compress=False + conn = Urllib3HttpConnection( + "httpbin.org", port=443, use_ssl=True, http_compress=False + ) + status, data = self.httpbin_anything(conn) + assert status == 200 + assert data["method"] == "GET" + assert data["headers"] == { + "Accept-Encoding": "identity", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # http_compress=True + conn = Urllib3HttpConnection( + "httpbin.org", port=443, use_ssl=True, http_compress=True + ) + status, data = self.httpbin_anything(conn) + assert status == 200 + assert data["headers"] == { + "Accept-Encoding": "gzip,deflate", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # Headers + conn = Urllib3HttpConnection( + "httpbin.org", + port=443, + use_ssl=True, + http_compress=True, + headers={"header1": "value1"}, + ) + status, data = self.httpbin_anything( + conn, headers={"header2": "value2", "header1": "override!"} + ) + assert status == 200 + assert data["headers"] == { + "Accept-Encoding": "gzip,deflate", + "Content-Type": "application/json", + "Host": "httpbin.org", + "Header1": "override!", + "Header2": "value2", + "User-Agent": user_agent, + } + + def test_requests_connection(self): + # Defaults + conn = RequestsHttpConnection("httpbin.org", port=443, use_ssl=True) + user_agent = conn._get_default_user_agent() + status, data = self.httpbin_anything(conn) + assert status == 200 + assert data["method"] == "GET" + assert data["headers"] == { + "Accept-Encoding": "identity", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # http_compress=False + conn = RequestsHttpConnection( + "httpbin.org", port=443, use_ssl=True, http_compress=False + ) + status, data = self.httpbin_anything(conn) + assert status == 200 + assert data["method"] == "GET" + assert data["headers"] == { + "Accept-Encoding": "identity", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # http_compress=True + conn = RequestsHttpConnection( + "httpbin.org", port=443, use_ssl=True, http_compress=True + ) + status, data = self.httpbin_anything(conn) + assert status == 200 + assert data["headers"] == { + "Accept-Encoding": "gzip,deflate", + "Content-Type": "application/json", + "Host": "httpbin.org", + "User-Agent": user_agent, + } + + # Headers + conn = RequestsHttpConnection( + "httpbin.org", + port=443, + use_ssl=True, + http_compress=True, + headers={"header1": "value1"}, + ) + status, data = self.httpbin_anything( + conn, headers={"header2": "value2", "header1": "override!"} + ) + assert status == 200 + assert data["headers"] == { + "Accept-Encoding": "gzip,deflate", + "Content-Type": "application/json", + "Host": "httpbin.org", + "Header1": "override!", + "Header2": "value2", + "User-Agent": user_agent, + }

AIOHttpConnection - http_compress does not change Accept-Encoding header Hi! I am experimenting with http_compress in our client options. I can confirm it is toggling whether to compress request bodies, but Accept-Encoding: gzip,deflate is always sent regardless of this setting. per the doc: ``` http_compress – Use gzip compression ``` ...which I interpreted potentially that this works as designed, except the code implies it should also be managing the responses ([here](https://github.com/elastic/elasticsearch-py/blob/db3fd7f5184be1c9aecfe0de95336c76fc112b1e/elasticsearch/connection/http_requests.py#L109) and [here](https://github.com/elastic/elasticsearch-py/blob/da42c81fa8d0087099f3a1d22f75ab8b2319c962/elasticsearch/connection/base.py#L139)).

2021-06-04T18:06:00Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1645

c36d31d1609b34db8da15e52f95e49c9f41b12d2

diff --git a/elasticsearch/_async/client/nodes.py b/elasticsearch/_async/client/nodes.py --- a/elasticsearch/_async/client/nodes.py +++ b/elasticsearch/_async/client/nodes.py @@ -15,7 +15,7 @@ # specific language governing permissions and limitations # under the License. -from .utils import NamespacedClient, _make_path, query_params, SKIP_IN_PATH +from .utils import SKIP_IN_PATH, NamespacedClient, _make_path, query_params class NodesClient(NamespacedClient): diff --git a/elasticsearch/_async/client/sql.py b/elasticsearch/_async/client/sql.py --- a/elasticsearch/_async/client/sql.py +++ b/elasticsearch/_async/client/sql.py @@ -15,7 +15,7 @@ # specific language governing permissions and limitations # under the License. -from .utils import SKIP_IN_PATH, NamespacedClient, query_params, _make_path +from .utils import SKIP_IN_PATH, NamespacedClient, _make_path, query_params class SqlClient(NamespacedClient): diff --git a/elasticsearch/_async/helpers.py b/elasticsearch/_async/helpers.py --- a/elasticsearch/_async/helpers.py +++ b/elasticsearch/_async/helpers.py @@ -19,7 +19,7 @@ import logging from ..compat import map -from ..exceptions import TransportError +from ..exceptions import NotFoundError, TransportError from ..helpers.actions import ( _ActionChunker, _process_bulk_chunk_error, @@ -406,6 +406,7 @@ async def async_reindex( target_client=None, chunk_size=500, scroll="5m", + op_type=None, scan_kwargs={}, bulk_kwargs={}, ): @@ -435,6 +436,9 @@ async def async_reindex( :arg chunk_size: number of docs in one chunk sent to es (default: 500) :arg scroll: Specify how long a consistent view of the index should be maintained for scrolled search + :arg op_type: Explicit operation type. Defaults to '_index'. Data streams must + be set to 'create'. If not specified, will auto-detect if target_index is a + data stream. :arg scan_kwargs: additional kwargs to be passed to :func:`~elasticsearch.helpers.async_scan` :arg bulk_kwargs: additional kwargs to be passed to @@ -445,18 +449,41 @@ async def async_reindex( client, query=query, index=source_index, scroll=scroll, **scan_kwargs ) - async def _change_doc_index(hits, index): + async def _change_doc_index(hits, index, op_type): async for h in hits: h["_index"] = index + if op_type is not None: + h["_op_type"] = op_type if "fields" in h: h.update(h.pop("fields")) yield h kwargs = {"stats_only": True} kwargs.update(bulk_kwargs) + + is_data_stream = False + try: + # Verify if the target_index is data stream or index + data_streams = await target_client.indices.get_data_stream( + target_index, expand_wildcards="all" + ) + is_data_stream = any( + data_stream["name"] == target_index + for data_stream in data_streams["data_streams"] + ) + except (TransportError, KeyError, NotFoundError): + # If its not data stream, might be index + pass + + if is_data_stream: + if op_type not in (None, "create"): + raise ValueError("Data streams must have 'op_type' set to 'create'") + else: + op_type = "create" + return await async_bulk( target_client, - _change_doc_index(docs, target_index), + _change_doc_index(docs, target_index, op_type), chunk_size=chunk_size, **kwargs, ) diff --git a/elasticsearch/client/nodes.py b/elasticsearch/client/nodes.py --- a/elasticsearch/client/nodes.py +++ b/elasticsearch/client/nodes.py @@ -15,7 +15,7 @@ # specific language governing permissions and limitations # under the License. -from .utils import NamespacedClient, _make_path, query_params, SKIP_IN_PATH +from .utils import SKIP_IN_PATH, NamespacedClient, _make_path, query_params class NodesClient(NamespacedClient): diff --git a/elasticsearch/client/sql.py b/elasticsearch/client/sql.py --- a/elasticsearch/client/sql.py +++ b/elasticsearch/client/sql.py @@ -15,7 +15,7 @@ # specific language governing permissions and limitations # under the License. -from .utils import SKIP_IN_PATH, NamespacedClient, query_params, _make_path +from .utils import SKIP_IN_PATH, NamespacedClient, _make_path, query_params class SqlClient(NamespacedClient): diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -20,7 +20,7 @@ from operator import methodcaller from ..compat import Mapping, Queue, map, string_types -from ..exceptions import TransportError +from ..exceptions import NotFoundError, TransportError from .errors import BulkIndexError, ScanError logger = logging.getLogger("elasticsearch.helpers") @@ -622,6 +622,7 @@ def reindex( target_client=None, chunk_size=500, scroll="5m", + op_type=None, scan_kwargs={}, bulk_kwargs={}, ): @@ -651,6 +652,9 @@ def reindex( :arg chunk_size: number of docs in one chunk sent to es (default: 500) :arg scroll: Specify how long a consistent view of the index should be maintained for scrolled search + :arg op_type: Explicit operation type. Defaults to '_index'. Data streams must + be set to 'create'. If not specified, will auto-detect if target_index is a + data stream. :arg scan_kwargs: additional kwargs to be passed to :func:`~elasticsearch.helpers.scan` :arg bulk_kwargs: additional kwargs to be passed to @@ -659,18 +663,41 @@ def reindex( target_client = client if target_client is None else target_client docs = scan(client, query=query, index=source_index, scroll=scroll, **scan_kwargs) - def _change_doc_index(hits, index): + def _change_doc_index(hits, index, op_type): for h in hits: h["_index"] = index + if op_type is not None: + h["_op_type"] = op_type if "fields" in h: h.update(h.pop("fields")) yield h kwargs = {"stats_only": True} kwargs.update(bulk_kwargs) + + is_data_stream = False + try: + # Verify if the target_index is data stream or index + data_streams = target_client.indices.get_data_stream( + target_index, expand_wildcards="all" + ) + is_data_stream = any( + data_stream["name"] == target_index + for data_stream in data_streams["data_streams"] + ) + except (TransportError, KeyError, NotFoundError): + # If its not data stream, might be index + pass + + if is_data_stream: + if op_type not in (None, "create"): + raise ValueError("Data streams must have 'op_type' set to 'create'") + else: + op_type = "create" + return bulk( target_client, - _change_doc_index(docs, target_index), + _change_doc_index(docs, target_index, op_type), chunk_size=chunk_size, **kwargs )

diff --git a/elasticsearch/helpers/test.py b/elasticsearch/helpers/test.py --- a/elasticsearch/helpers/test.py +++ b/elasticsearch/helpers/test.py @@ -73,6 +73,9 @@ def teardown_method(self, _): if self.es_version() >= (7, 7): expand_wildcards.append("hidden") + self.client.indices.delete_data_stream( + name="*", ignore=404, expand_wildcards=expand_wildcards + ) self.client.indices.delete( index="*", ignore=404, expand_wildcards=expand_wildcards ) diff --git a/test_elasticsearch/test_async/test_server/test_helpers.py b/test_elasticsearch/test_async/test_server/test_helpers.py --- a/test_elasticsearch/test_async/test_server/test_helpers.py +++ b/test_elasticsearch/test_async/test_server/test_helpers.py @@ -16,6 +16,7 @@ # under the License. import asyncio +from datetime import datetime, timedelta, timezone import pytest from mock import MagicMock, patch @@ -884,3 +885,67 @@ async def test_children_are_reindexed_correctly( "_version": 1, "found": True, } == q + + [email protected](scope="function") +async def reindex_data_stream_setup(async_client): + dt = datetime.now(tz=timezone.utc) + bulk = [] + for x in range(100): + bulk.append({"index": {"_index": "test_index_stream", "_id": x}}) + bulk.append( + { + "answer": x, + "correct": x == 42, + "type": "answers" if x % 2 == 0 else "questions", + "@timestamp": (dt - timedelta(days=x)).isoformat(), + } + ) + await async_client.bulk(bulk, refresh=True) + await async_client.indices.put_index_template( + name="my-index-template", + body={ + "index_patterns": ["py-*-*"], + "data_stream": {}, + }, + ) + await async_client.indices.create_data_stream(name="py-test-stream") + await async_client.indices.refresh() + yield + + +class TestAsyncDataStreamReindex(object): + @pytest.mark.parametrize("op_type", [None, "create"]) + async def test_reindex_index_datastream( + self, op_type, async_client, reindex_data_stream_setup + ): + await helpers.async_reindex( + async_client, + source_index="test_index_stream", + target_index="py-test-stream", + scan_kwargs={"q": "type:answers"}, + bulk_kwargs={"refresh": True}, + op_type=op_type, + ) + # await async_client.indices.refresh() + assert await async_client.indices.exists(index="py-test-stream") + assert ( + 50 + == (await async_client.count(index="py-test-stream", q="type:answers"))[ + "count" + ] + ) + + async def test_reindex_index_datastream_op_type_index( + self, async_client, reindex_data_stream_setup + ): + with pytest.raises( + ValueError, match="Data streams must have 'op_type' set to 'create'" + ): + await helpers.async_reindex( + async_client, + source_index="test_index_stream", + target_index="py-test-stream", + query={"query": {"bool": {"filter": {"term": {"type": "answers"}}}}}, + op_type="_index", + ) diff --git a/test_elasticsearch/test_server/test_helpers.py b/test_elasticsearch/test_server/test_helpers.py --- a/test_elasticsearch/test_server/test_helpers.py +++ b/test_elasticsearch/test_server/test_helpers.py @@ -15,6 +15,9 @@ # specific language governing permissions and limitations # under the License. +from datetime import datetime, timedelta, timezone + +import pytest from mock import patch from elasticsearch import TransportError, helpers @@ -754,3 +757,62 @@ def test_children_are_reindexed_correctly(self): }, q, ) + + [email protected](scope="function") +def reindex_data_stream_setup(sync_client): + dt = datetime.now(tz=timezone.utc) + bulk = [] + for x in range(100): + bulk.append({"index": {"_index": "test_index_stream", "_id": x}}) + bulk.append( + { + "answer": x, + "correct": x == 42, + "type": "answers" if x % 2 == 0 else "questions", + "@timestamp": (dt - timedelta(days=x)).isoformat(), + } + ) + sync_client.bulk(bulk, refresh=True) + sync_client.indices.put_index_template( + name="my-index-template", + body={ + "index_patterns": ["py-*-*"], + "data_stream": {}, + }, + ) + sync_client.indices.create_data_stream(name="py-test-stream") + sync_client.indices.refresh() + + +class TestDataStreamReindex(object): + @pytest.mark.parametrize("op_type", [None, "create"]) + def test_reindex_index_datastream( + self, op_type, sync_client, reindex_data_stream_setup + ): + helpers.reindex( + sync_client, + source_index="test_index_stream", + target_index="py-test-stream", + query={"query": {"bool": {"filter": {"term": {"type": "answers"}}}}}, + op_type=op_type, + ) + sync_client.indices.refresh() + assert sync_client.indices.exists(index="py-test-stream") + assert ( + 50 == sync_client.count(index="py-test-stream", q="type:answers")["count"] + ) + + def test_reindex_index_datastream_op_type_index( + self, sync_client, reindex_data_stream_setup + ): + with pytest.raises( + ValueError, match="Data streams must have 'op_type' set to 'create'" + ): + helpers.reindex( + sync_client, + source_index="test_index_stream", + target_index="py-test-stream", + query={"query": {"bool": {"filter": {"term": {"type": "answers"}}}}}, + op_type="_index", + )

Support using the reindex helper to target a datastream **Describe the feature**: The `elasticsearch.helpers.reindex` function currently does not support re-indexing into a data stream. If you run something like the following code: ```python helpers.reindex( client=source_client, source_index=source_index, target_client=target_client, target_index=target_data_stream, ) ``` it will return a `BulkIndexError` with the error `{'type': 'illegal_argument_exception', 'reason': 'only write ops with an op_type of create are allowed in data streams'}`. Per the Elastic API documentation for `reindex` under the [query parameters](https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-reindex.html#docs-reindex-api-query-params): `To reindex to a data stream destination, this argument must be create.` Would be great if this option was available in the helper!

@sethmlarson By any chance, Will I be able to implement this ? If Yes any inputs on what to do. I think @jeska is already looking at this! @V1NAY8 by all means go for it!

2021-07-16T14:26:26Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1790

538c1046f2c0624210e89f4640e2947e94e061be

diff --git a/elasticsearch/_async/helpers.py b/elasticsearch/_async/helpers.py --- a/elasticsearch/_async/helpers.py +++ b/elasticsearch/_async/helpers.py @@ -355,16 +355,19 @@ def pop_transport_kwargs(kw): ) client._client_meta = (("h", "s"),) - # initial search - search_kwargs = query.copy() if query else {} - search_kwargs.update(kwargs) - search_kwargs["scroll"] = scroll - search_kwargs["size"] = size - try: + search_kwargs = query.copy() if query else {} + search_kwargs.update(kwargs) + search_kwargs["scroll"] = scroll + search_kwargs["size"] = size resp = await client.search(**search_kwargs) + + # Try the old deprecated way if we fail immediately on parameters. except TypeError: - resp = await client.search(body=query, scroll=scroll, size=size, **kwargs) + search_kwargs = kwargs.copy() + search_kwargs["scroll"] = scroll + search_kwargs["size"] = size + resp = await client.search(body=query, **search_kwargs) scroll_id = resp.raw.get("_scroll_id") scroll_transport_kwargs = pop_transport_kwargs(scroll_kwargs) diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -566,15 +566,20 @@ def pop_transport_kwargs(kw): ) client._client_meta = (("h", "s"),) - # initial search - search_kwargs = query.copy() if query else {} - search_kwargs.update(kwargs) - search_kwargs["scroll"] = scroll - search_kwargs["size"] = size try: + search_kwargs = query.copy() if query else {} + search_kwargs.update(kwargs) + search_kwargs["scroll"] = scroll + search_kwargs["size"] = size resp = client.search(**search_kwargs) + + # Try the old deprecated way if we fail immediately on parameters. except TypeError: - resp = client.search(body=query, scroll=scroll, size=size, **kwargs) + search_kwargs = kwargs.copy() + search_kwargs["scroll"] = scroll + search_kwargs["size"] = size + resp = client.search(body=query, **search_kwargs) + scroll_id = resp.raw.get("_scroll_id") scroll_transport_kwargs = pop_transport_kwargs(scroll_kwargs) if scroll_transport_kwargs:

diff --git a/test_elasticsearch/test_async/test_server/test_rest_api_spec.py b/test_elasticsearch/test_async/test_server/test_rest_api_spec.py --- a/test_elasticsearch/test_async/test_server/test_rest_api_spec.py +++ b/test_elasticsearch/test_async/test_server/test_rest_api_spec.py @@ -147,7 +147,7 @@ async def run_do(self, action): warnings.simplefilter("always", category=ElasticsearchWarning) with warnings.catch_warnings(record=True) as caught_warnings: try: - self.last_response = await api(**args).raw + self.last_response = (await api(**args)).raw except Exception as e: if not catch: raise

Issue with ES >= v7.15.0: TypeError: search() got multiple values for keyword argument 'size' <!-- Bug report --> **Elasticsearch version** : 7.15.1 **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 7.15.1 Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: Hello, I'm upgrading ES from v7.14.2 to v7.15.1 and I'm getting an error on code that used to work fine: ``` resp = client.search( scroll=scroll, size=size, request_timeout=request_timeout, **search_kwargs ) E TypeError: search() got multiple values for keyword argument 'size' /usr/local/lib/python3.8/dist-packages/elasticsearch/helpers/actions.py:571: TypeError ``` I'm doing a search.scan() without any extra parameters, and the search itself has from=0 and size=0. If I remove the pagination entirely, it works. Any idea what might be going on? Downgrading ES to v7.14.2 solves the issue. I've checked recent bug reports on elasticsearch and elasticsearch-dsl but nothing similar shows up. The changelog of elasticsearch itself also doesn't give any clues. Greetings, Jan **Steps to reproduce**:

Could you print out what `search_kwargs` is in this API call? > Could you print out what `search_kwargs` is in this API call? Maybe not exactly the same but I did this print of search.to_dict() right before executing the scan() which causes the TypeError: ``` search.to_dict() = {'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range': {'start_time': {'gte': datetime.datetime(2016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0 } ``` Does that help? Printing `search_kwargs` is better, that way I can get an exact reproducer of the issue. > Printing `search_kwargs` is better, that way I can get an exact reproducer of the issue. ``` search_kwargs={'index': ['*test_watcher_2016_04'], 'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range' : {'start_time': {'gte': datetime.datetime(2016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0, 'sort': '_doc'} ``` By the way I just tested this again with my ES cluster at v7.14.2 and only the elasticsearch python library at v7.15.1 and the TypeError happens, the same way as when both ES and python lib are at v7.15.1 So it seems as if it's an issue in the library itself, not in ES? Looks like there are multiple values of `size` being specified, one in `body` and one outside of `body`. That's likely the source of the problem. > Looks like there are multiple values of `size` being specified, one in `body` and one outside of `body`. That's likely the source of the problem. Hmmm but how come my code works fine in v7.14.2 and breaks in v7.15.1 ? What's surprising here is that your code should fail with all versions of the client: Python is telling you that you cannot call a function with two keyword arguments with the same name. The following snippet fails with `TypeError: search() got multiple values for keyword argument 'size'` for both 7.14.2 and 7.15.1, as it should: ```python3 import elasticsearch search_kwargs = {"index": ["*test_watcher_2016_04"], "size": 0} elasticsearch.Elasticsearch().search(size=0, **search_kwargs) ``` Can you please share a [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example)? @sethmlarson @pquentin so the thing is, I compared what happens in `elasticsearch/helpers/actions.py` in v7.14.2 and v7.15.1. In the old version, what I'm doing results in this: ``` kwargs={'index': ['*test_watcher_2016_04']} query={'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range': {'start_time': {'gte': datetime.datetime(2 016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0, 'sort': '_doc'} ``` The query variable gets passed to client.search() as the **body** argument, while kwargs goes in as **kwargs. This means the size part goes into body, in v7.14.2, and that's why it does work there. However in version 7.15.1 the code in said actions.py has changed, and this results in: ``` search_kwargs={'index': ['*test_watcher_2016_04'], 'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range' : {'start_time': {'gte': datetime.datetime(2016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0, 'sort': '_doc'} ``` And the whole search_kwargs gets passed as **search_kwargs to client.search(): it no longer passes the body argument. And that's I guess the explanation as to why my code works fine in v7.14.2 and breaks in v7.15.1. I'm not sure if this was intentional, but at least now we understand where it's coming from: the code in actions.py does not work **exactly** like it did before since instead of passing both body and kwargs it just puts everything in kwargs. I'm not an expert on the matter, but to me this feels like a regression. Shouldn't it be valid to specify a size parameter inside of the query? With the new approach in v7.15.1 this suddenly becomes a conflict, but it hasn't ever been a problem, and I've been using ES and the assorted python libraries since v5. @jgb Thanks for digging into this, you're right that things have changed in 7.15 for some APIs, we're moving towards having all top-level API parameters (in path, query, **and body**) to be similarly top-level in the function signature. This means that we'll have to choose for the user where these parameters are serialized instead of having that be the user's responsibility (via `body=` and `params=`). Looks like we could be merging the parameters in `scan` better to not run into this issue, I'll open a PR fixing that.

2021-11-05T02:48:22Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1792

20efd21290f5c5dd492d7b705ed4cc422b2aca47

diff --git a/elasticsearch/_async/helpers.py b/elasticsearch/_async/helpers.py --- a/elasticsearch/_async/helpers.py +++ b/elasticsearch/_async/helpers.py @@ -26,6 +26,7 @@ from ..exceptions import NotFoundError, TransportError from ..helpers.actions import ( _ActionChunker, + _add_helper_meta_to_kwargs, _process_bulk_chunk_error, _process_bulk_chunk_success, expand_action, @@ -332,7 +333,9 @@ async def async_scan( ) """ - scroll_kwargs = scroll_kwargs or {} + scroll_kwargs = scroll_kwargs.copy() if scroll_kwargs else {} + scroll_kwargs["scroll"] = scroll + _add_helper_meta_to_kwargs(scroll_kwargs, "s") if not preserve_order: query = query.copy() if query else {} @@ -356,9 +359,11 @@ async def async_scan( search_kwargs = kwargs.copy() if query: search_kwargs.update(query) - resp = await client.search( - scroll=scroll, size=size, request_timeout=request_timeout, **search_kwargs - ) + search_kwargs["scroll"] = scroll + search_kwargs["size"] = size + search_kwargs["request_timeout"] = request_timeout + _add_helper_meta_to_kwargs(search_kwargs, "s") + resp = await client.search(**search_kwargs) scroll_id = resp.get("_scroll_id") try: @@ -390,9 +395,9 @@ async def async_scan( shards_total, ), ) - resp = await client.scroll( - scroll_id=scroll_id, scroll=scroll, **scroll_kwargs - ) + + scroll_kwargs["scroll_id"] = scroll_id + resp = await client.scroll(**scroll_kwargs) scroll_id = resp.get("_scroll_id") finally: diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -543,7 +543,8 @@ def scan( ) """ - scroll_kwargs = scroll_kwargs or {} + scroll_kwargs = scroll_kwargs.copy() if scroll_kwargs else {} + scroll_kwargs["scroll"] = scroll _add_helper_meta_to_kwargs(scroll_kwargs, "s") if not preserve_order: @@ -568,9 +569,11 @@ def scan( search_kwargs = kwargs.copy() if query: search_kwargs.update(query) - resp = client.search( - scroll=scroll, size=size, request_timeout=request_timeout, **search_kwargs - ) + search_kwargs["scroll"] = scroll + search_kwargs["size"] = size + search_kwargs["request_timeout"] = request_timeout + _add_helper_meta_to_kwargs(search_kwargs, "s") + resp = client.search(**search_kwargs) scroll_id = resp.get("_scroll_id") try: @@ -602,7 +605,9 @@ def scan( shards_total, ), ) - resp = client.scroll(scroll_id=scroll_id, scroll=scroll, **scroll_kwargs) + + scroll_kwargs["scroll_id"] = scroll_id + resp = client.scroll(**scroll_kwargs) scroll_id = resp.get("_scroll_id") finally:

diff --git a/test_elasticsearch/test_async/test_server/test_helpers.py b/test_elasticsearch/test_async/test_server/test_helpers.py --- a/test_elasticsearch/test_async/test_server/test_helpers.py +++ b/test_elasticsearch/test_async/test_server/test_helpers.py @@ -756,6 +756,59 @@ async def test_scan_auth_kwargs_favor_scroll_kwargs_option( } assert async_client.scroll.call_args[1]["sort"] == "asc" + async def test_scan_duplicate_parameters(self, async_client): + with patch.object(async_client, "search") as search_mock, patch.object( + async_client, "scroll" + ) as scroll_mock, patch.object( + async_client, "clear_scroll" + ) as clear_scroll_mock: + search_mock.return_value = MockResponse( + { + "_scroll_id": "scroll_id", + "_shards": {"successful": 5, "total": 5, "skipped": 0}, + "hits": {"hits": [{"field": "value"}]}, + } + ) + scroll_mock.return_value = MockResponse( + { + "_scroll_id": "scroll_id", + "_shards": {"successful": 5, "total": 5, "skipped": 0}, + "hits": {"hits": []}, + } + ) + clear_scroll_mock.return_value = MockResponse({"acknowledged": True}) + data = [ + x + async for x in helpers.async_scan( + async_client, + index="test_index", + size=10, + query={"size": 1}, + scroll_kwargs={"scroll": "10m", "rest_total_hits_as_int": True}, + ) + ] + + assert data == [{"field": "value"}] + search_mock.assert_called_with( + index="test_index", + size=10, + sort="_doc", + scroll="5m", + request_timeout=None, + params={"__elastic_client_meta": (("h", "s"),)}, + ) + scroll_mock.assert_called_with( + scroll="5m", + rest_total_hits_as_int=True, + params={"__elastic_client_meta": (("h", "s"),)}, + scroll_id="scroll_id", + ) + clear_scroll_mock.assert_called_with( + scroll_id="scroll_id", + ignore=(404,), + params={"__elastic_client_meta": (("h", "s"),)}, + ) + @pytest.fixture(scope="function") async def reindex_setup(async_client): diff --git a/test_elasticsearch/test_server/test_helpers.py b/test_elasticsearch/test_server/test_helpers.py --- a/test_elasticsearch/test_server/test_helpers.py +++ b/test_elasticsearch/test_server/test_helpers.py @@ -568,6 +568,57 @@ def test_scan_auth_kwargs_favor_scroll_kwargs_option(self): ) self.assertEqual(client_mock.scroll.call_args[1]["sort"], "asc") + def test_scan_duplicate_parameters(self): + with patch.object(self.client, "search") as search_mock, patch.object( + self.client, "scroll" + ) as scroll_mock, patch.object( + self.client, "clear_scroll" + ) as clear_scroll_mock: + search_mock.return_value = { + "_scroll_id": "scroll_id", + "_shards": {"successful": 5, "total": 5, "skipped": 0}, + "hits": {"hits": [{"field": "value"}]}, + } + + scroll_mock.return_value = { + "_scroll_id": "scroll_id", + "_shards": {"successful": 5, "total": 5, "skipped": 0}, + "hits": {"hits": []}, + } + + clear_scroll_mock.return_value = {"acknowledged": True} + data = [ + x + for x in helpers.scan( + self.client, + index="test_index", + size=10, + query={"size": 1}, + scroll_kwargs={"scroll": "10m", "rest_total_hits_as_int": True}, + ) + ] + + assert data == [{"field": "value"}] + search_mock.assert_called_with( + index="test_index", + size=10, + sort="_doc", + scroll="5m", + request_timeout=None, + params={"__elastic_client_meta": (("h", "s"),)}, + ) + scroll_mock.assert_called_with( + scroll="5m", + rest_total_hits_as_int=True, + params={"__elastic_client_meta": (("h", "s"),)}, + scroll_id="scroll_id", + ) + clear_scroll_mock.assert_called_with( + scroll_id="scroll_id", + ignore=(404,), + params={"__elastic_client_meta": (("h", "s"),)}, + ) + @patch("elasticsearch.helpers.actions.logger") def test_logger(self, logger_mock): bulk = []

Issue with ES >= v7.15.0: TypeError: search() got multiple values for keyword argument 'size' <!-- Bug report --> **Elasticsearch version** : 7.15.1 **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 7.15.1 Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: Hello, I'm upgrading ES from v7.14.2 to v7.15.1 and I'm getting an error on code that used to work fine: ``` resp = client.search( scroll=scroll, size=size, request_timeout=request_timeout, **search_kwargs ) E TypeError: search() got multiple values for keyword argument 'size' /usr/local/lib/python3.8/dist-packages/elasticsearch/helpers/actions.py:571: TypeError ``` I'm doing a search.scan() without any extra parameters, and the search itself has from=0 and size=0. If I remove the pagination entirely, it works. Any idea what might be going on? Downgrading ES to v7.14.2 solves the issue. I've checked recent bug reports on elasticsearch and elasticsearch-dsl but nothing similar shows up. The changelog of elasticsearch itself also doesn't give any clues. Greetings, Jan **Steps to reproduce**:

Could you print out what `search_kwargs` is in this API call? > Could you print out what `search_kwargs` is in this API call? Maybe not exactly the same but I did this print of search.to_dict() right before executing the scan() which causes the TypeError: ``` search.to_dict() = {'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range': {'start_time': {'gte': datetime.datetime(2016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0 } ``` Does that help? Printing `search_kwargs` is better, that way I can get an exact reproducer of the issue. > Printing `search_kwargs` is better, that way I can get an exact reproducer of the issue. ``` search_kwargs={'index': ['*test_watcher_2016_04'], 'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range' : {'start_time': {'gte': datetime.datetime(2016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0, 'sort': '_doc'} ``` By the way I just tested this again with my ES cluster at v7.14.2 and only the elasticsearch python library at v7.15.1 and the TypeError happens, the same way as when both ES and python lib are at v7.15.1 So it seems as if it's an issue in the library itself, not in ES? Looks like there are multiple values of `size` being specified, one in `body` and one outside of `body`. That's likely the source of the problem. > Looks like there are multiple values of `size` being specified, one in `body` and one outside of `body`. That's likely the source of the problem. Hmmm but how come my code works fine in v7.14.2 and breaks in v7.15.1 ? What's surprising here is that your code should fail with all versions of the client: Python is telling you that you cannot call a function with two keyword arguments with the same name. The following snippet fails with `TypeError: search() got multiple values for keyword argument 'size'` for both 7.14.2 and 7.15.1, as it should: ```python3 import elasticsearch search_kwargs = {"index": ["*test_watcher_2016_04"], "size": 0} elasticsearch.Elasticsearch().search(size=0, **search_kwargs) ``` Can you please share a [minimal, reproducible example](https://stackoverflow.com/help/minimal-reproducible-example)? @sethmlarson @pquentin so the thing is, I compared what happens in `elasticsearch/helpers/actions.py` in v7.14.2 and v7.15.1. In the old version, what I'm doing results in this: ``` kwargs={'index': ['*test_watcher_2016_04']} query={'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range': {'start_time': {'gte': datetime.datetime(2 016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0, 'sort': '_doc'} ``` The query variable gets passed to client.search() as the **body** argument, while kwargs goes in as **kwargs. This means the size part goes into body, in v7.14.2, and that's why it does work there. However in version 7.15.1 the code in said actions.py has changed, and this results in: ``` search_kwargs={'index': ['*test_watcher_2016_04'], 'query': {'bool': {'filter': [{'terms': {'location_id': [5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120]}}, {'range' : {'start_time': {'gte': datetime.datetime(2016, 4, 2, 12, 0), 'lte': datetime.datetime(2016, 4, 19, 12, 0)}}}]}}, 'track_total_hits': True, 'from': 0, 'size': 0, 'sort': '_doc'} ``` And the whole search_kwargs gets passed as **search_kwargs to client.search(): it no longer passes the body argument. And that's I guess the explanation as to why my code works fine in v7.14.2 and breaks in v7.15.1. I'm not sure if this was intentional, but at least now we understand where it's coming from: the code in actions.py does not work **exactly** like it did before since instead of passing both body and kwargs it just puts everything in kwargs. I'm not an expert on the matter, but to me this feels like a regression. Shouldn't it be valid to specify a size parameter inside of the query? With the new approach in v7.15.1 this suddenly becomes a conflict, but it hasn't ever been a problem, and I've been using ES and the assorted python libraries since v5. @jgb Thanks for digging into this, you're right that things have changed in 7.15 for some APIs, we're moving towards having all top-level API parameters (in path, query, **and body**) to be similarly top-level in the function signature. This means that we'll have to choose for the user where these parameters are serialized instead of having that be the user's responsibility (via `body=` and `params=`). Looks like we could be merging the parameters in `scan` better to not run into this issue, I'll open a PR fixing that. @sethmlarson awesome, thanks for following up on this! Looking forward to a future release where this is taken care of. Thanks again!

2021-11-05T14:09:03Z

elastic/elasticsearch-py

elastic__elasticsearch-py-1922

dfbf1ee271ce160a0adaed630cf032e707c46a32

diff --git a/elasticsearch/_async/client/__init__.py b/elasticsearch/_async/client/__init__.py --- a/elasticsearch/_async/client/__init__.py +++ b/elasticsearch/_async/client/__init__.py @@ -604,7 +604,7 @@ async def bulk( ] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -925,7 +925,7 @@ async def create( t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -1011,7 +1011,7 @@ async def delete( t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -1136,7 +1136,7 @@ async def delete_by_query( timeout: t.Optional[t.Union[int, str]] = None, version: t.Optional[bool] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: @@ -1205,6 +1205,17 @@ async def delete_by_query( __path = f"/{_quote(index)}/_delete_by_query" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if analyze_wildcard is not None: @@ -2127,7 +2138,7 @@ async def index( t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -2981,7 +2992,7 @@ async def reindex( source: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: @@ -3540,6 +3551,17 @@ async def search( __path = "/_search" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: @@ -3785,6 +3807,17 @@ async def search_mvt( __path = f"/{_quote(index)}/_mvt/{_quote(field)}/{_quote(zoom)}/{_quote(x)}/{_quote(y)}" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggs is not None: __body["aggs"] = aggs if error_trace is not None: @@ -4289,7 +4322,7 @@ async def update( timeout: t.Optional[t.Union[int, str]] = None, upsert: t.Optional[t.Mapping[str, t.Any]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -4454,7 +4487,7 @@ async def update_by_query( version: t.Optional[bool] = None, version_type: t.Optional[bool] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: @@ -4528,6 +4561,17 @@ async def update_by_query( __path = f"/{_quote(index)}/_update_by_query" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if analyze_wildcard is not None: diff --git a/elasticsearch/_async/client/async_search.py b/elasticsearch/_async/client/async_search.py --- a/elasticsearch/_async/client/async_search.py +++ b/elasticsearch/_async/client/async_search.py @@ -427,6 +427,17 @@ async def submit( __path = "/_async_search" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: diff --git a/elasticsearch/_async/client/ccr.py b/elasticsearch/_async/client/ccr.py --- a/elasticsearch/_async/client/ccr.py +++ b/elasticsearch/_async/client/ccr.py @@ -86,7 +86,7 @@ async def follow( read_poll_timeout: t.Optional[t.Union[int, str]] = None, remote_cluster: t.Optional[str] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ diff --git a/elasticsearch/_async/client/cluster.py b/elasticsearch/_async/client/cluster.py --- a/elasticsearch/_async/client/cluster.py +++ b/elasticsearch/_async/client/cluster.py @@ -360,7 +360,7 @@ async def health( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_events: t.Optional[ t.Union[ diff --git a/elasticsearch/_async/client/fleet.py b/elasticsearch/_async/client/fleet.py --- a/elasticsearch/_async/client/fleet.py +++ b/elasticsearch/_async/client/fleet.py @@ -489,6 +489,17 @@ async def search( __path = f"/{_quote(index)}/_fleet/_fleet_search" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: diff --git a/elasticsearch/_async/client/indices.py b/elasticsearch/_async/client/indices.py --- a/elasticsearch/_async/client/indices.py +++ b/elasticsearch/_async/client/indices.py @@ -304,7 +304,7 @@ async def clone( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -389,7 +389,7 @@ async def close( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -458,7 +458,7 @@ async def create( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -794,20 +794,28 @@ async def delete_data_stream( async def delete_index_template( self, *, - name: str, + name: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], error_trace: t.Optional[bool] = None, filter_path: t.Optional[ t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] ] = None, human: t.Optional[bool] = None, + master_timeout: t.Optional[t.Union[int, str]] = None, pretty: t.Optional[bool] = None, + timeout: t.Optional[t.Union[int, str]] = None, ) -> ObjectApiResponse[t.Any]: """ Deletes an index template. `<https://www.elastic.co/guide/en/elasticsearch/reference/master/indices-templates.html>`_ - :param name: The name of the template + :param name: Comma-separated list of index template names used to limit the request. + Wildcard (*) expressions are supported. + :param master_timeout: Period to wait for a connection to the master node. If + no response is received before the timeout expires, the request fails and + returns an error. + :param timeout: Period to wait for a response. If no response is received before + the timeout expires, the request fails and returns an error. """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") @@ -819,8 +827,12 @@ async def delete_index_template( __query["filter_path"] = filter_path if human is not None: __query["human"] = human + if master_timeout is not None: + __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty + if timeout is not None: + __query["timeout"] = timeout __headers = {"accept": "application/json"} return await self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers @@ -1232,6 +1244,106 @@ async def exists_template( "HEAD", __path, params=__query, headers=__headers ) + @_rewrite_parameters() + async def field_usage_stats( + self, + *, + index: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], + allow_no_indices: t.Optional[bool] = None, + error_trace: t.Optional[bool] = None, + expand_wildcards: t.Optional[ + t.Union[ + t.Union["t.Literal['all', 'closed', 'hidden', 'none', 'open']", str], + t.Union[ + t.List[ + t.Union[ + "t.Literal['all', 'closed', 'hidden', 'none', 'open']", str + ] + ], + t.Tuple[ + t.Union[ + "t.Literal['all', 'closed', 'hidden', 'none', 'open']", str + ], + ..., + ], + ], + ] + ] = None, + fields: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + ignore_unavailable: t.Optional[bool] = None, + master_timeout: t.Optional[t.Union[int, str]] = None, + pretty: t.Optional[bool] = None, + timeout: t.Optional[t.Union[int, str]] = None, + wait_for_active_shards: t.Optional[ + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] + ] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Returns the field usage stats for each field of an index + + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/field-usage-stats.html>`_ + + :param index: Comma-separated list or wildcard expression of index names used + to limit the request. + :param allow_no_indices: If false, the request returns an error if any wildcard + expression, index alias, or _all value targets only missing or closed indices. + This behavior applies even if the request targets other open indices. For + example, a request targeting `foo*,bar*` returns an error if an index starts + with `foo` but no index starts with `bar`. + :param expand_wildcards: Type of index that wildcard patterns can match. If the + request can target data streams, this argument determines whether wildcard + expressions match hidden data streams. Supports comma-separated values, such + as `open,hidden`. + :param fields: Comma-separated list or wildcard expressions of fields to include + in the statistics. + :param ignore_unavailable: If true, missing or closed indices are not included + in the response. + :param master_timeout: Period to wait for a connection to the master node. If + no response is received before the timeout expires, the request fails and + returns an error. + :param timeout: Period to wait for a response. If no response is received before + the timeout expires, the request fails and returns an error. + :param wait_for_active_shards: The number of shard copies that must be active + before proceeding with the operation. Set to all or any positive integer + up to the total number of shards in the index (`number_of_replicas+1`). + """ + if index in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'index'") + __path = f"/{_quote(index)}/_field_usage_stats" + __query: t.Dict[str, t.Any] = {} + if allow_no_indices is not None: + __query["allow_no_indices"] = allow_no_indices + if error_trace is not None: + __query["error_trace"] = error_trace + if expand_wildcards is not None: + __query["expand_wildcards"] = expand_wildcards + if fields is not None: + __query["fields"] = fields + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if ignore_unavailable is not None: + __query["ignore_unavailable"] = ignore_unavailable + if master_timeout is not None: + __query["master_timeout"] = master_timeout + if pretty is not None: + __query["pretty"] = pretty + if timeout is not None: + __query["timeout"] = timeout + if wait_for_active_shards is not None: + __query["wait_for_active_shards"] = wait_for_active_shards + __headers = {"accept": "application/json"} + return await self.perform_request( # type: ignore[return-value] + "GET", __path, params=__query, headers=__headers + ) + @_rewrite_parameters() async def flush( self, @@ -2074,7 +2186,7 @@ async def open( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -2927,7 +3039,7 @@ async def rollover( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -3171,7 +3283,7 @@ async def shrink( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -3393,7 +3505,7 @@ async def split( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ diff --git a/elasticsearch/_async/client/migration.py b/elasticsearch/_async/client/migration.py --- a/elasticsearch/_async/client/migration.py +++ b/elasticsearch/_async/client/migration.py @@ -63,3 +63,65 @@ async def deprecations( return await self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers ) + + @_rewrite_parameters() + async def get_feature_upgrade_status( + self, + *, + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Find out whether system features need to be upgraded or not + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/migration-api-feature-upgrade.html>`_ + """ + __path = "/_migration/system_features" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return await self.perform_request( # type: ignore[return-value] + "GET", __path, params=__query, headers=__headers + ) + + @_rewrite_parameters() + async def post_feature_upgrade( + self, + *, + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Begin upgrades for system features + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/migration-api-feature-upgrade.html>`_ + """ + __path = "/_migration/system_features" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return await self.perform_request( # type: ignore[return-value] + "POST", __path, params=__query, headers=__headers + ) diff --git a/elasticsearch/_async/client/ml.py b/elasticsearch/_async/client/ml.py --- a/elasticsearch/_async/client/ml.py +++ b/elasticsearch/_async/client/ml.py @@ -166,7 +166,7 @@ async def delete_calendar_job( self, *, calendar_id: str, - job_id: str, + job_id: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], error_trace: t.Optional[bool] = None, filter_path: t.Optional[ t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] @@ -1464,7 +1464,9 @@ async def get_datafeeds( async def get_filters( self, *, - filter_id: t.Optional[str] = None, + filter_id: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[ t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] @@ -2028,7 +2030,9 @@ async def get_trained_models( async def get_trained_models_stats( self, *, - model_id: t.Optional[str] = None, + model_id: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[ diff --git a/elasticsearch/_async/client/nodes.py b/elasticsearch/_async/client/nodes.py --- a/elasticsearch/_async/client/nodes.py +++ b/elasticsearch/_async/client/nodes.py @@ -24,6 +24,85 @@ class NodesClient(NamespacedClient): + @_rewrite_parameters() + async def clear_repositories_metering_archive( + self, + *, + node_id: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], + max_archive_version: int, + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Removes the archived repositories metering information present in the cluster. + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/clear-repositories-metering-archive-api.html>`_ + + :param node_id: Comma-separated list of node IDs or names used to limit returned + information. All the nodes selective options are explained [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/cluster.html#cluster-nodes). + :param max_archive_version: Specifies the maximum [archive_version](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-repositories-metering-api.html#get-repositories-metering-api-response-body) + to be cleared from the archive. + """ + if node_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'node_id'") + if max_archive_version in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'max_archive_version'") + __path = f"/_nodes/{_quote(node_id)}/_repositories_metering/{_quote(max_archive_version)}" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return await self.perform_request( # type: ignore[return-value] + "DELETE", __path, params=__query, headers=__headers + ) + + @_rewrite_parameters() + async def get_repositories_metering_info( + self, + *, + node_id: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Returns cluster repositories metering information. + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/get-repositories-metering-api.html>`_ + + :param node_id: Comma-separated list of node IDs or names used to limit returned + information. All the nodes selective options are explained [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/cluster.html#cluster-nodes). + """ + if node_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'node_id'") + __path = f"/_nodes/{_quote(node_id)}/_repositories_metering" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return await self.perform_request( # type: ignore[return-value] + "GET", __path, params=__query, headers=__headers + ) + @_rewrite_parameters() async def hot_threads( self, diff --git a/elasticsearch/_async/client/sql.py b/elasticsearch/_async/client/sql.py --- a/elasticsearch/_async/client/sql.py +++ b/elasticsearch/_async/client/sql.py @@ -199,11 +199,12 @@ async def get_async_status( @_rewrite_parameters( body_fields=True, - ignore_deprecated_options={"request_timeout"}, + ignore_deprecated_options={"params", "request_timeout"}, ) async def query( self, *, + catalog: t.Optional[str] = None, columnar: t.Optional[bool] = None, cursor: t.Optional[str] = None, error_trace: t.Optional[bool] = None, @@ -215,17 +216,36 @@ async def query( ] = None, format: t.Optional[str] = None, human: t.Optional[bool] = None, + index_using_frozen: t.Optional[bool] = None, + keep_alive: t.Optional[t.Union[int, str]] = None, + keep_on_completion: t.Optional[bool] = None, page_timeout: t.Optional[t.Union[int, str]] = None, + params: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, query: t.Optional[str] = None, request_timeout: t.Optional[t.Union[int, str]] = None, + runtime_mappings: t.Optional[ + t.Mapping[ + str, + t.Union[ + t.Mapping[str, t.Any], + t.Union[ + t.List[t.Mapping[str, t.Any]], + t.Tuple[t.Mapping[str, t.Any], ...], + ], + ], + ] + ] = None, time_zone: t.Optional[str] = None, + wait_for_completion_timeout: t.Optional[t.Union[int, str]] = None, ) -> ObjectApiResponse[t.Any]: """ Executes a SQL request `<https://www.elastic.co/guide/en/elasticsearch/reference/current/sql-search-api.html>`_ + :param catalog: Default catalog (cluster) for queries. If unspecified, the queries + execute on the data in the local cluster only. :param columnar: :param cursor: :param fetch_size: The maximum number of rows (or entries) to return in one response @@ -235,15 +255,29 @@ async def query( in natural ascending order). :param filter: Optional Elasticsearch query DSL for additional filtering. :param format: a short version of the Accept header, e.g. json, yaml + :param index_using_frozen: If true, the search can run on frozen indices. Defaults + to false. + :param keep_alive: Retention period for an async or saved synchronous search. + :param keep_on_completion: If true, Elasticsearch stores synchronous searches + if you also specify the wait_for_completion_timeout parameter. If false, + Elasticsearch only stores async searches that don’t finish before the wait_for_completion_timeout. :param page_timeout: The timeout before a pagination request fails. + :param params: Values for parameters in the query. :param query: SQL query to execute :param request_timeout: The timeout before the request fails. + :param runtime_mappings: Defines one or more runtime fields in the search request. + These fields take precedence over mapped fields with the same name. :param time_zone: Time-zone in ISO 8601 used for executing the query on the server. More information available here. + :param wait_for_completion_timeout: Period to wait for complete results. Defaults + to no timeout, meaning the request waits for complete search results. If + the search doesn’t finish within this period, the search becomes async. """ __path = "/_sql" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + if catalog is not None: + __body["catalog"] = catalog if columnar is not None: __body["columnar"] = columnar if cursor is not None: @@ -262,16 +296,28 @@ async def query( __query["format"] = format if human is not None: __query["human"] = human + if index_using_frozen is not None: + __body["index_using_frozen"] = index_using_frozen + if keep_alive is not None: + __body["keep_alive"] = keep_alive + if keep_on_completion is not None: + __body["keep_on_completion"] = keep_on_completion if page_timeout is not None: __body["page_timeout"] = page_timeout + if params is not None: + __body["params"] = params if pretty is not None: __query["pretty"] = pretty if query is not None: __body["query"] = query if request_timeout is not None: __body["request_timeout"] = request_timeout + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings if time_zone is not None: __body["time_zone"] = time_zone + if wait_for_completion_timeout is not None: + __body["wait_for_completion_timeout"] = wait_for_completion_timeout __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/transform.py b/elasticsearch/_async/client/transform.py --- a/elasticsearch/_async/client/transform.py +++ b/elasticsearch/_async/client/transform.py @@ -77,7 +77,9 @@ async def delete_transform( async def get_transform( self, *, - transform_id: t.Optional[str] = None, + transform_id: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, exclude_generated: t.Optional[bool] = None, diff --git a/elasticsearch/_sync/client/__init__.py b/elasticsearch/_sync/client/__init__.py --- a/elasticsearch/_sync/client/__init__.py +++ b/elasticsearch/_sync/client/__init__.py @@ -602,7 +602,7 @@ def bulk( ] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -923,7 +923,7 @@ def create( t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -1009,7 +1009,7 @@ def delete( t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -1134,7 +1134,7 @@ def delete_by_query( timeout: t.Optional[t.Union[int, str]] = None, version: t.Optional[bool] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: @@ -1203,6 +1203,17 @@ def delete_by_query( __path = f"/{_quote(index)}/_delete_by_query" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if analyze_wildcard is not None: @@ -2125,7 +2136,7 @@ def index( t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -2979,7 +2990,7 @@ def reindex( source: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: @@ -3538,6 +3549,17 @@ def search( __path = "/_search" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: @@ -3783,6 +3805,17 @@ def search_mvt( __path = f"/{_quote(index)}/_mvt/{_quote(field)}/{_quote(zoom)}/{_quote(x)}/{_quote(y)}" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggs is not None: __body["aggs"] = aggs if error_trace is not None: @@ -4287,7 +4320,7 @@ def update( timeout: t.Optional[t.Union[int, str]] = None, upsert: t.Optional[t.Mapping[str, t.Any]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -4452,7 +4485,7 @@ def update_by_query( version: t.Optional[bool] = None, version_type: t.Optional[bool] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, ) -> ObjectApiResponse[t.Any]: @@ -4526,6 +4559,17 @@ def update_by_query( __path = f"/{_quote(index)}/_update_by_query" __query: t.Dict[str, t.Any] = {} __body: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if analyze_wildcard is not None: diff --git a/elasticsearch/_sync/client/async_search.py b/elasticsearch/_sync/client/async_search.py --- a/elasticsearch/_sync/client/async_search.py +++ b/elasticsearch/_sync/client/async_search.py @@ -427,6 +427,17 @@ def submit( __path = "/_async_search" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: diff --git a/elasticsearch/_sync/client/ccr.py b/elasticsearch/_sync/client/ccr.py --- a/elasticsearch/_sync/client/ccr.py +++ b/elasticsearch/_sync/client/ccr.py @@ -86,7 +86,7 @@ def follow( read_poll_timeout: t.Optional[t.Union[int, str]] = None, remote_cluster: t.Optional[str] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ diff --git a/elasticsearch/_sync/client/cluster.py b/elasticsearch/_sync/client/cluster.py --- a/elasticsearch/_sync/client/cluster.py +++ b/elasticsearch/_sync/client/cluster.py @@ -360,7 +360,7 @@ def health( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_events: t.Optional[ t.Union[ diff --git a/elasticsearch/_sync/client/fleet.py b/elasticsearch/_sync/client/fleet.py --- a/elasticsearch/_sync/client/fleet.py +++ b/elasticsearch/_sync/client/fleet.py @@ -489,6 +489,17 @@ def search( __path = f"/{_quote(index)}/_fleet/_fleet_search" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + # The 'sort' parameter with a colon can't be encoded to the body. + if sort is not None and ( + (isinstance(sort, str) and ":" in sort) + or ( + isinstance(sort, (list, tuple)) + and all(isinstance(_x, str) for _x in sort) + and any(":" in _x for _x in sort) + ) + ): + __query["sort"] = sort + sort = None if aggregations is not None: __body["aggregations"] = aggregations if aggs is not None: diff --git a/elasticsearch/_sync/client/indices.py b/elasticsearch/_sync/client/indices.py --- a/elasticsearch/_sync/client/indices.py +++ b/elasticsearch/_sync/client/indices.py @@ -304,7 +304,7 @@ def clone( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -389,7 +389,7 @@ def close( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -458,7 +458,7 @@ def create( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -794,20 +794,28 @@ def delete_data_stream( def delete_index_template( self, *, - name: str, + name: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], error_trace: t.Optional[bool] = None, filter_path: t.Optional[ t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] ] = None, human: t.Optional[bool] = None, + master_timeout: t.Optional[t.Union[int, str]] = None, pretty: t.Optional[bool] = None, + timeout: t.Optional[t.Union[int, str]] = None, ) -> ObjectApiResponse[t.Any]: """ Deletes an index template. `<https://www.elastic.co/guide/en/elasticsearch/reference/master/indices-templates.html>`_ - :param name: The name of the template + :param name: Comma-separated list of index template names used to limit the request. + Wildcard (*) expressions are supported. + :param master_timeout: Period to wait for a connection to the master node. If + no response is received before the timeout expires, the request fails and + returns an error. + :param timeout: Period to wait for a response. If no response is received before + the timeout expires, the request fails and returns an error. """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") @@ -819,8 +827,12 @@ def delete_index_template( __query["filter_path"] = filter_path if human is not None: __query["human"] = human + if master_timeout is not None: + __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty + if timeout is not None: + __query["timeout"] = timeout __headers = {"accept": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers @@ -1232,6 +1244,106 @@ def exists_template( "HEAD", __path, params=__query, headers=__headers ) + @_rewrite_parameters() + def field_usage_stats( + self, + *, + index: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], + allow_no_indices: t.Optional[bool] = None, + error_trace: t.Optional[bool] = None, + expand_wildcards: t.Optional[ + t.Union[ + t.Union["t.Literal['all', 'closed', 'hidden', 'none', 'open']", str], + t.Union[ + t.List[ + t.Union[ + "t.Literal['all', 'closed', 'hidden', 'none', 'open']", str + ] + ], + t.Tuple[ + t.Union[ + "t.Literal['all', 'closed', 'hidden', 'none', 'open']", str + ], + ..., + ], + ], + ] + ] = None, + fields: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + ignore_unavailable: t.Optional[bool] = None, + master_timeout: t.Optional[t.Union[int, str]] = None, + pretty: t.Optional[bool] = None, + timeout: t.Optional[t.Union[int, str]] = None, + wait_for_active_shards: t.Optional[ + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] + ] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Returns the field usage stats for each field of an index + + `<https://www.elastic.co/guide/en/elasticsearch/reference/master/field-usage-stats.html>`_ + + :param index: Comma-separated list or wildcard expression of index names used + to limit the request. + :param allow_no_indices: If false, the request returns an error if any wildcard + expression, index alias, or _all value targets only missing or closed indices. + This behavior applies even if the request targets other open indices. For + example, a request targeting `foo*,bar*` returns an error if an index starts + with `foo` but no index starts with `bar`. + :param expand_wildcards: Type of index that wildcard patterns can match. If the + request can target data streams, this argument determines whether wildcard + expressions match hidden data streams. Supports comma-separated values, such + as `open,hidden`. + :param fields: Comma-separated list or wildcard expressions of fields to include + in the statistics. + :param ignore_unavailable: If true, missing or closed indices are not included + in the response. + :param master_timeout: Period to wait for a connection to the master node. If + no response is received before the timeout expires, the request fails and + returns an error. + :param timeout: Period to wait for a response. If no response is received before + the timeout expires, the request fails and returns an error. + :param wait_for_active_shards: The number of shard copies that must be active + before proceeding with the operation. Set to all or any positive integer + up to the total number of shards in the index (`number_of_replicas+1`). + """ + if index in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'index'") + __path = f"/{_quote(index)}/_field_usage_stats" + __query: t.Dict[str, t.Any] = {} + if allow_no_indices is not None: + __query["allow_no_indices"] = allow_no_indices + if error_trace is not None: + __query["error_trace"] = error_trace + if expand_wildcards is not None: + __query["expand_wildcards"] = expand_wildcards + if fields is not None: + __query["fields"] = fields + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if ignore_unavailable is not None: + __query["ignore_unavailable"] = ignore_unavailable + if master_timeout is not None: + __query["master_timeout"] = master_timeout + if pretty is not None: + __query["pretty"] = pretty + if timeout is not None: + __query["timeout"] = timeout + if wait_for_active_shards is not None: + __query["wait_for_active_shards"] = wait_for_active_shards + __headers = {"accept": "application/json"} + return self.perform_request( # type: ignore[return-value] + "GET", __path, params=__query, headers=__headers + ) + @_rewrite_parameters() def flush( self, @@ -2074,7 +2186,7 @@ def open( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -2927,7 +3039,7 @@ def rollover( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -3171,7 +3283,7 @@ def shrink( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ @@ -3393,7 +3505,7 @@ def split( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union[int, str]] = None, wait_for_active_shards: t.Optional[ - t.Union[int, t.Union["t.Literal['all']", str]] + t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, ) -> ObjectApiResponse[t.Any]: """ diff --git a/elasticsearch/_sync/client/migration.py b/elasticsearch/_sync/client/migration.py --- a/elasticsearch/_sync/client/migration.py +++ b/elasticsearch/_sync/client/migration.py @@ -63,3 +63,65 @@ def deprecations( return self.perform_request( # type: ignore[return-value] "GET", __path, params=__query, headers=__headers ) + + @_rewrite_parameters() + def get_feature_upgrade_status( + self, + *, + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Find out whether system features need to be upgraded or not + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/migration-api-feature-upgrade.html>`_ + """ + __path = "/_migration/system_features" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return self.perform_request( # type: ignore[return-value] + "GET", __path, params=__query, headers=__headers + ) + + @_rewrite_parameters() + def post_feature_upgrade( + self, + *, + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Begin upgrades for system features + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/migration-api-feature-upgrade.html>`_ + """ + __path = "/_migration/system_features" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return self.perform_request( # type: ignore[return-value] + "POST", __path, params=__query, headers=__headers + ) diff --git a/elasticsearch/_sync/client/ml.py b/elasticsearch/_sync/client/ml.py --- a/elasticsearch/_sync/client/ml.py +++ b/elasticsearch/_sync/client/ml.py @@ -166,7 +166,7 @@ def delete_calendar_job( self, *, calendar_id: str, - job_id: str, + job_id: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], error_trace: t.Optional[bool] = None, filter_path: t.Optional[ t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] @@ -1464,7 +1464,9 @@ def get_datafeeds( def get_filters( self, *, - filter_id: t.Optional[str] = None, + filter_id: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[ t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] @@ -2028,7 +2030,9 @@ def get_trained_models( def get_trained_models_stats( self, *, - model_id: t.Optional[str] = None, + model_id: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[ diff --git a/elasticsearch/_sync/client/nodes.py b/elasticsearch/_sync/client/nodes.py --- a/elasticsearch/_sync/client/nodes.py +++ b/elasticsearch/_sync/client/nodes.py @@ -24,6 +24,85 @@ class NodesClient(NamespacedClient): + @_rewrite_parameters() + def clear_repositories_metering_archive( + self, + *, + node_id: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], + max_archive_version: int, + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Removes the archived repositories metering information present in the cluster. + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/clear-repositories-metering-archive-api.html>`_ + + :param node_id: Comma-separated list of node IDs or names used to limit returned + information. All the nodes selective options are explained [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/cluster.html#cluster-nodes). + :param max_archive_version: Specifies the maximum [archive_version](https://www.elastic.co/guide/en/elasticsearch/reference/current/get-repositories-metering-api.html#get-repositories-metering-api-response-body) + to be cleared from the archive. + """ + if node_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'node_id'") + if max_archive_version in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'max_archive_version'") + __path = f"/_nodes/{_quote(node_id)}/_repositories_metering/{_quote(max_archive_version)}" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return self.perform_request( # type: ignore[return-value] + "DELETE", __path, params=__query, headers=__headers + ) + + @_rewrite_parameters() + def get_repositories_metering_info( + self, + *, + node_id: t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]], + error_trace: t.Optional[bool] = None, + filter_path: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, + human: t.Optional[bool] = None, + pretty: t.Optional[bool] = None, + ) -> ObjectApiResponse[t.Any]: + """ + Returns cluster repositories metering information. + + `<https://www.elastic.co/guide/en/elasticsearch/reference/current/get-repositories-metering-api.html>`_ + + :param node_id: Comma-separated list of node IDs or names used to limit returned + information. All the nodes selective options are explained [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/cluster.html#cluster-nodes). + """ + if node_id in SKIP_IN_PATH: + raise ValueError("Empty value passed for parameter 'node_id'") + __path = f"/_nodes/{_quote(node_id)}/_repositories_metering" + __query: t.Dict[str, t.Any] = {} + if error_trace is not None: + __query["error_trace"] = error_trace + if filter_path is not None: + __query["filter_path"] = filter_path + if human is not None: + __query["human"] = human + if pretty is not None: + __query["pretty"] = pretty + __headers = {"accept": "application/json"} + return self.perform_request( # type: ignore[return-value] + "GET", __path, params=__query, headers=__headers + ) + @_rewrite_parameters() def hot_threads( self, diff --git a/elasticsearch/_sync/client/sql.py b/elasticsearch/_sync/client/sql.py --- a/elasticsearch/_sync/client/sql.py +++ b/elasticsearch/_sync/client/sql.py @@ -199,11 +199,12 @@ def get_async_status( @_rewrite_parameters( body_fields=True, - ignore_deprecated_options={"request_timeout"}, + ignore_deprecated_options={"params", "request_timeout"}, ) def query( self, *, + catalog: t.Optional[str] = None, columnar: t.Optional[bool] = None, cursor: t.Optional[str] = None, error_trace: t.Optional[bool] = None, @@ -215,17 +216,36 @@ def query( ] = None, format: t.Optional[str] = None, human: t.Optional[bool] = None, + index_using_frozen: t.Optional[bool] = None, + keep_alive: t.Optional[t.Union[int, str]] = None, + keep_on_completion: t.Optional[bool] = None, page_timeout: t.Optional[t.Union[int, str]] = None, + params: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, query: t.Optional[str] = None, request_timeout: t.Optional[t.Union[int, str]] = None, + runtime_mappings: t.Optional[ + t.Mapping[ + str, + t.Union[ + t.Mapping[str, t.Any], + t.Union[ + t.List[t.Mapping[str, t.Any]], + t.Tuple[t.Mapping[str, t.Any], ...], + ], + ], + ] + ] = None, time_zone: t.Optional[str] = None, + wait_for_completion_timeout: t.Optional[t.Union[int, str]] = None, ) -> ObjectApiResponse[t.Any]: """ Executes a SQL request `<https://www.elastic.co/guide/en/elasticsearch/reference/current/sql-search-api.html>`_ + :param catalog: Default catalog (cluster) for queries. If unspecified, the queries + execute on the data in the local cluster only. :param columnar: :param cursor: :param fetch_size: The maximum number of rows (or entries) to return in one response @@ -235,15 +255,29 @@ def query( in natural ascending order). :param filter: Optional Elasticsearch query DSL for additional filtering. :param format: a short version of the Accept header, e.g. json, yaml + :param index_using_frozen: If true, the search can run on frozen indices. Defaults + to false. + :param keep_alive: Retention period for an async or saved synchronous search. + :param keep_on_completion: If true, Elasticsearch stores synchronous searches + if you also specify the wait_for_completion_timeout parameter. If false, + Elasticsearch only stores async searches that don’t finish before the wait_for_completion_timeout. :param page_timeout: The timeout before a pagination request fails. + :param params: Values for parameters in the query. :param query: SQL query to execute :param request_timeout: The timeout before the request fails. + :param runtime_mappings: Defines one or more runtime fields in the search request. + These fields take precedence over mapped fields with the same name. :param time_zone: Time-zone in ISO 8601 used for executing the query on the server. More information available here. + :param wait_for_completion_timeout: Period to wait for complete results. Defaults + to no timeout, meaning the request waits for complete search results. If + the search doesn’t finish within this period, the search becomes async. """ __path = "/_sql" __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + if catalog is not None: + __body["catalog"] = catalog if columnar is not None: __body["columnar"] = columnar if cursor is not None: @@ -262,16 +296,28 @@ def query( __query["format"] = format if human is not None: __query["human"] = human + if index_using_frozen is not None: + __body["index_using_frozen"] = index_using_frozen + if keep_alive is not None: + __body["keep_alive"] = keep_alive + if keep_on_completion is not None: + __body["keep_on_completion"] = keep_on_completion if page_timeout is not None: __body["page_timeout"] = page_timeout + if params is not None: + __body["params"] = params if pretty is not None: __query["pretty"] = pretty if query is not None: __body["query"] = query if request_timeout is not None: __body["request_timeout"] = request_timeout + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings if time_zone is not None: __body["time_zone"] = time_zone + if wait_for_completion_timeout is not None: + __body["wait_for_completion_timeout"] = wait_for_completion_timeout __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/transform.py b/elasticsearch/_sync/client/transform.py --- a/elasticsearch/_sync/client/transform.py +++ b/elasticsearch/_sync/client/transform.py @@ -77,7 +77,9 @@ def delete_transform( def get_transform( self, *, - transform_id: t.Optional[str] = None, + transform_id: t.Optional[ + t.Union[str, t.Union[t.List[str], t.Tuple[str, ...]]] + ] = None, allow_no_match: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, exclude_generated: t.Optional[bool] = None,

diff --git a/test_elasticsearch/test_client/test_overrides.py b/test_elasticsearch/test_client/test_overrides.py --- a/test_elasticsearch/test_client/test_overrides.py +++ b/test_elasticsearch/test_client/test_overrides.py @@ -71,3 +71,40 @@ def test_from_in_search(self, param_name): self.client.search(index="i", **{param_name: 10}) calls = self.assert_url_called("POST", "/i/_search") assert calls[0]["body"] == {"from": 10} + + def test_sort_in_search(self): + self.client.search(index="i", sort="@timestamp:asc") + calls = self.assert_url_called("POST", "/i/_search?sort=%40timestamp%3Aasc") + assert calls[0]["body"] is None + + self.client.search(index="i", sort=["@timestamp:asc", "field"]) + calls = self.assert_url_called( + "POST", "/i/_search?sort=%40timestamp%3Aasc,field" + ) + assert calls[0]["body"] is None + + self.client.search(index="i", sort=("field", "@timestamp:asc")) + calls = self.assert_url_called( + "POST", "/i/_search?sort=field,%40timestamp%3Aasc" + ) + assert calls[0]["body"] is None + + self.client.search(index="i", sort=("field", "@timestamp")) + calls = self.assert_url_called("POST", "/i/_search") + assert calls[-1]["body"] == {"sort": ("field", "@timestamp")} + + self.client.search(index="i2", sort=["@timestamp", "field"]) + calls = self.assert_url_called("POST", "/i2/_search") + assert calls[-1]["body"] == {"sort": ["@timestamp", "field"]} + + self.client.search( + index="i3", sort=("field", "@timestamp:asc", {"field": "desc"}) + ) + calls = self.assert_url_called("POST", "/i3/_search") + assert calls[-1]["body"] == { + "sort": ("field", "@timestamp:asc", {"field": "desc"}) + } + + self.client.search(index="i4", sort=["@timestamp:asc", {"field": "desc"}]) + calls = self.assert_url_called("POST", "/i4/_search") + assert calls[-1]["body"] == {"sort": ["@timestamp:asc", {"field": "desc"}]}

Shorthand format for sort parameter incorrectly serialized in request body **Elasticsearch version** (`bin/elasticsearch --version`): 7.15.0 **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 7.15.0 Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: Code that relies on Elasticsearch `search` API using the `sort` query parameter using `<field>:<direction>` breaks in the 7.15.0 library. Confirmed it still works using 7.14.2. In 7.14.2, the `perform_request` call produces a `full_url` with `sort` as a query parameter. Equivalent example: ```GET testindex/_search?sort=@timestamp:asc``` In 7.15.0, `sort` instead gets added to the request `body` with a key/value pair as part of `perform_request`. Equivalent example: ``` GET testindex/_search { "sort": "@timestamp:asc" } ``` Refs: * https://www.elastic.co/guide/en/elasticsearch/reference/current/search-search.html - Query parameters, sort **Steps to reproduce**: * Create a simple index and add a document with some basic information: ``` POST testindex/_doc { "@timestamp": "2021-09-29T12:00:00Z" } ``` * Run the following search using `elasticsearch-py`: ``` import elasticsearch es = elasticsearch.Elasticsearch('http://localhost:9200') es.search(index='testindex', sort='@timestamp:asc') ``` With 7.14.2 (and earlier), a successful result returns; with 7.15.0, the following returns: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<...>/testing/venv/lib/python3.8/site-packages/elasticsearch/client/utils.py", line 301, in _wrapped return func(*args, params=params, headers=headers, **kwargs) File "<...>/testing/venv/lib/python3.8/site-packages/elasticsearch/client/__init__.py", line 1765, in search return self.transport.perform_request( File "<...>/testing/venv/lib/python3.8/site-packages/elasticsearch/transport.py", line 458, in perform_request raise e File "<...>/testing/venv/lib/python3.8/site-packages/elasticsearch/transport.py", line 419, in perform_request status, headers_response, data = connection.perform_request( File "<...>/testing/venv/lib/python3.8/site-packages/elasticsearch/connection/http_urllib3.py", line 287, in perform_request self._raise_error(response.status, raw_data) File "<...>/testing/venv/lib/python3.8/site-packages/elasticsearch/connection/base.py", line 337, in _raise_error raise HTTP_EXCEPTIONS.get(status_code, TransportError)( elasticsearch.exceptions.RequestError: RequestError(400, 'search_phase_execution_exception', 'No mapping found for [@timestamp:asc] in order to sort on') ```

@jpittiglio Thanks for reporting this, I'll reevaluate including `sort` as a potential body field. For now you can either specify `params={"sort": "@timestamp:asc"}` or pin your version of `elasticsearch`.

2022-02-16T01:48:54Z

elastic/elasticsearch-py

elastic__elasticsearch-py-2030

2f64b0ccbaf71ac1f1dc7ab498bd0ccafd1777f4

diff --git a/elasticsearch/_async/client/__init__.py b/elasticsearch/_async/client/__init__.py --- a/elasticsearch/_async/client/__init__.py +++ b/elasticsearch/_async/client/__init__.py @@ -519,26 +519,36 @@ def options( if request_timeout is not DEFAULT: client._request_timeout = request_timeout + else: + client._request_timeout = self._request_timeout if ignore_status is not DEFAULT: if isinstance(ignore_status, int): ignore_status = (ignore_status,) client._ignore_status = ignore_status + else: + client._ignore_status = self._ignore_status if max_retries is not DEFAULT: if not isinstance(max_retries, int): raise TypeError("'max_retries' must be of type 'int'") client._max_retries = max_retries + else: + client._max_retries = self._max_retries if retry_on_status is not DEFAULT: if isinstance(retry_on_status, int): retry_on_status = (retry_on_status,) client._retry_on_status = retry_on_status + else: + client._retry_on_status = self._retry_on_status if retry_on_timeout is not DEFAULT: if not isinstance(retry_on_timeout, bool): raise TypeError("'retry_on_timeout' must be of type 'bool'") client._retry_on_timeout = retry_on_timeout + else: + client._retry_on_timeout = self._retry_on_timeout return client diff --git a/elasticsearch/_sync/client/__init__.py b/elasticsearch/_sync/client/__init__.py --- a/elasticsearch/_sync/client/__init__.py +++ b/elasticsearch/_sync/client/__init__.py @@ -519,26 +519,36 @@ def options( if request_timeout is not DEFAULT: client._request_timeout = request_timeout + else: + client._request_timeout = self._request_timeout if ignore_status is not DEFAULT: if isinstance(ignore_status, int): ignore_status = (ignore_status,) client._ignore_status = ignore_status + else: + client._ignore_status = self._ignore_status if max_retries is not DEFAULT: if not isinstance(max_retries, int): raise TypeError("'max_retries' must be of type 'int'") client._max_retries = max_retries + else: + client._max_retries = self._max_retries if retry_on_status is not DEFAULT: if isinstance(retry_on_status, int): retry_on_status = (retry_on_status,) client._retry_on_status = retry_on_status + else: + client._retry_on_status = self._retry_on_status if retry_on_timeout is not DEFAULT: if not isinstance(retry_on_timeout, bool): raise TypeError("'retry_on_timeout' must be of type 'bool'") client._retry_on_timeout = retry_on_timeout + else: + client._retry_on_timeout = self._retry_on_timeout return client

diff --git a/test_elasticsearch/test_client/test_options.py b/test_elasticsearch/test_client/test_options.py --- a/test_elasticsearch/test_client/test_options.py +++ b/test_elasticsearch/test_client/test_options.py @@ -368,3 +368,104 @@ def test_user_agent_override(self): "user-agent": "custom4", "accept": "application/vnd.elasticsearch+json; compatible-with=8", } + + def test_options_timeout_parameters(self): + client = Elasticsearch( + "http://localhost:9200", + transport_class=DummyTransport, + request_timeout=1, + max_retries=2, + retry_on_status=(404,), + retry_on_timeout=True, + ) + + # timeout parameters are preserved with .options() + client.options().indices.get(index="test") + + calls = client.transport.calls + call = calls[("GET", "/test")][0] + assert call.pop("client_meta") is DEFAULT + assert call == { + "headers": { + "accept": "application/vnd.elasticsearch+json; compatible-with=8", + }, + "body": None, + "request_timeout": 1, + "max_retries": 2, + "retry_on_status": (404,), + "retry_on_timeout": True, + } + + client = Elasticsearch( + "http://localhost:9200", + transport_class=DummyTransport, + request_timeout=1, + max_retries=2, + retry_on_status=(404,), + retry_on_timeout=True, + ) + client.options( + request_timeout=2, + max_retries=3, + retry_on_status=(400,), + retry_on_timeout=False, + ).indices.get(index="test") + + calls = client.transport.calls + call = calls[("GET", "/test")][0] + assert call.pop("client_meta") is DEFAULT + assert call == { + "headers": { + "accept": "application/vnd.elasticsearch+json; compatible-with=8", + }, + "body": None, + "request_timeout": 2, + "max_retries": 3, + "retry_on_status": (400,), + "retry_on_timeout": False, + } + + client = Elasticsearch( + "http://localhost:9200", + transport_class=DummyTransport, + ) + client.options().indices.get(index="test") + + calls = client.transport.calls + call = calls[("GET", "/test")][0] + assert call.pop("request_timeout") is DEFAULT + assert call.pop("max_retries") is DEFAULT + assert call.pop("retry_on_timeout") is DEFAULT + assert call.pop("retry_on_status") is DEFAULT + assert call.pop("client_meta") is DEFAULT + assert call == { + "headers": { + "accept": "application/vnd.elasticsearch+json; compatible-with=8", + }, + "body": None, + } + + client = Elasticsearch( + "http://localhost:9200", + transport_class=DummyTransport, + ) + client.options( + request_timeout=1, + max_retries=2, + retry_on_status=(404,), + retry_on_timeout=True, + ).indices.get(index="test") + + calls = client.transport.calls + call = calls[("GET", "/test")][0] + assert call.pop("client_meta") is DEFAULT + assert call == { + "headers": { + "accept": "application/vnd.elasticsearch+json; compatible-with=8", + }, + "body": None, + "request_timeout": 1, + "max_retries": 2, + "retry_on_status": (404,), + "retry_on_timeout": True, + }

Calling options() on a client resets timeout related attributes <!-- Bug report --> **Elasticsearch version** (8.1.1): **`elasticsearch-py` version (`8.1.1`)**: Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: When the `options()` method is called for an existing `ElasticSearch` client instance, the timeout related parameters are not preserved. These parameters are: - `self._request_timeout` - `self._max_retries` - `self._retry_on_timeout` - `self._retry_on_status` This is problematic in the `helpers` module, particularly `scan()` because that method calls the `options` and resets any timeout related settings we may have made on the client. We can re-specify the `request_timeout` there but retries parameters cannot be replicated **Steps to reproduce**: ``` >>> import elasticsearch as ES >>> es=ES.Elasticsearch("http://esmasters:9200", sniff_on_connection_fail=True, sniff_on_start=True, min_delay_between_sniffing=600, request_timeout=600, sniff_timeout=300, max_retries=5, retry_on_timeout=True) >>> es._retry_on_timeout True >>> es2=es.options() >>> es2._retry_on_timeout <DEFAULT> >>> es._max_retries 5 >>> es2._max_retries <DEFAULT> >>> es2._retry_on_status <DEFAULT> >>> es._retry_on_status <DEFAULT> >>> es._request_timeout 600 >>> es2._request_timeout <DEFAULT> ```

Thanks for reporting this, this does seem like something worth fixing. Let me take a look. +1 to this. It's called in the `streaming_bulk` method as well, and I believe it is not possible to re-specify any of the affected parameters at that point (most importantly `request_timeout`) Yes +1 it would be great to see this fixed - ran into this problem today

2022-07-14T11:09:50Z

elastic/elasticsearch-py

elastic__elasticsearch-py-2383

dc9693098d9b532b4fe7b148aa819fe05626e4b2

diff --git a/elasticsearch/_async/client/__init__.py b/elasticsearch/_async/client/__init__.py --- a/elasticsearch/_async/client/__init__.py +++ b/elasticsearch/_async/client/__init__.py @@ -612,7 +612,8 @@ async def ping( async def bulk( self, *, - operations: t.Sequence[t.Mapping[str, t.Any]], + operations: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -663,8 +664,12 @@ async def bulk( before proceeding with the operation. Set to all or any positive integer up to the total number of shards in the index (`number_of_replicas+1`). """ - if operations is None: - raise ValueError("Empty value passed for parameter 'operations'") + if operations is None and body is None: + raise ValueError( + "Empty value passed for parameters 'operations' and 'body', one of them should be set." + ) + elif operations is not None and body is not None: + raise ValueError("Cannot set both 'operations' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_bulk" else: @@ -696,7 +701,7 @@ async def bulk( __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards - __body = operations + __body = operations if operations is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -706,7 +711,7 @@ async def bulk( ) @_rewrite_parameters( - body_fields=True, + body_fields=("scroll_id",), ) async def clear_scroll( self, @@ -716,6 +721,7 @@ async def clear_scroll( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, scroll_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Explicitly clears the search context for a scroll. @@ -726,7 +732,7 @@ async def clear_scroll( """ __path = "/_search/scroll" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -735,8 +741,9 @@ async def clear_scroll( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if scroll_id is not None: - __body["scroll_id"] = scroll_id + if not __body: + if scroll_id is not None: + __body["scroll_id"] = scroll_id if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -747,16 +754,17 @@ async def clear_scroll( ) @_rewrite_parameters( - body_fields=True, + body_fields=("id",), ) async def close_point_in_time( self, *, - id: str, + id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Close a point in time @@ -765,13 +773,11 @@ async def close_point_in_time( :param id: The ID of the point-in-time. """ - if id is None: + if id is None and body is None: raise ValueError("Empty value passed for parameter 'id'") __path = "/_pit" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if id is not None: - __body["id"] = id + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -780,6 +786,9 @@ async def close_point_in_time( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if id is not None: + __body["id"] = id if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -790,7 +799,7 @@ async def close_point_in_time( ) @_rewrite_parameters( - body_fields=True, + body_fields=("query",), ) async def count( self, @@ -822,6 +831,7 @@ async def count( query: t.Optional[t.Mapping[str, t.Any]] = None, routing: t.Optional[str] = None, terminate_after: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns number of documents matching a query. @@ -870,7 +880,7 @@ async def count( else: __path = "/_count" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if analyze_wildcard is not None: @@ -903,12 +913,13 @@ async def count( __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if routing is not None: __query["routing"] = routing if terminate_after is not None: __query["terminate_after"] = terminate_after + if not __body: + if query is not None: + __body["query"] = query if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -926,7 +937,8 @@ async def create( *, index: str, id: str, - document: t.Mapping[str, t.Any], + document: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -982,8 +994,12 @@ async def create( raise ValueError("Empty value passed for parameter 'index'") if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if document is None: - raise ValueError("Empty value passed for parameter 'document'") + if document is None and body is None: + raise ValueError( + "Empty value passed for parameters 'document' and 'body', one of them should be set." + ) + elif document is not None and body is not None: + raise ValueError("Cannot set both 'document' and 'body'") __path = f"/{_quote(index)}/_create/{_quote(id)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -1008,7 +1024,7 @@ async def create( __query["version_type"] = version_type if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards - __body = document + __body = document if document is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -1100,7 +1116,7 @@ async def delete( ) @_rewrite_parameters( - body_fields=True, + body_fields=("max_docs", "query", "slice"), parameter_aliases={"from": "from_"}, ) async def delete_by_query( @@ -1155,6 +1171,7 @@ async def delete_by_query( t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Deletes documents matching the provided query. @@ -1228,7 +1245,7 @@ async def delete_by_query( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_delete_by_query" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -1266,16 +1283,12 @@ async def delete_by_query( __query["ignore_unavailable"] = ignore_unavailable if lenient is not None: __query["lenient"] = lenient - if max_docs is not None: - __body["max_docs"] = max_docs if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if refresh is not None: __query["refresh"] = refresh if request_cache is not None: @@ -1292,8 +1305,6 @@ async def delete_by_query( __query["search_timeout"] = search_timeout if search_type is not None: __query["search_type"] = search_type - if slice is not None: - __body["slice"] = slice if slices is not None: __query["slices"] = slices if sort is not None: @@ -1310,6 +1321,13 @@ async def delete_by_query( __query["wait_for_active_shards"] = wait_for_active_shards if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if max_docs is not None: + __body["max_docs"] = max_docs + if query is not None: + __body["query"] = query + if slice is not None: + __body["slice"] = slice __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -1588,7 +1606,7 @@ async def exists_source( ) @_rewrite_parameters( - body_fields=True, + body_fields=("query",), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -1617,6 +1635,7 @@ async def explain( source_excludes: t.Optional[t.Union[str, t.Sequence[str]]] = None, source_includes: t.Optional[t.Union[str, t.Sequence[str]]] = None, stored_fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns information about why a specific matches (or doesn't match) a query. @@ -1655,7 +1674,7 @@ async def explain( raise ValueError("Empty value passed for parameter 'id'") __path = f"/{_quote(index)}/_explain/{_quote(id)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if analyze_wildcard is not None: __query["analyze_wildcard"] = analyze_wildcard if analyzer is not None: @@ -1678,8 +1697,6 @@ async def explain( __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if routing is not None: __query["routing"] = routing if source is not None: @@ -1690,6 +1707,9 @@ async def explain( __query["_source_includes"] = source_includes if stored_fields is not None: __query["stored_fields"] = stored_fields + if not __body: + if query is not None: + __body["query"] = query if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1700,7 +1720,7 @@ async def explain( ) @_rewrite_parameters( - body_fields=True, + body_fields=("fields", "index_filter", "runtime_mappings"), ) async def field_caps( self, @@ -1726,6 +1746,7 @@ async def field_caps( pretty: t.Optional[bool] = None, runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, types: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns the information about the capabilities of fields among multiple indices. @@ -1764,15 +1785,13 @@ async def field_caps( else: __path = "/_field_caps" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path if filters is not None: @@ -1783,14 +1802,17 @@ async def field_caps( __query["ignore_unavailable"] = ignore_unavailable if include_unmapped is not None: __query["include_unmapped"] = include_unmapped - if index_filter is not None: - __body["index_filter"] = index_filter if pretty is not None: __query["pretty"] = pretty - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings if types is not None: __query["types"] = types + if not __body: + if fields is not None: + __body["fields"] = fields + if index_filter is not None: + __body["index_filter"] = index_filter + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2141,7 +2163,8 @@ async def index( self, *, index: str, - document: t.Mapping[str, t.Any], + document: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -2205,8 +2228,12 @@ async def index( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if document is None: - raise ValueError("Empty value passed for parameter 'document'") + if document is None and body is None: + raise ValueError( + "Empty value passed for parameters 'document' and 'body', one of them should be set." + ) + elif document is not None and body is not None: + raise ValueError("Cannot set both 'document' and 'body'") if index not in SKIP_IN_PATH and id not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_doc/{_quote(id)}" __method = "PUT" @@ -2246,7 +2273,7 @@ async def index( __query["version_type"] = version_type if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards - __body = document + __body = document if document is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] __method, __path, params=__query, headers=__headers, body=__body @@ -2282,14 +2309,21 @@ async def info( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "knn", + "docvalue_fields", + "fields", + "filter", + "source", + "stored_fields", + ), parameter_aliases={"_source": "source"}, ) async def knn_search( self, *, index: t.Union[str, t.Sequence[str]], - knn: t.Mapping[str, t.Any], + knn: t.Optional[t.Mapping[str, t.Any]] = None, docvalue_fields: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -2302,6 +2336,7 @@ async def knn_search( routing: t.Optional[str] = None, source: t.Optional[t.Union[bool, t.Mapping[str, t.Any]]] = None, stored_fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Performs a kNN search. @@ -2331,21 +2366,13 @@ async def knn_search( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if knn is None: + if knn is None and body is None: raise ValueError("Empty value passed for parameter 'knn'") __path = f"/{_quote(index)}/_knn_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if knn is not None: - __body["knn"] = knn - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if fields is not None: - __body["fields"] = fields - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: @@ -2354,10 +2381,19 @@ async def knn_search( __query["pretty"] = pretty if routing is not None: __query["routing"] = routing - if source is not None: - __body["_source"] = source - if stored_fields is not None: - __body["stored_fields"] = stored_fields + if not __body: + if knn is not None: + __body["knn"] = knn + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if fields is not None: + __body["fields"] = fields + if filter is not None: + __body["filter"] = filter + if source is not None: + __body["_source"] = source + if stored_fields is not None: + __body["stored_fields"] = stored_fields if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2368,7 +2404,7 @@ async def knn_search( ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "ids"), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -2393,6 +2429,7 @@ async def mget( source_excludes: t.Optional[t.Union[str, t.Sequence[str]]] = None, source_includes: t.Optional[t.Union[str, t.Sequence[str]]] = None, stored_fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to get multiple documents in one request. @@ -2428,18 +2465,14 @@ async def mget( __path = f"/{_quote(index)}/_mget" else: __path = "/_mget" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ids is not None: - __body["ids"] = ids if preference is not None: __query["preference"] = preference if pretty is not None: @@ -2458,6 +2491,11 @@ async def mget( __query["_source_includes"] = source_includes if stored_fields is not None: __query["stored_fields"] = stored_fields + if not __body: + if docs is not None: + __body["docs"] = docs + if ids is not None: + __body["ids"] = ids __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2469,7 +2507,8 @@ async def mget( async def msearch( self, *, - searches: t.Sequence[t.Mapping[str, t.Any]], + searches: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_indices: t.Optional[bool] = None, ccs_minimize_roundtrips: t.Optional[bool] = None, @@ -2538,8 +2577,12 @@ async def msearch( :param typed_keys: Specifies whether aggregation and suggester names should be prefixed by their respective types in the response. """ - if searches is None: - raise ValueError("Empty value passed for parameter 'searches'") + if searches is None and body is None: + raise ValueError( + "Empty value passed for parameters 'searches' and 'body', one of them should be set." + ) + elif searches is not None and body is not None: + raise ValueError("Cannot set both 'searches' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_msearch" else: @@ -2577,7 +2620,7 @@ async def msearch( __query["search_type"] = search_type if typed_keys is not None: __query["typed_keys"] = typed_keys - __body = searches + __body = searches if searches is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -2592,7 +2635,8 @@ async def msearch( async def msearch_template( self, *, - search_templates: t.Sequence[t.Mapping[str, t.Any]], + search_templates: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, ccs_minimize_roundtrips: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -2626,8 +2670,12 @@ async def msearch_template( :param typed_keys: If `true`, the response prefixes aggregation and suggester names with their respective types. """ - if search_templates is None: - raise ValueError("Empty value passed for parameter 'search_templates'") + if search_templates is None and body is None: + raise ValueError( + "Empty value passed for parameters 'search_templates' and 'body', one of them should be set." + ) + elif search_templates is not None and body is not None: + raise ValueError("Cannot set both 'search_templates' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_msearch/template" else: @@ -2651,7 +2699,7 @@ async def msearch_template( __query["search_type"] = search_type if typed_keys is not None: __query["typed_keys"] = typed_keys - __body = search_templates + __body = search_templates if search_templates is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -2661,7 +2709,7 @@ async def msearch_template( ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "ids"), ) async def mtermvectors( self, @@ -2686,6 +2734,7 @@ async def mtermvectors( version_type: t.Optional[ t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns multiple termvectors in one request. @@ -2717,10 +2766,8 @@ async def mtermvectors( __path = f"/{_quote(index)}/_mtermvectors" else: __path = "/_mtermvectors" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if field_statistics is not None: @@ -2731,8 +2778,6 @@ async def mtermvectors( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ids is not None: - __body["ids"] = ids if offsets is not None: __query["offsets"] = offsets if payloads is not None: @@ -2753,6 +2798,11 @@ async def mtermvectors( __query["version"] = version if version_type is not None: __query["version_type"] = version_type + if not __body: + if docs is not None: + __body["docs"] = docs + if ids is not None: + __body["ids"] = ids if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2832,13 +2882,13 @@ async def open_point_in_time( ) @_rewrite_parameters( - body_fields=True, + body_fields=("script",), ) async def put_script( self, *, id: str, - script: t.Mapping[str, t.Any], + script: t.Optional[t.Mapping[str, t.Any]] = None, context: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -2848,6 +2898,7 @@ async def put_script( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a script. @@ -2869,7 +2920,7 @@ async def put_script( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if script is None: + if script is None and body is None: raise ValueError("Empty value passed for parameter 'script'") if id not in SKIP_IN_PATH and context not in SKIP_IN_PATH: __path = f"/_scripts/{_quote(id)}/{_quote(context)}" @@ -2877,10 +2928,8 @@ async def put_script( __path = f"/_scripts/{_quote(id)}" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if script is not None: - __body["script"] = script + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2893,18 +2942,21 @@ async def put_script( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if script is not None: + __body["script"] = script __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("requests", "metric"), ) async def rank_eval( self, *, - requests: t.Sequence[t.Mapping[str, t.Any]], + requests: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_indices: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -2922,6 +2974,7 @@ async def rank_eval( metric: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, search_type: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to evaluate the quality of ranked search results over a set of typical @@ -2947,16 +3000,14 @@ async def rank_eval( :param metric: Definition of the evaluation metric to calculate. :param search_type: Search operation type """ - if requests is None: + if requests is None and body is None: raise ValueError("Empty value passed for parameter 'requests'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_rank_eval" else: __path = "/_rank_eval" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if requests is not None: - __body["requests"] = requests + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: @@ -2969,25 +3020,28 @@ async def rank_eval( __query["human"] = human if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if metric is not None: - __body["metric"] = metric if pretty is not None: __query["pretty"] = pretty if search_type is not None: __query["search_type"] = search_type + if not __body: + if requests is not None: + __body["requests"] = requests + if metric is not None: + __body["metric"] = metric __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("dest", "source", "conflicts", "max_docs", "script", "size"), ) async def reindex( self, *, - dest: t.Mapping[str, t.Any], - source: t.Mapping[str, t.Any], + dest: t.Optional[t.Mapping[str, t.Any]] = None, + source: t.Optional[t.Mapping[str, t.Any]] = None, conflicts: t.Optional[t.Union["t.Literal['abort', 'proceed']", str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -3006,6 +3060,7 @@ async def reindex( t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to copy documents from one index to another, optionally filtering the @@ -3038,27 +3093,19 @@ async def reindex( :param wait_for_completion: If `true`, the request blocks until the operation is complete. """ - if dest is None: + if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") - if source is None: + if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path = "/_reindex" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if dest is not None: - __body["dest"] = dest - if source is not None: - __body["source"] = source - if conflicts is not None: - __body["conflicts"] = conflicts + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_docs is not None: - __body["max_docs"] = max_docs if pretty is not None: __query["pretty"] = pretty if refresh is not None: @@ -3067,12 +3114,8 @@ async def reindex( __query["requests_per_second"] = requests_per_second if require_alias is not None: __query["require_alias"] = require_alias - if script is not None: - __body["script"] = script if scroll is not None: __query["scroll"] = scroll - if size is not None: - __body["size"] = size if slices is not None: __query["slices"] = slices if timeout is not None: @@ -3081,6 +3124,19 @@ async def reindex( __query["wait_for_active_shards"] = wait_for_active_shards if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if dest is not None: + __body["dest"] = dest + if source is not None: + __body["source"] = source + if conflicts is not None: + __body["conflicts"] = conflicts + if max_docs is not None: + __body["max_docs"] = max_docs + if script is not None: + __body["script"] = script + if size is not None: + __body["size"] = size __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -3126,7 +3182,7 @@ async def reindex_rethrottle( ) @_rewrite_parameters( - body_fields=True, + body_fields=("file", "params", "source"), ignore_deprecated_options={"params"}, ) async def render_search_template( @@ -3140,6 +3196,7 @@ async def render_search_template( params: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, source: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to use the Mustache language to pre-render a search definition. @@ -3160,21 +3217,22 @@ async def render_search_template( else: __path = "/_render/template" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if file is not None: - __body["file"] = file if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if params is not None: - __body["params"] = params if pretty is not None: __query["pretty"] = pretty - if source is not None: - __body["source"] = source + if not __body: + if file is not None: + __body["file"] = file + if params is not None: + __body["params"] = params + if source is not None: + __body["source"] = source if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3185,7 +3243,7 @@ async def render_search_template( ) @_rewrite_parameters( - body_fields=True, + body_fields=("context", "context_setup", "script"), ) async def scripts_painless_execute( self, @@ -3197,6 +3255,7 @@ async def scripts_painless_execute( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, script: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows an arbitrary script to be executed and a result to be returned @@ -3208,12 +3267,8 @@ async def scripts_painless_execute( :param script: The Painless script to execute. """ __path = "/_scripts/painless/_execute" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if context is not None: - __body["context"] = context - if context_setup is not None: - __body["context_setup"] = context_setup + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3222,8 +3277,13 @@ async def scripts_painless_execute( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if script is not None: - __body["script"] = script + if not __body: + if context is not None: + __body["context"] = context + if context_setup is not None: + __body["context_setup"] = context_setup + if script is not None: + __body["script"] = script if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3234,18 +3294,19 @@ async def scripts_painless_execute( ) @_rewrite_parameters( - body_fields=True, + body_fields=("scroll_id", "scroll"), ) async def scroll( self, *, - scroll_id: str, + scroll_id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, rest_total_hits_as_int: t.Optional[bool] = None, scroll: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to retrieve a large numbers of results from a single search request. @@ -3258,13 +3319,11 @@ async def scroll( is returned as an object. :param scroll: Period to retain the search context for scrolling. """ - if scroll_id is None: + if scroll_id is None and body is None: raise ValueError("Empty value passed for parameter 'scroll_id'") __path = "/_search/scroll" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if scroll_id is not None: - __body["scroll_id"] = scroll_id + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3275,8 +3334,11 @@ async def scroll( __query["pretty"] = pretty if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int - if scroll is not None: - __body["scroll"] = scroll + if not __body: + if scroll_id is not None: + __body["scroll_id"] = scroll_id + if scroll is not None: + __body["scroll"] = scroll if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3287,7 +3349,42 @@ async def scroll( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "aggs", + "collapse", + "docvalue_fields", + "explain", + "ext", + "fields", + "from_", + "highlight", + "indices_boost", + "knn", + "min_score", + "pit", + "post_filter", + "profile", + "query", + "rank", + "rescore", + "runtime_mappings", + "script_fields", + "search_after", + "seq_no_primary_term", + "size", + "slice", + "sort", + "source", + "stats", + "stored_fields", + "suggest", + "terminate_after", + "timeout", + "track_scores", + "track_total_hits", + "version", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -3388,6 +3485,7 @@ async def search( track_total_hits: t.Optional[t.Union[bool, int]] = None, typed_keys: t.Optional[bool] = None, version: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns results matching a query. @@ -3580,8 +3678,8 @@ async def search( __path = f"/{_quote(index)}/_search" else: __path = "/_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -3593,10 +3691,6 @@ async def search( ): __query["sort"] = sort sort = None - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if allow_partial_search_results is not None: @@ -3609,104 +3703,50 @@ async def search( __query["batched_reduce_size"] = batched_reduce_size if ccs_minimize_roundtrips is not None: __query["ccs_minimize_roundtrips"] = ccs_minimize_roundtrips - if collapse is not None: - __body["collapse"] = collapse if default_operator is not None: __query["default_operator"] = default_operator if df is not None: __query["df"] = df - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain - if ext is not None: - __body["ext"] = ext - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ - if highlight is not None: - __body["highlight"] = highlight if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indices_boost is not None: - __body["indices_boost"] = indices_boost - if knn is not None: - __body["knn"] = knn if lenient is not None: __query["lenient"] = lenient if max_concurrent_shard_requests is not None: __query["max_concurrent_shard_requests"] = max_concurrent_shard_requests if min_compatible_shard_node is not None: __query["min_compatible_shard_node"] = min_compatible_shard_node - if min_score is not None: - __body["min_score"] = min_score - if pit is not None: - __body["pit"] = pit - if post_filter is not None: - __body["post_filter"] = post_filter if pre_filter_shard_size is not None: __query["pre_filter_shard_size"] = pre_filter_shard_size if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query - if rank is not None: - __body["rank"] = rank if request_cache is not None: __query["request_cache"] = request_cache - if rescore is not None: - __body["rescore"] = rescore if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: __query["routing"] = routing - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields if scroll is not None: __query["scroll"] = scroll - if search_after is not None: - __body["search_after"] = search_after if search_type is not None: __query["search_type"] = search_type - if seq_no_primary_term is not None: - __body["seq_no_primary_term"] = seq_no_primary_term - if size is not None: - __body["size"] = size - if slice is not None: - __body["slice"] = slice - if sort is not None: - __body["sort"] = sort - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes - if stats is not None: - __body["stats"] = stats - if stored_fields is not None: - __body["stored_fields"] = stored_fields - if suggest is not None: - __body["suggest"] = suggest if suggest_field is not None: __query["suggest_field"] = suggest_field if suggest_mode is not None: @@ -3715,18 +3755,77 @@ async def search( __query["suggest_size"] = suggest_size if suggest_text is not None: __query["suggest_text"] = suggest_text - if terminate_after is not None: - __body["terminate_after"] = terminate_after - if timeout is not None: - __body["timeout"] = timeout - if track_scores is not None: - __body["track_scores"] = track_scores - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits if typed_keys is not None: __query["typed_keys"] = typed_keys - if version is not None: - __body["version"] = version + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if collapse is not None: + __body["collapse"] = collapse + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if explain is not None: + __body["explain"] = explain + if ext is not None: + __body["ext"] = ext + if fields is not None: + __body["fields"] = fields + if from_ is not None: + __body["from"] = from_ + if highlight is not None: + __body["highlight"] = highlight + if indices_boost is not None: + __body["indices_boost"] = indices_boost + if knn is not None: + __body["knn"] = knn + if min_score is not None: + __body["min_score"] = min_score + if pit is not None: + __body["pit"] = pit + if post_filter is not None: + __body["post_filter"] = post_filter + if profile is not None: + __body["profile"] = profile + if query is not None: + __body["query"] = query + if rank is not None: + __body["rank"] = rank + if rescore is not None: + __body["rescore"] = rescore + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if search_after is not None: + __body["search_after"] = search_after + if seq_no_primary_term is not None: + __body["seq_no_primary_term"] = seq_no_primary_term + if size is not None: + __body["size"] = size + if slice is not None: + __body["slice"] = slice + if sort is not None: + __body["sort"] = sort + if source is not None: + __body["_source"] = source + if stats is not None: + __body["stats"] = stats + if stored_fields is not None: + __body["stored_fields"] = stored_fields + if suggest is not None: + __body["suggest"] = suggest + if terminate_after is not None: + __body["terminate_after"] = terminate_after + if timeout is not None: + __body["timeout"] = timeout + if track_scores is not None: + __body["track_scores"] = track_scores + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3737,7 +3836,22 @@ async def search( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggs", + "buffer", + "exact_bounds", + "extent", + "fields", + "grid_agg", + "grid_precision", + "grid_type", + "query", + "runtime_mappings", + "size", + "sort", + "track_total_hits", + "with_labels", + ), ) async def search_mvt( self, @@ -3772,6 +3886,7 @@ async def search_mvt( ] = None, track_total_hits: t.Optional[t.Union[bool, int]] = None, with_labels: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> BinaryApiResponse: """ Searches a vector tile for geospatial values. Returns results as a binary Mapbox @@ -3833,8 +3948,8 @@ async def search_mvt( if y in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'y'") __path = f"/{_quote(index)}/_mvt/{_quote(field)}/{_quote(zoom)}/{_quote(x)}/{_quote(y)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -3846,42 +3961,43 @@ async def search_mvt( ): __query["sort"] = sort sort = None - if aggs is not None: - __body["aggs"] = aggs - if buffer is not None: - __body["buffer"] = buffer if error_trace is not None: __query["error_trace"] = error_trace - if exact_bounds is not None: - __body["exact_bounds"] = exact_bounds - if extent is not None: - __body["extent"] = extent - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if grid_agg is not None: - __body["grid_agg"] = grid_agg - if grid_precision is not None: - __body["grid_precision"] = grid_precision - if grid_type is not None: - __body["grid_type"] = grid_type if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if size is not None: - __body["size"] = size - if sort is not None: - __body["sort"] = sort - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits - if with_labels is not None: - __body["with_labels"] = with_labels + if not __body: + if aggs is not None: + __body["aggs"] = aggs + if buffer is not None: + __body["buffer"] = buffer + if exact_bounds is not None: + __body["exact_bounds"] = exact_bounds + if extent is not None: + __body["extent"] = extent + if fields is not None: + __body["fields"] = fields + if grid_agg is not None: + __body["grid_agg"] = grid_agg + if grid_precision is not None: + __body["grid_precision"] = grid_precision + if grid_type is not None: + __body["grid_type"] = grid_type + if query is not None: + __body["query"] = query + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if size is not None: + __body["size"] = size + if sort is not None: + __body["sort"] = sort + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if with_labels is not None: + __body["with_labels"] = with_labels if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/vnd.mapbox-vector-tile"} @@ -3970,7 +4086,7 @@ async def search_shards( ) @_rewrite_parameters( - body_fields=True, + body_fields=("explain", "id", "params", "profile", "source"), ignore_deprecated_options={"params"}, ) async def search_template( @@ -4006,6 +4122,7 @@ async def search_template( ] = None, source: t.Optional[str] = None, typed_keys: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to use the Mustache language to pre-render a search definition. @@ -4055,7 +4172,7 @@ async def search_template( else: __path = "/_search/template" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if ccs_minimize_roundtrips is not None: @@ -4064,26 +4181,18 @@ async def search_template( __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if id is not None: - __body["id"] = id if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if params is not None: - __body["params"] = params if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: @@ -4092,23 +4201,40 @@ async def search_template( __query["scroll"] = scroll if search_type is not None: __query["search_type"] = search_type - if source is not None: - __body["source"] = source if typed_keys is not None: __query["typed_keys"] = typed_keys + if not __body: + if explain is not None: + __body["explain"] = explain + if id is not None: + __body["id"] = id + if params is not None: + __body["params"] = params + if profile is not None: + __body["profile"] = profile + if source is not None: + __body["source"] = source __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "field", + "case_insensitive", + "index_filter", + "search_after", + "size", + "string", + "timeout", + ), ) async def terms_enum( self, *, index: str, - field: str, + field: t.Optional[str] = None, case_insensitive: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -4119,6 +4245,7 @@ async def terms_enum( size: t.Optional[int] = None, string: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ The terms enum API can be used to discover terms in the index that begin with @@ -4146,33 +4273,34 @@ async def terms_enum( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if field is None: + if field is None and body is None: raise ValueError("Empty value passed for parameter 'field'") __path = f"/{_quote(index)}/_terms_enum" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if field is not None: - __body["field"] = field - if case_insensitive is not None: - __body["case_insensitive"] = case_insensitive + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index_filter is not None: - __body["index_filter"] = index_filter if pretty is not None: __query["pretty"] = pretty - if search_after is not None: - __body["search_after"] = search_after - if size is not None: - __body["size"] = size - if string is not None: - __body["string"] = string - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if field is not None: + __body["field"] = field + if case_insensitive is not None: + __body["case_insensitive"] = case_insensitive + if index_filter is not None: + __body["index_filter"] = index_filter + if search_after is not None: + __body["search_after"] = search_after + if size is not None: + __body["size"] = size + if string is not None: + __body["string"] = string + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -4183,7 +4311,7 @@ async def terms_enum( ) @_rewrite_parameters( - body_fields=True, + body_fields=("doc", "filter", "per_field_analyzer"), ) async def termvectors( self, @@ -4210,6 +4338,7 @@ async def termvectors( version_type: t.Optional[ t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns information and statistics about terms in the fields of a particular @@ -4248,18 +4377,14 @@ async def termvectors( __path = f"/{_quote(index)}/_termvectors" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if doc is not None: - __body["doc"] = doc + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if field_statistics is not None: __query["field_statistics"] = field_statistics if fields is not None: __query["fields"] = fields - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: @@ -4268,8 +4393,6 @@ async def termvectors( __query["offsets"] = offsets if payloads is not None: __query["payloads"] = payloads - if per_field_analyzer is not None: - __body["per_field_analyzer"] = per_field_analyzer if positions is not None: __query["positions"] = positions if preference is not None: @@ -4286,6 +4409,13 @@ async def termvectors( __query["version"] = version if version_type is not None: __query["version_type"] = version_type + if not __body: + if doc is not None: + __body["doc"] = doc + if filter is not None: + __body["filter"] = filter + if per_field_analyzer is not None: + __body["per_field_analyzer"] = per_field_analyzer if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -4296,7 +4426,15 @@ async def termvectors( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "detect_noop", + "doc", + "doc_as_upsert", + "script", + "scripted_upsert", + "source", + "upsert", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -4334,6 +4472,7 @@ async def update( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates a document with a script or partial document. @@ -4381,14 +4520,8 @@ async def update( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/{_quote(index)}/_update/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if detect_noop is not None: - __body["detect_noop"] = detect_noop - if doc is not None: - __body["doc"] = doc - if doc_as_upsert is not None: - __body["doc_as_upsert"] = doc_as_upsert + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -4411,29 +4544,36 @@ async def update( __query["retry_on_conflict"] = retry_on_conflict if routing is not None: __query["routing"] = routing - if script is not None: - __body["script"] = script - if scripted_upsert is not None: - __body["scripted_upsert"] = scripted_upsert - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes if timeout is not None: __query["timeout"] = timeout - if upsert is not None: - __body["upsert"] = upsert if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if detect_noop is not None: + __body["detect_noop"] = detect_noop + if doc is not None: + __body["doc"] = doc + if doc_as_upsert is not None: + __body["doc_as_upsert"] = doc_as_upsert + if script is not None: + __body["script"] = script + if scripted_upsert is not None: + __body["scripted_upsert"] = scripted_upsert + if source is not None: + __body["_source"] = source + if upsert is not None: + __body["upsert"] = upsert __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("conflicts", "max_docs", "query", "script", "slice"), parameter_aliases={"from": "from_"}, ) async def update_by_query( @@ -4490,6 +4630,7 @@ async def update_by_query( t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Performs an update on every document in the index without changing the source, @@ -4571,7 +4712,7 @@ async def update_by_query( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_update_by_query" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -4589,8 +4730,6 @@ async def update_by_query( __query["analyze_wildcard"] = analyze_wildcard if analyzer is not None: __query["analyzer"] = analyzer - if conflicts is not None: - __body["conflicts"] = conflicts if default_operator is not None: __query["default_operator"] = default_operator if df is not None: @@ -4609,16 +4748,12 @@ async def update_by_query( __query["ignore_unavailable"] = ignore_unavailable if lenient is not None: __query["lenient"] = lenient - if max_docs is not None: - __body["max_docs"] = max_docs if pipeline is not None: __query["pipeline"] = pipeline if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query if refresh is not None: __query["refresh"] = refresh if request_cache is not None: @@ -4627,8 +4762,6 @@ async def update_by_query( __query["requests_per_second"] = requests_per_second if routing is not None: __query["routing"] = routing - if script is not None: - __body["script"] = script if scroll is not None: __query["scroll"] = scroll if scroll_size is not None: @@ -4637,8 +4770,6 @@ async def update_by_query( __query["search_timeout"] = search_timeout if search_type is not None: __query["search_type"] = search_type - if slice is not None: - __body["slice"] = slice if slices is not None: __query["slices"] = slices if sort is not None: @@ -4657,6 +4788,17 @@ async def update_by_query( __query["wait_for_active_shards"] = wait_for_active_shards if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if conflicts is not None: + __body["conflicts"] = conflicts + if max_docs is not None: + __body["max_docs"] = max_docs + if query is not None: + __body["query"] = query + if script is not None: + __body["script"] = script + if slice is not None: + __body["slice"] = slice if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/async_search.py b/elasticsearch/_async/client/async_search.py --- a/elasticsearch/_async/client/async_search.py +++ b/elasticsearch/_async/client/async_search.py @@ -155,7 +155,41 @@ async def status( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "aggs", + "collapse", + "docvalue_fields", + "explain", + "ext", + "fields", + "from_", + "highlight", + "indices_boost", + "knn", + "min_score", + "pit", + "post_filter", + "profile", + "query", + "rescore", + "runtime_mappings", + "script_fields", + "search_after", + "seq_no_primary_term", + "size", + "slice", + "sort", + "source", + "stats", + "stored_fields", + "suggest", + "terminate_after", + "timeout", + "track_scores", + "track_total_hits", + "version", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -260,6 +294,7 @@ async def submit( wait_for_completion_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Executes a search request asynchronously. @@ -396,8 +431,8 @@ async def submit( __path = f"/{_quote(index)}/_async_search" else: __path = "/_async_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -409,10 +444,6 @@ async def submit( ): __query["sort"] = sort sort = None - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if allow_partial_search_results is not None: @@ -425,106 +456,54 @@ async def submit( __query["batched_reduce_size"] = batched_reduce_size if ccs_minimize_roundtrips is not None: __query["ccs_minimize_roundtrips"] = ccs_minimize_roundtrips - if collapse is not None: - __body["collapse"] = collapse if default_operator is not None: __query["default_operator"] = default_operator if df is not None: __query["df"] = df - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain - if ext is not None: - __body["ext"] = ext - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ - if highlight is not None: - __body["highlight"] = highlight if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indices_boost is not None: - __body["indices_boost"] = indices_boost if keep_alive is not None: __query["keep_alive"] = keep_alive if keep_on_completion is not None: __query["keep_on_completion"] = keep_on_completion - if knn is not None: - __body["knn"] = knn if lenient is not None: __query["lenient"] = lenient if max_concurrent_shard_requests is not None: __query["max_concurrent_shard_requests"] = max_concurrent_shard_requests if min_compatible_shard_node is not None: __query["min_compatible_shard_node"] = min_compatible_shard_node - if min_score is not None: - __body["min_score"] = min_score - if pit is not None: - __body["pit"] = pit - if post_filter is not None: - __body["post_filter"] = post_filter if pre_filter_shard_size is not None: __query["pre_filter_shard_size"] = pre_filter_shard_size if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if request_cache is not None: __query["request_cache"] = request_cache - if rescore is not None: - __body["rescore"] = rescore if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: __query["routing"] = routing - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields if scroll is not None: __query["scroll"] = scroll - if search_after is not None: - __body["search_after"] = search_after if search_type is not None: __query["search_type"] = search_type - if seq_no_primary_term is not None: - __body["seq_no_primary_term"] = seq_no_primary_term - if size is not None: - __body["size"] = size - if slice is not None: - __body["slice"] = slice - if sort is not None: - __body["sort"] = sort - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes - if stats is not None: - __body["stats"] = stats - if stored_fields is not None: - __body["stored_fields"] = stored_fields - if suggest is not None: - __body["suggest"] = suggest if suggest_field is not None: __query["suggest_field"] = suggest_field if suggest_mode is not None: @@ -533,20 +512,77 @@ async def submit( __query["suggest_size"] = suggest_size if suggest_text is not None: __query["suggest_text"] = suggest_text - if terminate_after is not None: - __body["terminate_after"] = terminate_after - if timeout is not None: - __body["timeout"] = timeout - if track_scores is not None: - __body["track_scores"] = track_scores - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits if typed_keys is not None: __query["typed_keys"] = typed_keys - if version is not None: - __body["version"] = version if wait_for_completion_timeout is not None: __query["wait_for_completion_timeout"] = wait_for_completion_timeout + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if collapse is not None: + __body["collapse"] = collapse + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if explain is not None: + __body["explain"] = explain + if ext is not None: + __body["ext"] = ext + if fields is not None: + __body["fields"] = fields + if from_ is not None: + __body["from"] = from_ + if highlight is not None: + __body["highlight"] = highlight + if indices_boost is not None: + __body["indices_boost"] = indices_boost + if knn is not None: + __body["knn"] = knn + if min_score is not None: + __body["min_score"] = min_score + if pit is not None: + __body["pit"] = pit + if post_filter is not None: + __body["post_filter"] = post_filter + if profile is not None: + __body["profile"] = profile + if query is not None: + __body["query"] = query + if rescore is not None: + __body["rescore"] = rescore + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if search_after is not None: + __body["search_after"] = search_after + if seq_no_primary_term is not None: + __body["seq_no_primary_term"] = seq_no_primary_term + if size is not None: + __body["size"] = size + if slice is not None: + __body["slice"] = slice + if sort is not None: + __body["sort"] = sort + if source is not None: + __body["_source"] = source + if stats is not None: + __body["stats"] = stats + if stored_fields is not None: + __body["stored_fields"] = stored_fields + if suggest is not None: + __body["suggest"] = suggest + if terminate_after is not None: + __body["terminate_after"] = terminate_after + if timeout is not None: + __body["timeout"] = timeout + if track_scores is not None: + __body["track_scores"] = track_scores + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/autoscaling.py b/elasticsearch/_async/client/autoscaling.py --- a/elasticsearch/_async/client/autoscaling.py +++ b/elasticsearch/_async/client/autoscaling.py @@ -131,7 +131,8 @@ async def put_autoscaling_policy( self, *, name: str, - policy: t.Mapping[str, t.Any], + policy: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -148,8 +149,12 @@ async def put_autoscaling_policy( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if policy is None: - raise ValueError("Empty value passed for parameter 'policy'") + if policy is None and body is None: + raise ValueError( + "Empty value passed for parameters 'policy' and 'body', one of them should be set." + ) + elif policy is not None and body is not None: + raise ValueError("Cannot set both 'policy' and 'body'") __path = f"/_autoscaling/policy/{_quote(name)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -160,7 +165,7 @@ async def put_autoscaling_policy( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = policy + __body = policy if policy is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/ccr.py b/elasticsearch/_async/client/ccr.py --- a/elasticsearch/_async/client/ccr.py +++ b/elasticsearch/_async/client/ccr.py @@ -59,7 +59,20 @@ async def delete_auto_follow_pattern( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "leader_index", + "max_outstanding_read_requests", + "max_outstanding_write_requests", + "max_read_request_operation_count", + "max_read_request_size", + "max_retry_delay", + "max_write_buffer_count", + "max_write_buffer_size", + "max_write_request_operation_count", + "max_write_request_size", + "read_poll_timeout", + "remote_cluster", + ), ) async def follow( self, @@ -88,6 +101,7 @@ async def follow( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new follower index configured to follow the referenced leader index. @@ -116,45 +130,48 @@ async def follow( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_ccr/follow" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if leader_index is not None: - __body["leader_index"] = leader_index - if max_outstanding_read_requests is not None: - __body["max_outstanding_read_requests"] = max_outstanding_read_requests - if max_outstanding_write_requests is not None: - __body["max_outstanding_write_requests"] = max_outstanding_write_requests - if max_read_request_operation_count is not None: - __body[ - "max_read_request_operation_count" - ] = max_read_request_operation_count - if max_read_request_size is not None: - __body["max_read_request_size"] = max_read_request_size - if max_retry_delay is not None: - __body["max_retry_delay"] = max_retry_delay - if max_write_buffer_count is not None: - __body["max_write_buffer_count"] = max_write_buffer_count - if max_write_buffer_size is not None: - __body["max_write_buffer_size"] = max_write_buffer_size - if max_write_request_operation_count is not None: - __body[ - "max_write_request_operation_count" - ] = max_write_request_operation_count - if max_write_request_size is not None: - __body["max_write_request_size"] = max_write_request_size if pretty is not None: __query["pretty"] = pretty - if read_poll_timeout is not None: - __body["read_poll_timeout"] = read_poll_timeout - if remote_cluster is not None: - __body["remote_cluster"] = remote_cluster if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if leader_index is not None: + __body["leader_index"] = leader_index + if max_outstanding_read_requests is not None: + __body["max_outstanding_read_requests"] = max_outstanding_read_requests + if max_outstanding_write_requests is not None: + __body[ + "max_outstanding_write_requests" + ] = max_outstanding_write_requests + if max_read_request_operation_count is not None: + __body[ + "max_read_request_operation_count" + ] = max_read_request_operation_count + if max_read_request_size is not None: + __body["max_read_request_size"] = max_read_request_size + if max_retry_delay is not None: + __body["max_retry_delay"] = max_retry_delay + if max_write_buffer_count is not None: + __body["max_write_buffer_count"] = max_write_buffer_count + if max_write_buffer_size is not None: + __body["max_write_buffer_size"] = max_write_buffer_size + if max_write_request_operation_count is not None: + __body[ + "max_write_request_operation_count" + ] = max_write_request_operation_count + if max_write_request_size is not None: + __body["max_write_request_size"] = max_write_request_size + if read_poll_timeout is not None: + __body["read_poll_timeout"] = read_poll_timeout + if remote_cluster is not None: + __body["remote_cluster"] = remote_cluster __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -233,7 +250,12 @@ async def follow_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "follower_cluster", + "follower_index", + "follower_index_uuid", + "leader_remote_cluster", + ), ) async def forget_follower( self, @@ -247,6 +269,7 @@ async def forget_follower( human: t.Optional[bool] = None, leader_remote_cluster: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Removes the follower retention leases from the leader. @@ -264,23 +287,24 @@ async def forget_follower( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_ccr/forget_follower" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if follower_cluster is not None: - __body["follower_cluster"] = follower_cluster - if follower_index is not None: - __body["follower_index"] = follower_index - if follower_index_uuid is not None: - __body["follower_index_uuid"] = follower_index_uuid if human is not None: __query["human"] = human - if leader_remote_cluster is not None: - __body["leader_remote_cluster"] = leader_remote_cluster if pretty is not None: __query["pretty"] = pretty + if not __body: + if follower_cluster is not None: + __body["follower_cluster"] = follower_cluster + if follower_index is not None: + __body["follower_index"] = follower_index + if follower_index_uuid is not None: + __body["follower_index_uuid"] = follower_index_uuid + if leader_remote_cluster is not None: + __body["leader_remote_cluster"] = leader_remote_cluster __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -395,13 +419,29 @@ async def pause_follow( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "remote_cluster", + "follow_index_pattern", + "leader_index_exclusion_patterns", + "leader_index_patterns", + "max_outstanding_read_requests", + "max_outstanding_write_requests", + "max_read_request_operation_count", + "max_read_request_size", + "max_retry_delay", + "max_write_buffer_count", + "max_write_buffer_size", + "max_write_request_operation_count", + "max_write_request_size", + "read_poll_timeout", + "settings", + ), ) async def put_auto_follow_pattern( self, *, name: str, - remote_cluster: str, + remote_cluster: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, follow_index_pattern: t.Optional[str] = None, @@ -424,6 +464,7 @@ async def put_auto_follow_pattern( t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, settings: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new named collection of auto-follow patterns against a specified remote @@ -477,53 +518,58 @@ async def put_auto_follow_pattern( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if remote_cluster is None: + if remote_cluster is None and body is None: raise ValueError("Empty value passed for parameter 'remote_cluster'") __path = f"/_ccr/auto_follow/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if remote_cluster is not None: - __body["remote_cluster"] = remote_cluster + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if follow_index_pattern is not None: - __body["follow_index_pattern"] = follow_index_pattern if human is not None: __query["human"] = human - if leader_index_exclusion_patterns is not None: - __body["leader_index_exclusion_patterns"] = leader_index_exclusion_patterns - if leader_index_patterns is not None: - __body["leader_index_patterns"] = leader_index_patterns - if max_outstanding_read_requests is not None: - __body["max_outstanding_read_requests"] = max_outstanding_read_requests - if max_outstanding_write_requests is not None: - __body["max_outstanding_write_requests"] = max_outstanding_write_requests - if max_read_request_operation_count is not None: - __body[ - "max_read_request_operation_count" - ] = max_read_request_operation_count - if max_read_request_size is not None: - __body["max_read_request_size"] = max_read_request_size - if max_retry_delay is not None: - __body["max_retry_delay"] = max_retry_delay - if max_write_buffer_count is not None: - __body["max_write_buffer_count"] = max_write_buffer_count - if max_write_buffer_size is not None: - __body["max_write_buffer_size"] = max_write_buffer_size - if max_write_request_operation_count is not None: - __body[ - "max_write_request_operation_count" - ] = max_write_request_operation_count - if max_write_request_size is not None: - __body["max_write_request_size"] = max_write_request_size if pretty is not None: __query["pretty"] = pretty - if read_poll_timeout is not None: - __body["read_poll_timeout"] = read_poll_timeout - if settings is not None: - __body["settings"] = settings + if not __body: + if remote_cluster is not None: + __body["remote_cluster"] = remote_cluster + if follow_index_pattern is not None: + __body["follow_index_pattern"] = follow_index_pattern + if leader_index_exclusion_patterns is not None: + __body[ + "leader_index_exclusion_patterns" + ] = leader_index_exclusion_patterns + if leader_index_patterns is not None: + __body["leader_index_patterns"] = leader_index_patterns + if max_outstanding_read_requests is not None: + __body["max_outstanding_read_requests"] = max_outstanding_read_requests + if max_outstanding_write_requests is not None: + __body[ + "max_outstanding_write_requests" + ] = max_outstanding_write_requests + if max_read_request_operation_count is not None: + __body[ + "max_read_request_operation_count" + ] = max_read_request_operation_count + if max_read_request_size is not None: + __body["max_read_request_size"] = max_read_request_size + if max_retry_delay is not None: + __body["max_retry_delay"] = max_retry_delay + if max_write_buffer_count is not None: + __body["max_write_buffer_count"] = max_write_buffer_count + if max_write_buffer_size is not None: + __body["max_write_buffer_size"] = max_write_buffer_size + if max_write_request_operation_count is not None: + __body[ + "max_write_request_operation_count" + ] = max_write_request_operation_count + if max_write_request_size is not None: + __body["max_write_request_size"] = max_write_request_size + if read_poll_timeout is not None: + __body["read_poll_timeout"] = read_poll_timeout + if settings is not None: + __body["settings"] = settings __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -565,7 +611,18 @@ async def resume_auto_follow_pattern( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "max_outstanding_read_requests", + "max_outstanding_write_requests", + "max_read_request_operation_count", + "max_read_request_size", + "max_retry_delay", + "max_write_buffer_count", + "max_write_buffer_size", + "max_write_request_operation_count", + "max_write_request_size", + "read_poll_timeout", + ), ) async def resume_follow( self, @@ -589,6 +646,7 @@ async def resume_follow( read_poll_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Resumes a follower index that has been paused @@ -611,39 +669,42 @@ async def resume_follow( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_ccr/resume_follow" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_outstanding_read_requests is not None: - __body["max_outstanding_read_requests"] = max_outstanding_read_requests - if max_outstanding_write_requests is not None: - __body["max_outstanding_write_requests"] = max_outstanding_write_requests - if max_read_request_operation_count is not None: - __body[ - "max_read_request_operation_count" - ] = max_read_request_operation_count - if max_read_request_size is not None: - __body["max_read_request_size"] = max_read_request_size - if max_retry_delay is not None: - __body["max_retry_delay"] = max_retry_delay - if max_write_buffer_count is not None: - __body["max_write_buffer_count"] = max_write_buffer_count - if max_write_buffer_size is not None: - __body["max_write_buffer_size"] = max_write_buffer_size - if max_write_request_operation_count is not None: - __body[ - "max_write_request_operation_count" - ] = max_write_request_operation_count - if max_write_request_size is not None: - __body["max_write_request_size"] = max_write_request_size if pretty is not None: __query["pretty"] = pretty - if read_poll_timeout is not None: - __body["read_poll_timeout"] = read_poll_timeout + if not __body: + if max_outstanding_read_requests is not None: + __body["max_outstanding_read_requests"] = max_outstanding_read_requests + if max_outstanding_write_requests is not None: + __body[ + "max_outstanding_write_requests" + ] = max_outstanding_write_requests + if max_read_request_operation_count is not None: + __body[ + "max_read_request_operation_count" + ] = max_read_request_operation_count + if max_read_request_size is not None: + __body["max_read_request_size"] = max_read_request_size + if max_retry_delay is not None: + __body["max_retry_delay"] = max_retry_delay + if max_write_buffer_count is not None: + __body["max_write_buffer_count"] = max_write_buffer_count + if max_write_buffer_size is not None: + __body["max_write_buffer_size"] = max_write_buffer_size + if max_write_request_operation_count is not None: + __body[ + "max_write_request_operation_count" + ] = max_write_request_operation_count + if max_write_request_size is not None: + __body["max_write_request_size"] = max_write_request_size + if read_poll_timeout is not None: + __body["read_poll_timeout"] = read_poll_timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/cluster.py b/elasticsearch/_async/client/cluster.py --- a/elasticsearch/_async/client/cluster.py +++ b/elasticsearch/_async/client/cluster.py @@ -25,7 +25,7 @@ class ClusterClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("current_node", "index", "primary", "shard"), ) async def allocation_explain( self, @@ -40,6 +40,7 @@ async def allocation_explain( pretty: t.Optional[bool] = None, primary: t.Optional[bool] = None, shard: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Provides explanations for shard allocations in the cluster. @@ -59,10 +60,8 @@ async def allocation_explain( for. """ __path = "/_cluster/allocation/explain" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if current_node is not None: - __body["current_node"] = current_node + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -73,14 +72,17 @@ async def allocation_explain( __query["include_disk_info"] = include_disk_info if include_yes_decisions is not None: __query["include_yes_decisions"] = include_yes_decisions - if index is not None: - __body["index"] = index if pretty is not None: __query["pretty"] = pretty - if primary is not None: - __body["primary"] = primary - if shard is not None: - __body["shard"] = shard + if not __body: + if current_node is not None: + __body["current_node"] = current_node + if index is not None: + __body["index"] = index + if primary is not None: + __body["primary"] = primary + if shard is not None: + __body["shard"] = shard if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -593,14 +595,14 @@ async def post_voting_config_exclusions( ) @_rewrite_parameters( - body_fields=True, + body_fields=("template", "allow_auto_create", "meta", "version"), parameter_aliases={"_meta": "meta"}, ) async def put_component_template( self, *, name: str, - template: t.Mapping[str, t.Any], + template: t.Optional[t.Mapping[str, t.Any]] = None, allow_auto_create: t.Optional[bool] = None, create: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -612,6 +614,7 @@ async def put_component_template( meta: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a component template @@ -649,15 +652,11 @@ async def put_component_template( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if template is None: + if template is None and body is None: raise ValueError("Empty value passed for parameter 'template'") __path = f"/_component_template/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if template is not None: - __body["template"] = template - if allow_auto_create is not None: - __body["allow_auto_create"] = allow_auto_create + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create if error_trace is not None: @@ -668,19 +667,24 @@ async def put_component_template( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if version is not None: - __body["version"] = version + if not __body: + if template is not None: + __body["template"] = template + if allow_auto_create is not None: + __body["allow_auto_create"] = allow_auto_create + if meta is not None: + __body["_meta"] = meta + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("persistent", "transient"), ) async def put_settings( self, @@ -696,6 +700,7 @@ async def put_settings( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, transient: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the cluster settings. @@ -710,7 +715,7 @@ async def put_settings( """ __path = "/_cluster/settings" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -721,14 +726,15 @@ async def put_settings( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if persistent is not None: - __body["persistent"] = persistent if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout - if transient is not None: - __body["transient"] = transient + if not __body: + if persistent is not None: + __body["persistent"] = persistent + if transient is not None: + __body["transient"] = transient __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -764,7 +770,7 @@ async def remote_info( ) @_rewrite_parameters( - body_fields=True, + body_fields=("commands",), ) async def reroute( self, @@ -782,6 +788,7 @@ async def reroute( pretty: t.Optional[bool] = None, retry_failed: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to manually change the allocation of individual shards in the cluster. @@ -803,10 +810,8 @@ async def reroute( the timeout expires, the request fails and returns an error. """ __path = "/_cluster/reroute" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if commands is not None: - __body["commands"] = commands + __body: t.Dict[str, t.Any] = body if body is not None else {} if dry_run is not None: __query["dry_run"] = dry_run if error_trace is not None: @@ -827,6 +832,9 @@ async def reroute( __query["retry_failed"] = retry_failed if timeout is not None: __query["timeout"] = timeout + if not __body: + if commands is not None: + __body["commands"] = commands if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/enrich.py b/elasticsearch/_async/client/enrich.py --- a/elasticsearch/_async/client/enrich.py +++ b/elasticsearch/_async/client/enrich.py @@ -134,7 +134,7 @@ async def get_policy( ) @_rewrite_parameters( - body_fields=True, + body_fields=("geo_match", "match", "range"), ) async def put_policy( self, @@ -147,6 +147,7 @@ async def put_policy( match: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, range: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new enrich policy. @@ -164,21 +165,22 @@ async def put_policy( raise ValueError("Empty value passed for parameter 'name'") __path = f"/_enrich/policy/{_quote(name)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if geo_match is not None: - __body["geo_match"] = geo_match if human is not None: __query["human"] = human - if match is not None: - __body["match"] = match if pretty is not None: __query["pretty"] = pretty - if range is not None: - __body["range"] = range + if not __body: + if geo_match is not None: + __body["geo_match"] = geo_match + if match is not None: + __body["match"] = match + if range is not None: + __body["range"] = range __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/eql.py b/elasticsearch/_async/client/eql.py --- a/elasticsearch/_async/client/eql.py +++ b/elasticsearch/_async/client/eql.py @@ -145,13 +145,28 @@ async def get_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "query", + "case_sensitive", + "event_category_field", + "fetch_size", + "fields", + "filter", + "keep_alive", + "keep_on_completion", + "result_position", + "runtime_mappings", + "size", + "tiebreaker_field", + "timestamp_field", + "wait_for_completion_timeout", + ), ) async def search( self, *, index: t.Union[str, t.Sequence[str]], - query: str, + query: t.Optional[str] = None, allow_no_indices: t.Optional[bool] = None, case_sensitive: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -185,6 +200,7 @@ async def search( wait_for_completion_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns results matching a query expressed in Event Query Language (EQL) @@ -219,53 +235,54 @@ async def search( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if query is None: + if query is None and body is None: raise ValueError("Empty value passed for parameter 'query'") __path = f"/{_quote(index)}/_eql/search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if query is not None: - __body["query"] = query + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if case_sensitive is not None: - __body["case_sensitive"] = case_sensitive if error_trace is not None: __query["error_trace"] = error_trace - if event_category_field is not None: - __body["event_category_field"] = event_category_field if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if fetch_size is not None: - __body["fetch_size"] = fetch_size - if fields is not None: - __body["fields"] = fields - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if keep_alive is not None: - __body["keep_alive"] = keep_alive - if keep_on_completion is not None: - __body["keep_on_completion"] = keep_on_completion if pretty is not None: __query["pretty"] = pretty - if result_position is not None: - __body["result_position"] = result_position - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if size is not None: - __body["size"] = size - if tiebreaker_field is not None: - __body["tiebreaker_field"] = tiebreaker_field - if timestamp_field is not None: - __body["timestamp_field"] = timestamp_field - if wait_for_completion_timeout is not None: - __body["wait_for_completion_timeout"] = wait_for_completion_timeout + if not __body: + if query is not None: + __body["query"] = query + if case_sensitive is not None: + __body["case_sensitive"] = case_sensitive + if event_category_field is not None: + __body["event_category_field"] = event_category_field + if fetch_size is not None: + __body["fetch_size"] = fetch_size + if fields is not None: + __body["fields"] = fields + if filter is not None: + __body["filter"] = filter + if keep_alive is not None: + __body["keep_alive"] = keep_alive + if keep_on_completion is not None: + __body["keep_on_completion"] = keep_on_completion + if result_position is not None: + __body["result_position"] = result_position + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if size is not None: + __body["size"] = size + if tiebreaker_field is not None: + __body["tiebreaker_field"] = tiebreaker_field + if timestamp_field is not None: + __body["timestamp_field"] = timestamp_field + if wait_for_completion_timeout is not None: + __body["wait_for_completion_timeout"] = wait_for_completion_timeout __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/fleet.py b/elasticsearch/_async/client/fleet.py --- a/elasticsearch/_async/client/fleet.py +++ b/elasticsearch/_async/client/fleet.py @@ -87,7 +87,8 @@ async def global_checkpoints( async def msearch( self, *, - searches: t.Sequence[t.Mapping[str, t.Any]], + searches: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[str] = None, allow_no_indices: t.Optional[bool] = None, allow_partial_search_results: t.Optional[bool] = None, @@ -163,8 +164,12 @@ async def msearch( has become visible for search. Defaults to an empty list which will cause Elasticsearch to immediately execute the search. """ - if searches is None: - raise ValueError("Empty value passed for parameter 'searches'") + if searches is None and body is None: + raise ValueError( + "Empty value passed for parameters 'searches' and 'body', one of them should be set." + ) + elif searches is not None and body is not None: + raise ValueError("Cannot set both 'searches' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_fleet/_fleet_msearch" else: @@ -204,7 +209,7 @@ async def msearch( __query["typed_keys"] = typed_keys if wait_for_checkpoints is not None: __query["wait_for_checkpoints"] = wait_for_checkpoints - __body = searches + __body = searches if searches is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -214,7 +219,40 @@ async def msearch( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "aggs", + "collapse", + "docvalue_fields", + "explain", + "ext", + "fields", + "from_", + "highlight", + "indices_boost", + "min_score", + "pit", + "post_filter", + "profile", + "query", + "rescore", + "runtime_mappings", + "script_fields", + "search_after", + "seq_no_primary_term", + "size", + "slice", + "sort", + "source", + "stats", + "stored_fields", + "suggest", + "terminate_after", + "timeout", + "track_scores", + "track_total_hits", + "version", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -312,6 +350,7 @@ async def search( typed_keys: t.Optional[bool] = None, version: t.Optional[bool] = None, wait_for_checkpoints: t.Optional[t.Sequence[int]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Search API where the search will only be executed after specified checkpoints @@ -421,8 +460,8 @@ async def search( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_fleet/_fleet_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -434,10 +473,6 @@ async def search( ): __query["sort"] = sort sort = None - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if allow_partial_search_results is not None: @@ -450,100 +485,50 @@ async def search( __query["batched_reduce_size"] = batched_reduce_size if ccs_minimize_roundtrips is not None: __query["ccs_minimize_roundtrips"] = ccs_minimize_roundtrips - if collapse is not None: - __body["collapse"] = collapse if default_operator is not None: __query["default_operator"] = default_operator if df is not None: __query["df"] = df - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain - if ext is not None: - __body["ext"] = ext - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ - if highlight is not None: - __body["highlight"] = highlight if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indices_boost is not None: - __body["indices_boost"] = indices_boost if lenient is not None: __query["lenient"] = lenient if max_concurrent_shard_requests is not None: __query["max_concurrent_shard_requests"] = max_concurrent_shard_requests if min_compatible_shard_node is not None: __query["min_compatible_shard_node"] = min_compatible_shard_node - if min_score is not None: - __body["min_score"] = min_score - if pit is not None: - __body["pit"] = pit - if post_filter is not None: - __body["post_filter"] = post_filter if pre_filter_shard_size is not None: __query["pre_filter_shard_size"] = pre_filter_shard_size if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if request_cache is not None: __query["request_cache"] = request_cache - if rescore is not None: - __body["rescore"] = rescore if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: __query["routing"] = routing - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields if scroll is not None: __query["scroll"] = scroll - if search_after is not None: - __body["search_after"] = search_after if search_type is not None: __query["search_type"] = search_type - if seq_no_primary_term is not None: - __body["seq_no_primary_term"] = seq_no_primary_term - if size is not None: - __body["size"] = size - if slice is not None: - __body["slice"] = slice - if sort is not None: - __body["sort"] = sort - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes - if stats is not None: - __body["stats"] = stats - if stored_fields is not None: - __body["stored_fields"] = stored_fields - if suggest is not None: - __body["suggest"] = suggest if suggest_field is not None: __query["suggest_field"] = suggest_field if suggest_mode is not None: @@ -552,20 +537,75 @@ async def search( __query["suggest_size"] = suggest_size if suggest_text is not None: __query["suggest_text"] = suggest_text - if terminate_after is not None: - __body["terminate_after"] = terminate_after - if timeout is not None: - __body["timeout"] = timeout - if track_scores is not None: - __body["track_scores"] = track_scores - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits if typed_keys is not None: __query["typed_keys"] = typed_keys - if version is not None: - __body["version"] = version if wait_for_checkpoints is not None: __query["wait_for_checkpoints"] = wait_for_checkpoints + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if collapse is not None: + __body["collapse"] = collapse + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if explain is not None: + __body["explain"] = explain + if ext is not None: + __body["ext"] = ext + if fields is not None: + __body["fields"] = fields + if from_ is not None: + __body["from"] = from_ + if highlight is not None: + __body["highlight"] = highlight + if indices_boost is not None: + __body["indices_boost"] = indices_boost + if min_score is not None: + __body["min_score"] = min_score + if pit is not None: + __body["pit"] = pit + if post_filter is not None: + __body["post_filter"] = post_filter + if profile is not None: + __body["profile"] = profile + if query is not None: + __body["query"] = query + if rescore is not None: + __body["rescore"] = rescore + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if search_after is not None: + __body["search_after"] = search_after + if seq_no_primary_term is not None: + __body["seq_no_primary_term"] = seq_no_primary_term + if size is not None: + __body["size"] = size + if slice is not None: + __body["slice"] = slice + if sort is not None: + __body["sort"] = sort + if source is not None: + __body["_source"] = source + if stats is not None: + __body["stats"] = stats + if stored_fields is not None: + __body["stored_fields"] = stored_fields + if suggest is not None: + __body["suggest"] = suggest + if terminate_after is not None: + __body["terminate_after"] = terminate_after + if timeout is not None: + __body["timeout"] = timeout + if track_scores is not None: + __body["track_scores"] = track_scores + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/graph.py b/elasticsearch/_async/client/graph.py --- a/elasticsearch/_async/client/graph.py +++ b/elasticsearch/_async/client/graph.py @@ -25,7 +25,7 @@ class GraphClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("connections", "controls", "query", "vertices"), ) async def explore( self, @@ -41,6 +41,7 @@ async def explore( routing: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, vertices: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Explore extracted and summarized information about the documents and terms in @@ -64,12 +65,8 @@ async def explore( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_graph/explore" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if connections is not None: - __body["connections"] = connections - if controls is not None: - __body["controls"] = controls + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -78,14 +75,19 @@ async def explore( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query if routing is not None: __query["routing"] = routing if timeout is not None: __query["timeout"] = timeout - if vertices is not None: - __body["vertices"] = vertices + if not __body: + if connections is not None: + __body["connections"] = connections + if controls is not None: + __body["controls"] = controls + if query is not None: + __body["query"] = query + if vertices is not None: + __body["vertices"] = vertices if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/ilm.py b/elasticsearch/_async/client/ilm.py --- a/elasticsearch/_async/client/ilm.py +++ b/elasticsearch/_async/client/ilm.py @@ -212,7 +212,7 @@ async def get_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=("legacy_template_to_delete", "node_attribute"), ) async def migrate_to_data_tiers( self, @@ -224,6 +224,7 @@ async def migrate_to_data_tiers( legacy_template_to_delete: t.Optional[str] = None, node_attribute: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Migrates the indices and ILM policies away from custom node attribute allocation @@ -239,7 +240,7 @@ async def migrate_to_data_tiers( """ __path = "/_ilm/migrate_to_data_tiers" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if dry_run is not None: __query["dry_run"] = dry_run if error_trace is not None: @@ -248,12 +249,13 @@ async def migrate_to_data_tiers( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if legacy_template_to_delete is not None: - __body["legacy_template_to_delete"] = legacy_template_to_delete - if node_attribute is not None: - __body["node_attribute"] = node_attribute if pretty is not None: __query["pretty"] = pretty + if not __body: + if legacy_template_to_delete is not None: + __body["legacy_template_to_delete"] = legacy_template_to_delete + if node_attribute is not None: + __body["node_attribute"] = node_attribute if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -264,7 +266,7 @@ async def migrate_to_data_tiers( ) @_rewrite_parameters( - body_fields=True, + body_fields=("current_step", "next_step"), ) async def move_to_step( self, @@ -276,6 +278,7 @@ async def move_to_step( human: t.Optional[bool] = None, next_step: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Manually moves an index into the specified step and executes that step. @@ -289,20 +292,21 @@ async def move_to_step( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/_ilm/move/{_quote(index)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if current_step is not None: - __body["current_step"] = current_step + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if next_step is not None: - __body["next_step"] = next_step if pretty is not None: __query["pretty"] = pretty + if not __body: + if current_step is not None: + __body["current_step"] = current_step + if next_step is not None: + __body["next_step"] = next_step if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -313,7 +317,7 @@ async def move_to_step( ) @_rewrite_parameters( - body_fields=True, + body_fields=("policy",), ) async def put_lifecycle( self, @@ -328,6 +332,7 @@ async def put_lifecycle( policy: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a lifecycle policy @@ -346,7 +351,7 @@ async def put_lifecycle( raise ValueError("Empty value passed for parameter 'name'") __path = f"/_ilm/policy/{_quote(name)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -355,12 +360,13 @@ async def put_lifecycle( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if policy is not None: - __body["policy"] = policy if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if policy is not None: + __body["policy"] = policy if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/indices.py b/elasticsearch/_async/client/indices.py --- a/elasticsearch/_async/client/indices.py +++ b/elasticsearch/_async/client/indices.py @@ -96,7 +96,17 @@ async def add_block( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analyzer", + "attributes", + "char_filter", + "explain", + "field", + "filter", + "normalizer", + "text", + "tokenizer", + ), ) async def analyze( self, @@ -115,6 +125,7 @@ async def analyze( pretty: t.Optional[bool] = None, text: t.Optional[t.Union[str, t.Sequence[str]]] = None, tokenizer: t.Optional[t.Union[str, t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Performs the analysis process on a text and return the tokens breakdown of the @@ -147,34 +158,35 @@ async def analyze( __path = f"/{_quote(index)}/_analyze" else: __path = "/_analyze" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analyzer is not None: - __body["analyzer"] = analyzer - if attributes is not None: - __body["attributes"] = attributes - if char_filter is not None: - __body["char_filter"] = char_filter + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if explain is not None: - __body["explain"] = explain - if field is not None: - __body["field"] = field - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if normalizer is not None: - __body["normalizer"] = normalizer if pretty is not None: __query["pretty"] = pretty - if text is not None: - __body["text"] = text - if tokenizer is not None: - __body["tokenizer"] = tokenizer + if not __body: + if analyzer is not None: + __body["analyzer"] = analyzer + if attributes is not None: + __body["attributes"] = attributes + if char_filter is not None: + __body["char_filter"] = char_filter + if explain is not None: + __body["explain"] = explain + if field is not None: + __body["field"] = field + if filter is not None: + __body["filter"] = filter + if normalizer is not None: + __body["normalizer"] = normalizer + if text is not None: + __body["text"] = text + if tokenizer is not None: + __body["tokenizer"] = tokenizer if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -265,7 +277,7 @@ async def clear_cache( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "settings"), ) async def clone( self, @@ -285,6 +297,7 @@ async def clone( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Clones an index @@ -309,10 +322,8 @@ async def clone( if target in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target'") __path = f"/{_quote(index)}/_clone/{_quote(target)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -323,12 +334,15 @@ async def clone( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -420,7 +434,7 @@ async def close( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "mappings", "settings"), ) async def create( self, @@ -440,6 +454,7 @@ async def create( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an index with optional settings and mappings. @@ -463,28 +478,29 @@ async def create( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if mappings is not None: - __body["mappings"] = mappings if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if mappings is not None: + __body["mappings"] = mappings + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -991,7 +1007,8 @@ async def downsample( *, index: str, target_index: str, - config: t.Mapping[str, t.Any], + config: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -1010,8 +1027,12 @@ async def downsample( raise ValueError("Empty value passed for parameter 'index'") if target_index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target_index'") - if config is None: - raise ValueError("Empty value passed for parameter 'config'") + if config is None and body is None: + raise ValueError( + "Empty value passed for parameters 'config' and 'body', one of them should be set." + ) + elif config is not None and body is not None: + raise ValueError("Cannot set both 'config' and 'body'") __path = f"/{_quote(index)}/_downsample/{_quote(target_index)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -1022,7 +1043,7 @@ async def downsample( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = config + __body = config if config is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2220,16 +2241,17 @@ async def migrate_to_data_stream( ) @_rewrite_parameters( - body_fields=True, + body_fields=("actions",), ) async def modify_data_stream( self, *, - actions: t.Sequence[t.Mapping[str, t.Any]], + actions: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Modifies a data stream @@ -2238,13 +2260,11 @@ async def modify_data_stream( :param actions: Actions to perform. """ - if actions is None: + if actions is None and body is None: raise ValueError("Empty value passed for parameter 'actions'") __path = "/_data_stream/_modify" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if actions is not None: - __body["actions"] = actions + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2253,6 +2273,9 @@ async def modify_data_stream( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if actions is not None: + __body["actions"] = actions __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2379,7 +2402,13 @@ async def promote_data_stream( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "filter", + "index_routing", + "is_write_index", + "routing", + "search_routing", + ), ) async def put_alias( self, @@ -2399,6 +2428,7 @@ async def put_alias( routing: t.Optional[str] = None, search_routing: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates an alias. @@ -2437,29 +2467,30 @@ async def put_alias( raise ValueError("Empty value passed for parameter 'name'") __path = f"/{_quote(index)}/_alias/{_quote(name)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index_routing is not None: - __body["index_routing"] = index_routing - if is_write_index is not None: - __body["is_write_index"] = is_write_index if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if routing is not None: - __body["routing"] = routing - if search_routing is not None: - __body["search_routing"] = search_routing if timeout is not None: __query["timeout"] = timeout + if not __body: + if filter is not None: + __body["filter"] = filter + if index_routing is not None: + __body["index_routing"] = index_routing + if is_write_index is not None: + __body["is_write_index"] = is_write_index + if routing is not None: + __body["routing"] = routing + if search_routing is not None: + __body["search_routing"] = search_routing if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2470,7 +2501,7 @@ async def put_alias( ) @_rewrite_parameters( - body_fields=True, + body_fields=("data_retention", "downsampling"), ) async def put_data_lifecycle( self, @@ -2496,6 +2527,7 @@ async def put_data_lifecycle( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the data stream lifecycle of the selected data streams. @@ -2523,12 +2555,8 @@ async def put_data_lifecycle( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_data_stream/{_quote(name)}/_lifecycle" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if data_retention is not None: - __body["data_retention"] = data_retention - if downsampling is not None: - __body["downsampling"] = downsampling + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: @@ -2543,6 +2571,11 @@ async def put_data_lifecycle( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if data_retention is not None: + __body["data_retention"] = data_retention + if downsampling is not None: + __body["downsampling"] = downsampling if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2553,7 +2586,15 @@ async def put_data_lifecycle( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "composed_of", + "data_stream", + "index_patterns", + "meta", + "priority", + "template", + "version", + ), parameter_aliases={"_meta": "meta"}, ) async def put_index_template( @@ -2572,6 +2613,7 @@ async def put_index_template( priority: t.Optional[int] = None, template: t.Optional[t.Mapping[str, t.Any]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates an index template. @@ -2603,39 +2645,52 @@ async def put_index_template( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_index_template/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if composed_of is not None: - __body["composed_of"] = composed_of + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create - if data_stream is not None: - __body["data_stream"] = data_stream if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index_patterns is not None: - __body["index_patterns"] = index_patterns - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if priority is not None: - __body["priority"] = priority - if template is not None: - __body["template"] = template - if version is not None: - __body["version"] = version + if not __body: + if composed_of is not None: + __body["composed_of"] = composed_of + if data_stream is not None: + __body["data_stream"] = data_stream + if index_patterns is not None: + __body["index_patterns"] = index_patterns + if meta is not None: + __body["_meta"] = meta + if priority is not None: + __body["priority"] = priority + if template is not None: + __body["template"] = template + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "date_detection", + "dynamic", + "dynamic_date_formats", + "dynamic_templates", + "field_names", + "meta", + "numeric_detection", + "properties", + "routing", + "runtime", + "source", + ), parameter_aliases={ "_field_names": "field_names", "_meta": "meta", @@ -2684,6 +2739,7 @@ async def put_mapping( source: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, write_index_only: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the index mappings. @@ -2730,23 +2786,13 @@ async def put_mapping( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_mapping" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if date_detection is not None: - __body["date_detection"] = date_detection - if dynamic is not None: - __body["dynamic"] = dynamic - if dynamic_date_formats is not None: - __body["dynamic_date_formats"] = dynamic_date_formats - if dynamic_templates is not None: - __body["dynamic_templates"] = dynamic_templates if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if field_names is not None: - __body["_field_names"] = field_names if filter_path is not None: __query["filter_path"] = filter_path if human is not None: @@ -2755,24 +2801,35 @@ async def put_mapping( __query["ignore_unavailable"] = ignore_unavailable if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta - if numeric_detection is not None: - __body["numeric_detection"] = numeric_detection if pretty is not None: __query["pretty"] = pretty - if properties is not None: - __body["properties"] = properties - if routing is not None: - __body["_routing"] = routing - if runtime is not None: - __body["runtime"] = runtime - if source is not None: - __body["_source"] = source if timeout is not None: __query["timeout"] = timeout if write_index_only is not None: __query["write_index_only"] = write_index_only + if not __body: + if date_detection is not None: + __body["date_detection"] = date_detection + if dynamic is not None: + __body["dynamic"] = dynamic + if dynamic_date_formats is not None: + __body["dynamic_date_formats"] = dynamic_date_formats + if dynamic_templates is not None: + __body["dynamic_templates"] = dynamic_templates + if field_names is not None: + __body["_field_names"] = field_names + if meta is not None: + __body["_meta"] = meta + if numeric_detection is not None: + __body["numeric_detection"] = numeric_detection + if properties is not None: + __body["properties"] = properties + if routing is not None: + __body["_routing"] = routing + if runtime is not None: + __body["runtime"] = runtime + if source is not None: + __body["_source"] = source __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -2784,7 +2841,8 @@ async def put_mapping( async def put_settings( self, *, - settings: t.Mapping[str, t.Any], + settings: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_indices: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -2834,8 +2892,12 @@ async def put_settings( :param timeout: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error. """ - if settings is None: - raise ValueError("Empty value passed for parameter 'settings'") + if settings is None and body is None: + raise ValueError( + "Empty value passed for parameters 'settings' and 'body', one of them should be set." + ) + elif settings is not None and body is not None: + raise ValueError("Cannot set both 'settings' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_settings" else: @@ -2863,14 +2925,21 @@ async def put_settings( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout - __body = settings + __body = settings if settings is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aliases", + "index_patterns", + "mappings", + "order", + "settings", + "version", + ), ) async def put_template( self, @@ -2892,6 +2961,7 @@ async def put_template( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates an index template. @@ -2922,10 +2992,8 @@ async def put_template( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_template/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create if error_trace is not None: @@ -2936,22 +3004,25 @@ async def put_template( __query["flat_settings"] = flat_settings if human is not None: __query["human"] = human - if index_patterns is not None: - __body["index_patterns"] = index_patterns - if mappings is not None: - __body["mappings"] = mappings if master_timeout is not None: __query["master_timeout"] = master_timeout - if order is not None: - __body["order"] = order if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout - if version is not None: - __body["version"] = version + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if index_patterns is not None: + __body["index_patterns"] = index_patterns + if mappings is not None: + __body["mappings"] = mappings + if order is not None: + __body["order"] = order + if settings is not None: + __body["settings"] = settings + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -3173,7 +3244,7 @@ async def resolve_index( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "conditions", "mappings", "settings"), ) async def rollover( self, @@ -3196,6 +3267,7 @@ async def rollover( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates an alias to point to a new index when the existing index is considered @@ -3237,12 +3309,8 @@ async def rollover( __path = f"/{_quote(alias)}/_rollover" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases - if conditions is not None: - __body["conditions"] = conditions + __body: t.Dict[str, t.Any] = body if body is not None else {} if dry_run is not None: __query["dry_run"] = dry_run if error_trace is not None: @@ -3251,18 +3319,23 @@ async def rollover( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if mappings is not None: - __body["mappings"] = mappings if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if conditions is not None: + __body["conditions"] = conditions + if mappings is not None: + __body["mappings"] = mappings + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3407,7 +3480,7 @@ async def shard_stores( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "settings"), ) async def shrink( self, @@ -3427,6 +3500,7 @@ async def shrink( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allow to shrink an existing index into a new index with fewer primary shards. @@ -3451,10 +3525,8 @@ async def shrink( if target in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target'") __path = f"/{_quote(index)}/_shrink/{_quote(target)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3465,12 +3537,15 @@ async def shrink( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3481,7 +3556,16 @@ async def shrink( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_auto_create", + "composed_of", + "data_stream", + "index_patterns", + "meta", + "priority", + "template", + "version", + ), parameter_aliases={"_meta": "meta"}, ) async def simulate_index_template( @@ -3505,6 +3589,7 @@ async def simulate_index_template( priority: t.Optional[int] = None, template: t.Optional[t.Mapping[str, t.Any]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Simulate matching the given index name against the index templates in the system @@ -3550,16 +3635,10 @@ async def simulate_index_template( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_index_template/_simulate_index/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_auto_create is not None: - __body["allow_auto_create"] = allow_auto_create - if composed_of is not None: - __body["composed_of"] = composed_of + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create - if data_stream is not None: - __body["data_stream"] = data_stream if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3568,20 +3647,27 @@ async def simulate_index_template( __query["human"] = human if include_defaults is not None: __query["include_defaults"] = include_defaults - if index_patterns is not None: - __body["index_patterns"] = index_patterns if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if priority is not None: - __body["priority"] = priority - if template is not None: - __body["template"] = template - if version is not None: - __body["version"] = version + if not __body: + if allow_auto_create is not None: + __body["allow_auto_create"] = allow_auto_create + if composed_of is not None: + __body["composed_of"] = composed_of + if data_stream is not None: + __body["data_stream"] = data_stream + if index_patterns is not None: + __body["index_patterns"] = index_patterns + if meta is not None: + __body["_meta"] = meta + if priority is not None: + __body["priority"] = priority + if template is not None: + __body["template"] = template + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3608,6 +3694,7 @@ async def simulate_template( ] = None, pretty: t.Optional[bool] = None, template: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Simulate resolving the given template name or body @@ -3628,6 +3715,12 @@ async def simulate_template( returns an error. :param template: """ + if template is None and body is None: + raise ValueError( + "Empty value passed for parameters 'template' and 'body', one of them should be set." + ) + elif template is not None and body is not None: + raise ValueError("Cannot set both 'template' and 'body'") if name not in SKIP_IN_PATH: __path = f"/_index_template/_simulate/{_quote(name)}" else: @@ -3647,7 +3740,7 @@ async def simulate_template( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - __body = template + __body = template if template is not None else body if not __body: __body = None __headers = {"accept": "application/json"} @@ -3658,7 +3751,7 @@ async def simulate_template( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "settings"), ) async def split( self, @@ -3678,6 +3771,7 @@ async def split( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows you to split an existing index into a new index with more primary shards. @@ -3702,10 +3796,8 @@ async def split( if target in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target'") __path = f"/{_quote(index)}/_split/{_quote(target)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3716,12 +3808,15 @@ async def split( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3910,7 +4005,7 @@ async def unfreeze( ) @_rewrite_parameters( - body_fields=True, + body_fields=("actions",), ) async def update_aliases( self, @@ -3924,6 +4019,7 @@ async def update_aliases( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates index aliases. @@ -3938,10 +4034,8 @@ async def update_aliases( the timeout expires, the request fails and returns an error. """ __path = "/_aliases" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if actions is not None: - __body["actions"] = actions + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3954,13 +4048,16 @@ async def update_aliases( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if actions is not None: + __body["actions"] = actions __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("query",), ) async def validate_query( self, @@ -3990,6 +4087,7 @@ async def validate_query( q: t.Optional[str] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, rewrite: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows a user to validate a potentially expensive query without executing it. @@ -4032,7 +4130,7 @@ async def validate_query( else: __path = "/_validate/query" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if all_shards is not None: __query["all_shards"] = all_shards if allow_no_indices is not None: @@ -4063,10 +4161,11 @@ async def validate_query( __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if rewrite is not None: __query["rewrite"] = rewrite + if not __body: + if query is not None: + __body["query"] = query if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/ingest.py b/elasticsearch/_async/client/ingest.py --- a/elasticsearch/_async/client/ingest.py +++ b/elasticsearch/_async/client/ingest.py @@ -179,7 +179,7 @@ async def processor_grok( ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "meta", "on_failure", "processors", "version"), parameter_aliases={"_meta": "meta"}, ) async def put_pipeline( @@ -200,6 +200,7 @@ async def put_pipeline( processors: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a pipeline. @@ -232,10 +233,8 @@ async def put_pipeline( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/_ingest/pipeline/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -246,25 +245,28 @@ async def put_pipeline( __query["if_version"] = if_version if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta - if on_failure is not None: - __body["on_failure"] = on_failure if pretty is not None: __query["pretty"] = pretty - if processors is not None: - __body["processors"] = processors if timeout is not None: __query["timeout"] = timeout - if version is not None: - __body["version"] = version + if not __body: + if description is not None: + __body["description"] = description + if meta is not None: + __body["_meta"] = meta + if on_failure is not None: + __body["on_failure"] = on_failure + if processors is not None: + __body["processors"] = processors + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "pipeline"), ) async def simulate( self, @@ -277,6 +279,7 @@ async def simulate( pipeline: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, verbose: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to simulate a pipeline with example documents. @@ -296,22 +299,23 @@ async def simulate( __path = f"/_ingest/pipeline/{_quote(id)}/_simulate" else: __path = "/_ingest/pipeline/_simulate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if pipeline is not None: - __body["pipeline"] = pipeline if pretty is not None: __query["pretty"] = pretty if verbose is not None: __query["verbose"] = verbose + if not __body: + if docs is not None: + __body["docs"] = docs + if pipeline is not None: + __body["pipeline"] = pipeline __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/license.py b/elasticsearch/_async/client/license.py --- a/elasticsearch/_async/client/license.py +++ b/elasticsearch/_async/client/license.py @@ -154,7 +154,7 @@ async def get_trial_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=("license", "licenses"), ) async def post( self, @@ -166,6 +166,7 @@ async def post( license: t.Optional[t.Mapping[str, t.Any]] = None, licenses: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the license for the cluster. @@ -179,7 +180,7 @@ async def post( """ __path = "/_license" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if acknowledge is not None: __query["acknowledge"] = acknowledge if error_trace is not None: @@ -188,12 +189,13 @@ async def post( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if license is not None: - __body["license"] = license - if licenses is not None: - __body["licenses"] = licenses if pretty is not None: __query["pretty"] = pretty + if not __body: + if license is not None: + __body["license"] = license + if licenses is not None: + __body["licenses"] = licenses if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/logstash.py b/elasticsearch/_async/client/logstash.py --- a/elasticsearch/_async/client/logstash.py +++ b/elasticsearch/_async/client/logstash.py @@ -100,7 +100,8 @@ async def put_pipeline( self, *, id: str, - pipeline: t.Mapping[str, t.Any], + pipeline: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -116,8 +117,12 @@ async def put_pipeline( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if pipeline is None: - raise ValueError("Empty value passed for parameter 'pipeline'") + if pipeline is None and body is None: + raise ValueError( + "Empty value passed for parameters 'pipeline' and 'body', one of them should be set." + ) + elif pipeline is not None and body is not None: + raise ValueError("Cannot set both 'pipeline' and 'body'") __path = f"/_logstash/pipeline/{_quote(id)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -128,7 +133,7 @@ async def put_pipeline( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = pipeline + __body = pipeline if pipeline is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/ml.py b/elasticsearch/_async/client/ml.py --- a/elasticsearch/_async/client/ml.py +++ b/elasticsearch/_async/client/ml.py @@ -59,7 +59,7 @@ async def clear_trained_model_deployment_cache( ) @_rewrite_parameters( - body_fields=True, + body_fields=("allow_no_match", "force", "timeout"), ) async def close_job( self, @@ -72,6 +72,7 @@ async def close_job( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Closes one or more anomaly detection jobs. A job can be opened and closed multiple @@ -92,22 +93,23 @@ async def close_job( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_close" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_no_match is not None: - __body["allow_no_match"] = allow_no_match + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if force is not None: - __body["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if allow_no_match is not None: + __body["allow_no_match"] = allow_no_match + if force is not None: + __body["force"] = force + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -315,7 +317,7 @@ async def delete_datafeed( ) @_rewrite_parameters( - body_fields=True, + body_fields=("requests_per_second", "timeout"), ) async def delete_expired_data( self, @@ -327,6 +329,7 @@ async def delete_expired_data( pretty: t.Optional[bool] = None, requests_per_second: t.Optional[float] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Deletes expired and unused machine learning data. @@ -345,7 +348,7 @@ async def delete_expired_data( else: __path = "/_ml/_delete_expired_data" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -354,10 +357,11 @@ async def delete_expired_data( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if requests_per_second is not None: - __body["requests_per_second"] = requests_per_second - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if requests_per_second is not None: + __body["requests_per_second"] = requests_per_second + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -624,7 +628,11 @@ async def delete_trained_model_alias( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis_config", + "max_bucket_cardinality", + "overall_cardinality", + ), ) async def estimate_model_memory( self, @@ -636,6 +644,7 @@ async def estimate_model_memory( max_bucket_cardinality: t.Optional[t.Mapping[str, int]] = None, overall_cardinality: t.Optional[t.Mapping[str, int]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Estimates the model memory @@ -658,40 +667,42 @@ async def estimate_model_memory( or `partition_field_name`. """ __path = "/_ml/anomaly_detectors/_estimate_model_memory" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis_config is not None: - __body["analysis_config"] = analysis_config + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_bucket_cardinality is not None: - __body["max_bucket_cardinality"] = max_bucket_cardinality - if overall_cardinality is not None: - __body["overall_cardinality"] = overall_cardinality if pretty is not None: __query["pretty"] = pretty + if not __body: + if analysis_config is not None: + __body["analysis_config"] = analysis_config + if max_bucket_cardinality is not None: + __body["max_bucket_cardinality"] = max_bucket_cardinality + if overall_cardinality is not None: + __body["overall_cardinality"] = overall_cardinality __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("evaluation", "index", "query"), ) async def evaluate_data_frame( self, *, - evaluation: t.Mapping[str, t.Any], - index: str, + evaluation: t.Optional[t.Mapping[str, t.Any]] = None, + index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Evaluates the data frame analytics for an annotated index. @@ -703,17 +714,13 @@ async def evaluate_data_frame( :param query: A query clause that retrieves a subset of data from the source index. """ - if evaluation is None: + if evaluation is None and body is None: raise ValueError("Empty value passed for parameter 'evaluation'") - if index is None: + if index is None and body is None: raise ValueError("Empty value passed for parameter 'index'") __path = "/_ml/data_frame/_evaluate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if evaluation is not None: - __body["evaluation"] = evaluation - if index is not None: - __body["index"] = index + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -722,15 +729,29 @@ async def evaluate_data_frame( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query + if not __body: + if evaluation is not None: + __body["evaluation"] = evaluation + if index is not None: + __body["index"] = index + if query is not None: + __body["query"] = query __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_lazy_start", + "analysis", + "analyzed_fields", + "description", + "dest", + "max_num_threads", + "model_memory_limit", + "source", + ), ) async def explain_data_frame_analytics( self, @@ -748,6 +769,7 @@ async def explain_data_frame_analytics( model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, source: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Explains a data frame analytics config. @@ -786,32 +808,33 @@ async def explain_data_frame_analytics( __path = f"/_ml/data_frame/analytics/{_quote(id)}/_explain" else: __path = "/_ml/data_frame/analytics/_explain" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_lazy_start is not None: - __body["allow_lazy_start"] = allow_lazy_start - if analysis is not None: - __body["analysis"] = analysis - if analyzed_fields is not None: - __body["analyzed_fields"] = analyzed_fields - if description is not None: - __body["description"] = description - if dest is not None: - __body["dest"] = dest + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_num_threads is not None: - __body["max_num_threads"] = max_num_threads - if model_memory_limit is not None: - __body["model_memory_limit"] = model_memory_limit if pretty is not None: __query["pretty"] = pretty - if source is not None: - __body["source"] = source + if not __body: + if allow_lazy_start is not None: + __body["allow_lazy_start"] = allow_lazy_start + if analysis is not None: + __body["analysis"] = analysis + if analyzed_fields is not None: + __body["analyzed_fields"] = analyzed_fields + if description is not None: + __body["description"] = description + if dest is not None: + __body["dest"] = dest + if max_num_threads is not None: + __body["max_num_threads"] = max_num_threads + if model_memory_limit is not None: + __body["model_memory_limit"] = model_memory_limit + if source is not None: + __body["source"] = source if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -822,7 +845,7 @@ async def explain_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("advance_time", "calc_interim", "end", "skip_time", "start"), ) async def flush_job( self, @@ -837,6 +860,7 @@ async def flush_job( pretty: t.Optional[bool] = None, skip_time: t.Optional[t.Union[str, t.Any]] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Forces any buffered data to be processed by the job. @@ -853,14 +877,8 @@ async def flush_job( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_flush" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if advance_time is not None: - __body["advance_time"] = advance_time - if calc_interim is not None: - __body["calc_interim"] = calc_interim - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -869,10 +887,17 @@ async def flush_job( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if skip_time is not None: - __body["skip_time"] = skip_time - if start is not None: - __body["start"] = start + if not __body: + if advance_time is not None: + __body["advance_time"] = advance_time + if calc_interim is not None: + __body["calc_interim"] = calc_interim + if end is not None: + __body["end"] = end + if skip_time is not None: + __body["skip_time"] = skip_time + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -883,7 +908,7 @@ async def flush_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=("duration", "expires_in", "max_model_memory"), ) async def forecast( self, @@ -896,6 +921,7 @@ async def forecast( human: t.Optional[bool] = None, max_model_memory: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Predicts the future behavior of a time series by using its historical behavior. @@ -912,22 +938,23 @@ async def forecast( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_forecast" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if duration is not None: - __body["duration"] = duration + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if expires_in is not None: - __body["expires_in"] = expires_in if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_model_memory is not None: - __body["max_model_memory"] = max_model_memory if pretty is not None: __query["pretty"] = pretty + if not __body: + if duration is not None: + __body["duration"] = duration + if expires_in is not None: + __body["expires_in"] = expires_in + if max_model_memory is not None: + __body["max_model_memory"] = max_model_memory if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -938,7 +965,16 @@ async def forecast( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "anomaly_score", + "desc", + "end", + "exclude_interim", + "expand", + "page", + "sort", + "start", + ), parameter_aliases={"from": "from_"}, ) async def get_buckets( @@ -960,6 +996,7 @@ async def get_buckets( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly detection job results for one or more buckets. @@ -990,36 +1027,37 @@ async def get_buckets( __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/buckets" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if anomaly_score is not None: - __body["anomaly_score"] = anomaly_score - if desc is not None: - __body["desc"] = desc - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if exclude_interim is not None: - __body["exclude_interim"] = exclude_interim - if expand is not None: - __body["expand"] = expand if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size - if sort is not None: - __body["sort"] = sort - if start is not None: - __body["start"] = start + if not __body: + if anomaly_score is not None: + __body["anomaly_score"] = anomaly_score + if desc is not None: + __body["desc"] = desc + if end is not None: + __body["end"] = end + if exclude_interim is not None: + __body["exclude_interim"] = exclude_interim + if expand is not None: + __body["expand"] = expand + if page is not None: + __body["page"] = page + if sort is not None: + __body["sort"] = sort + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1090,7 +1128,7 @@ async def get_calendar_events( ) @_rewrite_parameters( - body_fields=True, + body_fields=("page",), parameter_aliases={"from": "from_"}, ) async def get_calendars( @@ -1104,6 +1142,7 @@ async def get_calendars( page: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves configuration information for calendars. @@ -1125,7 +1164,7 @@ async def get_calendars( else: __path = "/_ml/calendars" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1134,12 +1173,13 @@ async def get_calendars( __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size + if not __body: + if page is not None: + __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1150,7 +1190,7 @@ async def get_calendars( ) @_rewrite_parameters( - body_fields=True, + body_fields=("page",), parameter_aliases={"from": "from_"}, ) async def get_categories( @@ -1166,6 +1206,7 @@ async def get_categories( partition_field_value: t.Optional[str] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly detection job results for one or more categories. @@ -1192,7 +1233,7 @@ async def get_categories( else: raise ValueError("Couldn't find a path for the given parameters") __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1201,14 +1242,15 @@ async def get_categories( __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if partition_field_value is not None: __query["partition_field_value"] = partition_field_value if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size + if not __body: + if page is not None: + __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1490,7 +1532,7 @@ async def get_filters( ) @_rewrite_parameters( - body_fields=True, + body_fields=("page",), parameter_aliases={"from": "from_"}, ) async def get_influencers( @@ -1510,6 +1552,7 @@ async def get_influencers( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly detection job results for one or more influencers. @@ -1537,7 +1580,7 @@ async def get_influencers( raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/influencers" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if desc is not None: __query["desc"] = desc if end is not None: @@ -1554,8 +1597,6 @@ async def get_influencers( __query["human"] = human if influencer_score is not None: __query["influencer_score"] = influencer_score - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: @@ -1564,6 +1605,9 @@ async def get_influencers( __query["sort"] = sort if start is not None: __query["start"] = start + if not __body: + if page is not None: + __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1776,7 +1820,7 @@ async def get_model_snapshot_upgrade_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=("desc", "end", "page", "sort", "start"), parameter_aliases={"from": "from_"}, ) async def get_model_snapshots( @@ -1795,6 +1839,7 @@ async def get_model_snapshots( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves information about model snapshots. @@ -1823,12 +1868,8 @@ async def get_model_snapshots( __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/model_snapshots" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if desc is not None: - __body["desc"] = desc - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1837,16 +1878,21 @@ async def get_model_snapshots( __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size - if sort is not None: - __body["sort"] = sort - if start is not None: - __body["start"] = start + if not __body: + if desc is not None: + __body["desc"] = desc + if end is not None: + __body["end"] = end + if page is not None: + __body["page"] = page + if sort is not None: + __body["sort"] = sort + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1857,7 +1903,15 @@ async def get_model_snapshots( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_no_match", + "bucket_span", + "end", + "exclude_interim", + "overall_score", + "start", + "top_n", + ), ) async def get_overall_buckets( self, @@ -1874,6 +1928,7 @@ async def get_overall_buckets( pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, top_n: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves overall bucket results that summarize the bucket results of multiple @@ -1899,30 +1954,31 @@ async def get_overall_buckets( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/overall_buckets" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_no_match is not None: - __body["allow_no_match"] = allow_no_match - if bucket_span is not None: - __body["bucket_span"] = bucket_span - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if exclude_interim is not None: - __body["exclude_interim"] = exclude_interim if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if overall_score is not None: - __body["overall_score"] = overall_score if pretty is not None: __query["pretty"] = pretty - if start is not None: - __body["start"] = start - if top_n is not None: - __body["top_n"] = top_n + if not __body: + if allow_no_match is not None: + __body["allow_no_match"] = allow_no_match + if bucket_span is not None: + __body["bucket_span"] = bucket_span + if end is not None: + __body["end"] = end + if exclude_interim is not None: + __body["exclude_interim"] = exclude_interim + if overall_score is not None: + __body["overall_score"] = overall_score + if start is not None: + __body["start"] = start + if top_n is not None: + __body["top_n"] = top_n if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1933,7 +1989,15 @@ async def get_overall_buckets( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "desc", + "end", + "exclude_interim", + "page", + "record_score", + "sort", + "start", + ), parameter_aliases={"from": "from_"}, ) async def get_records( @@ -1953,6 +2017,7 @@ async def get_records( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly records for an anomaly detection job. @@ -1974,34 +2039,35 @@ async def get_records( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/records" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if desc is not None: - __body["desc"] = desc - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if exclude_interim is not None: - __body["exclude_interim"] = exclude_interim if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty - if record_score is not None: - __body["record_score"] = record_score if size is not None: __query["size"] = size - if sort is not None: - __body["sort"] = sort - if start is not None: - __body["start"] = start + if not __body: + if desc is not None: + __body["desc"] = desc + if end is not None: + __body["end"] = end + if exclude_interim is not None: + __body["exclude_interim"] = exclude_interim + if page is not None: + __body["page"] = page + if record_score is not None: + __body["record_score"] = record_score + if sort is not None: + __body["sort"] = sort + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2147,19 +2213,20 @@ async def get_trained_models_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "inference_config"), ) async def infer_trained_model( self, *, model_id: str, - docs: t.Sequence[t.Mapping[str, t.Any]], + docs: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, inference_config: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Evaluate a trained model. @@ -2177,25 +2244,26 @@ async def infer_trained_model( """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") - if docs is None: + if docs is None and body is None: raise ValueError("Empty value passed for parameter 'docs'") __path = f"/_ml/trained_models/{_quote(model_id)}/_infer" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if inference_config is not None: - __body["inference_config"] = inference_config if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if docs is not None: + __body["docs"] = docs + if inference_config is not None: + __body["inference_config"] = inference_config __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2231,7 +2299,7 @@ async def info( ) @_rewrite_parameters( - body_fields=True, + body_fields=("timeout",), ) async def open_job( self, @@ -2242,6 +2310,7 @@ async def open_job( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Opens one or more anomaly detection jobs. @@ -2255,7 +2324,7 @@ async def open_job( raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_open" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2264,8 +2333,9 @@ async def open_job( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2276,17 +2346,18 @@ async def open_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=("events",), ) async def post_calendar_events( self, *, calendar_id: str, - events: t.Sequence[t.Mapping[str, t.Any]], + events: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Posts scheduled events in a calendar. @@ -2300,13 +2371,11 @@ async def post_calendar_events( """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") - if events is None: + if events is None and body is None: raise ValueError("Empty value passed for parameter 'events'") __path = f"/_ml/calendars/{_quote(calendar_id)}/events" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if events is not None: - __body["events"] = events + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2315,6 +2384,9 @@ async def post_calendar_events( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if events is not None: + __body["events"] = events __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2327,7 +2399,8 @@ async def post_data( self, *, job_id: str, - data: t.Sequence[t.Any], + data: t.Optional[t.Sequence[t.Any]] = None, + body: t.Optional[t.Sequence[t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -2348,8 +2421,12 @@ async def post_data( """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") - if data is None: - raise ValueError("Empty value passed for parameter 'data'") + if data is None and body is None: + raise ValueError( + "Empty value passed for parameters 'data' and 'body', one of them should be set." + ) + elif data is not None and body is not None: + raise ValueError("Cannot set both 'data' and 'body'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_data" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -2364,7 +2441,7 @@ async def post_data( __query["reset_end"] = reset_end if reset_start is not None: __query["reset_start"] = reset_start - __body = data + __body = data if data is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -2374,7 +2451,7 @@ async def post_data( ) @_rewrite_parameters( - body_fields=True, + body_fields=("config",), ) async def preview_data_frame_analytics( self, @@ -2385,6 +2462,7 @@ async def preview_data_frame_analytics( filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Previews that will be analyzed given a data frame analytics config. @@ -2400,10 +2478,8 @@ async def preview_data_frame_analytics( __path = f"/_ml/data_frame/analytics/{_quote(id)}/_preview" else: __path = "/_ml/data_frame/analytics/_preview" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if config is not None: - __body["config"] = config + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2412,6 +2488,9 @@ async def preview_data_frame_analytics( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if config is not None: + __body["config"] = config if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2422,7 +2501,7 @@ async def preview_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("datafeed_config", "job_config"), ) async def preview_datafeed( self, @@ -2436,6 +2515,7 @@ async def preview_datafeed( job_config: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Previews a datafeed. @@ -2461,10 +2541,8 @@ async def preview_datafeed( __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_preview" else: __path = "/_ml/datafeeds/_preview" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if datafeed_config is not None: - __body["datafeed_config"] = datafeed_config + __body: t.Dict[str, t.Any] = body if body is not None else {} if end is not None: __query["end"] = end if error_trace is not None: @@ -2473,12 +2551,15 @@ async def preview_datafeed( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if job_config is not None: - __body["job_config"] = job_config if pretty is not None: __query["pretty"] = pretty if start is not None: __query["start"] = start + if not __body: + if datafeed_config is not None: + __body["datafeed_config"] = datafeed_config + if job_config is not None: + __body["job_config"] = job_config if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2489,7 +2570,7 @@ async def preview_datafeed( ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "job_ids"), ) async def put_calendar( self, @@ -2501,6 +2582,7 @@ async def put_calendar( human: t.Optional[bool] = None, job_ids: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a calendar. @@ -2514,20 +2596,21 @@ async def put_calendar( if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") __path = f"/_ml/calendars/{_quote(calendar_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if job_ids is not None: - __body["job_ids"] = job_ids if pretty is not None: __query["pretty"] = pretty + if not __body: + if description is not None: + __body["description"] = description + if job_ids is not None: + __body["job_ids"] = job_ids if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2577,16 +2660,27 @@ async def put_calendar_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis", + "dest", + "source", + "allow_lazy_start", + "analyzed_fields", + "description", + "headers", + "max_num_threads", + "model_memory_limit", + "version", + ), ignore_deprecated_options={"headers"}, ) async def put_data_frame_analytics( self, *, id: str, - analysis: t.Mapping[str, t.Any], - dest: t.Mapping[str, t.Any], - source: t.Mapping[str, t.Any], + analysis: t.Optional[t.Mapping[str, t.Any]] = None, + dest: t.Optional[t.Mapping[str, t.Any]] = None, + source: t.Optional[t.Mapping[str, t.Any]] = None, allow_lazy_start: t.Optional[bool] = None, analyzed_fields: t.Optional[t.Mapping[str, t.Any]] = None, description: t.Optional[str] = None, @@ -2598,6 +2692,7 @@ async def put_data_frame_analytics( model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, version: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a data frame analytics job. @@ -2661,50 +2756,67 @@ async def put_data_frame_analytics( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if analysis is None: + if analysis is None and body is None: raise ValueError("Empty value passed for parameter 'analysis'") - if dest is None: + if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") - if source is None: + if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path = f"/_ml/data_frame/analytics/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis is not None: - __body["analysis"] = analysis - if dest is not None: - __body["dest"] = dest - if source is not None: - __body["source"] = source - if allow_lazy_start is not None: - __body["allow_lazy_start"] = allow_lazy_start - if analyzed_fields is not None: - __body["analyzed_fields"] = analyzed_fields - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if headers is not None: - __body["headers"] = headers if human is not None: __query["human"] = human - if max_num_threads is not None: - __body["max_num_threads"] = max_num_threads - if model_memory_limit is not None: - __body["model_memory_limit"] = model_memory_limit if pretty is not None: __query["pretty"] = pretty - if version is not None: - __body["version"] = version + if not __body: + if analysis is not None: + __body["analysis"] = analysis + if dest is not None: + __body["dest"] = dest + if source is not None: + __body["source"] = source + if allow_lazy_start is not None: + __body["allow_lazy_start"] = allow_lazy_start + if analyzed_fields is not None: + __body["analyzed_fields"] = analyzed_fields + if description is not None: + __body["description"] = description + if headers is not None: + __body["headers"] = headers + if max_num_threads is not None: + __body["max_num_threads"] = max_num_threads + if model_memory_limit is not None: + __body["model_memory_limit"] = model_memory_limit + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "chunking_config", + "delayed_data_check_config", + "frequency", + "headers", + "indexes", + "indices", + "indices_options", + "job_id", + "max_empty_searches", + "query", + "query_delay", + "runtime_mappings", + "script_fields", + "scroll_size", + ), ignore_deprecated_options={"headers"}, ) async def put_datafeed( @@ -2741,6 +2853,7 @@ async def put_datafeed( runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, script_fields: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, scroll_size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a datafeed. @@ -2819,61 +2932,62 @@ async def put_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aggregations is not None: - __body["aggregations"] = aggregations + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if chunking_config is not None: - __body["chunking_config"] = chunking_config - if delayed_data_check_config is not None: - __body["delayed_data_check_config"] = delayed_data_check_config if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency - if headers is not None: - __body["headers"] = headers if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indexes is not None: - __body["indexes"] = indexes - if indices is not None: - __body["indices"] = indices - if indices_options is not None: - __body["indices_options"] = indices_options - if job_id is not None: - __body["job_id"] = job_id - if max_empty_searches is not None: - __body["max_empty_searches"] = max_empty_searches if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if query_delay is not None: - __body["query_delay"] = query_delay - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields - if scroll_size is not None: - __body["scroll_size"] = scroll_size + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if chunking_config is not None: + __body["chunking_config"] = chunking_config + if delayed_data_check_config is not None: + __body["delayed_data_check_config"] = delayed_data_check_config + if frequency is not None: + __body["frequency"] = frequency + if headers is not None: + __body["headers"] = headers + if indexes is not None: + __body["indexes"] = indexes + if indices is not None: + __body["indices"] = indices + if indices_options is not None: + __body["indices_options"] = indices_options + if job_id is not None: + __body["job_id"] = job_id + if max_empty_searches is not None: + __body["max_empty_searches"] = max_empty_searches + if query is not None: + __body["query"] = query + if query_delay is not None: + __body["query_delay"] = query_delay + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if scroll_size is not None: + __body["scroll_size"] = scroll_size __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "items"), ) async def put_filter( self, @@ -2885,6 +2999,7 @@ async def put_filter( human: t.Optional[bool] = None, items: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a filter. @@ -2899,34 +3014,51 @@ async def put_filter( if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path = f"/_ml/filters/{_quote(filter_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if items is not None: - __body["items"] = items if pretty is not None: __query["pretty"] = pretty + if not __body: + if description is not None: + __body["description"] = description + if items is not None: + __body["items"] = items __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis_config", + "data_description", + "allow_lazy_open", + "analysis_limits", + "background_persist_interval", + "custom_settings", + "daily_model_snapshot_retention_after_days", + "datafeed_config", + "description", + "groups", + "model_plot_config", + "model_snapshot_retention_days", + "renormalization_window_days", + "results_index_name", + "results_retention_days", + ), ) async def put_job( self, *, job_id: str, - analysis_config: t.Mapping[str, t.Any], - data_description: t.Mapping[str, t.Any], + analysis_config: t.Optional[t.Mapping[str, t.Any]] = None, + data_description: t.Optional[t.Mapping[str, t.Any]] = None, allow_lazy_open: t.Optional[bool] = None, analysis_limits: t.Optional[t.Mapping[str, t.Any]] = None, background_persist_interval: t.Optional[ @@ -2946,6 +3078,7 @@ async def put_job( renormalization_window_days: t.Optional[int] = None, results_index_name: t.Optional[str] = None, results_retention_days: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates an anomaly detection job. @@ -3027,60 +3160,72 @@ async def put_job( """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") - if analysis_config is None: + if analysis_config is None and body is None: raise ValueError("Empty value passed for parameter 'analysis_config'") - if data_description is None: + if data_description is None and body is None: raise ValueError("Empty value passed for parameter 'data_description'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis_config is not None: - __body["analysis_config"] = analysis_config - if data_description is not None: - __body["data_description"] = data_description - if allow_lazy_open is not None: - __body["allow_lazy_open"] = allow_lazy_open - if analysis_limits is not None: - __body["analysis_limits"] = analysis_limits - if background_persist_interval is not None: - __body["background_persist_interval"] = background_persist_interval - if custom_settings is not None: - __body["custom_settings"] = custom_settings - if daily_model_snapshot_retention_after_days is not None: - __body[ - "daily_model_snapshot_retention_after_days" - ] = daily_model_snapshot_retention_after_days - if datafeed_config is not None: - __body["datafeed_config"] = datafeed_config - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if groups is not None: - __body["groups"] = groups if human is not None: __query["human"] = human - if model_plot_config is not None: - __body["model_plot_config"] = model_plot_config - if model_snapshot_retention_days is not None: - __body["model_snapshot_retention_days"] = model_snapshot_retention_days if pretty is not None: __query["pretty"] = pretty - if renormalization_window_days is not None: - __body["renormalization_window_days"] = renormalization_window_days - if results_index_name is not None: - __body["results_index_name"] = results_index_name - if results_retention_days is not None: - __body["results_retention_days"] = results_retention_days + if not __body: + if analysis_config is not None: + __body["analysis_config"] = analysis_config + if data_description is not None: + __body["data_description"] = data_description + if allow_lazy_open is not None: + __body["allow_lazy_open"] = allow_lazy_open + if analysis_limits is not None: + __body["analysis_limits"] = analysis_limits + if background_persist_interval is not None: + __body["background_persist_interval"] = background_persist_interval + if custom_settings is not None: + __body["custom_settings"] = custom_settings + if daily_model_snapshot_retention_after_days is not None: + __body[ + "daily_model_snapshot_retention_after_days" + ] = daily_model_snapshot_retention_after_days + if datafeed_config is not None: + __body["datafeed_config"] = datafeed_config + if description is not None: + __body["description"] = description + if groups is not None: + __body["groups"] = groups + if model_plot_config is not None: + __body["model_plot_config"] = model_plot_config + if model_snapshot_retention_days is not None: + __body["model_snapshot_retention_days"] = model_snapshot_retention_days + if renormalization_window_days is not None: + __body["renormalization_window_days"] = renormalization_window_days + if results_index_name is not None: + __body["results_index_name"] = results_index_name + if results_retention_days is not None: + __body["results_retention_days"] = results_retention_days __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "compressed_definition", + "definition", + "description", + "inference_config", + "input", + "metadata", + "model_size_bytes", + "model_type", + "platform_architecture", + "tags", + ), ) async def put_trained_model( self, @@ -3103,6 +3248,7 @@ async def put_trained_model( platform_architecture: t.Optional[str] = None, pretty: t.Optional[bool] = None, tags: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an inference trained model. @@ -3142,38 +3288,39 @@ async def put_trained_model( if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path = f"/_ml/trained_models/{_quote(model_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if compressed_definition is not None: - __body["compressed_definition"] = compressed_definition + __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_definition_decompression is not None: __query["defer_definition_decompression"] = defer_definition_decompression - if definition is not None: - __body["definition"] = definition - if description is not None: - __body["description"] = description if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if inference_config is not None: - __body["inference_config"] = inference_config - if input is not None: - __body["input"] = input - if metadata is not None: - __body["metadata"] = metadata - if model_size_bytes is not None: - __body["model_size_bytes"] = model_size_bytes - if model_type is not None: - __body["model_type"] = model_type - if platform_architecture is not None: - __body["platform_architecture"] = platform_architecture if pretty is not None: __query["pretty"] = pretty - if tags is not None: - __body["tags"] = tags + if not __body: + if compressed_definition is not None: + __body["compressed_definition"] = compressed_definition + if definition is not None: + __body["definition"] = definition + if description is not None: + __body["description"] = description + if inference_config is not None: + __body["inference_config"] = inference_config + if input is not None: + __body["input"] = input + if metadata is not None: + __body["metadata"] = metadata + if model_size_bytes is not None: + __body["model_size_bytes"] = model_size_bytes + if model_type is not None: + __body["model_type"] = model_type + if platform_architecture is not None: + __body["platform_architecture"] = platform_architecture + if tags is not None: + __body["tags"] = tags __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -3225,20 +3372,21 @@ async def put_trained_model_alias( ) @_rewrite_parameters( - body_fields=True, + body_fields=("definition", "total_definition_length", "total_parts"), ) async def put_trained_model_definition_part( self, *, model_id: str, part: int, - definition: str, - total_definition_length: int, - total_parts: int, + definition: t.Optional[str] = None, + total_definition_length: t.Optional[int] = None, + total_parts: t.Optional[int] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates part of a trained model definition @@ -3260,23 +3408,17 @@ async def put_trained_model_definition_part( raise ValueError("Empty value passed for parameter 'model_id'") if part in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'part'") - if definition is None: + if definition is None and body is None: raise ValueError("Empty value passed for parameter 'definition'") - if total_definition_length is None: + if total_definition_length is None and body is None: raise ValueError( "Empty value passed for parameter 'total_definition_length'" ) - if total_parts is None: + if total_parts is None and body is None: raise ValueError("Empty value passed for parameter 'total_parts'") __path = f"/_ml/trained_models/{_quote(model_id)}/definition/{_quote(part)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if definition is not None: - __body["definition"] = definition - if total_definition_length is not None: - __body["total_definition_length"] = total_definition_length - if total_parts is not None: - __body["total_parts"] = total_parts + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3285,25 +3427,33 @@ async def put_trained_model_definition_part( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if definition is not None: + __body["definition"] = definition + if total_definition_length is not None: + __body["total_definition_length"] = total_definition_length + if total_parts is not None: + __body["total_parts"] = total_parts __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("vocabulary", "merges", "scores"), ) async def put_trained_model_vocabulary( self, *, model_id: str, - vocabulary: t.Sequence[str], + vocabulary: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, merges: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, scores: t.Optional[t.Sequence[float]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a trained model vocabulary @@ -3317,25 +3467,26 @@ async def put_trained_model_vocabulary( """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") - if vocabulary is None: + if vocabulary is None and body is None: raise ValueError("Empty value passed for parameter 'vocabulary'") __path = f"/_ml/trained_models/{_quote(model_id)}/vocabulary" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if vocabulary is not None: - __body["vocabulary"] = vocabulary + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if merges is not None: - __body["merges"] = merges if pretty is not None: __query["pretty"] = pretty - if scores is not None: - __body["scores"] = scores + if not __body: + if vocabulary is not None: + __body["vocabulary"] = vocabulary + if merges is not None: + __body["merges"] = merges + if scores is not None: + __body["scores"] = scores __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -3387,7 +3538,7 @@ async def reset_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=("delete_intervening_results",), ) async def revert_model_snapshot( self, @@ -3399,6 +3550,7 @@ async def revert_model_snapshot( filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Reverts to a specific snapshot. @@ -3417,10 +3569,8 @@ async def revert_model_snapshot( if snapshot_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'snapshot_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/model_snapshots/{_quote(snapshot_id)}/_revert" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if delete_intervening_results is not None: - __body["delete_intervening_results"] = delete_intervening_results + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3429,6 +3579,9 @@ async def revert_model_snapshot( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if delete_intervening_results is not None: + __body["delete_intervening_results"] = delete_intervening_results if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3521,7 +3674,7 @@ async def start_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("end", "start", "timeout"), ) async def start_datafeed( self, @@ -3534,6 +3687,7 @@ async def start_datafeed( pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Starts one or more datafeeds. @@ -3551,10 +3705,8 @@ async def start_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_start" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3563,10 +3715,13 @@ async def start_datafeed( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if start is not None: - __body["start"] = start - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if end is not None: + __body["end"] = end + if start is not None: + __body["start"] = start + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3717,7 +3872,7 @@ async def stop_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("allow_no_match", "force", "timeout"), ) async def stop_datafeed( self, @@ -3730,6 +3885,7 @@ async def stop_datafeed( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Stops one or more datafeeds. @@ -3748,22 +3904,23 @@ async def stop_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_stop" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_no_match is not None: - __body["allow_no_match"] = allow_no_match + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if force is not None: - __body["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if allow_no_match is not None: + __body["allow_no_match"] = allow_no_match + if force is not None: + __body["force"] = force + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3823,7 +3980,12 @@ async def stop_trained_model_deployment( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_lazy_start", + "description", + "max_num_threads", + "model_memory_limit", + ), ) async def update_data_frame_analytics( self, @@ -3837,6 +3999,7 @@ async def update_data_frame_analytics( max_num_threads: t.Optional[int] = None, model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a data frame analytics job. @@ -3862,31 +4025,47 @@ async def update_data_frame_analytics( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/_ml/data_frame/analytics/{_quote(id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_lazy_start is not None: - __body["allow_lazy_start"] = allow_lazy_start - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_num_threads is not None: - __body["max_num_threads"] = max_num_threads - if model_memory_limit is not None: - __body["model_memory_limit"] = model_memory_limit if pretty is not None: __query["pretty"] = pretty + if not __body: + if allow_lazy_start is not None: + __body["allow_lazy_start"] = allow_lazy_start + if description is not None: + __body["description"] = description + if max_num_threads is not None: + __body["max_num_threads"] = max_num_threads + if model_memory_limit is not None: + __body["model_memory_limit"] = model_memory_limit __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "chunking_config", + "delayed_data_check_config", + "frequency", + "indexes", + "indices", + "indices_options", + "job_id", + "max_empty_searches", + "query", + "query_delay", + "runtime_mappings", + "script_fields", + "scroll_size", + ), ) async def update_datafeed( self, @@ -3921,6 +4100,7 @@ async def update_datafeed( runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, script_fields: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, scroll_size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a datafeed. @@ -4010,59 +4190,60 @@ async def update_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aggregations is not None: - __body["aggregations"] = aggregations + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if chunking_config is not None: - __body["chunking_config"] = chunking_config - if delayed_data_check_config is not None: - __body["delayed_data_check_config"] = delayed_data_check_config if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indexes is not None: - __body["indexes"] = indexes - if indices is not None: - __body["indices"] = indices - if indices_options is not None: - __body["indices_options"] = indices_options - if job_id is not None: - __body["job_id"] = job_id - if max_empty_searches is not None: - __body["max_empty_searches"] = max_empty_searches if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if query_delay is not None: - __body["query_delay"] = query_delay - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields - if scroll_size is not None: - __body["scroll_size"] = scroll_size + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if chunking_config is not None: + __body["chunking_config"] = chunking_config + if delayed_data_check_config is not None: + __body["delayed_data_check_config"] = delayed_data_check_config + if frequency is not None: + __body["frequency"] = frequency + if indexes is not None: + __body["indexes"] = indexes + if indices is not None: + __body["indices"] = indices + if indices_options is not None: + __body["indices_options"] = indices_options + if job_id is not None: + __body["job_id"] = job_id + if max_empty_searches is not None: + __body["max_empty_searches"] = max_empty_searches + if query is not None: + __body["query"] = query + if query_delay is not None: + __body["query_delay"] = query_delay + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if scroll_size is not None: + __body["scroll_size"] = scroll_size __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("add_items", "description", "remove_items"), ) async def update_filter( self, @@ -4075,6 +4256,7 @@ async def update_filter( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, remove_items: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the description of a filter, adds items, or removes items. @@ -4089,12 +4271,8 @@ async def update_filter( if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path = f"/_ml/filters/{_quote(filter_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if add_items is not None: - __body["add_items"] = add_items - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -4103,15 +4281,36 @@ async def update_filter( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if remove_items is not None: - __body["remove_items"] = remove_items + if not __body: + if add_items is not None: + __body["add_items"] = add_items + if description is not None: + __body["description"] = description + if remove_items is not None: + __body["remove_items"] = remove_items __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_lazy_open", + "analysis_limits", + "background_persist_interval", + "categorization_filters", + "custom_settings", + "daily_model_snapshot_retention_after_days", + "description", + "detectors", + "groups", + "model_plot_config", + "model_prune_window", + "model_snapshot_retention_days", + "per_partition_categorization", + "renormalization_window_days", + "results_retention_days", + ), ) async def update_job( self, @@ -4140,6 +4339,7 @@ async def update_job( pretty: t.Optional[bool] = None, renormalization_window_days: t.Optional[int] = None, results_retention_days: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of an anomaly detection job. @@ -4198,55 +4398,56 @@ async def update_job( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_lazy_open is not None: - __body["allow_lazy_open"] = allow_lazy_open - if analysis_limits is not None: - __body["analysis_limits"] = analysis_limits - if background_persist_interval is not None: - __body["background_persist_interval"] = background_persist_interval - if categorization_filters is not None: - __body["categorization_filters"] = categorization_filters - if custom_settings is not None: - __body["custom_settings"] = custom_settings - if daily_model_snapshot_retention_after_days is not None: - __body[ - "daily_model_snapshot_retention_after_days" - ] = daily_model_snapshot_retention_after_days - if description is not None: - __body["description"] = description - if detectors is not None: - __body["detectors"] = detectors + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if groups is not None: - __body["groups"] = groups if human is not None: __query["human"] = human - if model_plot_config is not None: - __body["model_plot_config"] = model_plot_config - if model_prune_window is not None: - __body["model_prune_window"] = model_prune_window - if model_snapshot_retention_days is not None: - __body["model_snapshot_retention_days"] = model_snapshot_retention_days - if per_partition_categorization is not None: - __body["per_partition_categorization"] = per_partition_categorization if pretty is not None: __query["pretty"] = pretty - if renormalization_window_days is not None: - __body["renormalization_window_days"] = renormalization_window_days - if results_retention_days is not None: - __body["results_retention_days"] = results_retention_days + if not __body: + if allow_lazy_open is not None: + __body["allow_lazy_open"] = allow_lazy_open + if analysis_limits is not None: + __body["analysis_limits"] = analysis_limits + if background_persist_interval is not None: + __body["background_persist_interval"] = background_persist_interval + if categorization_filters is not None: + __body["categorization_filters"] = categorization_filters + if custom_settings is not None: + __body["custom_settings"] = custom_settings + if daily_model_snapshot_retention_after_days is not None: + __body[ + "daily_model_snapshot_retention_after_days" + ] = daily_model_snapshot_retention_after_days + if description is not None: + __body["description"] = description + if detectors is not None: + __body["detectors"] = detectors + if groups is not None: + __body["groups"] = groups + if model_plot_config is not None: + __body["model_plot_config"] = model_plot_config + if model_prune_window is not None: + __body["model_prune_window"] = model_prune_window + if model_snapshot_retention_days is not None: + __body["model_snapshot_retention_days"] = model_snapshot_retention_days + if per_partition_categorization is not None: + __body["per_partition_categorization"] = per_partition_categorization + if renormalization_window_days is not None: + __body["renormalization_window_days"] = renormalization_window_days + if results_retention_days is not None: + __body["results_retention_days"] = results_retention_days __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "retain"), ) async def update_model_snapshot( self, @@ -4259,6 +4460,7 @@ async def update_model_snapshot( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, retain: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a snapshot. @@ -4277,10 +4479,8 @@ async def update_model_snapshot( if snapshot_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'snapshot_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/model_snapshots/{_quote(snapshot_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -4289,8 +4489,11 @@ async def update_model_snapshot( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if retain is not None: - __body["retain"] = retain + if not __body: + if description is not None: + __body["description"] = description + if retain is not None: + __body["retain"] = retain __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -4346,7 +4549,17 @@ async def upgrade_job_snapshot( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis_config", + "analysis_limits", + "data_description", + "description", + "job_id", + "model_plot", + "model_snapshot_id", + "model_snapshot_retention_days", + "results_index_name", + ), ) async def validate( self, @@ -4364,6 +4577,7 @@ async def validate( model_snapshot_retention_days: t.Optional[int] = None, pretty: t.Optional[bool] = None, results_index_name: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Validates an anomaly detection job. @@ -4381,34 +4595,35 @@ async def validate( :param results_index_name: """ __path = "/_ml/anomaly_detectors/_validate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis_config is not None: - __body["analysis_config"] = analysis_config - if analysis_limits is not None: - __body["analysis_limits"] = analysis_limits - if data_description is not None: - __body["data_description"] = data_description - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if job_id is not None: - __body["job_id"] = job_id - if model_plot is not None: - __body["model_plot"] = model_plot - if model_snapshot_id is not None: - __body["model_snapshot_id"] = model_snapshot_id - if model_snapshot_retention_days is not None: - __body["model_snapshot_retention_days"] = model_snapshot_retention_days if pretty is not None: __query["pretty"] = pretty - if results_index_name is not None: - __body["results_index_name"] = results_index_name + if not __body: + if analysis_config is not None: + __body["analysis_config"] = analysis_config + if analysis_limits is not None: + __body["analysis_limits"] = analysis_limits + if data_description is not None: + __body["data_description"] = data_description + if description is not None: + __body["description"] = description + if job_id is not None: + __body["job_id"] = job_id + if model_plot is not None: + __body["model_plot"] = model_plot + if model_snapshot_id is not None: + __body["model_snapshot_id"] = model_snapshot_id + if model_snapshot_retention_days is not None: + __body["model_snapshot_retention_days"] = model_snapshot_retention_days + if results_index_name is not None: + __body["results_index_name"] = results_index_name __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -4420,7 +4635,8 @@ async def validate( async def validate_detector( self, *, - detector: t.Mapping[str, t.Any], + detector: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -4433,8 +4649,12 @@ async def validate_detector( :param detector: """ - if detector is None: - raise ValueError("Empty value passed for parameter 'detector'") + if detector is None and body is None: + raise ValueError( + "Empty value passed for parameters 'detector' and 'body', one of them should be set." + ) + elif detector is not None and body is not None: + raise ValueError("Cannot set both 'detector' and 'body'") __path = "/_ml/anomaly_detectors/_validate/detector" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -4445,7 +4665,7 @@ async def validate_detector( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = detector + __body = detector if detector is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/monitoring.py b/elasticsearch/_async/client/monitoring.py --- a/elasticsearch/_async/client/monitoring.py +++ b/elasticsearch/_async/client/monitoring.py @@ -31,7 +31,8 @@ async def bulk( self, *, interval: t.Union["t.Literal[-1]", "t.Literal[0]", str], - operations: t.Sequence[t.Mapping[str, t.Any]], + operations: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, system_api_version: str, system_id: str, error_trace: t.Optional[bool] = None, @@ -51,8 +52,12 @@ async def bulk( """ if interval is None: raise ValueError("Empty value passed for parameter 'interval'") - if operations is None: - raise ValueError("Empty value passed for parameter 'operations'") + if operations is None and body is None: + raise ValueError( + "Empty value passed for parameters 'operations' and 'body', one of them should be set." + ) + elif operations is not None and body is not None: + raise ValueError("Cannot set both 'operations' and 'body'") if system_api_version is None: raise ValueError("Empty value passed for parameter 'system_api_version'") if system_id is None: @@ -73,7 +78,7 @@ async def bulk( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = operations + __body = operations if operations is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", diff --git a/elasticsearch/_async/client/nodes.py b/elasticsearch/_async/client/nodes.py --- a/elasticsearch/_async/client/nodes.py +++ b/elasticsearch/_async/client/nodes.py @@ -237,7 +237,7 @@ async def info( ) @_rewrite_parameters( - body_fields=True, + body_fields=("secure_settings_password",), ) async def reload_secure_settings( self, @@ -249,6 +249,7 @@ async def reload_secure_settings( pretty: t.Optional[bool] = None, secure_settings_password: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Reloads secure settings. @@ -265,7 +266,7 @@ async def reload_secure_settings( else: __path = "/_nodes/reload_secure_settings" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -274,10 +275,11 @@ async def reload_secure_settings( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if secure_settings_password is not None: - __body["secure_settings_password"] = secure_settings_password if timeout is not None: __query["timeout"] = timeout + if not __body: + if secure_settings_password is not None: + __body["secure_settings_password"] = secure_settings_password if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/query_ruleset.py b/elasticsearch/_async/client/query_ruleset.py --- a/elasticsearch/_async/client/query_ruleset.py +++ b/elasticsearch/_async/client/query_ruleset.py @@ -133,17 +133,18 @@ async def list( ) @_rewrite_parameters( - body_fields=True, + body_fields=("rules",), ) async def put( self, *, ruleset_id: str, - rules: t.Sequence[t.Mapping[str, t.Any]], + rules: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a query ruleset. @@ -156,13 +157,11 @@ async def put( """ if ruleset_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'ruleset_id'") - if rules is None: + if rules is None and body is None: raise ValueError("Empty value passed for parameter 'rules'") __path = f"/_query_rules/{_quote(ruleset_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if rules is not None: - __body["rules"] = rules + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -171,6 +170,9 @@ async def put( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if rules is not None: + __body["rules"] = rules __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/rollup.py b/elasticsearch/_async/client/rollup.py --- a/elasticsearch/_async/client/rollup.py +++ b/elasticsearch/_async/client/rollup.py @@ -168,18 +168,27 @@ async def get_rollup_index_caps( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "cron", + "groups", + "index_pattern", + "page_size", + "rollup_index", + "headers", + "metrics", + "timeout", + ), ignore_deprecated_options={"headers"}, ) async def put_job( self, *, id: str, - cron: str, - groups: t.Mapping[str, t.Any], - index_pattern: str, - page_size: int, - rollup_index: str, + cron: t.Optional[str] = None, + groups: t.Optional[t.Mapping[str, t.Any]] = None, + index_pattern: t.Optional[str] = None, + page_size: t.Optional[int] = None, + rollup_index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, headers: t.Optional[t.Mapping[str, t.Union[str, t.Sequence[str]]]] = None, @@ -187,6 +196,7 @@ async def put_job( metrics: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a rollup job. @@ -235,50 +245,51 @@ async def put_job( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if cron is None: + if cron is None and body is None: raise ValueError("Empty value passed for parameter 'cron'") - if groups is None: + if groups is None and body is None: raise ValueError("Empty value passed for parameter 'groups'") - if index_pattern is None: + if index_pattern is None and body is None: raise ValueError("Empty value passed for parameter 'index_pattern'") - if page_size is None: + if page_size is None and body is None: raise ValueError("Empty value passed for parameter 'page_size'") - if rollup_index is None: + if rollup_index is None and body is None: raise ValueError("Empty value passed for parameter 'rollup_index'") __path = f"/_rollup/job/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if cron is not None: - __body["cron"] = cron - if groups is not None: - __body["groups"] = groups - if index_pattern is not None: - __body["index_pattern"] = index_pattern - if page_size is not None: - __body["page_size"] = page_size - if rollup_index is not None: - __body["rollup_index"] = rollup_index + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if headers is not None: - __body["headers"] = headers if human is not None: __query["human"] = human - if metrics is not None: - __body["metrics"] = metrics if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if cron is not None: + __body["cron"] = cron + if groups is not None: + __body["groups"] = groups + if index_pattern is not None: + __body["index_pattern"] = index_pattern + if page_size is not None: + __body["page_size"] = page_size + if rollup_index is not None: + __body["rollup_index"] = rollup_index + if headers is not None: + __body["headers"] = headers + if metrics is not None: + __body["metrics"] = metrics + if timeout is not None: + __body["timeout"] = timeout __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("aggregations", "aggs", "query", "size"), ) async def rollup_search( self, @@ -294,6 +305,7 @@ async def rollup_search( rest_total_hits_as_int: t.Optional[bool] = None, size: t.Optional[int] = None, typed_keys: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Enables searching rolled-up data using the standard query DSL. @@ -313,12 +325,8 @@ async def rollup_search( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_rollup_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -327,14 +335,19 @@ async def rollup_search( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int - if size is not None: - __body["size"] = size if typed_keys is not None: __query["typed_keys"] = typed_keys + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if query is not None: + __body["query"] = query + if size is not None: + __body["size"] = size __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/search_application.py b/elasticsearch/_async/client/search_application.py --- a/elasticsearch/_async/client/search_application.py +++ b/elasticsearch/_async/client/search_application.py @@ -212,7 +212,8 @@ async def put( self, *, name: str, - search_application: t.Mapping[str, t.Any], + search_application: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, create: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -231,8 +232,12 @@ async def put( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if search_application is None: - raise ValueError("Empty value passed for parameter 'search_application'") + if search_application is None and body is None: + raise ValueError( + "Empty value passed for parameters 'search_application' and 'body', one of them should be set." + ) + elif search_application is not None and body is not None: + raise ValueError("Cannot set both 'search_application' and 'body'") __path = f"/_application/search_application/{_quote(name)}" __query: t.Dict[str, t.Any] = {} if create is not None: @@ -245,7 +250,7 @@ async def put( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = search_application + __body = search_application if search_application is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -286,7 +291,7 @@ async def put_behavioral_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("params",), ignore_deprecated_options={"params"}, ) async def search( @@ -298,6 +303,7 @@ async def search( human: t.Optional[bool] = None, params: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Perform a search against a search application @@ -312,17 +318,18 @@ async def search( raise ValueError("Empty value passed for parameter 'name'") __path = f"/_application/search_application/{_quote(name)}/_search" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if params is not None: - __body["params"] = params if pretty is not None: __query["pretty"] = pretty + if not __body: + if params is not None: + __body["params"] = params if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/searchable_snapshots.py b/elasticsearch/_async/client/searchable_snapshots.py --- a/elasticsearch/_async/client/searchable_snapshots.py +++ b/elasticsearch/_async/client/searchable_snapshots.py @@ -126,14 +126,19 @@ async def clear_cache( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "index", + "ignore_index_settings", + "index_settings", + "renamed_index", + ), ) async def mount( self, *, repository: str, snapshot: str, - index: str, + index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -146,6 +151,7 @@ async def mount( renamed_index: t.Optional[str] = None, storage: t.Optional[str] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Mount a snapshot as a searchable index. @@ -169,33 +175,34 @@ async def mount( raise ValueError("Empty value passed for parameter 'repository'") if snapshot in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'snapshot'") - if index is None: + if index is None and body is None: raise ValueError("Empty value passed for parameter 'index'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}/_mount" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if index is not None: - __body["index"] = index + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_index_settings is not None: - __body["ignore_index_settings"] = ignore_index_settings - if index_settings is not None: - __body["index_settings"] = index_settings if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if renamed_index is not None: - __body["renamed_index"] = renamed_index if storage is not None: __query["storage"] = storage if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if index is not None: + __body["index"] = index + if ignore_index_settings is not None: + __body["ignore_index_settings"] = ignore_index_settings + if index_settings is not None: + __body["index_settings"] = index_settings + if renamed_index is not None: + __body["renamed_index"] = renamed_index __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/security.py b/elasticsearch/_async/client/security.py --- a/elasticsearch/_async/client/security.py +++ b/elasticsearch/_async/client/security.py @@ -25,12 +25,14 @@ class SecurityClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("grant_type", "access_token", "password", "username"), ) async def activate_user_profile( self, *, - grant_type: t.Union["t.Literal['access_token', 'password']", str], + grant_type: t.Optional[ + t.Union["t.Literal['access_token', 'password']", str] + ] = None, access_token: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -38,6 +40,7 @@ async def activate_user_profile( password: t.Optional[str] = None, pretty: t.Optional[bool] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates the user profile on behalf of another user. @@ -49,27 +52,28 @@ async def activate_user_profile( :param password: :param username: """ - if grant_type is None: + if grant_type is None and body is None: raise ValueError("Empty value passed for parameter 'grant_type'") __path = "/_security/profile/_activate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if grant_type is not None: - __body["grant_type"] = grant_type - if access_token is not None: - __body["access_token"] = access_token + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if password is not None: - __body["password"] = password if pretty is not None: __query["pretty"] = pretty - if username is not None: - __body["username"] = username + if not __body: + if grant_type is not None: + __body["grant_type"] = grant_type + if access_token is not None: + __body["access_token"] = access_token + if password is not None: + __body["password"] = password + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -106,7 +110,7 @@ async def authenticate( ) @_rewrite_parameters( - body_fields=True, + body_fields=("password", "password_hash"), ) async def change_password( self, @@ -121,6 +125,7 @@ async def change_password( refresh: t.Optional[ t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Changes the passwords of users in the native realm and built-in users. @@ -144,21 +149,22 @@ async def change_password( else: __path = "/_security/user/_password" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if password is not None: - __body["password"] = password - if password_hash is not None: - __body["password_hash"] = password_hash if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh + if not __body: + if password is not None: + __body["password"] = password + if password_hash is not None: + __body["password_hash"] = password_hash __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -349,7 +355,7 @@ async def clear_cached_service_tokens( ) @_rewrite_parameters( - body_fields=True, + body_fields=("expiration", "metadata", "name", "role_descriptors"), ) async def create_api_key( self, @@ -365,6 +371,7 @@ async def create_api_key( t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, role_descriptors: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an API key for access without requiring basic authentication. @@ -391,25 +398,26 @@ async def create_api_key( """ __path = "/_security/api_key" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if expiration is not None: - __body["expiration"] = expiration if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata - if name is not None: - __body["name"] = name if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if role_descriptors is not None: - __body["role_descriptors"] = role_descriptors + if not __body: + if expiration is not None: + __body["expiration"] = expiration + if metadata is not None: + __body["metadata"] = metadata + if name is not None: + __body["name"] = name + if role_descriptors is not None: + __body["role_descriptors"] = role_descriptors __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -1212,7 +1220,14 @@ async def get_service_credentials( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "grant_type", + "kerberos_ticket", + "password", + "refresh_token", + "scope", + "username", + ), ) async def get_token( self, @@ -1232,6 +1247,7 @@ async def get_token( refresh_token: t.Optional[str] = None, scope: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a bearer token for access without requiring basic authentication. @@ -1247,27 +1263,28 @@ async def get_token( """ __path = "/_security/oauth2/token" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if grant_type is not None: - __body["grant_type"] = grant_type if human is not None: __query["human"] = human - if kerberos_ticket is not None: - __body["kerberos_ticket"] = kerberos_ticket - if password is not None: - __body["password"] = password if pretty is not None: __query["pretty"] = pretty - if refresh_token is not None: - __body["refresh_token"] = refresh_token - if scope is not None: - __body["scope"] = scope - if username is not None: - __body["username"] = username + if not __body: + if grant_type is not None: + __body["grant_type"] = grant_type + if kerberos_ticket is not None: + __body["kerberos_ticket"] = kerberos_ticket + if password is not None: + __body["password"] = password + if refresh_token is not None: + __body["refresh_token"] = refresh_token + if scope is not None: + __body["scope"] = scope + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -1402,14 +1419,23 @@ async def get_user_profile( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "api_key", + "grant_type", + "access_token", + "password", + "run_as", + "username", + ), ignore_deprecated_options={"api_key"}, ) async def grant_api_key( self, *, - api_key: t.Mapping[str, t.Any], - grant_type: t.Union["t.Literal['access_token', 'password']", str], + api_key: t.Optional[t.Mapping[str, t.Any]] = None, + grant_type: t.Optional[ + t.Union["t.Literal['access_token', 'password']", str] + ] = None, access_token: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -1418,6 +1444,7 @@ async def grant_api_key( pretty: t.Optional[bool] = None, run_as: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an API key on behalf of another user. @@ -1437,40 +1464,41 @@ async def grant_api_key( grant type, this parameter is required. It is not valid with other grant types. """ - if api_key is None: + if api_key is None and body is None: raise ValueError("Empty value passed for parameter 'api_key'") - if grant_type is None: + if grant_type is None and body is None: raise ValueError("Empty value passed for parameter 'grant_type'") __path = "/_security/api_key/grant" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if api_key is not None: - __body["api_key"] = api_key - if grant_type is not None: - __body["grant_type"] = grant_type - if access_token is not None: - __body["access_token"] = access_token + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if password is not None: - __body["password"] = password if pretty is not None: __query["pretty"] = pretty - if run_as is not None: - __body["run_as"] = run_as - if username is not None: - __body["username"] = username + if not __body: + if api_key is not None: + __body["api_key"] = api_key + if grant_type is not None: + __body["grant_type"] = grant_type + if access_token is not None: + __body["access_token"] = access_token + if password is not None: + __body["password"] = password + if run_as is not None: + __body["run_as"] = run_as + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("application", "cluster", "index"), ) async def has_privileges( self, @@ -1490,6 +1518,7 @@ async def has_privileges( human: t.Optional[bool] = None, index: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Determines whether the specified user has a specified list of privileges. @@ -1505,39 +1534,41 @@ async def has_privileges( __path = f"/_security/user/{_quote(user)}/_has_privileges" else: __path = "/_security/user/_has_privileges" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if application is not None: - __body["application"] = application - if cluster is not None: - __body["cluster"] = cluster + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index is not None: - __body["index"] = index if pretty is not None: __query["pretty"] = pretty + if not __body: + if application is not None: + __body["application"] = application + if cluster is not None: + __body["cluster"] = cluster + if index is not None: + __body["index"] = index __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("privileges", "uids"), ) async def has_privileges_user_profile( self, *, - privileges: t.Mapping[str, t.Any], - uids: t.Sequence[str], + privileges: t.Optional[t.Mapping[str, t.Any]] = None, + uids: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Determines whether the users associated with the specified profile IDs have all @@ -1549,17 +1580,13 @@ async def has_privileges_user_profile( :param uids: A list of profile IDs. The privileges are checked for associated users of the profiles. """ - if privileges is None: + if privileges is None and body is None: raise ValueError("Empty value passed for parameter 'privileges'") - if uids is None: + if uids is None and body is None: raise ValueError("Empty value passed for parameter 'uids'") __path = "/_security/profile/_has_privileges" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if privileges is not None: - __body["privileges"] = privileges - if uids is not None: - __body["uids"] = uids + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1568,13 +1595,18 @@ async def has_privileges_user_profile( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if privileges is not None: + __body["privileges"] = privileges + if uids is not None: + __body["uids"] = uids __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("id", "ids", "name", "owner", "realm_name", "username"), ) async def invalidate_api_key( self, @@ -1589,6 +1621,7 @@ async def invalidate_api_key( pretty: t.Optional[bool] = None, realm_name: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Invalidates one or more API keys. @@ -1611,34 +1644,35 @@ async def invalidate_api_key( """ __path = "/_security/api_key" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if id is not None: - __body["id"] = id - if ids is not None: - __body["ids"] = ids - if name is not None: - __body["name"] = name - if owner is not None: - __body["owner"] = owner if pretty is not None: __query["pretty"] = pretty - if realm_name is not None: - __body["realm_name"] = realm_name - if username is not None: - __body["username"] = username + if not __body: + if id is not None: + __body["id"] = id + if ids is not None: + __body["ids"] = ids + if name is not None: + __body["name"] = name + if owner is not None: + __body["owner"] = owner + if realm_name is not None: + __body["realm_name"] = realm_name + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("realm_name", "refresh_token", "token", "username"), ) async def invalidate_token( self, @@ -1651,6 +1685,7 @@ async def invalidate_token( refresh_token: t.Optional[str] = None, token: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Invalidates one or more access tokens or refresh tokens. @@ -1664,7 +1699,7 @@ async def invalidate_token( """ __path = "/_security/oauth2/token" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1673,14 +1708,15 @@ async def invalidate_token( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm_name is not None: - __body["realm_name"] = realm_name - if refresh_token is not None: - __body["refresh_token"] = refresh_token - if token is not None: - __body["token"] = token - if username is not None: - __body["username"] = username + if not __body: + if realm_name is not None: + __body["realm_name"] = realm_name + if refresh_token is not None: + __body["refresh_token"] = refresh_token + if token is not None: + __body["token"] = token + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, body=__body @@ -1692,7 +1728,10 @@ async def invalidate_token( async def put_privileges( self, *, - privileges: t.Mapping[str, t.Mapping[str, t.Mapping[str, t.Any]]], + privileges: t.Optional[ + t.Mapping[str, t.Mapping[str, t.Mapping[str, t.Any]]] + ] = None, + body: t.Optional[t.Mapping[str, t.Mapping[str, t.Mapping[str, t.Any]]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -1711,8 +1750,12 @@ async def put_privileges( this operation visible to search, if `wait_for` then wait for a refresh to make this operation visible to search, if `false` then do nothing with refreshes. """ - if privileges is None: - raise ValueError("Empty value passed for parameter 'privileges'") + if privileges is None and body is None: + raise ValueError( + "Empty value passed for parameters 'privileges' and 'body', one of them should be set." + ) + elif privileges is not None and body is not None: + raise ValueError("Cannot set both 'privileges' and 'body'") __path = "/_security/privilege" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -1725,14 +1768,22 @@ async def put_privileges( __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - __body = privileges + __body = privileges if privileges is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "applications", + "cluster", + "global_", + "indices", + "metadata", + "run_as", + "transient_metadata", + ), parameter_aliases={"global": "global_"}, ) async def put_role( @@ -1760,6 +1811,7 @@ async def put_role( ] = None, run_as: t.Optional[t.Sequence[str]] = None, transient_metadata: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Adds and updates roles in the native realm. @@ -1790,39 +1842,40 @@ async def put_role( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_security/role/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if applications is not None: - __body["applications"] = applications - if cluster is not None: - __body["cluster"] = cluster + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if global_ is not None: - __body["global"] = global_ if human is not None: __query["human"] = human - if indices is not None: - __body["indices"] = indices - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if run_as is not None: - __body["run_as"] = run_as - if transient_metadata is not None: - __body["transient_metadata"] = transient_metadata + if not __body: + if applications is not None: + __body["applications"] = applications + if cluster is not None: + __body["cluster"] = cluster + if global_ is not None: + __body["global"] = global_ + if indices is not None: + __body["indices"] = indices + if metadata is not None: + __body["metadata"] = metadata + if run_as is not None: + __body["run_as"] = run_as + if transient_metadata is not None: + __body["transient_metadata"] = transient_metadata __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("enabled", "metadata", "roles", "rules", "run_as"), ) async def put_role_mapping( self, @@ -1840,6 +1893,7 @@ async def put_role_mapping( roles: t.Optional[t.Sequence[str]] = None, rules: t.Optional[t.Mapping[str, t.Any]] = None, run_as: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates and updates role mappings. @@ -1859,35 +1913,44 @@ async def put_role_mapping( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_security/role_mapping/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if enabled is not None: - __body["enabled"] = enabled + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if roles is not None: - __body["roles"] = roles - if rules is not None: - __body["rules"] = rules - if run_as is not None: - __body["run_as"] = run_as + if not __body: + if enabled is not None: + __body["enabled"] = enabled + if metadata is not None: + __body["metadata"] = metadata + if roles is not None: + __body["roles"] = roles + if rules is not None: + __body["rules"] = rules + if run_as is not None: + __body["run_as"] = run_as __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "email", + "enabled", + "full_name", + "metadata", + "password", + "password_hash", + "roles", + ), ) async def put_user( self, @@ -1907,6 +1970,7 @@ async def put_user( t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, roles: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Adds and updates users in the native realm. These users are commonly referred @@ -1929,39 +1993,40 @@ async def put_user( if username in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'username'") __path = f"/_security/user/{_quote(username)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if email is not None: - __body["email"] = email - if enabled is not None: - __body["enabled"] = enabled + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if full_name is not None: - __body["full_name"] = full_name if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata - if password is not None: - __body["password"] = password - if password_hash is not None: - __body["password_hash"] = password_hash if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if roles is not None: - __body["roles"] = roles + if not __body: + if email is not None: + __body["email"] = email + if enabled is not None: + __body["enabled"] = enabled + if full_name is not None: + __body["full_name"] = full_name + if metadata is not None: + __body["metadata"] = metadata + if password is not None: + __body["password"] = password + if password_hash is not None: + __body["password_hash"] = password_hash + if roles is not None: + __body["roles"] = roles __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("from_", "query", "search_after", "size", "sort"), parameter_aliases={"from": "from_"}, ) async def query_api_keys( @@ -1984,6 +2049,7 @@ async def query_api_keys( ] ] = None, with_limited_by: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves information for API keys using a subset of query DSL @@ -2010,7 +2076,7 @@ async def query_api_keys( """ __path = "/_security/_query/api_key" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -2026,22 +2092,23 @@ async def query_api_keys( __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if search_after is not None: - __body["search_after"] = search_after - if size is not None: - __body["size"] = size - if sort is not None: - __body["sort"] = sort if with_limited_by is not None: __query["with_limited_by"] = with_limited_by + if not __body: + if from_ is not None: + __body["from"] = from_ + if query is not None: + __body["query"] = query + if search_after is not None: + __body["search_after"] = search_after + if size is not None: + __body["size"] = size + if sort is not None: + __body["sort"] = sort if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2052,18 +2119,19 @@ async def query_api_keys( ) @_rewrite_parameters( - body_fields=True, + body_fields=("content", "ids", "realm"), ) async def saml_authenticate( self, *, - content: str, - ids: t.Union[str, t.Sequence[str]], + content: t.Optional[str] = None, + ids: t.Optional[t.Union[str, t.Sequence[str]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, realm: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Exchanges a SAML Response message for an Elasticsearch access token and refresh @@ -2078,17 +2146,13 @@ async def saml_authenticate( :param realm: The name of the realm that should authenticate the SAML response. Useful in cases where many SAML realms are defined. """ - if content is None: + if content is None and body is None: raise ValueError("Empty value passed for parameter 'content'") - if ids is None: + if ids is None and body is None: raise ValueError("Empty value passed for parameter 'ids'") __path = "/_security/saml/authenticate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if content is not None: - __body["content"] = content - if ids is not None: - __body["ids"] = ids + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2097,27 +2161,33 @@ async def saml_authenticate( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm is not None: - __body["realm"] = realm + if not __body: + if content is not None: + __body["content"] = content + if ids is not None: + __body["ids"] = ids + if realm is not None: + __body["realm"] = realm __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("ids", "realm", "content", "query_string"), ) async def saml_complete_logout( self, *, - ids: t.Union[str, t.Sequence[str]], - realm: str, + ids: t.Optional[t.Union[str, t.Sequence[str]]] = None, + realm: t.Optional[str] = None, content: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, query_string: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Verifies the logout response sent from the SAML IdP @@ -2134,19 +2204,13 @@ async def saml_complete_logout( :param query_string: If the SAML IdP sends the logout response with the HTTP-Redirect binding, this field must be set to the query string of the redirect URI. """ - if ids is None: + if ids is None and body is None: raise ValueError("Empty value passed for parameter 'ids'") - if realm is None: + if realm is None and body is None: raise ValueError("Empty value passed for parameter 'realm'") __path = "/_security/saml/complete_logout" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if ids is not None: - __body["ids"] = ids - if realm is not None: - __body["realm"] = realm - if content is not None: - __body["content"] = content + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2155,26 +2219,34 @@ async def saml_complete_logout( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query_string is not None: - __body["query_string"] = query_string + if not __body: + if ids is not None: + __body["ids"] = ids + if realm is not None: + __body["realm"] = realm + if content is not None: + __body["content"] = content + if query_string is not None: + __body["query_string"] = query_string __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("query_string", "acs", "realm"), ) async def saml_invalidate( self, *, - query_string: str, + query_string: t.Optional[str] = None, acs: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, realm: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Consumes a SAML LogoutRequest @@ -2197,15 +2269,11 @@ async def saml_invalidate( :param realm: The name of the SAML realm in Elasticsearch the configuration. You must specify either this parameter or the acs parameter. """ - if query_string is None: + if query_string is None and body is None: raise ValueError("Empty value passed for parameter 'query_string'") __path = "/_security/saml/invalidate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if query_string is not None: - __body["query_string"] = query_string - if acs is not None: - __body["acs"] = acs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2214,25 +2282,31 @@ async def saml_invalidate( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm is not None: - __body["realm"] = realm + if not __body: + if query_string is not None: + __body["query_string"] = query_string + if acs is not None: + __body["acs"] = acs + if realm is not None: + __body["realm"] = realm __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("token", "refresh_token"), ) async def saml_logout( self, *, - token: str, + token: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, refresh_token: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Invalidates an access token and a refresh token that were generated via the SAML @@ -2247,13 +2321,11 @@ async def saml_logout( the SAML authenticate API. Alternatively, the most recent refresh token that was received after refreshing the original access token. """ - if token is None: + if token is None and body is None: raise ValueError("Empty value passed for parameter 'token'") __path = "/_security/saml/logout" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if token is not None: - __body["token"] = token + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2262,15 +2334,18 @@ async def saml_logout( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if refresh_token is not None: - __body["refresh_token"] = refresh_token + if not __body: + if token is not None: + __body["token"] = token + if refresh_token is not None: + __body["refresh_token"] = refresh_token __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("acs", "realm", "relay_state"), ) async def saml_prepare_authentication( self, @@ -2282,6 +2357,7 @@ async def saml_prepare_authentication( pretty: t.Optional[bool] = None, realm: t.Optional[str] = None, relay_state: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a SAML authentication request @@ -2299,10 +2375,8 @@ async def saml_prepare_authentication( is signed, this value is used as part of the signature computation. """ __path = "/_security/saml/prepare" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if acs is not None: - __body["acs"] = acs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2311,10 +2385,13 @@ async def saml_prepare_authentication( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm is not None: - __body["realm"] = realm - if relay_state is not None: - __body["relay_state"] = relay_state + if not __body: + if acs is not None: + __body["acs"] = acs + if realm is not None: + __body["realm"] = realm + if relay_state is not None: + __body["relay_state"] = relay_state __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2355,7 +2432,7 @@ async def saml_service_provider_metadata( ) @_rewrite_parameters( - body_fields=True, + body_fields=("data", "hint", "name", "size"), ) async def suggest_user_profiles( self, @@ -2368,6 +2445,7 @@ async def suggest_user_profiles( name: t.Optional[str] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Get suggestions for user profiles that match specified search criteria. @@ -2387,24 +2465,25 @@ async def suggest_user_profiles( :param size: Number of profiles to return. """ __path = "/_security/profile/_suggest" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if data is not None: - __body["data"] = data + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if hint is not None: - __body["hint"] = hint if human is not None: __query["human"] = human - if name is not None: - __body["name"] = name if pretty is not None: __query["pretty"] = pretty - if size is not None: - __body["size"] = size + if not __body: + if data is not None: + __body["data"] = data + if hint is not None: + __body["hint"] = hint + if name is not None: + __body["name"] = name + if size is not None: + __body["size"] = size if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2415,7 +2494,7 @@ async def suggest_user_profiles( ) @_rewrite_parameters( - body_fields=True, + body_fields=("metadata", "role_descriptors"), ) async def update_api_key( self, @@ -2427,6 +2506,7 @@ async def update_api_key( metadata: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, role_descriptors: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates attributes of an existing API key. @@ -2450,19 +2530,20 @@ async def update_api_key( raise ValueError("Empty value passed for parameter 'id'") __path = f"/_security/api_key/{_quote(id)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty - if role_descriptors is not None: - __body["role_descriptors"] = role_descriptors + if not __body: + if metadata is not None: + __body["metadata"] = metadata + if role_descriptors is not None: + __body["role_descriptors"] = role_descriptors if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2473,7 +2554,7 @@ async def update_api_key( ) @_rewrite_parameters( - body_fields=True, + body_fields=("data", "labels"), ) async def update_user_profile_data( self, @@ -2490,6 +2571,7 @@ async def update_user_profile_data( refresh: t.Optional[ t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Update application specific data for the user profile of the given unique ID. @@ -2512,10 +2594,8 @@ async def update_user_profile_data( if uid in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'uid'") __path = f"/_security/profile/{_quote(uid)}/_data" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if data is not None: - __body["data"] = data + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2526,12 +2606,15 @@ async def update_user_profile_data( __query["if_primary_term"] = if_primary_term if if_seq_no is not None: __query["if_seq_no"] = if_seq_no - if labels is not None: - __body["labels"] = labels if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh + if not __body: + if data is not None: + __body["data"] = data + if labels is not None: + __body["labels"] = labels __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/shutdown.py b/elasticsearch/_async/client/shutdown.py --- a/elasticsearch/_async/client/shutdown.py +++ b/elasticsearch/_async/client/shutdown.py @@ -126,14 +126,16 @@ async def get_node( ) @_rewrite_parameters( - body_fields=True, + body_fields=("reason", "type", "allocation_delay", "target_node_name"), ) async def put_node( self, *, node_id: str, - reason: str, - type: t.Union["t.Literal['remove', 'replace', 'restart']", str], + reason: t.Optional[str] = None, + type: t.Optional[ + t.Union["t.Literal['remove', 'replace', 'restart']", str] + ] = None, allocation_delay: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -146,6 +148,7 @@ async def put_node( timeout: t.Optional[ t.Union["t.Literal['d', 'h', 'm', 'micros', 'ms', 'nanos', 's']", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Adds a node to be shut down. Designed for indirect use by ECE/ESS and ECK. Direct @@ -188,19 +191,13 @@ async def put_node( """ if node_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'node_id'") - if reason is None: + if reason is None and body is None: raise ValueError("Empty value passed for parameter 'reason'") - if type is None: + if type is None and body is None: raise ValueError("Empty value passed for parameter 'type'") __path = f"/_nodes/{_quote(node_id)}/shutdown" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if reason is not None: - __body["reason"] = reason - if type is not None: - __body["type"] = type - if allocation_delay is not None: - __body["allocation_delay"] = allocation_delay + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -211,10 +208,17 @@ async def put_node( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if target_node_name is not None: - __body["target_node_name"] = target_node_name if timeout is not None: __query["timeout"] = timeout + if not __body: + if reason is not None: + __body["reason"] = reason + if type is not None: + __body["type"] = type + if allocation_delay is not None: + __body["allocation_delay"] = allocation_delay + if target_node_name is not None: + __body["target_node_name"] = target_node_name __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/slm.py b/elasticsearch/_async/client/slm.py --- a/elasticsearch/_async/client/slm.py +++ b/elasticsearch/_async/client/slm.py @@ -218,7 +218,7 @@ async def get_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=("config", "name", "repository", "retention", "schedule"), ) async def put_lifecycle( self, @@ -237,6 +237,7 @@ async def put_lifecycle( retention: t.Optional[t.Mapping[str, t.Any]] = None, schedule: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a snapshot lifecycle policy. @@ -265,10 +266,8 @@ async def put_lifecycle( if policy_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'policy_id'") __path = f"/_slm/policy/{_quote(policy_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if config is not None: - __body["config"] = config + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -277,18 +276,21 @@ async def put_lifecycle( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if name is not None: - __body["name"] = name if pretty is not None: __query["pretty"] = pretty - if repository is not None: - __body["repository"] = repository - if retention is not None: - __body["retention"] = retention - if schedule is not None: - __body["schedule"] = schedule if timeout is not None: __query["timeout"] = timeout + if not __body: + if config is not None: + __body["config"] = config + if name is not None: + __body["name"] = name + if repository is not None: + __body["repository"] = repository + if retention is not None: + __body["retention"] = retention + if schedule is not None: + __body["schedule"] = schedule if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/snapshot.py b/elasticsearch/_async/client/snapshot.py --- a/elasticsearch/_async/client/snapshot.py +++ b/elasticsearch/_async/client/snapshot.py @@ -69,7 +69,7 @@ async def cleanup_repository( ) @_rewrite_parameters( - body_fields=True, + body_fields=("indices",), ) async def clone( self, @@ -77,7 +77,7 @@ async def clone( repository: str, snapshot: str, target_snapshot: str, - indices: str, + indices: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -86,6 +86,7 @@ async def clone( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Clones indices from one snapshot into another snapshot in the same repository. @@ -105,13 +106,11 @@ async def clone( raise ValueError("Empty value passed for parameter 'snapshot'") if target_snapshot in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target_snapshot'") - if indices is None: + if indices is None and body is None: raise ValueError("Empty value passed for parameter 'indices'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}/_clone/{_quote(target_snapshot)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if indices is not None: - __body["indices"] = indices + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -124,13 +123,23 @@ async def clone( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if indices is not None: + __body["indices"] = indices __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "feature_states", + "ignore_unavailable", + "include_global_state", + "indices", + "metadata", + "partial", + ), ) async def create( self, @@ -151,6 +160,7 @@ async def create( partial: t.Optional[bool] = None, pretty: t.Optional[bool] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a snapshot in a repository. @@ -194,31 +204,32 @@ async def create( raise ValueError("Empty value passed for parameter 'snapshot'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if feature_states is not None: - __body["feature_states"] = feature_states if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_unavailable is not None: - __body["ignore_unavailable"] = ignore_unavailable - if include_global_state is not None: - __body["include_global_state"] = include_global_state - if indices is not None: - __body["indices"] = indices if master_timeout is not None: __query["master_timeout"] = master_timeout - if metadata is not None: - __body["metadata"] = metadata - if partial is not None: - __body["partial"] = partial if pretty is not None: __query["pretty"] = pretty if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if feature_states is not None: + __body["feature_states"] = feature_states + if ignore_unavailable is not None: + __body["ignore_unavailable"] = ignore_unavailable + if include_global_state is not None: + __body["include_global_state"] = include_global_state + if indices is not None: + __body["indices"] = indices + if metadata is not None: + __body["metadata"] = metadata + if partial is not None: + __body["partial"] = partial if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -229,14 +240,14 @@ async def create( ) @_rewrite_parameters( - body_fields=True, + body_fields=("settings", "type", "repository"), ) async def create_repository( self, *, name: str, - settings: t.Mapping[str, t.Any], - type: str, + settings: t.Optional[t.Mapping[str, t.Any]] = None, + type: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -247,6 +258,7 @@ async def create_repository( repository: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, verify: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a repository. @@ -263,17 +275,13 @@ async def create_repository( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if settings is None: + if settings is None and body is None: raise ValueError("Empty value passed for parameter 'settings'") - if type is None: + if type is None and body is None: raise ValueError("Empty value passed for parameter 'type'") __path = f"/_snapshot/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if settings is not None: - __body["settings"] = settings - if type is not None: - __body["type"] = type + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -284,12 +292,17 @@ async def create_repository( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if repository is not None: - __body["repository"] = repository if timeout is not None: __query["timeout"] = timeout if verify is not None: __query["verify"] = verify + if not __body: + if settings is not None: + __body["settings"] = settings + if type is not None: + __body["type"] = type + if repository is not None: + __body["repository"] = repository __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -553,7 +566,18 @@ async def get_repository( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "feature_states", + "ignore_index_settings", + "ignore_unavailable", + "include_aliases", + "include_global_state", + "index_settings", + "indices", + "partial", + "rename_pattern", + "rename_replacement", + ), ) async def restore( self, @@ -578,6 +602,7 @@ async def restore( rename_pattern: t.Optional[str] = None, rename_replacement: t.Optional[str] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Restores a snapshot. @@ -606,39 +631,40 @@ async def restore( raise ValueError("Empty value passed for parameter 'snapshot'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}/_restore" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if feature_states is not None: - __body["feature_states"] = feature_states if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_index_settings is not None: - __body["ignore_index_settings"] = ignore_index_settings - if ignore_unavailable is not None: - __body["ignore_unavailable"] = ignore_unavailable - if include_aliases is not None: - __body["include_aliases"] = include_aliases - if include_global_state is not None: - __body["include_global_state"] = include_global_state - if index_settings is not None: - __body["index_settings"] = index_settings - if indices is not None: - __body["indices"] = indices if master_timeout is not None: __query["master_timeout"] = master_timeout - if partial is not None: - __body["partial"] = partial if pretty is not None: __query["pretty"] = pretty - if rename_pattern is not None: - __body["rename_pattern"] = rename_pattern - if rename_replacement is not None: - __body["rename_replacement"] = rename_replacement if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if feature_states is not None: + __body["feature_states"] = feature_states + if ignore_index_settings is not None: + __body["ignore_index_settings"] = ignore_index_settings + if ignore_unavailable is not None: + __body["ignore_unavailable"] = ignore_unavailable + if include_aliases is not None: + __body["include_aliases"] = include_aliases + if include_global_state is not None: + __body["include_global_state"] = include_global_state + if index_settings is not None: + __body["index_settings"] = index_settings + if indices is not None: + __body["indices"] = indices + if partial is not None: + __body["partial"] = partial + if rename_pattern is not None: + __body["rename_pattern"] = rename_pattern + if rename_replacement is not None: + __body["rename_replacement"] = rename_replacement if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/client/sql.py b/elasticsearch/_async/client/sql.py --- a/elasticsearch/_async/client/sql.py +++ b/elasticsearch/_async/client/sql.py @@ -25,16 +25,17 @@ class SqlClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("cursor",), ) async def clear_cursor( self, *, - cursor: str, + cursor: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Clears the SQL cursor @@ -43,13 +44,11 @@ async def clear_cursor( :param cursor: Cursor to clear. """ - if cursor is None: + if cursor is None and body is None: raise ValueError("Empty value passed for parameter 'cursor'") __path = "/_sql/close" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if cursor is not None: - __body["cursor"] = cursor + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -58,6 +57,9 @@ async def clear_cursor( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if cursor is not None: + __body["cursor"] = cursor __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -192,7 +194,24 @@ async def get_async_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "catalog", + "columnar", + "cursor", + "fetch_size", + "field_multi_value_leniency", + "filter", + "index_using_frozen", + "keep_alive", + "keep_on_completion", + "page_timeout", + "params", + "query", + "request_timeout", + "runtime_mappings", + "time_zone", + "wait_for_completion_timeout", + ), ignore_deprecated_options={"params", "request_timeout"}, ) async def query( @@ -223,6 +242,7 @@ async def query( wait_for_completion_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Executes a SQL request @@ -261,62 +281,63 @@ async def query( the search doesn’t finish within this period, the search becomes async. """ __path = "/_sql" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if catalog is not None: - __body["catalog"] = catalog - if columnar is not None: - __body["columnar"] = columnar - if cursor is not None: - __body["cursor"] = cursor + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if fetch_size is not None: - __body["fetch_size"] = fetch_size - if field_multi_value_leniency is not None: - __body["field_multi_value_leniency"] = field_multi_value_leniency - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if format is not None: __query["format"] = format if human is not None: __query["human"] = human - if index_using_frozen is not None: - __body["index_using_frozen"] = index_using_frozen - if keep_alive is not None: - __body["keep_alive"] = keep_alive - if keep_on_completion is not None: - __body["keep_on_completion"] = keep_on_completion - if page_timeout is not None: - __body["page_timeout"] = page_timeout - if params is not None: - __body["params"] = params if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if request_timeout is not None: - __body["request_timeout"] = request_timeout - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if time_zone is not None: - __body["time_zone"] = time_zone - if wait_for_completion_timeout is not None: - __body["wait_for_completion_timeout"] = wait_for_completion_timeout + if not __body: + if catalog is not None: + __body["catalog"] = catalog + if columnar is not None: + __body["columnar"] = columnar + if cursor is not None: + __body["cursor"] = cursor + if fetch_size is not None: + __body["fetch_size"] = fetch_size + if field_multi_value_leniency is not None: + __body["field_multi_value_leniency"] = field_multi_value_leniency + if filter is not None: + __body["filter"] = filter + if index_using_frozen is not None: + __body["index_using_frozen"] = index_using_frozen + if keep_alive is not None: + __body["keep_alive"] = keep_alive + if keep_on_completion is not None: + __body["keep_on_completion"] = keep_on_completion + if page_timeout is not None: + __body["page_timeout"] = page_timeout + if params is not None: + __body["params"] = params + if query is not None: + __body["query"] = query + if request_timeout is not None: + __body["request_timeout"] = request_timeout + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if time_zone is not None: + __body["time_zone"] = time_zone + if wait_for_completion_timeout is not None: + __body["wait_for_completion_timeout"] = wait_for_completion_timeout __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("query", "fetch_size", "filter", "time_zone"), ) async def translate( self, *, - query: str, + query: t.Optional[str] = None, error_trace: t.Optional[bool] = None, fetch_size: t.Optional[int] = None, filter: t.Optional[t.Mapping[str, t.Any]] = None, @@ -324,6 +345,7 @@ async def translate( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, time_zone: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Translates SQL into Elasticsearch queries @@ -335,27 +357,28 @@ async def translate( :param filter: Elasticsearch query DSL for additional filtering. :param time_zone: ISO-8601 time zone ID for the search. """ - if query is None: + if query is None and body is None: raise ValueError("Empty value passed for parameter 'query'") __path = "/_sql/translate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if query is not None: - __body["query"] = query + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if fetch_size is not None: - __body["fetch_size"] = fetch_size - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if time_zone is not None: - __body["time_zone"] = time_zone + if not __body: + if query is not None: + __body["query"] = query + if fetch_size is not None: + __body["fetch_size"] = fetch_size + if filter is not None: + __body["filter"] = filter + if time_zone is not None: + __body["time_zone"] = time_zone __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/synonyms.py b/elasticsearch/_async/client/synonyms.py --- a/elasticsearch/_async/client/synonyms.py +++ b/elasticsearch/_async/client/synonyms.py @@ -219,17 +219,18 @@ async def get_synonyms_sets( ) @_rewrite_parameters( - body_fields=True, + body_fields=("synonyms_set",), ) async def put_synonym( self, *, id: str, - synonyms_set: t.Sequence[t.Mapping[str, t.Any]], + synonyms_set: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a synonyms set @@ -241,13 +242,11 @@ async def put_synonym( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if synonyms_set is None: + if synonyms_set is None and body is None: raise ValueError("Empty value passed for parameter 'synonyms_set'") __path = f"/_synonyms/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if synonyms_set is not None: - __body["synonyms_set"] = synonyms_set + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -256,24 +255,28 @@ async def put_synonym( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if synonyms_set is not None: + __body["synonyms_set"] = synonyms_set __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("synonyms",), ) async def put_synonym_rule( self, *, set_id: str, rule_id: str, - synonyms: t.Sequence[str], + synonyms: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a synonym rule in a synonym set @@ -288,13 +291,11 @@ async def put_synonym_rule( raise ValueError("Empty value passed for parameter 'set_id'") if rule_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'rule_id'") - if synonyms is None: + if synonyms is None and body is None: raise ValueError("Empty value passed for parameter 'synonyms'") __path = f"/_synonyms/{_quote(set_id)}/{_quote(rule_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if synonyms is not None: - __body["synonyms"] = synonyms + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -303,6 +304,9 @@ async def put_synonym_rule( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if synonyms is not None: + __body["synonyms"] = synonyms __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/text_structure.py b/elasticsearch/_async/client/text_structure.py --- a/elasticsearch/_async/client/text_structure.py +++ b/elasticsearch/_async/client/text_structure.py @@ -30,7 +30,8 @@ class TextStructureClient(NamespacedClient): async def find_structure( self, *, - text_files: t.Sequence[t.Any], + text_files: t.Optional[t.Sequence[t.Any]] = None, + body: t.Optional[t.Sequence[t.Any]] = None, charset: t.Optional[str] = None, column_names: t.Optional[str] = None, delimiter: t.Optional[str] = None, @@ -116,8 +117,12 @@ async def find_structure( the file :param timestamp_format: The Java time format of the timestamp field in the text. """ - if text_files is None: - raise ValueError("Empty value passed for parameter 'text_files'") + if text_files is None and body is None: + raise ValueError( + "Empty value passed for parameters 'text_files' and 'body', one of them should be set." + ) + elif text_files is not None and body is not None: + raise ValueError("Cannot set both 'text_files' and 'body'") __path = "/_text_structure/find_structure" __query: t.Dict[str, t.Any] = {} if charset is not None: @@ -148,7 +153,7 @@ async def find_structure( __query["timestamp_field"] = timestamp_field if timestamp_format is not None: __query["timestamp_format"] = timestamp_format - __body = text_files + __body = text_files if text_files is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", diff --git a/elasticsearch/_async/client/transform.py b/elasticsearch/_async/client/transform.py --- a/elasticsearch/_async/client/transform.py +++ b/elasticsearch/_async/client/transform.py @@ -195,7 +195,17 @@ async def get_transform_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "description", + "dest", + "frequency", + "latest", + "pivot", + "retention_policy", + "settings", + "source", + "sync", + ), ) async def preview_transform( self, @@ -215,6 +225,7 @@ async def preview_transform( source: t.Optional[t.Mapping[str, t.Any]] = None, sync: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Previews a transform. @@ -247,36 +258,37 @@ async def preview_transform( __path = f"/_transform/{_quote(transform_id)}/_preview" else: __path = "/_transform/_preview" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description - if dest is not None: - __body["dest"] = dest + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human - if latest is not None: - __body["latest"] = latest - if pivot is not None: - __body["pivot"] = pivot if pretty is not None: __query["pretty"] = pretty - if retention_policy is not None: - __body["retention_policy"] = retention_policy - if settings is not None: - __body["settings"] = settings - if source is not None: - __body["source"] = source - if sync is not None: - __body["sync"] = sync if timeout is not None: __query["timeout"] = timeout + if not __body: + if description is not None: + __body["description"] = description + if dest is not None: + __body["dest"] = dest + if frequency is not None: + __body["frequency"] = frequency + if latest is not None: + __body["latest"] = latest + if pivot is not None: + __body["pivot"] = pivot + if retention_policy is not None: + __body["retention_policy"] = retention_policy + if settings is not None: + __body["settings"] = settings + if source is not None: + __body["source"] = source + if sync is not None: + __body["sync"] = sync if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -287,15 +299,26 @@ async def preview_transform( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "dest", + "source", + "description", + "frequency", + "latest", + "meta", + "pivot", + "retention_policy", + "settings", + "sync", + ), parameter_aliases={"_meta": "meta"}, ) async def put_transform( self, *, transform_id: str, - dest: t.Mapping[str, t.Any], - source: t.Mapping[str, t.Any], + dest: t.Optional[t.Mapping[str, t.Any]] = None, + source: t.Optional[t.Mapping[str, t.Any]] = None, defer_validation: t.Optional[bool] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, @@ -310,6 +333,7 @@ async def put_transform( settings: t.Optional[t.Mapping[str, t.Any]] = None, sync: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a transform. @@ -348,45 +372,46 @@ async def put_transform( """ if transform_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'transform_id'") - if dest is None: + if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") - if source is None: + if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path = f"/_transform/{_quote(transform_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if dest is not None: - __body["dest"] = dest - if source is not None: - __body["source"] = source + __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_validation is not None: __query["defer_validation"] = defer_validation - if description is not None: - __body["description"] = description if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human - if latest is not None: - __body["latest"] = latest - if meta is not None: - __body["_meta"] = meta - if pivot is not None: - __body["pivot"] = pivot if pretty is not None: __query["pretty"] = pretty - if retention_policy is not None: - __body["retention_policy"] = retention_policy - if settings is not None: - __body["settings"] = settings - if sync is not None: - __body["sync"] = sync if timeout is not None: __query["timeout"] = timeout + if not __body: + if dest is not None: + __body["dest"] = dest + if source is not None: + __body["source"] = source + if description is not None: + __body["description"] = description + if frequency is not None: + __body["frequency"] = frequency + if latest is not None: + __body["latest"] = latest + if meta is not None: + __body["_meta"] = meta + if pivot is not None: + __body["pivot"] = pivot + if retention_policy is not None: + __body["retention_policy"] = retention_policy + if settings is not None: + __body["settings"] = settings + if sync is not None: + __body["sync"] = sync __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -589,7 +614,16 @@ async def stop_transform( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "description", + "dest", + "frequency", + "meta", + "retention_policy", + "settings", + "source", + "sync", + ), parameter_aliases={"_meta": "meta"}, ) async def update_transform( @@ -610,6 +644,7 @@ async def update_transform( source: t.Optional[t.Mapping[str, t.Any]] = None, sync: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a transform. @@ -639,35 +674,36 @@ async def update_transform( raise ValueError("Empty value passed for parameter 'transform_id'") __path = f"/_transform/{_quote(transform_id)}/_update" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_validation is not None: __query["defer_validation"] = defer_validation - if description is not None: - __body["description"] = description - if dest is not None: - __body["dest"] = dest if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if retention_policy is not None: - __body["retention_policy"] = retention_policy - if settings is not None: - __body["settings"] = settings - if source is not None: - __body["source"] = source - if sync is not None: - __body["sync"] = sync if timeout is not None: __query["timeout"] = timeout + if not __body: + if description is not None: + __body["description"] = description + if dest is not None: + __body["dest"] = dest + if frequency is not None: + __body["frequency"] = frequency + if meta is not None: + __body["_meta"] = meta + if retention_policy is not None: + __body["retention_policy"] = retention_policy + if settings is not None: + __body["settings"] = settings + if source is not None: + __body["source"] = source + if sync is not None: + __body["sync"] = sync __headers = {"accept": "application/json", "content-type": "application/json"} return await self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_async/client/watcher.py b/elasticsearch/_async/client/watcher.py --- a/elasticsearch/_async/client/watcher.py +++ b/elasticsearch/_async/client/watcher.py @@ -168,7 +168,15 @@ async def delete_watch( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "action_modes", + "alternative_input", + "ignore_condition", + "record_execution", + "simulated_actions", + "trigger_data", + "watch", + ), ) async def execute_watch( self, @@ -194,6 +202,7 @@ async def execute_watch( simulated_actions: t.Optional[t.Mapping[str, t.Any]] = None, trigger_data: t.Optional[t.Mapping[str, t.Any]] = None, watch: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Forces the execution of a stored watch. @@ -223,12 +232,8 @@ async def execute_watch( __path = f"/_watcher/watch/{_quote(id)}/_execute" else: __path = "/_watcher/watch/_execute" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if action_modes is not None: - __body["action_modes"] = action_modes - if alternative_input is not None: - __body["alternative_input"] = alternative_input + __body: t.Dict[str, t.Any] = body if body is not None else {} if debug is not None: __query["debug"] = debug if error_trace is not None: @@ -237,18 +242,23 @@ async def execute_watch( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_condition is not None: - __body["ignore_condition"] = ignore_condition if pretty is not None: __query["pretty"] = pretty - if record_execution is not None: - __body["record_execution"] = record_execution - if simulated_actions is not None: - __body["simulated_actions"] = simulated_actions - if trigger_data is not None: - __body["trigger_data"] = trigger_data - if watch is not None: - __body["watch"] = watch + if not __body: + if action_modes is not None: + __body["action_modes"] = action_modes + if alternative_input is not None: + __body["alternative_input"] = alternative_input + if ignore_condition is not None: + __body["ignore_condition"] = ignore_condition + if record_execution is not None: + __body["record_execution"] = record_execution + if simulated_actions is not None: + __body["simulated_actions"] = simulated_actions + if trigger_data is not None: + __body["trigger_data"] = trigger_data + if watch is not None: + __body["watch"] = watch if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -293,7 +303,15 @@ async def get_watch( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "actions", + "condition", + "input", + "metadata", + "throttle_period", + "transform", + "trigger", + ), ) async def put_watch( self, @@ -314,6 +332,7 @@ async def put_watch( transform: t.Optional[t.Mapping[str, t.Any]] = None, trigger: t.Optional[t.Mapping[str, t.Any]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new watch, or updates an existing one. @@ -338,14 +357,10 @@ async def put_watch( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/_watcher/watch/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if actions is not None: - __body["actions"] = actions + __body: t.Dict[str, t.Any] = body if body is not None else {} if active is not None: __query["active"] = active - if condition is not None: - __body["condition"] = condition if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -356,20 +371,25 @@ async def put_watch( __query["if_primary_term"] = if_primary_term if if_seq_no is not None: __query["if_seq_no"] = if_seq_no - if input is not None: - __body["input"] = input - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty - if throttle_period is not None: - __body["throttle_period"] = throttle_period - if transform is not None: - __body["transform"] = transform - if trigger is not None: - __body["trigger"] = trigger if version is not None: __query["version"] = version + if not __body: + if actions is not None: + __body["actions"] = actions + if condition is not None: + __body["condition"] = condition + if input is not None: + __body["input"] = input + if metadata is not None: + __body["metadata"] = metadata + if throttle_period is not None: + __body["throttle_period"] = throttle_period + if transform is not None: + __body["transform"] = transform + if trigger is not None: + __body["trigger"] = trigger if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -380,7 +400,7 @@ async def put_watch( ) @_rewrite_parameters( - body_fields=True, + body_fields=("from_", "query", "search_after", "size", "sort"), parameter_aliases={"from": "from_"}, ) async def query_watches( @@ -402,6 +422,7 @@ async def query_watches( t.Union[str, t.Mapping[str, t.Any]], ] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves stored watches. @@ -417,7 +438,7 @@ async def query_watches( """ __path = "/_watcher/_query/watches" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -433,20 +454,21 @@ async def query_watches( __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if search_after is not None: - __body["search_after"] = search_after - if size is not None: - __body["size"] = size - if sort is not None: - __body["sort"] = sort + if not __body: + if from_ is not None: + __body["from"] = from_ + if query is not None: + __body["query"] = query + if search_after is not None: + __body["search_after"] = search_after + if size is not None: + __body["size"] = size + if sort is not None: + __body["sort"] = sort if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_async/helpers.py b/elasticsearch/_async/helpers.py --- a/elasticsearch/_async/helpers.py +++ b/elasticsearch/_async/helpers.py @@ -445,7 +445,7 @@ def normalize_from_keyword(kw: MutableMapping[str, Any]) -> None: search_kwargs = kwargs.copy() search_kwargs["scroll"] = scroll search_kwargs["size"] = size - resp = await client.search(body=query, **search_kwargs) # type: ignore[call-arg] + resp = await client.search(body=query, **search_kwargs) scroll_id: Optional[str] = resp.get("_scroll_id") scroll_transport_kwargs = pop_transport_kwargs(scroll_kwargs) diff --git a/elasticsearch/_sync/client/__init__.py b/elasticsearch/_sync/client/__init__.py --- a/elasticsearch/_sync/client/__init__.py +++ b/elasticsearch/_sync/client/__init__.py @@ -610,7 +610,8 @@ def ping( def bulk( self, *, - operations: t.Sequence[t.Mapping[str, t.Any]], + operations: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -661,8 +662,12 @@ def bulk( before proceeding with the operation. Set to all or any positive integer up to the total number of shards in the index (`number_of_replicas+1`). """ - if operations is None: - raise ValueError("Empty value passed for parameter 'operations'") + if operations is None and body is None: + raise ValueError( + "Empty value passed for parameters 'operations' and 'body', one of them should be set." + ) + elif operations is not None and body is not None: + raise ValueError("Cannot set both 'operations' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_bulk" else: @@ -694,7 +699,7 @@ def bulk( __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards - __body = operations + __body = operations if operations is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -704,7 +709,7 @@ def bulk( ) @_rewrite_parameters( - body_fields=True, + body_fields=("scroll_id",), ) def clear_scroll( self, @@ -714,6 +719,7 @@ def clear_scroll( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, scroll_id: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Explicitly clears the search context for a scroll. @@ -724,7 +730,7 @@ def clear_scroll( """ __path = "/_search/scroll" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -733,8 +739,9 @@ def clear_scroll( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if scroll_id is not None: - __body["scroll_id"] = scroll_id + if not __body: + if scroll_id is not None: + __body["scroll_id"] = scroll_id if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -745,16 +752,17 @@ def clear_scroll( ) @_rewrite_parameters( - body_fields=True, + body_fields=("id",), ) def close_point_in_time( self, *, - id: str, + id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Close a point in time @@ -763,13 +771,11 @@ def close_point_in_time( :param id: The ID of the point-in-time. """ - if id is None: + if id is None and body is None: raise ValueError("Empty value passed for parameter 'id'") __path = "/_pit" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if id is not None: - __body["id"] = id + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -778,6 +784,9 @@ def close_point_in_time( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if id is not None: + __body["id"] = id if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -788,7 +797,7 @@ def close_point_in_time( ) @_rewrite_parameters( - body_fields=True, + body_fields=("query",), ) def count( self, @@ -820,6 +829,7 @@ def count( query: t.Optional[t.Mapping[str, t.Any]] = None, routing: t.Optional[str] = None, terminate_after: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns number of documents matching a query. @@ -868,7 +878,7 @@ def count( else: __path = "/_count" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if analyze_wildcard is not None: @@ -901,12 +911,13 @@ def count( __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if routing is not None: __query["routing"] = routing if terminate_after is not None: __query["terminate_after"] = terminate_after + if not __body: + if query is not None: + __body["query"] = query if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -924,7 +935,8 @@ def create( *, index: str, id: str, - document: t.Mapping[str, t.Any], + document: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -980,8 +992,12 @@ def create( raise ValueError("Empty value passed for parameter 'index'") if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if document is None: - raise ValueError("Empty value passed for parameter 'document'") + if document is None and body is None: + raise ValueError( + "Empty value passed for parameters 'document' and 'body', one of them should be set." + ) + elif document is not None and body is not None: + raise ValueError("Cannot set both 'document' and 'body'") __path = f"/{_quote(index)}/_create/{_quote(id)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -1006,7 +1022,7 @@ def create( __query["version_type"] = version_type if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards - __body = document + __body = document if document is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -1098,7 +1114,7 @@ def delete( ) @_rewrite_parameters( - body_fields=True, + body_fields=("max_docs", "query", "slice"), parameter_aliases={"from": "from_"}, ) def delete_by_query( @@ -1153,6 +1169,7 @@ def delete_by_query( t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Deletes documents matching the provided query. @@ -1226,7 +1243,7 @@ def delete_by_query( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_delete_by_query" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -1264,16 +1281,12 @@ def delete_by_query( __query["ignore_unavailable"] = ignore_unavailable if lenient is not None: __query["lenient"] = lenient - if max_docs is not None: - __body["max_docs"] = max_docs if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if refresh is not None: __query["refresh"] = refresh if request_cache is not None: @@ -1290,8 +1303,6 @@ def delete_by_query( __query["search_timeout"] = search_timeout if search_type is not None: __query["search_type"] = search_type - if slice is not None: - __body["slice"] = slice if slices is not None: __query["slices"] = slices if sort is not None: @@ -1308,6 +1319,13 @@ def delete_by_query( __query["wait_for_active_shards"] = wait_for_active_shards if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if max_docs is not None: + __body["max_docs"] = max_docs + if query is not None: + __body["query"] = query + if slice is not None: + __body["slice"] = slice __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -1586,7 +1604,7 @@ def exists_source( ) @_rewrite_parameters( - body_fields=True, + body_fields=("query",), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -1615,6 +1633,7 @@ def explain( source_excludes: t.Optional[t.Union[str, t.Sequence[str]]] = None, source_includes: t.Optional[t.Union[str, t.Sequence[str]]] = None, stored_fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns information about why a specific matches (or doesn't match) a query. @@ -1653,7 +1672,7 @@ def explain( raise ValueError("Empty value passed for parameter 'id'") __path = f"/{_quote(index)}/_explain/{_quote(id)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if analyze_wildcard is not None: __query["analyze_wildcard"] = analyze_wildcard if analyzer is not None: @@ -1676,8 +1695,6 @@ def explain( __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if routing is not None: __query["routing"] = routing if source is not None: @@ -1688,6 +1705,9 @@ def explain( __query["_source_includes"] = source_includes if stored_fields is not None: __query["stored_fields"] = stored_fields + if not __body: + if query is not None: + __body["query"] = query if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1698,7 +1718,7 @@ def explain( ) @_rewrite_parameters( - body_fields=True, + body_fields=("fields", "index_filter", "runtime_mappings"), ) def field_caps( self, @@ -1724,6 +1744,7 @@ def field_caps( pretty: t.Optional[bool] = None, runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, types: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns the information about the capabilities of fields among multiple indices. @@ -1762,15 +1783,13 @@ def field_caps( else: __path = "/_field_caps" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path if filters is not None: @@ -1781,14 +1800,17 @@ def field_caps( __query["ignore_unavailable"] = ignore_unavailable if include_unmapped is not None: __query["include_unmapped"] = include_unmapped - if index_filter is not None: - __body["index_filter"] = index_filter if pretty is not None: __query["pretty"] = pretty - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings if types is not None: __query["types"] = types + if not __body: + if fields is not None: + __body["fields"] = fields + if index_filter is not None: + __body["index_filter"] = index_filter + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2139,7 +2161,8 @@ def index( self, *, index: str, - document: t.Mapping[str, t.Any], + document: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -2203,8 +2226,12 @@ def index( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if document is None: - raise ValueError("Empty value passed for parameter 'document'") + if document is None and body is None: + raise ValueError( + "Empty value passed for parameters 'document' and 'body', one of them should be set." + ) + elif document is not None and body is not None: + raise ValueError("Cannot set both 'document' and 'body'") if index not in SKIP_IN_PATH and id not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_doc/{_quote(id)}" __method = "PUT" @@ -2244,7 +2271,7 @@ def index( __query["version_type"] = version_type if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards - __body = document + __body = document if document is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] __method, __path, params=__query, headers=__headers, body=__body @@ -2280,14 +2307,21 @@ def info( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "knn", + "docvalue_fields", + "fields", + "filter", + "source", + "stored_fields", + ), parameter_aliases={"_source": "source"}, ) def knn_search( self, *, index: t.Union[str, t.Sequence[str]], - knn: t.Mapping[str, t.Any], + knn: t.Optional[t.Mapping[str, t.Any]] = None, docvalue_fields: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -2300,6 +2334,7 @@ def knn_search( routing: t.Optional[str] = None, source: t.Optional[t.Union[bool, t.Mapping[str, t.Any]]] = None, stored_fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Performs a kNN search. @@ -2329,21 +2364,13 @@ def knn_search( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if knn is None: + if knn is None and body is None: raise ValueError("Empty value passed for parameter 'knn'") __path = f"/{_quote(index)}/_knn_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if knn is not None: - __body["knn"] = knn - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if fields is not None: - __body["fields"] = fields - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: @@ -2352,10 +2379,19 @@ def knn_search( __query["pretty"] = pretty if routing is not None: __query["routing"] = routing - if source is not None: - __body["_source"] = source - if stored_fields is not None: - __body["stored_fields"] = stored_fields + if not __body: + if knn is not None: + __body["knn"] = knn + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if fields is not None: + __body["fields"] = fields + if filter is not None: + __body["filter"] = filter + if source is not None: + __body["_source"] = source + if stored_fields is not None: + __body["stored_fields"] = stored_fields if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2366,7 +2402,7 @@ def knn_search( ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "ids"), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -2391,6 +2427,7 @@ def mget( source_excludes: t.Optional[t.Union[str, t.Sequence[str]]] = None, source_includes: t.Optional[t.Union[str, t.Sequence[str]]] = None, stored_fields: t.Optional[t.Union[str, t.Sequence[str]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to get multiple documents in one request. @@ -2426,18 +2463,14 @@ def mget( __path = f"/{_quote(index)}/_mget" else: __path = "/_mget" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ids is not None: - __body["ids"] = ids if preference is not None: __query["preference"] = preference if pretty is not None: @@ -2456,6 +2489,11 @@ def mget( __query["_source_includes"] = source_includes if stored_fields is not None: __query["stored_fields"] = stored_fields + if not __body: + if docs is not None: + __body["docs"] = docs + if ids is not None: + __body["ids"] = ids __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2467,7 +2505,8 @@ def mget( def msearch( self, *, - searches: t.Sequence[t.Mapping[str, t.Any]], + searches: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_indices: t.Optional[bool] = None, ccs_minimize_roundtrips: t.Optional[bool] = None, @@ -2536,8 +2575,12 @@ def msearch( :param typed_keys: Specifies whether aggregation and suggester names should be prefixed by their respective types in the response. """ - if searches is None: - raise ValueError("Empty value passed for parameter 'searches'") + if searches is None and body is None: + raise ValueError( + "Empty value passed for parameters 'searches' and 'body', one of them should be set." + ) + elif searches is not None and body is not None: + raise ValueError("Cannot set both 'searches' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_msearch" else: @@ -2575,7 +2618,7 @@ def msearch( __query["search_type"] = search_type if typed_keys is not None: __query["typed_keys"] = typed_keys - __body = searches + __body = searches if searches is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -2590,7 +2633,8 @@ def msearch( def msearch_template( self, *, - search_templates: t.Sequence[t.Mapping[str, t.Any]], + search_templates: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, ccs_minimize_roundtrips: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -2624,8 +2668,12 @@ def msearch_template( :param typed_keys: If `true`, the response prefixes aggregation and suggester names with their respective types. """ - if search_templates is None: - raise ValueError("Empty value passed for parameter 'search_templates'") + if search_templates is None and body is None: + raise ValueError( + "Empty value passed for parameters 'search_templates' and 'body', one of them should be set." + ) + elif search_templates is not None and body is not None: + raise ValueError("Cannot set both 'search_templates' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_msearch/template" else: @@ -2649,7 +2697,7 @@ def msearch_template( __query["search_type"] = search_type if typed_keys is not None: __query["typed_keys"] = typed_keys - __body = search_templates + __body = search_templates if search_templates is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -2659,7 +2707,7 @@ def msearch_template( ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "ids"), ) def mtermvectors( self, @@ -2684,6 +2732,7 @@ def mtermvectors( version_type: t.Optional[ t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns multiple termvectors in one request. @@ -2715,10 +2764,8 @@ def mtermvectors( __path = f"/{_quote(index)}/_mtermvectors" else: __path = "/_mtermvectors" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if field_statistics is not None: @@ -2729,8 +2776,6 @@ def mtermvectors( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ids is not None: - __body["ids"] = ids if offsets is not None: __query["offsets"] = offsets if payloads is not None: @@ -2751,6 +2796,11 @@ def mtermvectors( __query["version"] = version if version_type is not None: __query["version_type"] = version_type + if not __body: + if docs is not None: + __body["docs"] = docs + if ids is not None: + __body["ids"] = ids if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2830,13 +2880,13 @@ def open_point_in_time( ) @_rewrite_parameters( - body_fields=True, + body_fields=("script",), ) def put_script( self, *, id: str, - script: t.Mapping[str, t.Any], + script: t.Optional[t.Mapping[str, t.Any]] = None, context: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -2846,6 +2896,7 @@ def put_script( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a script. @@ -2867,7 +2918,7 @@ def put_script( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if script is None: + if script is None and body is None: raise ValueError("Empty value passed for parameter 'script'") if id not in SKIP_IN_PATH and context not in SKIP_IN_PATH: __path = f"/_scripts/{_quote(id)}/{_quote(context)}" @@ -2875,10 +2926,8 @@ def put_script( __path = f"/_scripts/{_quote(id)}" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if script is not None: - __body["script"] = script + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2891,18 +2940,21 @@ def put_script( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if script is not None: + __body["script"] = script __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("requests", "metric"), ) def rank_eval( self, *, - requests: t.Sequence[t.Mapping[str, t.Any]], + requests: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_indices: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -2920,6 +2972,7 @@ def rank_eval( metric: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, search_type: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to evaluate the quality of ranked search results over a set of typical @@ -2945,16 +2998,14 @@ def rank_eval( :param metric: Definition of the evaluation metric to calculate. :param search_type: Search operation type """ - if requests is None: + if requests is None and body is None: raise ValueError("Empty value passed for parameter 'requests'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_rank_eval" else: __path = "/_rank_eval" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if requests is not None: - __body["requests"] = requests + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if error_trace is not None: @@ -2967,25 +3018,28 @@ def rank_eval( __query["human"] = human if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if metric is not None: - __body["metric"] = metric if pretty is not None: __query["pretty"] = pretty if search_type is not None: __query["search_type"] = search_type + if not __body: + if requests is not None: + __body["requests"] = requests + if metric is not None: + __body["metric"] = metric __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("dest", "source", "conflicts", "max_docs", "script", "size"), ) def reindex( self, *, - dest: t.Mapping[str, t.Any], - source: t.Mapping[str, t.Any], + dest: t.Optional[t.Mapping[str, t.Any]] = None, + source: t.Optional[t.Mapping[str, t.Any]] = None, conflicts: t.Optional[t.Union["t.Literal['abort', 'proceed']", str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -3004,6 +3058,7 @@ def reindex( t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to copy documents from one index to another, optionally filtering the @@ -3036,27 +3091,19 @@ def reindex( :param wait_for_completion: If `true`, the request blocks until the operation is complete. """ - if dest is None: + if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") - if source is None: + if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path = "/_reindex" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if dest is not None: - __body["dest"] = dest - if source is not None: - __body["source"] = source - if conflicts is not None: - __body["conflicts"] = conflicts + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_docs is not None: - __body["max_docs"] = max_docs if pretty is not None: __query["pretty"] = pretty if refresh is not None: @@ -3065,12 +3112,8 @@ def reindex( __query["requests_per_second"] = requests_per_second if require_alias is not None: __query["require_alias"] = require_alias - if script is not None: - __body["script"] = script if scroll is not None: __query["scroll"] = scroll - if size is not None: - __body["size"] = size if slices is not None: __query["slices"] = slices if timeout is not None: @@ -3079,6 +3122,19 @@ def reindex( __query["wait_for_active_shards"] = wait_for_active_shards if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if dest is not None: + __body["dest"] = dest + if source is not None: + __body["source"] = source + if conflicts is not None: + __body["conflicts"] = conflicts + if max_docs is not None: + __body["max_docs"] = max_docs + if script is not None: + __body["script"] = script + if size is not None: + __body["size"] = size __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -3124,7 +3180,7 @@ def reindex_rethrottle( ) @_rewrite_parameters( - body_fields=True, + body_fields=("file", "params", "source"), ignore_deprecated_options={"params"}, ) def render_search_template( @@ -3138,6 +3194,7 @@ def render_search_template( params: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, source: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to use the Mustache language to pre-render a search definition. @@ -3158,21 +3215,22 @@ def render_search_template( else: __path = "/_render/template" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if file is not None: - __body["file"] = file if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if params is not None: - __body["params"] = params if pretty is not None: __query["pretty"] = pretty - if source is not None: - __body["source"] = source + if not __body: + if file is not None: + __body["file"] = file + if params is not None: + __body["params"] = params + if source is not None: + __body["source"] = source if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3183,7 +3241,7 @@ def render_search_template( ) @_rewrite_parameters( - body_fields=True, + body_fields=("context", "context_setup", "script"), ) def scripts_painless_execute( self, @@ -3195,6 +3253,7 @@ def scripts_painless_execute( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, script: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows an arbitrary script to be executed and a result to be returned @@ -3206,12 +3265,8 @@ def scripts_painless_execute( :param script: The Painless script to execute. """ __path = "/_scripts/painless/_execute" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if context is not None: - __body["context"] = context - if context_setup is not None: - __body["context_setup"] = context_setup + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3220,8 +3275,13 @@ def scripts_painless_execute( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if script is not None: - __body["script"] = script + if not __body: + if context is not None: + __body["context"] = context + if context_setup is not None: + __body["context_setup"] = context_setup + if script is not None: + __body["script"] = script if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3232,18 +3292,19 @@ def scripts_painless_execute( ) @_rewrite_parameters( - body_fields=True, + body_fields=("scroll_id", "scroll"), ) def scroll( self, *, - scroll_id: str, + scroll_id: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, rest_total_hits_as_int: t.Optional[bool] = None, scroll: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to retrieve a large numbers of results from a single search request. @@ -3256,13 +3317,11 @@ def scroll( is returned as an object. :param scroll: Period to retain the search context for scrolling. """ - if scroll_id is None: + if scroll_id is None and body is None: raise ValueError("Empty value passed for parameter 'scroll_id'") __path = "/_search/scroll" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if scroll_id is not None: - __body["scroll_id"] = scroll_id + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3273,8 +3332,11 @@ def scroll( __query["pretty"] = pretty if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int - if scroll is not None: - __body["scroll"] = scroll + if not __body: + if scroll_id is not None: + __body["scroll_id"] = scroll_id + if scroll is not None: + __body["scroll"] = scroll if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3285,7 +3347,42 @@ def scroll( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "aggs", + "collapse", + "docvalue_fields", + "explain", + "ext", + "fields", + "from_", + "highlight", + "indices_boost", + "knn", + "min_score", + "pit", + "post_filter", + "profile", + "query", + "rank", + "rescore", + "runtime_mappings", + "script_fields", + "search_after", + "seq_no_primary_term", + "size", + "slice", + "sort", + "source", + "stats", + "stored_fields", + "suggest", + "terminate_after", + "timeout", + "track_scores", + "track_total_hits", + "version", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -3386,6 +3483,7 @@ def search( track_total_hits: t.Optional[t.Union[bool, int]] = None, typed_keys: t.Optional[bool] = None, version: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns results matching a query. @@ -3578,8 +3676,8 @@ def search( __path = f"/{_quote(index)}/_search" else: __path = "/_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -3591,10 +3689,6 @@ def search( ): __query["sort"] = sort sort = None - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if allow_partial_search_results is not None: @@ -3607,104 +3701,50 @@ def search( __query["batched_reduce_size"] = batched_reduce_size if ccs_minimize_roundtrips is not None: __query["ccs_minimize_roundtrips"] = ccs_minimize_roundtrips - if collapse is not None: - __body["collapse"] = collapse if default_operator is not None: __query["default_operator"] = default_operator if df is not None: __query["df"] = df - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain - if ext is not None: - __body["ext"] = ext - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ - if highlight is not None: - __body["highlight"] = highlight if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indices_boost is not None: - __body["indices_boost"] = indices_boost - if knn is not None: - __body["knn"] = knn if lenient is not None: __query["lenient"] = lenient if max_concurrent_shard_requests is not None: __query["max_concurrent_shard_requests"] = max_concurrent_shard_requests if min_compatible_shard_node is not None: __query["min_compatible_shard_node"] = min_compatible_shard_node - if min_score is not None: - __body["min_score"] = min_score - if pit is not None: - __body["pit"] = pit - if post_filter is not None: - __body["post_filter"] = post_filter if pre_filter_shard_size is not None: __query["pre_filter_shard_size"] = pre_filter_shard_size if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query - if rank is not None: - __body["rank"] = rank if request_cache is not None: __query["request_cache"] = request_cache - if rescore is not None: - __body["rescore"] = rescore if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: __query["routing"] = routing - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields if scroll is not None: __query["scroll"] = scroll - if search_after is not None: - __body["search_after"] = search_after if search_type is not None: __query["search_type"] = search_type - if seq_no_primary_term is not None: - __body["seq_no_primary_term"] = seq_no_primary_term - if size is not None: - __body["size"] = size - if slice is not None: - __body["slice"] = slice - if sort is not None: - __body["sort"] = sort - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes - if stats is not None: - __body["stats"] = stats - if stored_fields is not None: - __body["stored_fields"] = stored_fields - if suggest is not None: - __body["suggest"] = suggest if suggest_field is not None: __query["suggest_field"] = suggest_field if suggest_mode is not None: @@ -3713,18 +3753,77 @@ def search( __query["suggest_size"] = suggest_size if suggest_text is not None: __query["suggest_text"] = suggest_text - if terminate_after is not None: - __body["terminate_after"] = terminate_after - if timeout is not None: - __body["timeout"] = timeout - if track_scores is not None: - __body["track_scores"] = track_scores - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits if typed_keys is not None: __query["typed_keys"] = typed_keys - if version is not None: - __body["version"] = version + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if collapse is not None: + __body["collapse"] = collapse + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if explain is not None: + __body["explain"] = explain + if ext is not None: + __body["ext"] = ext + if fields is not None: + __body["fields"] = fields + if from_ is not None: + __body["from"] = from_ + if highlight is not None: + __body["highlight"] = highlight + if indices_boost is not None: + __body["indices_boost"] = indices_boost + if knn is not None: + __body["knn"] = knn + if min_score is not None: + __body["min_score"] = min_score + if pit is not None: + __body["pit"] = pit + if post_filter is not None: + __body["post_filter"] = post_filter + if profile is not None: + __body["profile"] = profile + if query is not None: + __body["query"] = query + if rank is not None: + __body["rank"] = rank + if rescore is not None: + __body["rescore"] = rescore + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if search_after is not None: + __body["search_after"] = search_after + if seq_no_primary_term is not None: + __body["seq_no_primary_term"] = seq_no_primary_term + if size is not None: + __body["size"] = size + if slice is not None: + __body["slice"] = slice + if sort is not None: + __body["sort"] = sort + if source is not None: + __body["_source"] = source + if stats is not None: + __body["stats"] = stats + if stored_fields is not None: + __body["stored_fields"] = stored_fields + if suggest is not None: + __body["suggest"] = suggest + if terminate_after is not None: + __body["terminate_after"] = terminate_after + if timeout is not None: + __body["timeout"] = timeout + if track_scores is not None: + __body["track_scores"] = track_scores + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3735,7 +3834,22 @@ def search( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggs", + "buffer", + "exact_bounds", + "extent", + "fields", + "grid_agg", + "grid_precision", + "grid_type", + "query", + "runtime_mappings", + "size", + "sort", + "track_total_hits", + "with_labels", + ), ) def search_mvt( self, @@ -3770,6 +3884,7 @@ def search_mvt( ] = None, track_total_hits: t.Optional[t.Union[bool, int]] = None, with_labels: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> BinaryApiResponse: """ Searches a vector tile for geospatial values. Returns results as a binary Mapbox @@ -3831,8 +3946,8 @@ def search_mvt( if y in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'y'") __path = f"/{_quote(index)}/_mvt/{_quote(field)}/{_quote(zoom)}/{_quote(x)}/{_quote(y)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -3844,42 +3959,43 @@ def search_mvt( ): __query["sort"] = sort sort = None - if aggs is not None: - __body["aggs"] = aggs - if buffer is not None: - __body["buffer"] = buffer if error_trace is not None: __query["error_trace"] = error_trace - if exact_bounds is not None: - __body["exact_bounds"] = exact_bounds - if extent is not None: - __body["extent"] = extent - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if grid_agg is not None: - __body["grid_agg"] = grid_agg - if grid_precision is not None: - __body["grid_precision"] = grid_precision - if grid_type is not None: - __body["grid_type"] = grid_type if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if size is not None: - __body["size"] = size - if sort is not None: - __body["sort"] = sort - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits - if with_labels is not None: - __body["with_labels"] = with_labels + if not __body: + if aggs is not None: + __body["aggs"] = aggs + if buffer is not None: + __body["buffer"] = buffer + if exact_bounds is not None: + __body["exact_bounds"] = exact_bounds + if extent is not None: + __body["extent"] = extent + if fields is not None: + __body["fields"] = fields + if grid_agg is not None: + __body["grid_agg"] = grid_agg + if grid_precision is not None: + __body["grid_precision"] = grid_precision + if grid_type is not None: + __body["grid_type"] = grid_type + if query is not None: + __body["query"] = query + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if size is not None: + __body["size"] = size + if sort is not None: + __body["sort"] = sort + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if with_labels is not None: + __body["with_labels"] = with_labels if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/vnd.mapbox-vector-tile"} @@ -3968,7 +4084,7 @@ def search_shards( ) @_rewrite_parameters( - body_fields=True, + body_fields=("explain", "id", "params", "profile", "source"), ignore_deprecated_options={"params"}, ) def search_template( @@ -4004,6 +4120,7 @@ def search_template( ] = None, source: t.Optional[str] = None, typed_keys: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to use the Mustache language to pre-render a search definition. @@ -4053,7 +4170,7 @@ def search_template( else: __path = "/_search/template" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if ccs_minimize_roundtrips is not None: @@ -4062,26 +4179,18 @@ def search_template( __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if id is not None: - __body["id"] = id if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if params is not None: - __body["params"] = params if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: @@ -4090,23 +4199,40 @@ def search_template( __query["scroll"] = scroll if search_type is not None: __query["search_type"] = search_type - if source is not None: - __body["source"] = source if typed_keys is not None: __query["typed_keys"] = typed_keys + if not __body: + if explain is not None: + __body["explain"] = explain + if id is not None: + __body["id"] = id + if params is not None: + __body["params"] = params + if profile is not None: + __body["profile"] = profile + if source is not None: + __body["source"] = source __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "field", + "case_insensitive", + "index_filter", + "search_after", + "size", + "string", + "timeout", + ), ) def terms_enum( self, *, index: str, - field: str, + field: t.Optional[str] = None, case_insensitive: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -4117,6 +4243,7 @@ def terms_enum( size: t.Optional[int] = None, string: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ The terms enum API can be used to discover terms in the index that begin with @@ -4144,33 +4271,34 @@ def terms_enum( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if field is None: + if field is None and body is None: raise ValueError("Empty value passed for parameter 'field'") __path = f"/{_quote(index)}/_terms_enum" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if field is not None: - __body["field"] = field - if case_insensitive is not None: - __body["case_insensitive"] = case_insensitive + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index_filter is not None: - __body["index_filter"] = index_filter if pretty is not None: __query["pretty"] = pretty - if search_after is not None: - __body["search_after"] = search_after - if size is not None: - __body["size"] = size - if string is not None: - __body["string"] = string - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if field is not None: + __body["field"] = field + if case_insensitive is not None: + __body["case_insensitive"] = case_insensitive + if index_filter is not None: + __body["index_filter"] = index_filter + if search_after is not None: + __body["search_after"] = search_after + if size is not None: + __body["size"] = size + if string is not None: + __body["string"] = string + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -4181,7 +4309,7 @@ def terms_enum( ) @_rewrite_parameters( - body_fields=True, + body_fields=("doc", "filter", "per_field_analyzer"), ) def termvectors( self, @@ -4208,6 +4336,7 @@ def termvectors( version_type: t.Optional[ t.Union["t.Literal['external', 'external_gte', 'force', 'internal']", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns information and statistics about terms in the fields of a particular @@ -4246,18 +4375,14 @@ def termvectors( __path = f"/{_quote(index)}/_termvectors" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if doc is not None: - __body["doc"] = doc + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if field_statistics is not None: __query["field_statistics"] = field_statistics if fields is not None: __query["fields"] = fields - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: @@ -4266,8 +4391,6 @@ def termvectors( __query["offsets"] = offsets if payloads is not None: __query["payloads"] = payloads - if per_field_analyzer is not None: - __body["per_field_analyzer"] = per_field_analyzer if positions is not None: __query["positions"] = positions if preference is not None: @@ -4284,6 +4407,13 @@ def termvectors( __query["version"] = version if version_type is not None: __query["version_type"] = version_type + if not __body: + if doc is not None: + __body["doc"] = doc + if filter is not None: + __body["filter"] = filter + if per_field_analyzer is not None: + __body["per_field_analyzer"] = per_field_analyzer if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -4294,7 +4424,15 @@ def termvectors( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "detect_noop", + "doc", + "doc_as_upsert", + "script", + "scripted_upsert", + "source", + "upsert", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -4332,6 +4470,7 @@ def update( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates a document with a script or partial document. @@ -4379,14 +4518,8 @@ def update( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/{_quote(index)}/_update/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if detect_noop is not None: - __body["detect_noop"] = detect_noop - if doc is not None: - __body["doc"] = doc - if doc_as_upsert is not None: - __body["doc_as_upsert"] = doc_as_upsert + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -4409,29 +4542,36 @@ def update( __query["retry_on_conflict"] = retry_on_conflict if routing is not None: __query["routing"] = routing - if script is not None: - __body["script"] = script - if scripted_upsert is not None: - __body["scripted_upsert"] = scripted_upsert - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes if timeout is not None: __query["timeout"] = timeout - if upsert is not None: - __body["upsert"] = upsert if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if detect_noop is not None: + __body["detect_noop"] = detect_noop + if doc is not None: + __body["doc"] = doc + if doc_as_upsert is not None: + __body["doc_as_upsert"] = doc_as_upsert + if script is not None: + __body["script"] = script + if scripted_upsert is not None: + __body["scripted_upsert"] = scripted_upsert + if source is not None: + __body["_source"] = source + if upsert is not None: + __body["upsert"] = upsert __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("conflicts", "max_docs", "query", "script", "slice"), parameter_aliases={"from": "from_"}, ) def update_by_query( @@ -4488,6 +4628,7 @@ def update_by_query( t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Performs an update on every document in the index without changing the source, @@ -4569,7 +4710,7 @@ def update_by_query( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_update_by_query" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -4587,8 +4728,6 @@ def update_by_query( __query["analyze_wildcard"] = analyze_wildcard if analyzer is not None: __query["analyzer"] = analyzer - if conflicts is not None: - __body["conflicts"] = conflicts if default_operator is not None: __query["default_operator"] = default_operator if df is not None: @@ -4607,16 +4746,12 @@ def update_by_query( __query["ignore_unavailable"] = ignore_unavailable if lenient is not None: __query["lenient"] = lenient - if max_docs is not None: - __body["max_docs"] = max_docs if pipeline is not None: __query["pipeline"] = pipeline if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query if refresh is not None: __query["refresh"] = refresh if request_cache is not None: @@ -4625,8 +4760,6 @@ def update_by_query( __query["requests_per_second"] = requests_per_second if routing is not None: __query["routing"] = routing - if script is not None: - __body["script"] = script if scroll is not None: __query["scroll"] = scroll if scroll_size is not None: @@ -4635,8 +4768,6 @@ def update_by_query( __query["search_timeout"] = search_timeout if search_type is not None: __query["search_type"] = search_type - if slice is not None: - __body["slice"] = slice if slices is not None: __query["slices"] = slices if sort is not None: @@ -4655,6 +4786,17 @@ def update_by_query( __query["wait_for_active_shards"] = wait_for_active_shards if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if conflicts is not None: + __body["conflicts"] = conflicts + if max_docs is not None: + __body["max_docs"] = max_docs + if query is not None: + __body["query"] = query + if script is not None: + __body["script"] = script + if slice is not None: + __body["slice"] = slice if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/async_search.py b/elasticsearch/_sync/client/async_search.py --- a/elasticsearch/_sync/client/async_search.py +++ b/elasticsearch/_sync/client/async_search.py @@ -155,7 +155,41 @@ def status( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "aggs", + "collapse", + "docvalue_fields", + "explain", + "ext", + "fields", + "from_", + "highlight", + "indices_boost", + "knn", + "min_score", + "pit", + "post_filter", + "profile", + "query", + "rescore", + "runtime_mappings", + "script_fields", + "search_after", + "seq_no_primary_term", + "size", + "slice", + "sort", + "source", + "stats", + "stored_fields", + "suggest", + "terminate_after", + "timeout", + "track_scores", + "track_total_hits", + "version", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -260,6 +294,7 @@ def submit( wait_for_completion_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Executes a search request asynchronously. @@ -396,8 +431,8 @@ def submit( __path = f"/{_quote(index)}/_async_search" else: __path = "/_async_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -409,10 +444,6 @@ def submit( ): __query["sort"] = sort sort = None - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if allow_partial_search_results is not None: @@ -425,106 +456,54 @@ def submit( __query["batched_reduce_size"] = batched_reduce_size if ccs_minimize_roundtrips is not None: __query["ccs_minimize_roundtrips"] = ccs_minimize_roundtrips - if collapse is not None: - __body["collapse"] = collapse if default_operator is not None: __query["default_operator"] = default_operator if df is not None: __query["df"] = df - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain - if ext is not None: - __body["ext"] = ext - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ - if highlight is not None: - __body["highlight"] = highlight if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indices_boost is not None: - __body["indices_boost"] = indices_boost if keep_alive is not None: __query["keep_alive"] = keep_alive if keep_on_completion is not None: __query["keep_on_completion"] = keep_on_completion - if knn is not None: - __body["knn"] = knn if lenient is not None: __query["lenient"] = lenient if max_concurrent_shard_requests is not None: __query["max_concurrent_shard_requests"] = max_concurrent_shard_requests if min_compatible_shard_node is not None: __query["min_compatible_shard_node"] = min_compatible_shard_node - if min_score is not None: - __body["min_score"] = min_score - if pit is not None: - __body["pit"] = pit - if post_filter is not None: - __body["post_filter"] = post_filter if pre_filter_shard_size is not None: __query["pre_filter_shard_size"] = pre_filter_shard_size if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if request_cache is not None: __query["request_cache"] = request_cache - if rescore is not None: - __body["rescore"] = rescore if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: __query["routing"] = routing - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields if scroll is not None: __query["scroll"] = scroll - if search_after is not None: - __body["search_after"] = search_after if search_type is not None: __query["search_type"] = search_type - if seq_no_primary_term is not None: - __body["seq_no_primary_term"] = seq_no_primary_term - if size is not None: - __body["size"] = size - if slice is not None: - __body["slice"] = slice - if sort is not None: - __body["sort"] = sort - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes - if stats is not None: - __body["stats"] = stats - if stored_fields is not None: - __body["stored_fields"] = stored_fields - if suggest is not None: - __body["suggest"] = suggest if suggest_field is not None: __query["suggest_field"] = suggest_field if suggest_mode is not None: @@ -533,20 +512,77 @@ def submit( __query["suggest_size"] = suggest_size if suggest_text is not None: __query["suggest_text"] = suggest_text - if terminate_after is not None: - __body["terminate_after"] = terminate_after - if timeout is not None: - __body["timeout"] = timeout - if track_scores is not None: - __body["track_scores"] = track_scores - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits if typed_keys is not None: __query["typed_keys"] = typed_keys - if version is not None: - __body["version"] = version if wait_for_completion_timeout is not None: __query["wait_for_completion_timeout"] = wait_for_completion_timeout + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if collapse is not None: + __body["collapse"] = collapse + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if explain is not None: + __body["explain"] = explain + if ext is not None: + __body["ext"] = ext + if fields is not None: + __body["fields"] = fields + if from_ is not None: + __body["from"] = from_ + if highlight is not None: + __body["highlight"] = highlight + if indices_boost is not None: + __body["indices_boost"] = indices_boost + if knn is not None: + __body["knn"] = knn + if min_score is not None: + __body["min_score"] = min_score + if pit is not None: + __body["pit"] = pit + if post_filter is not None: + __body["post_filter"] = post_filter + if profile is not None: + __body["profile"] = profile + if query is not None: + __body["query"] = query + if rescore is not None: + __body["rescore"] = rescore + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if search_after is not None: + __body["search_after"] = search_after + if seq_no_primary_term is not None: + __body["seq_no_primary_term"] = seq_no_primary_term + if size is not None: + __body["size"] = size + if slice is not None: + __body["slice"] = slice + if sort is not None: + __body["sort"] = sort + if source is not None: + __body["_source"] = source + if stats is not None: + __body["stats"] = stats + if stored_fields is not None: + __body["stored_fields"] = stored_fields + if suggest is not None: + __body["suggest"] = suggest + if terminate_after is not None: + __body["terminate_after"] = terminate_after + if timeout is not None: + __body["timeout"] = timeout + if track_scores is not None: + __body["track_scores"] = track_scores + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/autoscaling.py b/elasticsearch/_sync/client/autoscaling.py --- a/elasticsearch/_sync/client/autoscaling.py +++ b/elasticsearch/_sync/client/autoscaling.py @@ -131,7 +131,8 @@ def put_autoscaling_policy( self, *, name: str, - policy: t.Mapping[str, t.Any], + policy: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -148,8 +149,12 @@ def put_autoscaling_policy( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if policy is None: - raise ValueError("Empty value passed for parameter 'policy'") + if policy is None and body is None: + raise ValueError( + "Empty value passed for parameters 'policy' and 'body', one of them should be set." + ) + elif policy is not None and body is not None: + raise ValueError("Cannot set both 'policy' and 'body'") __path = f"/_autoscaling/policy/{_quote(name)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -160,7 +165,7 @@ def put_autoscaling_policy( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = policy + __body = policy if policy is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/ccr.py b/elasticsearch/_sync/client/ccr.py --- a/elasticsearch/_sync/client/ccr.py +++ b/elasticsearch/_sync/client/ccr.py @@ -59,7 +59,20 @@ def delete_auto_follow_pattern( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "leader_index", + "max_outstanding_read_requests", + "max_outstanding_write_requests", + "max_read_request_operation_count", + "max_read_request_size", + "max_retry_delay", + "max_write_buffer_count", + "max_write_buffer_size", + "max_write_request_operation_count", + "max_write_request_size", + "read_poll_timeout", + "remote_cluster", + ), ) def follow( self, @@ -88,6 +101,7 @@ def follow( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new follower index configured to follow the referenced leader index. @@ -116,45 +130,48 @@ def follow( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_ccr/follow" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if leader_index is not None: - __body["leader_index"] = leader_index - if max_outstanding_read_requests is not None: - __body["max_outstanding_read_requests"] = max_outstanding_read_requests - if max_outstanding_write_requests is not None: - __body["max_outstanding_write_requests"] = max_outstanding_write_requests - if max_read_request_operation_count is not None: - __body[ - "max_read_request_operation_count" - ] = max_read_request_operation_count - if max_read_request_size is not None: - __body["max_read_request_size"] = max_read_request_size - if max_retry_delay is not None: - __body["max_retry_delay"] = max_retry_delay - if max_write_buffer_count is not None: - __body["max_write_buffer_count"] = max_write_buffer_count - if max_write_buffer_size is not None: - __body["max_write_buffer_size"] = max_write_buffer_size - if max_write_request_operation_count is not None: - __body[ - "max_write_request_operation_count" - ] = max_write_request_operation_count - if max_write_request_size is not None: - __body["max_write_request_size"] = max_write_request_size if pretty is not None: __query["pretty"] = pretty - if read_poll_timeout is not None: - __body["read_poll_timeout"] = read_poll_timeout - if remote_cluster is not None: - __body["remote_cluster"] = remote_cluster if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if leader_index is not None: + __body["leader_index"] = leader_index + if max_outstanding_read_requests is not None: + __body["max_outstanding_read_requests"] = max_outstanding_read_requests + if max_outstanding_write_requests is not None: + __body[ + "max_outstanding_write_requests" + ] = max_outstanding_write_requests + if max_read_request_operation_count is not None: + __body[ + "max_read_request_operation_count" + ] = max_read_request_operation_count + if max_read_request_size is not None: + __body["max_read_request_size"] = max_read_request_size + if max_retry_delay is not None: + __body["max_retry_delay"] = max_retry_delay + if max_write_buffer_count is not None: + __body["max_write_buffer_count"] = max_write_buffer_count + if max_write_buffer_size is not None: + __body["max_write_buffer_size"] = max_write_buffer_size + if max_write_request_operation_count is not None: + __body[ + "max_write_request_operation_count" + ] = max_write_request_operation_count + if max_write_request_size is not None: + __body["max_write_request_size"] = max_write_request_size + if read_poll_timeout is not None: + __body["read_poll_timeout"] = read_poll_timeout + if remote_cluster is not None: + __body["remote_cluster"] = remote_cluster __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -233,7 +250,12 @@ def follow_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "follower_cluster", + "follower_index", + "follower_index_uuid", + "leader_remote_cluster", + ), ) def forget_follower( self, @@ -247,6 +269,7 @@ def forget_follower( human: t.Optional[bool] = None, leader_remote_cluster: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Removes the follower retention leases from the leader. @@ -264,23 +287,24 @@ def forget_follower( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_ccr/forget_follower" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if follower_cluster is not None: - __body["follower_cluster"] = follower_cluster - if follower_index is not None: - __body["follower_index"] = follower_index - if follower_index_uuid is not None: - __body["follower_index_uuid"] = follower_index_uuid if human is not None: __query["human"] = human - if leader_remote_cluster is not None: - __body["leader_remote_cluster"] = leader_remote_cluster if pretty is not None: __query["pretty"] = pretty + if not __body: + if follower_cluster is not None: + __body["follower_cluster"] = follower_cluster + if follower_index is not None: + __body["follower_index"] = follower_index + if follower_index_uuid is not None: + __body["follower_index_uuid"] = follower_index_uuid + if leader_remote_cluster is not None: + __body["leader_remote_cluster"] = leader_remote_cluster __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -395,13 +419,29 @@ def pause_follow( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "remote_cluster", + "follow_index_pattern", + "leader_index_exclusion_patterns", + "leader_index_patterns", + "max_outstanding_read_requests", + "max_outstanding_write_requests", + "max_read_request_operation_count", + "max_read_request_size", + "max_retry_delay", + "max_write_buffer_count", + "max_write_buffer_size", + "max_write_request_operation_count", + "max_write_request_size", + "read_poll_timeout", + "settings", + ), ) def put_auto_follow_pattern( self, *, name: str, - remote_cluster: str, + remote_cluster: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, follow_index_pattern: t.Optional[str] = None, @@ -424,6 +464,7 @@ def put_auto_follow_pattern( t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, settings: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new named collection of auto-follow patterns against a specified remote @@ -477,53 +518,58 @@ def put_auto_follow_pattern( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if remote_cluster is None: + if remote_cluster is None and body is None: raise ValueError("Empty value passed for parameter 'remote_cluster'") __path = f"/_ccr/auto_follow/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if remote_cluster is not None: - __body["remote_cluster"] = remote_cluster + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if follow_index_pattern is not None: - __body["follow_index_pattern"] = follow_index_pattern if human is not None: __query["human"] = human - if leader_index_exclusion_patterns is not None: - __body["leader_index_exclusion_patterns"] = leader_index_exclusion_patterns - if leader_index_patterns is not None: - __body["leader_index_patterns"] = leader_index_patterns - if max_outstanding_read_requests is not None: - __body["max_outstanding_read_requests"] = max_outstanding_read_requests - if max_outstanding_write_requests is not None: - __body["max_outstanding_write_requests"] = max_outstanding_write_requests - if max_read_request_operation_count is not None: - __body[ - "max_read_request_operation_count" - ] = max_read_request_operation_count - if max_read_request_size is not None: - __body["max_read_request_size"] = max_read_request_size - if max_retry_delay is not None: - __body["max_retry_delay"] = max_retry_delay - if max_write_buffer_count is not None: - __body["max_write_buffer_count"] = max_write_buffer_count - if max_write_buffer_size is not None: - __body["max_write_buffer_size"] = max_write_buffer_size - if max_write_request_operation_count is not None: - __body[ - "max_write_request_operation_count" - ] = max_write_request_operation_count - if max_write_request_size is not None: - __body["max_write_request_size"] = max_write_request_size if pretty is not None: __query["pretty"] = pretty - if read_poll_timeout is not None: - __body["read_poll_timeout"] = read_poll_timeout - if settings is not None: - __body["settings"] = settings + if not __body: + if remote_cluster is not None: + __body["remote_cluster"] = remote_cluster + if follow_index_pattern is not None: + __body["follow_index_pattern"] = follow_index_pattern + if leader_index_exclusion_patterns is not None: + __body[ + "leader_index_exclusion_patterns" + ] = leader_index_exclusion_patterns + if leader_index_patterns is not None: + __body["leader_index_patterns"] = leader_index_patterns + if max_outstanding_read_requests is not None: + __body["max_outstanding_read_requests"] = max_outstanding_read_requests + if max_outstanding_write_requests is not None: + __body[ + "max_outstanding_write_requests" + ] = max_outstanding_write_requests + if max_read_request_operation_count is not None: + __body[ + "max_read_request_operation_count" + ] = max_read_request_operation_count + if max_read_request_size is not None: + __body["max_read_request_size"] = max_read_request_size + if max_retry_delay is not None: + __body["max_retry_delay"] = max_retry_delay + if max_write_buffer_count is not None: + __body["max_write_buffer_count"] = max_write_buffer_count + if max_write_buffer_size is not None: + __body["max_write_buffer_size"] = max_write_buffer_size + if max_write_request_operation_count is not None: + __body[ + "max_write_request_operation_count" + ] = max_write_request_operation_count + if max_write_request_size is not None: + __body["max_write_request_size"] = max_write_request_size + if read_poll_timeout is not None: + __body["read_poll_timeout"] = read_poll_timeout + if settings is not None: + __body["settings"] = settings __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -565,7 +611,18 @@ def resume_auto_follow_pattern( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "max_outstanding_read_requests", + "max_outstanding_write_requests", + "max_read_request_operation_count", + "max_read_request_size", + "max_retry_delay", + "max_write_buffer_count", + "max_write_buffer_size", + "max_write_request_operation_count", + "max_write_request_size", + "read_poll_timeout", + ), ) def resume_follow( self, @@ -589,6 +646,7 @@ def resume_follow( read_poll_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Resumes a follower index that has been paused @@ -611,39 +669,42 @@ def resume_follow( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_ccr/resume_follow" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_outstanding_read_requests is not None: - __body["max_outstanding_read_requests"] = max_outstanding_read_requests - if max_outstanding_write_requests is not None: - __body["max_outstanding_write_requests"] = max_outstanding_write_requests - if max_read_request_operation_count is not None: - __body[ - "max_read_request_operation_count" - ] = max_read_request_operation_count - if max_read_request_size is not None: - __body["max_read_request_size"] = max_read_request_size - if max_retry_delay is not None: - __body["max_retry_delay"] = max_retry_delay - if max_write_buffer_count is not None: - __body["max_write_buffer_count"] = max_write_buffer_count - if max_write_buffer_size is not None: - __body["max_write_buffer_size"] = max_write_buffer_size - if max_write_request_operation_count is not None: - __body[ - "max_write_request_operation_count" - ] = max_write_request_operation_count - if max_write_request_size is not None: - __body["max_write_request_size"] = max_write_request_size if pretty is not None: __query["pretty"] = pretty - if read_poll_timeout is not None: - __body["read_poll_timeout"] = read_poll_timeout + if not __body: + if max_outstanding_read_requests is not None: + __body["max_outstanding_read_requests"] = max_outstanding_read_requests + if max_outstanding_write_requests is not None: + __body[ + "max_outstanding_write_requests" + ] = max_outstanding_write_requests + if max_read_request_operation_count is not None: + __body[ + "max_read_request_operation_count" + ] = max_read_request_operation_count + if max_read_request_size is not None: + __body["max_read_request_size"] = max_read_request_size + if max_retry_delay is not None: + __body["max_retry_delay"] = max_retry_delay + if max_write_buffer_count is not None: + __body["max_write_buffer_count"] = max_write_buffer_count + if max_write_buffer_size is not None: + __body["max_write_buffer_size"] = max_write_buffer_size + if max_write_request_operation_count is not None: + __body[ + "max_write_request_operation_count" + ] = max_write_request_operation_count + if max_write_request_size is not None: + __body["max_write_request_size"] = max_write_request_size + if read_poll_timeout is not None: + __body["read_poll_timeout"] = read_poll_timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/cluster.py b/elasticsearch/_sync/client/cluster.py --- a/elasticsearch/_sync/client/cluster.py +++ b/elasticsearch/_sync/client/cluster.py @@ -25,7 +25,7 @@ class ClusterClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("current_node", "index", "primary", "shard"), ) def allocation_explain( self, @@ -40,6 +40,7 @@ def allocation_explain( pretty: t.Optional[bool] = None, primary: t.Optional[bool] = None, shard: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Provides explanations for shard allocations in the cluster. @@ -59,10 +60,8 @@ def allocation_explain( for. """ __path = "/_cluster/allocation/explain" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if current_node is not None: - __body["current_node"] = current_node + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -73,14 +72,17 @@ def allocation_explain( __query["include_disk_info"] = include_disk_info if include_yes_decisions is not None: __query["include_yes_decisions"] = include_yes_decisions - if index is not None: - __body["index"] = index if pretty is not None: __query["pretty"] = pretty - if primary is not None: - __body["primary"] = primary - if shard is not None: - __body["shard"] = shard + if not __body: + if current_node is not None: + __body["current_node"] = current_node + if index is not None: + __body["index"] = index + if primary is not None: + __body["primary"] = primary + if shard is not None: + __body["shard"] = shard if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -593,14 +595,14 @@ def post_voting_config_exclusions( ) @_rewrite_parameters( - body_fields=True, + body_fields=("template", "allow_auto_create", "meta", "version"), parameter_aliases={"_meta": "meta"}, ) def put_component_template( self, *, name: str, - template: t.Mapping[str, t.Any], + template: t.Optional[t.Mapping[str, t.Any]] = None, allow_auto_create: t.Optional[bool] = None, create: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -612,6 +614,7 @@ def put_component_template( meta: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a component template @@ -649,15 +652,11 @@ def put_component_template( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if template is None: + if template is None and body is None: raise ValueError("Empty value passed for parameter 'template'") __path = f"/_component_template/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if template is not None: - __body["template"] = template - if allow_auto_create is not None: - __body["allow_auto_create"] = allow_auto_create + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create if error_trace is not None: @@ -668,19 +667,24 @@ def put_component_template( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if version is not None: - __body["version"] = version + if not __body: + if template is not None: + __body["template"] = template + if allow_auto_create is not None: + __body["allow_auto_create"] = allow_auto_create + if meta is not None: + __body["_meta"] = meta + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("persistent", "transient"), ) def put_settings( self, @@ -696,6 +700,7 @@ def put_settings( pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, transient: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the cluster settings. @@ -710,7 +715,7 @@ def put_settings( """ __path = "/_cluster/settings" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -721,14 +726,15 @@ def put_settings( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if persistent is not None: - __body["persistent"] = persistent if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout - if transient is not None: - __body["transient"] = transient + if not __body: + if persistent is not None: + __body["persistent"] = persistent + if transient is not None: + __body["transient"] = transient __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -764,7 +770,7 @@ def remote_info( ) @_rewrite_parameters( - body_fields=True, + body_fields=("commands",), ) def reroute( self, @@ -782,6 +788,7 @@ def reroute( pretty: t.Optional[bool] = None, retry_failed: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to manually change the allocation of individual shards in the cluster. @@ -803,10 +810,8 @@ def reroute( the timeout expires, the request fails and returns an error. """ __path = "/_cluster/reroute" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if commands is not None: - __body["commands"] = commands + __body: t.Dict[str, t.Any] = body if body is not None else {} if dry_run is not None: __query["dry_run"] = dry_run if error_trace is not None: @@ -827,6 +832,9 @@ def reroute( __query["retry_failed"] = retry_failed if timeout is not None: __query["timeout"] = timeout + if not __body: + if commands is not None: + __body["commands"] = commands if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/enrich.py b/elasticsearch/_sync/client/enrich.py --- a/elasticsearch/_sync/client/enrich.py +++ b/elasticsearch/_sync/client/enrich.py @@ -134,7 +134,7 @@ def get_policy( ) @_rewrite_parameters( - body_fields=True, + body_fields=("geo_match", "match", "range"), ) def put_policy( self, @@ -147,6 +147,7 @@ def put_policy( match: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, range: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new enrich policy. @@ -164,21 +165,22 @@ def put_policy( raise ValueError("Empty value passed for parameter 'name'") __path = f"/_enrich/policy/{_quote(name)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if geo_match is not None: - __body["geo_match"] = geo_match if human is not None: __query["human"] = human - if match is not None: - __body["match"] = match if pretty is not None: __query["pretty"] = pretty - if range is not None: - __body["range"] = range + if not __body: + if geo_match is not None: + __body["geo_match"] = geo_match + if match is not None: + __body["match"] = match + if range is not None: + __body["range"] = range __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/eql.py b/elasticsearch/_sync/client/eql.py --- a/elasticsearch/_sync/client/eql.py +++ b/elasticsearch/_sync/client/eql.py @@ -145,13 +145,28 @@ def get_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "query", + "case_sensitive", + "event_category_field", + "fetch_size", + "fields", + "filter", + "keep_alive", + "keep_on_completion", + "result_position", + "runtime_mappings", + "size", + "tiebreaker_field", + "timestamp_field", + "wait_for_completion_timeout", + ), ) def search( self, *, index: t.Union[str, t.Sequence[str]], - query: str, + query: t.Optional[str] = None, allow_no_indices: t.Optional[bool] = None, case_sensitive: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -185,6 +200,7 @@ def search( wait_for_completion_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Returns results matching a query expressed in Event Query Language (EQL) @@ -219,53 +235,54 @@ def search( """ if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") - if query is None: + if query is None and body is None: raise ValueError("Empty value passed for parameter 'query'") __path = f"/{_quote(index)}/_eql/search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if query is not None: - __body["query"] = query + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if case_sensitive is not None: - __body["case_sensitive"] = case_sensitive if error_trace is not None: __query["error_trace"] = error_trace - if event_category_field is not None: - __body["event_category_field"] = event_category_field if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if fetch_size is not None: - __body["fetch_size"] = fetch_size - if fields is not None: - __body["fields"] = fields - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if keep_alive is not None: - __body["keep_alive"] = keep_alive - if keep_on_completion is not None: - __body["keep_on_completion"] = keep_on_completion if pretty is not None: __query["pretty"] = pretty - if result_position is not None: - __body["result_position"] = result_position - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if size is not None: - __body["size"] = size - if tiebreaker_field is not None: - __body["tiebreaker_field"] = tiebreaker_field - if timestamp_field is not None: - __body["timestamp_field"] = timestamp_field - if wait_for_completion_timeout is not None: - __body["wait_for_completion_timeout"] = wait_for_completion_timeout + if not __body: + if query is not None: + __body["query"] = query + if case_sensitive is not None: + __body["case_sensitive"] = case_sensitive + if event_category_field is not None: + __body["event_category_field"] = event_category_field + if fetch_size is not None: + __body["fetch_size"] = fetch_size + if fields is not None: + __body["fields"] = fields + if filter is not None: + __body["filter"] = filter + if keep_alive is not None: + __body["keep_alive"] = keep_alive + if keep_on_completion is not None: + __body["keep_on_completion"] = keep_on_completion + if result_position is not None: + __body["result_position"] = result_position + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if size is not None: + __body["size"] = size + if tiebreaker_field is not None: + __body["tiebreaker_field"] = tiebreaker_field + if timestamp_field is not None: + __body["timestamp_field"] = timestamp_field + if wait_for_completion_timeout is not None: + __body["wait_for_completion_timeout"] = wait_for_completion_timeout __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/fleet.py b/elasticsearch/_sync/client/fleet.py --- a/elasticsearch/_sync/client/fleet.py +++ b/elasticsearch/_sync/client/fleet.py @@ -87,7 +87,8 @@ def global_checkpoints( def msearch( self, *, - searches: t.Sequence[t.Mapping[str, t.Any]], + searches: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, index: t.Optional[str] = None, allow_no_indices: t.Optional[bool] = None, allow_partial_search_results: t.Optional[bool] = None, @@ -163,8 +164,12 @@ def msearch( has become visible for search. Defaults to an empty list which will cause Elasticsearch to immediately execute the search. """ - if searches is None: - raise ValueError("Empty value passed for parameter 'searches'") + if searches is None and body is None: + raise ValueError( + "Empty value passed for parameters 'searches' and 'body', one of them should be set." + ) + elif searches is not None and body is not None: + raise ValueError("Cannot set both 'searches' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_fleet/_fleet_msearch" else: @@ -204,7 +209,7 @@ def msearch( __query["typed_keys"] = typed_keys if wait_for_checkpoints is not None: __query["wait_for_checkpoints"] = wait_for_checkpoints - __body = searches + __body = searches if searches is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -214,7 +219,40 @@ def msearch( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "aggs", + "collapse", + "docvalue_fields", + "explain", + "ext", + "fields", + "from_", + "highlight", + "indices_boost", + "min_score", + "pit", + "post_filter", + "profile", + "query", + "rescore", + "runtime_mappings", + "script_fields", + "search_after", + "seq_no_primary_term", + "size", + "slice", + "sort", + "source", + "stats", + "stored_fields", + "suggest", + "terminate_after", + "timeout", + "track_scores", + "track_total_hits", + "version", + ), parameter_aliases={ "_source": "source", "_source_excludes": "source_excludes", @@ -312,6 +350,7 @@ def search( typed_keys: t.Optional[bool] = None, version: t.Optional[bool] = None, wait_for_checkpoints: t.Optional[t.Sequence[int]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Search API where the search will only be executed after specified checkpoints @@ -421,8 +460,8 @@ def search( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_fleet/_fleet_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -434,10 +473,6 @@ def search( ): __query["sort"] = sort sort = None - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices if allow_partial_search_results is not None: @@ -450,100 +485,50 @@ def search( __query["batched_reduce_size"] = batched_reduce_size if ccs_minimize_roundtrips is not None: __query["ccs_minimize_roundtrips"] = ccs_minimize_roundtrips - if collapse is not None: - __body["collapse"] = collapse if default_operator is not None: __query["default_operator"] = default_operator if df is not None: __query["df"] = df - if docvalue_fields is not None: - __body["docvalue_fields"] = docvalue_fields if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if explain is not None: - __body["explain"] = explain - if ext is not None: - __body["ext"] = ext - if fields is not None: - __body["fields"] = fields if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ - if highlight is not None: - __body["highlight"] = highlight if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indices_boost is not None: - __body["indices_boost"] = indices_boost if lenient is not None: __query["lenient"] = lenient if max_concurrent_shard_requests is not None: __query["max_concurrent_shard_requests"] = max_concurrent_shard_requests if min_compatible_shard_node is not None: __query["min_compatible_shard_node"] = min_compatible_shard_node - if min_score is not None: - __body["min_score"] = min_score - if pit is not None: - __body["pit"] = pit - if post_filter is not None: - __body["post_filter"] = post_filter if pre_filter_shard_size is not None: __query["pre_filter_shard_size"] = pre_filter_shard_size if preference is not None: __query["preference"] = preference if pretty is not None: __query["pretty"] = pretty - if profile is not None: - __body["profile"] = profile if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if request_cache is not None: __query["request_cache"] = request_cache - if rescore is not None: - __body["rescore"] = rescore if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int if routing is not None: __query["routing"] = routing - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields if scroll is not None: __query["scroll"] = scroll - if search_after is not None: - __body["search_after"] = search_after if search_type is not None: __query["search_type"] = search_type - if seq_no_primary_term is not None: - __body["seq_no_primary_term"] = seq_no_primary_term - if size is not None: - __body["size"] = size - if slice is not None: - __body["slice"] = slice - if sort is not None: - __body["sort"] = sort - if source is not None: - __body["_source"] = source if source_excludes is not None: __query["_source_excludes"] = source_excludes if source_includes is not None: __query["_source_includes"] = source_includes - if stats is not None: - __body["stats"] = stats - if stored_fields is not None: - __body["stored_fields"] = stored_fields - if suggest is not None: - __body["suggest"] = suggest if suggest_field is not None: __query["suggest_field"] = suggest_field if suggest_mode is not None: @@ -552,20 +537,75 @@ def search( __query["suggest_size"] = suggest_size if suggest_text is not None: __query["suggest_text"] = suggest_text - if terminate_after is not None: - __body["terminate_after"] = terminate_after - if timeout is not None: - __body["timeout"] = timeout - if track_scores is not None: - __body["track_scores"] = track_scores - if track_total_hits is not None: - __body["track_total_hits"] = track_total_hits if typed_keys is not None: __query["typed_keys"] = typed_keys - if version is not None: - __body["version"] = version if wait_for_checkpoints is not None: __query["wait_for_checkpoints"] = wait_for_checkpoints + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if collapse is not None: + __body["collapse"] = collapse + if docvalue_fields is not None: + __body["docvalue_fields"] = docvalue_fields + if explain is not None: + __body["explain"] = explain + if ext is not None: + __body["ext"] = ext + if fields is not None: + __body["fields"] = fields + if from_ is not None: + __body["from"] = from_ + if highlight is not None: + __body["highlight"] = highlight + if indices_boost is not None: + __body["indices_boost"] = indices_boost + if min_score is not None: + __body["min_score"] = min_score + if pit is not None: + __body["pit"] = pit + if post_filter is not None: + __body["post_filter"] = post_filter + if profile is not None: + __body["profile"] = profile + if query is not None: + __body["query"] = query + if rescore is not None: + __body["rescore"] = rescore + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if search_after is not None: + __body["search_after"] = search_after + if seq_no_primary_term is not None: + __body["seq_no_primary_term"] = seq_no_primary_term + if size is not None: + __body["size"] = size + if slice is not None: + __body["slice"] = slice + if sort is not None: + __body["sort"] = sort + if source is not None: + __body["_source"] = source + if stats is not None: + __body["stats"] = stats + if stored_fields is not None: + __body["stored_fields"] = stored_fields + if suggest is not None: + __body["suggest"] = suggest + if terminate_after is not None: + __body["terminate_after"] = terminate_after + if timeout is not None: + __body["timeout"] = timeout + if track_scores is not None: + __body["track_scores"] = track_scores + if track_total_hits is not None: + __body["track_total_hits"] = track_total_hits + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/graph.py b/elasticsearch/_sync/client/graph.py --- a/elasticsearch/_sync/client/graph.py +++ b/elasticsearch/_sync/client/graph.py @@ -25,7 +25,7 @@ class GraphClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("connections", "controls", "query", "vertices"), ) def explore( self, @@ -41,6 +41,7 @@ def explore( routing: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, vertices: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Explore extracted and summarized information about the documents and terms in @@ -64,12 +65,8 @@ def explore( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_graph/explore" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if connections is not None: - __body["connections"] = connections - if controls is not None: - __body["controls"] = controls + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -78,14 +75,19 @@ def explore( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query if routing is not None: __query["routing"] = routing if timeout is not None: __query["timeout"] = timeout - if vertices is not None: - __body["vertices"] = vertices + if not __body: + if connections is not None: + __body["connections"] = connections + if controls is not None: + __body["controls"] = controls + if query is not None: + __body["query"] = query + if vertices is not None: + __body["vertices"] = vertices if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/ilm.py b/elasticsearch/_sync/client/ilm.py --- a/elasticsearch/_sync/client/ilm.py +++ b/elasticsearch/_sync/client/ilm.py @@ -212,7 +212,7 @@ def get_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=("legacy_template_to_delete", "node_attribute"), ) def migrate_to_data_tiers( self, @@ -224,6 +224,7 @@ def migrate_to_data_tiers( legacy_template_to_delete: t.Optional[str] = None, node_attribute: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Migrates the indices and ILM policies away from custom node attribute allocation @@ -239,7 +240,7 @@ def migrate_to_data_tiers( """ __path = "/_ilm/migrate_to_data_tiers" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if dry_run is not None: __query["dry_run"] = dry_run if error_trace is not None: @@ -248,12 +249,13 @@ def migrate_to_data_tiers( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if legacy_template_to_delete is not None: - __body["legacy_template_to_delete"] = legacy_template_to_delete - if node_attribute is not None: - __body["node_attribute"] = node_attribute if pretty is not None: __query["pretty"] = pretty + if not __body: + if legacy_template_to_delete is not None: + __body["legacy_template_to_delete"] = legacy_template_to_delete + if node_attribute is not None: + __body["node_attribute"] = node_attribute if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -264,7 +266,7 @@ def migrate_to_data_tiers( ) @_rewrite_parameters( - body_fields=True, + body_fields=("current_step", "next_step"), ) def move_to_step( self, @@ -276,6 +278,7 @@ def move_to_step( human: t.Optional[bool] = None, next_step: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Manually moves an index into the specified step and executes that step. @@ -289,20 +292,21 @@ def move_to_step( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/_ilm/move/{_quote(index)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if current_step is not None: - __body["current_step"] = current_step + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if next_step is not None: - __body["next_step"] = next_step if pretty is not None: __query["pretty"] = pretty + if not __body: + if current_step is not None: + __body["current_step"] = current_step + if next_step is not None: + __body["next_step"] = next_step if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -313,7 +317,7 @@ def move_to_step( ) @_rewrite_parameters( - body_fields=True, + body_fields=("policy",), ) def put_lifecycle( self, @@ -328,6 +332,7 @@ def put_lifecycle( policy: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a lifecycle policy @@ -346,7 +351,7 @@ def put_lifecycle( raise ValueError("Empty value passed for parameter 'name'") __path = f"/_ilm/policy/{_quote(name)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -355,12 +360,13 @@ def put_lifecycle( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if policy is not None: - __body["policy"] = policy if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if policy is not None: + __body["policy"] = policy if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/indices.py b/elasticsearch/_sync/client/indices.py --- a/elasticsearch/_sync/client/indices.py +++ b/elasticsearch/_sync/client/indices.py @@ -96,7 +96,17 @@ def add_block( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analyzer", + "attributes", + "char_filter", + "explain", + "field", + "filter", + "normalizer", + "text", + "tokenizer", + ), ) def analyze( self, @@ -115,6 +125,7 @@ def analyze( pretty: t.Optional[bool] = None, text: t.Optional[t.Union[str, t.Sequence[str]]] = None, tokenizer: t.Optional[t.Union[str, t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Performs the analysis process on a text and return the tokens breakdown of the @@ -147,34 +158,35 @@ def analyze( __path = f"/{_quote(index)}/_analyze" else: __path = "/_analyze" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analyzer is not None: - __body["analyzer"] = analyzer - if attributes is not None: - __body["attributes"] = attributes - if char_filter is not None: - __body["char_filter"] = char_filter + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if explain is not None: - __body["explain"] = explain - if field is not None: - __body["field"] = field - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if normalizer is not None: - __body["normalizer"] = normalizer if pretty is not None: __query["pretty"] = pretty - if text is not None: - __body["text"] = text - if tokenizer is not None: - __body["tokenizer"] = tokenizer + if not __body: + if analyzer is not None: + __body["analyzer"] = analyzer + if attributes is not None: + __body["attributes"] = attributes + if char_filter is not None: + __body["char_filter"] = char_filter + if explain is not None: + __body["explain"] = explain + if field is not None: + __body["field"] = field + if filter is not None: + __body["filter"] = filter + if normalizer is not None: + __body["normalizer"] = normalizer + if text is not None: + __body["text"] = text + if tokenizer is not None: + __body["tokenizer"] = tokenizer if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -265,7 +277,7 @@ def clear_cache( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "settings"), ) def clone( self, @@ -285,6 +297,7 @@ def clone( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Clones an index @@ -309,10 +322,8 @@ def clone( if target in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target'") __path = f"/{_quote(index)}/_clone/{_quote(target)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -323,12 +334,15 @@ def clone( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -420,7 +434,7 @@ def close( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "mappings", "settings"), ) def create( self, @@ -440,6 +454,7 @@ def create( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an index with optional settings and mappings. @@ -463,28 +478,29 @@ def create( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if mappings is not None: - __body["mappings"] = mappings if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if mappings is not None: + __body["mappings"] = mappings + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -991,7 +1007,8 @@ def downsample( *, index: str, target_index: str, - config: t.Mapping[str, t.Any], + config: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -1010,8 +1027,12 @@ def downsample( raise ValueError("Empty value passed for parameter 'index'") if target_index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target_index'") - if config is None: - raise ValueError("Empty value passed for parameter 'config'") + if config is None and body is None: + raise ValueError( + "Empty value passed for parameters 'config' and 'body', one of them should be set." + ) + elif config is not None and body is not None: + raise ValueError("Cannot set both 'config' and 'body'") __path = f"/{_quote(index)}/_downsample/{_quote(target_index)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -1022,7 +1043,7 @@ def downsample( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = config + __body = config if config is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2220,16 +2241,17 @@ def migrate_to_data_stream( ) @_rewrite_parameters( - body_fields=True, + body_fields=("actions",), ) def modify_data_stream( self, *, - actions: t.Sequence[t.Mapping[str, t.Any]], + actions: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Modifies a data stream @@ -2238,13 +2260,11 @@ def modify_data_stream( :param actions: Actions to perform. """ - if actions is None: + if actions is None and body is None: raise ValueError("Empty value passed for parameter 'actions'") __path = "/_data_stream/_modify" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if actions is not None: - __body["actions"] = actions + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2253,6 +2273,9 @@ def modify_data_stream( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if actions is not None: + __body["actions"] = actions __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2379,7 +2402,13 @@ def promote_data_stream( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "filter", + "index_routing", + "is_write_index", + "routing", + "search_routing", + ), ) def put_alias( self, @@ -2399,6 +2428,7 @@ def put_alias( routing: t.Optional[str] = None, search_routing: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates an alias. @@ -2437,29 +2467,30 @@ def put_alias( raise ValueError("Empty value passed for parameter 'name'") __path = f"/{_quote(index)}/_alias/{_quote(name)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index_routing is not None: - __body["index_routing"] = index_routing - if is_write_index is not None: - __body["is_write_index"] = is_write_index if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if routing is not None: - __body["routing"] = routing - if search_routing is not None: - __body["search_routing"] = search_routing if timeout is not None: __query["timeout"] = timeout + if not __body: + if filter is not None: + __body["filter"] = filter + if index_routing is not None: + __body["index_routing"] = index_routing + if is_write_index is not None: + __body["is_write_index"] = is_write_index + if routing is not None: + __body["routing"] = routing + if search_routing is not None: + __body["search_routing"] = search_routing if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2470,7 +2501,7 @@ def put_alias( ) @_rewrite_parameters( - body_fields=True, + body_fields=("data_retention", "downsampling"), ) def put_data_lifecycle( self, @@ -2496,6 +2527,7 @@ def put_data_lifecycle( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the data stream lifecycle of the selected data streams. @@ -2523,12 +2555,8 @@ def put_data_lifecycle( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_data_stream/{_quote(name)}/_lifecycle" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if data_retention is not None: - __body["data_retention"] = data_retention - if downsampling is not None: - __body["downsampling"] = downsampling + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: @@ -2543,6 +2571,11 @@ def put_data_lifecycle( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if data_retention is not None: + __body["data_retention"] = data_retention + if downsampling is not None: + __body["downsampling"] = downsampling if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2553,7 +2586,15 @@ def put_data_lifecycle( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "composed_of", + "data_stream", + "index_patterns", + "meta", + "priority", + "template", + "version", + ), parameter_aliases={"_meta": "meta"}, ) def put_index_template( @@ -2572,6 +2613,7 @@ def put_index_template( priority: t.Optional[int] = None, template: t.Optional[t.Mapping[str, t.Any]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates an index template. @@ -2603,39 +2645,52 @@ def put_index_template( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_index_template/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if composed_of is not None: - __body["composed_of"] = composed_of + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create - if data_stream is not None: - __body["data_stream"] = data_stream if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index_patterns is not None: - __body["index_patterns"] = index_patterns - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if priority is not None: - __body["priority"] = priority - if template is not None: - __body["template"] = template - if version is not None: - __body["version"] = version + if not __body: + if composed_of is not None: + __body["composed_of"] = composed_of + if data_stream is not None: + __body["data_stream"] = data_stream + if index_patterns is not None: + __body["index_patterns"] = index_patterns + if meta is not None: + __body["_meta"] = meta + if priority is not None: + __body["priority"] = priority + if template is not None: + __body["template"] = template + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "date_detection", + "dynamic", + "dynamic_date_formats", + "dynamic_templates", + "field_names", + "meta", + "numeric_detection", + "properties", + "routing", + "runtime", + "source", + ), parameter_aliases={ "_field_names": "field_names", "_meta": "meta", @@ -2684,6 +2739,7 @@ def put_mapping( source: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, write_index_only: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the index mappings. @@ -2730,23 +2786,13 @@ def put_mapping( raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_mapping" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if date_detection is not None: - __body["date_detection"] = date_detection - if dynamic is not None: - __body["dynamic"] = dynamic - if dynamic_date_formats is not None: - __body["dynamic_date_formats"] = dynamic_date_formats - if dynamic_templates is not None: - __body["dynamic_templates"] = dynamic_templates if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards - if field_names is not None: - __body["_field_names"] = field_names if filter_path is not None: __query["filter_path"] = filter_path if human is not None: @@ -2755,24 +2801,35 @@ def put_mapping( __query["ignore_unavailable"] = ignore_unavailable if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta - if numeric_detection is not None: - __body["numeric_detection"] = numeric_detection if pretty is not None: __query["pretty"] = pretty - if properties is not None: - __body["properties"] = properties - if routing is not None: - __body["_routing"] = routing - if runtime is not None: - __body["runtime"] = runtime - if source is not None: - __body["_source"] = source if timeout is not None: __query["timeout"] = timeout if write_index_only is not None: __query["write_index_only"] = write_index_only + if not __body: + if date_detection is not None: + __body["date_detection"] = date_detection + if dynamic is not None: + __body["dynamic"] = dynamic + if dynamic_date_formats is not None: + __body["dynamic_date_formats"] = dynamic_date_formats + if dynamic_templates is not None: + __body["dynamic_templates"] = dynamic_templates + if field_names is not None: + __body["_field_names"] = field_names + if meta is not None: + __body["_meta"] = meta + if numeric_detection is not None: + __body["numeric_detection"] = numeric_detection + if properties is not None: + __body["properties"] = properties + if routing is not None: + __body["_routing"] = routing + if runtime is not None: + __body["runtime"] = runtime + if source is not None: + __body["_source"] = source __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -2784,7 +2841,8 @@ def put_mapping( def put_settings( self, *, - settings: t.Mapping[str, t.Any], + settings: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, index: t.Optional[t.Union[str, t.Sequence[str]]] = None, allow_no_indices: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, @@ -2834,8 +2892,12 @@ def put_settings( :param timeout: Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error. """ - if settings is None: - raise ValueError("Empty value passed for parameter 'settings'") + if settings is None and body is None: + raise ValueError( + "Empty value passed for parameters 'settings' and 'body', one of them should be set." + ) + elif settings is not None and body is not None: + raise ValueError("Cannot set both 'settings' and 'body'") if index not in SKIP_IN_PATH: __path = f"/{_quote(index)}/_settings" else: @@ -2863,14 +2925,21 @@ def put_settings( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout - __body = settings + __body = settings if settings is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aliases", + "index_patterns", + "mappings", + "order", + "settings", + "version", + ), ) def put_template( self, @@ -2892,6 +2961,7 @@ def put_template( settings: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates an index template. @@ -2922,10 +2992,8 @@ def put_template( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_template/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create if error_trace is not None: @@ -2936,22 +3004,25 @@ def put_template( __query["flat_settings"] = flat_settings if human is not None: __query["human"] = human - if index_patterns is not None: - __body["index_patterns"] = index_patterns - if mappings is not None: - __body["mappings"] = mappings if master_timeout is not None: __query["master_timeout"] = master_timeout - if order is not None: - __body["order"] = order if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout - if version is not None: - __body["version"] = version + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if index_patterns is not None: + __body["index_patterns"] = index_patterns + if mappings is not None: + __body["mappings"] = mappings + if order is not None: + __body["order"] = order + if settings is not None: + __body["settings"] = settings + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -3173,7 +3244,7 @@ def resolve_index( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "conditions", "mappings", "settings"), ) def rollover( self, @@ -3196,6 +3267,7 @@ def rollover( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates an alias to point to a new index when the existing index is considered @@ -3237,12 +3309,8 @@ def rollover( __path = f"/{_quote(alias)}/_rollover" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases - if conditions is not None: - __body["conditions"] = conditions + __body: t.Dict[str, t.Any] = body if body is not None else {} if dry_run is not None: __query["dry_run"] = dry_run if error_trace is not None: @@ -3251,18 +3319,23 @@ def rollover( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if mappings is not None: - __body["mappings"] = mappings if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if conditions is not None: + __body["conditions"] = conditions + if mappings is not None: + __body["mappings"] = mappings + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3407,7 +3480,7 @@ def shard_stores( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "settings"), ) def shrink( self, @@ -3427,6 +3500,7 @@ def shrink( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allow to shrink an existing index into a new index with fewer primary shards. @@ -3451,10 +3525,8 @@ def shrink( if target in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target'") __path = f"/{_quote(index)}/_shrink/{_quote(target)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3465,12 +3537,15 @@ def shrink( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3481,7 +3556,16 @@ def shrink( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_auto_create", + "composed_of", + "data_stream", + "index_patterns", + "meta", + "priority", + "template", + "version", + ), parameter_aliases={"_meta": "meta"}, ) def simulate_index_template( @@ -3505,6 +3589,7 @@ def simulate_index_template( priority: t.Optional[int] = None, template: t.Optional[t.Mapping[str, t.Any]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Simulate matching the given index name against the index templates in the system @@ -3550,16 +3635,10 @@ def simulate_index_template( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_index_template/_simulate_index/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_auto_create is not None: - __body["allow_auto_create"] = allow_auto_create - if composed_of is not None: - __body["composed_of"] = composed_of + __body: t.Dict[str, t.Any] = body if body is not None else {} if create is not None: __query["create"] = create - if data_stream is not None: - __body["data_stream"] = data_stream if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3568,20 +3647,27 @@ def simulate_index_template( __query["human"] = human if include_defaults is not None: __query["include_defaults"] = include_defaults - if index_patterns is not None: - __body["index_patterns"] = index_patterns if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if priority is not None: - __body["priority"] = priority - if template is not None: - __body["template"] = template - if version is not None: - __body["version"] = version + if not __body: + if allow_auto_create is not None: + __body["allow_auto_create"] = allow_auto_create + if composed_of is not None: + __body["composed_of"] = composed_of + if data_stream is not None: + __body["data_stream"] = data_stream + if index_patterns is not None: + __body["index_patterns"] = index_patterns + if meta is not None: + __body["_meta"] = meta + if priority is not None: + __body["priority"] = priority + if template is not None: + __body["template"] = template + if version is not None: + __body["version"] = version if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3608,6 +3694,7 @@ def simulate_template( ] = None, pretty: t.Optional[bool] = None, template: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Simulate resolving the given template name or body @@ -3628,6 +3715,12 @@ def simulate_template( returns an error. :param template: """ + if template is None and body is None: + raise ValueError( + "Empty value passed for parameters 'template' and 'body', one of them should be set." + ) + elif template is not None and body is not None: + raise ValueError("Cannot set both 'template' and 'body'") if name not in SKIP_IN_PATH: __path = f"/_index_template/_simulate/{_quote(name)}" else: @@ -3647,7 +3740,7 @@ def simulate_template( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - __body = template + __body = template if template is not None else body if not __body: __body = None __headers = {"accept": "application/json"} @@ -3658,7 +3751,7 @@ def simulate_template( ) @_rewrite_parameters( - body_fields=True, + body_fields=("aliases", "settings"), ) def split( self, @@ -3678,6 +3771,7 @@ def split( wait_for_active_shards: t.Optional[ t.Union[int, t.Union["t.Literal['all', 'index-setting']", str]] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows you to split an existing index into a new index with more primary shards. @@ -3702,10 +3796,8 @@ def split( if target in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target'") __path = f"/{_quote(index)}/_split/{_quote(target)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aliases is not None: - __body["aliases"] = aliases + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3716,12 +3808,15 @@ def split( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if settings is not None: - __body["settings"] = settings if timeout is not None: __query["timeout"] = timeout if wait_for_active_shards is not None: __query["wait_for_active_shards"] = wait_for_active_shards + if not __body: + if aliases is not None: + __body["aliases"] = aliases + if settings is not None: + __body["settings"] = settings if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3910,7 +4005,7 @@ def unfreeze( ) @_rewrite_parameters( - body_fields=True, + body_fields=("actions",), ) def update_aliases( self, @@ -3924,6 +4019,7 @@ def update_aliases( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates index aliases. @@ -3938,10 +4034,8 @@ def update_aliases( the timeout expires, the request fails and returns an error. """ __path = "/_aliases" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if actions is not None: - __body["actions"] = actions + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3954,13 +4048,16 @@ def update_aliases( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if actions is not None: + __body["actions"] = actions __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("query",), ) def validate_query( self, @@ -3990,6 +4087,7 @@ def validate_query( q: t.Optional[str] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, rewrite: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows a user to validate a potentially expensive query without executing it. @@ -4032,7 +4130,7 @@ def validate_query( else: __path = "/_validate/query" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if all_shards is not None: __query["all_shards"] = all_shards if allow_no_indices is not None: @@ -4063,10 +4161,11 @@ def validate_query( __query["pretty"] = pretty if q is not None: __query["q"] = q - if query is not None: - __body["query"] = query if rewrite is not None: __query["rewrite"] = rewrite + if not __body: + if query is not None: + __body["query"] = query if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/ingest.py b/elasticsearch/_sync/client/ingest.py --- a/elasticsearch/_sync/client/ingest.py +++ b/elasticsearch/_sync/client/ingest.py @@ -179,7 +179,7 @@ def processor_grok( ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "meta", "on_failure", "processors", "version"), parameter_aliases={"_meta": "meta"}, ) def put_pipeline( @@ -200,6 +200,7 @@ def put_pipeline( processors: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a pipeline. @@ -232,10 +233,8 @@ def put_pipeline( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/_ingest/pipeline/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -246,25 +245,28 @@ def put_pipeline( __query["if_version"] = if_version if master_timeout is not None: __query["master_timeout"] = master_timeout - if meta is not None: - __body["_meta"] = meta - if on_failure is not None: - __body["on_failure"] = on_failure if pretty is not None: __query["pretty"] = pretty - if processors is not None: - __body["processors"] = processors if timeout is not None: __query["timeout"] = timeout - if version is not None: - __body["version"] = version + if not __body: + if description is not None: + __body["description"] = description + if meta is not None: + __body["_meta"] = meta + if on_failure is not None: + __body["on_failure"] = on_failure + if processors is not None: + __body["processors"] = processors + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "pipeline"), ) def simulate( self, @@ -277,6 +279,7 @@ def simulate( pipeline: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, verbose: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Allows to simulate a pipeline with example documents. @@ -296,22 +299,23 @@ def simulate( __path = f"/_ingest/pipeline/{_quote(id)}/_simulate" else: __path = "/_ingest/pipeline/_simulate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if pipeline is not None: - __body["pipeline"] = pipeline if pretty is not None: __query["pretty"] = pretty if verbose is not None: __query["verbose"] = verbose + if not __body: + if docs is not None: + __body["docs"] = docs + if pipeline is not None: + __body["pipeline"] = pipeline __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/license.py b/elasticsearch/_sync/client/license.py --- a/elasticsearch/_sync/client/license.py +++ b/elasticsearch/_sync/client/license.py @@ -154,7 +154,7 @@ def get_trial_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=("license", "licenses"), ) def post( self, @@ -166,6 +166,7 @@ def post( license: t.Optional[t.Mapping[str, t.Any]] = None, licenses: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the license for the cluster. @@ -179,7 +180,7 @@ def post( """ __path = "/_license" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if acknowledge is not None: __query["acknowledge"] = acknowledge if error_trace is not None: @@ -188,12 +189,13 @@ def post( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if license is not None: - __body["license"] = license - if licenses is not None: - __body["licenses"] = licenses if pretty is not None: __query["pretty"] = pretty + if not __body: + if license is not None: + __body["license"] = license + if licenses is not None: + __body["licenses"] = licenses if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/logstash.py b/elasticsearch/_sync/client/logstash.py --- a/elasticsearch/_sync/client/logstash.py +++ b/elasticsearch/_sync/client/logstash.py @@ -100,7 +100,8 @@ def put_pipeline( self, *, id: str, - pipeline: t.Mapping[str, t.Any], + pipeline: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -116,8 +117,12 @@ def put_pipeline( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if pipeline is None: - raise ValueError("Empty value passed for parameter 'pipeline'") + if pipeline is None and body is None: + raise ValueError( + "Empty value passed for parameters 'pipeline' and 'body', one of them should be set." + ) + elif pipeline is not None and body is not None: + raise ValueError("Cannot set both 'pipeline' and 'body'") __path = f"/_logstash/pipeline/{_quote(id)}" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -128,7 +133,7 @@ def put_pipeline( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = pipeline + __body = pipeline if pipeline is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/ml.py b/elasticsearch/_sync/client/ml.py --- a/elasticsearch/_sync/client/ml.py +++ b/elasticsearch/_sync/client/ml.py @@ -59,7 +59,7 @@ def clear_trained_model_deployment_cache( ) @_rewrite_parameters( - body_fields=True, + body_fields=("allow_no_match", "force", "timeout"), ) def close_job( self, @@ -72,6 +72,7 @@ def close_job( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Closes one or more anomaly detection jobs. A job can be opened and closed multiple @@ -92,22 +93,23 @@ def close_job( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_close" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_no_match is not None: - __body["allow_no_match"] = allow_no_match + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if force is not None: - __body["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if allow_no_match is not None: + __body["allow_no_match"] = allow_no_match + if force is not None: + __body["force"] = force + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -315,7 +317,7 @@ def delete_datafeed( ) @_rewrite_parameters( - body_fields=True, + body_fields=("requests_per_second", "timeout"), ) def delete_expired_data( self, @@ -327,6 +329,7 @@ def delete_expired_data( pretty: t.Optional[bool] = None, requests_per_second: t.Optional[float] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Deletes expired and unused machine learning data. @@ -345,7 +348,7 @@ def delete_expired_data( else: __path = "/_ml/_delete_expired_data" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -354,10 +357,11 @@ def delete_expired_data( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if requests_per_second is not None: - __body["requests_per_second"] = requests_per_second - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if requests_per_second is not None: + __body["requests_per_second"] = requests_per_second + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -624,7 +628,11 @@ def delete_trained_model_alias( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis_config", + "max_bucket_cardinality", + "overall_cardinality", + ), ) def estimate_model_memory( self, @@ -636,6 +644,7 @@ def estimate_model_memory( max_bucket_cardinality: t.Optional[t.Mapping[str, int]] = None, overall_cardinality: t.Optional[t.Mapping[str, int]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Estimates the model memory @@ -658,40 +667,42 @@ def estimate_model_memory( or `partition_field_name`. """ __path = "/_ml/anomaly_detectors/_estimate_model_memory" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis_config is not None: - __body["analysis_config"] = analysis_config + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_bucket_cardinality is not None: - __body["max_bucket_cardinality"] = max_bucket_cardinality - if overall_cardinality is not None: - __body["overall_cardinality"] = overall_cardinality if pretty is not None: __query["pretty"] = pretty + if not __body: + if analysis_config is not None: + __body["analysis_config"] = analysis_config + if max_bucket_cardinality is not None: + __body["max_bucket_cardinality"] = max_bucket_cardinality + if overall_cardinality is not None: + __body["overall_cardinality"] = overall_cardinality __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("evaluation", "index", "query"), ) def evaluate_data_frame( self, *, - evaluation: t.Mapping[str, t.Any], - index: str, + evaluation: t.Optional[t.Mapping[str, t.Any]] = None, + index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, query: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Evaluates the data frame analytics for an annotated index. @@ -703,17 +714,13 @@ def evaluate_data_frame( :param query: A query clause that retrieves a subset of data from the source index. """ - if evaluation is None: + if evaluation is None and body is None: raise ValueError("Empty value passed for parameter 'evaluation'") - if index is None: + if index is None and body is None: raise ValueError("Empty value passed for parameter 'index'") __path = "/_ml/data_frame/_evaluate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if evaluation is not None: - __body["evaluation"] = evaluation - if index is not None: - __body["index"] = index + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -722,15 +729,29 @@ def evaluate_data_frame( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query + if not __body: + if evaluation is not None: + __body["evaluation"] = evaluation + if index is not None: + __body["index"] = index + if query is not None: + __body["query"] = query __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_lazy_start", + "analysis", + "analyzed_fields", + "description", + "dest", + "max_num_threads", + "model_memory_limit", + "source", + ), ) def explain_data_frame_analytics( self, @@ -748,6 +769,7 @@ def explain_data_frame_analytics( model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, source: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Explains a data frame analytics config. @@ -786,32 +808,33 @@ def explain_data_frame_analytics( __path = f"/_ml/data_frame/analytics/{_quote(id)}/_explain" else: __path = "/_ml/data_frame/analytics/_explain" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_lazy_start is not None: - __body["allow_lazy_start"] = allow_lazy_start - if analysis is not None: - __body["analysis"] = analysis - if analyzed_fields is not None: - __body["analyzed_fields"] = analyzed_fields - if description is not None: - __body["description"] = description - if dest is not None: - __body["dest"] = dest + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_num_threads is not None: - __body["max_num_threads"] = max_num_threads - if model_memory_limit is not None: - __body["model_memory_limit"] = model_memory_limit if pretty is not None: __query["pretty"] = pretty - if source is not None: - __body["source"] = source + if not __body: + if allow_lazy_start is not None: + __body["allow_lazy_start"] = allow_lazy_start + if analysis is not None: + __body["analysis"] = analysis + if analyzed_fields is not None: + __body["analyzed_fields"] = analyzed_fields + if description is not None: + __body["description"] = description + if dest is not None: + __body["dest"] = dest + if max_num_threads is not None: + __body["max_num_threads"] = max_num_threads + if model_memory_limit is not None: + __body["model_memory_limit"] = model_memory_limit + if source is not None: + __body["source"] = source if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -822,7 +845,7 @@ def explain_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("advance_time", "calc_interim", "end", "skip_time", "start"), ) def flush_job( self, @@ -837,6 +860,7 @@ def flush_job( pretty: t.Optional[bool] = None, skip_time: t.Optional[t.Union[str, t.Any]] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Forces any buffered data to be processed by the job. @@ -853,14 +877,8 @@ def flush_job( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_flush" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if advance_time is not None: - __body["advance_time"] = advance_time - if calc_interim is not None: - __body["calc_interim"] = calc_interim - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -869,10 +887,17 @@ def flush_job( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if skip_time is not None: - __body["skip_time"] = skip_time - if start is not None: - __body["start"] = start + if not __body: + if advance_time is not None: + __body["advance_time"] = advance_time + if calc_interim is not None: + __body["calc_interim"] = calc_interim + if end is not None: + __body["end"] = end + if skip_time is not None: + __body["skip_time"] = skip_time + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -883,7 +908,7 @@ def flush_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=("duration", "expires_in", "max_model_memory"), ) def forecast( self, @@ -896,6 +921,7 @@ def forecast( human: t.Optional[bool] = None, max_model_memory: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Predicts the future behavior of a time series by using its historical behavior. @@ -912,22 +938,23 @@ def forecast( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_forecast" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if duration is not None: - __body["duration"] = duration + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if expires_in is not None: - __body["expires_in"] = expires_in if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_model_memory is not None: - __body["max_model_memory"] = max_model_memory if pretty is not None: __query["pretty"] = pretty + if not __body: + if duration is not None: + __body["duration"] = duration + if expires_in is not None: + __body["expires_in"] = expires_in + if max_model_memory is not None: + __body["max_model_memory"] = max_model_memory if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -938,7 +965,16 @@ def forecast( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "anomaly_score", + "desc", + "end", + "exclude_interim", + "expand", + "page", + "sort", + "start", + ), parameter_aliases={"from": "from_"}, ) def get_buckets( @@ -960,6 +996,7 @@ def get_buckets( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly detection job results for one or more buckets. @@ -990,36 +1027,37 @@ def get_buckets( __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/buckets" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if anomaly_score is not None: - __body["anomaly_score"] = anomaly_score - if desc is not None: - __body["desc"] = desc - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if exclude_interim is not None: - __body["exclude_interim"] = exclude_interim - if expand is not None: - __body["expand"] = expand if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size - if sort is not None: - __body["sort"] = sort - if start is not None: - __body["start"] = start + if not __body: + if anomaly_score is not None: + __body["anomaly_score"] = anomaly_score + if desc is not None: + __body["desc"] = desc + if end is not None: + __body["end"] = end + if exclude_interim is not None: + __body["exclude_interim"] = exclude_interim + if expand is not None: + __body["expand"] = expand + if page is not None: + __body["page"] = page + if sort is not None: + __body["sort"] = sort + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1090,7 +1128,7 @@ def get_calendar_events( ) @_rewrite_parameters( - body_fields=True, + body_fields=("page",), parameter_aliases={"from": "from_"}, ) def get_calendars( @@ -1104,6 +1142,7 @@ def get_calendars( page: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves configuration information for calendars. @@ -1125,7 +1164,7 @@ def get_calendars( else: __path = "/_ml/calendars" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1134,12 +1173,13 @@ def get_calendars( __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size + if not __body: + if page is not None: + __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1150,7 +1190,7 @@ def get_calendars( ) @_rewrite_parameters( - body_fields=True, + body_fields=("page",), parameter_aliases={"from": "from_"}, ) def get_categories( @@ -1166,6 +1206,7 @@ def get_categories( partition_field_value: t.Optional[str] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly detection job results for one or more categories. @@ -1192,7 +1233,7 @@ def get_categories( else: raise ValueError("Couldn't find a path for the given parameters") __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1201,14 +1242,15 @@ def get_categories( __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if partition_field_value is not None: __query["partition_field_value"] = partition_field_value if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size + if not __body: + if page is not None: + __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1490,7 +1532,7 @@ def get_filters( ) @_rewrite_parameters( - body_fields=True, + body_fields=("page",), parameter_aliases={"from": "from_"}, ) def get_influencers( @@ -1510,6 +1552,7 @@ def get_influencers( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly detection job results for one or more influencers. @@ -1537,7 +1580,7 @@ def get_influencers( raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/influencers" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if desc is not None: __query["desc"] = desc if end is not None: @@ -1554,8 +1597,6 @@ def get_influencers( __query["human"] = human if influencer_score is not None: __query["influencer_score"] = influencer_score - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: @@ -1564,6 +1605,9 @@ def get_influencers( __query["sort"] = sort if start is not None: __query["start"] = start + if not __body: + if page is not None: + __body["page"] = page if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1776,7 +1820,7 @@ def get_model_snapshot_upgrade_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=("desc", "end", "page", "sort", "start"), parameter_aliases={"from": "from_"}, ) def get_model_snapshots( @@ -1795,6 +1839,7 @@ def get_model_snapshots( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves information about model snapshots. @@ -1823,12 +1868,8 @@ def get_model_snapshots( __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/model_snapshots" else: raise ValueError("Couldn't find a path for the given parameters") - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if desc is not None: - __body["desc"] = desc - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1837,16 +1878,21 @@ def get_model_snapshots( __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty if size is not None: __query["size"] = size - if sort is not None: - __body["sort"] = sort - if start is not None: - __body["start"] = start + if not __body: + if desc is not None: + __body["desc"] = desc + if end is not None: + __body["end"] = end + if page is not None: + __body["page"] = page + if sort is not None: + __body["sort"] = sort + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1857,7 +1903,15 @@ def get_model_snapshots( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_no_match", + "bucket_span", + "end", + "exclude_interim", + "overall_score", + "start", + "top_n", + ), ) def get_overall_buckets( self, @@ -1874,6 +1928,7 @@ def get_overall_buckets( pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, top_n: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves overall bucket results that summarize the bucket results of multiple @@ -1899,30 +1954,31 @@ def get_overall_buckets( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/overall_buckets" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_no_match is not None: - __body["allow_no_match"] = allow_no_match - if bucket_span is not None: - __body["bucket_span"] = bucket_span - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if exclude_interim is not None: - __body["exclude_interim"] = exclude_interim if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if overall_score is not None: - __body["overall_score"] = overall_score if pretty is not None: __query["pretty"] = pretty - if start is not None: - __body["start"] = start - if top_n is not None: - __body["top_n"] = top_n + if not __body: + if allow_no_match is not None: + __body["allow_no_match"] = allow_no_match + if bucket_span is not None: + __body["bucket_span"] = bucket_span + if end is not None: + __body["end"] = end + if exclude_interim is not None: + __body["exclude_interim"] = exclude_interim + if overall_score is not None: + __body["overall_score"] = overall_score + if start is not None: + __body["start"] = start + if top_n is not None: + __body["top_n"] = top_n if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -1933,7 +1989,15 @@ def get_overall_buckets( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "desc", + "end", + "exclude_interim", + "page", + "record_score", + "sort", + "start", + ), parameter_aliases={"from": "from_"}, ) def get_records( @@ -1953,6 +2017,7 @@ def get_records( size: t.Optional[int] = None, sort: t.Optional[str] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves anomaly records for an anomaly detection job. @@ -1974,34 +2039,35 @@ def get_records( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/results/records" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if desc is not None: - __body["desc"] = desc - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if exclude_interim is not None: - __body["exclude_interim"] = exclude_interim if filter_path is not None: __query["filter_path"] = filter_path if from_ is not None: __query["from"] = from_ if human is not None: __query["human"] = human - if page is not None: - __body["page"] = page if pretty is not None: __query["pretty"] = pretty - if record_score is not None: - __body["record_score"] = record_score if size is not None: __query["size"] = size - if sort is not None: - __body["sort"] = sort - if start is not None: - __body["start"] = start + if not __body: + if desc is not None: + __body["desc"] = desc + if end is not None: + __body["end"] = end + if exclude_interim is not None: + __body["exclude_interim"] = exclude_interim + if page is not None: + __body["page"] = page + if record_score is not None: + __body["record_score"] = record_score + if sort is not None: + __body["sort"] = sort + if start is not None: + __body["start"] = start if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2147,19 +2213,20 @@ def get_trained_models_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=("docs", "inference_config"), ) def infer_trained_model( self, *, model_id: str, - docs: t.Sequence[t.Mapping[str, t.Any]], + docs: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, inference_config: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Evaluate a trained model. @@ -2177,25 +2244,26 @@ def infer_trained_model( """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") - if docs is None: + if docs is None and body is None: raise ValueError("Empty value passed for parameter 'docs'") __path = f"/_ml/trained_models/{_quote(model_id)}/_infer" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if docs is not None: - __body["docs"] = docs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if inference_config is not None: - __body["inference_config"] = inference_config if pretty is not None: __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if docs is not None: + __body["docs"] = docs + if inference_config is not None: + __body["inference_config"] = inference_config __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2231,7 +2299,7 @@ def info( ) @_rewrite_parameters( - body_fields=True, + body_fields=("timeout",), ) def open_job( self, @@ -2242,6 +2310,7 @@ def open_job( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Opens one or more anomaly detection jobs. @@ -2255,7 +2324,7 @@ def open_job( raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_open" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2264,8 +2333,9 @@ def open_job( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2276,17 +2346,18 @@ def open_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=("events",), ) def post_calendar_events( self, *, calendar_id: str, - events: t.Sequence[t.Mapping[str, t.Any]], + events: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Posts scheduled events in a calendar. @@ -2300,13 +2371,11 @@ def post_calendar_events( """ if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") - if events is None: + if events is None and body is None: raise ValueError("Empty value passed for parameter 'events'") __path = f"/_ml/calendars/{_quote(calendar_id)}/events" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if events is not None: - __body["events"] = events + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2315,6 +2384,9 @@ def post_calendar_events( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if events is not None: + __body["events"] = events __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2327,7 +2399,8 @@ def post_data( self, *, job_id: str, - data: t.Sequence[t.Any], + data: t.Optional[t.Sequence[t.Any]] = None, + body: t.Optional[t.Sequence[t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -2348,8 +2421,12 @@ def post_data( """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") - if data is None: - raise ValueError("Empty value passed for parameter 'data'") + if data is None and body is None: + raise ValueError( + "Empty value passed for parameters 'data' and 'body', one of them should be set." + ) + elif data is not None and body is not None: + raise ValueError("Cannot set both 'data' and 'body'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_data" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -2364,7 +2441,7 @@ def post_data( __query["reset_end"] = reset_end if reset_start is not None: __query["reset_start"] = reset_start - __body = data + __body = data if data is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", @@ -2374,7 +2451,7 @@ def post_data( ) @_rewrite_parameters( - body_fields=True, + body_fields=("config",), ) def preview_data_frame_analytics( self, @@ -2385,6 +2462,7 @@ def preview_data_frame_analytics( filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Previews that will be analyzed given a data frame analytics config. @@ -2400,10 +2478,8 @@ def preview_data_frame_analytics( __path = f"/_ml/data_frame/analytics/{_quote(id)}/_preview" else: __path = "/_ml/data_frame/analytics/_preview" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if config is not None: - __body["config"] = config + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2412,6 +2488,9 @@ def preview_data_frame_analytics( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if config is not None: + __body["config"] = config if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2422,7 +2501,7 @@ def preview_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("datafeed_config", "job_config"), ) def preview_datafeed( self, @@ -2436,6 +2515,7 @@ def preview_datafeed( job_config: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Previews a datafeed. @@ -2461,10 +2541,8 @@ def preview_datafeed( __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_preview" else: __path = "/_ml/datafeeds/_preview" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if datafeed_config is not None: - __body["datafeed_config"] = datafeed_config + __body: t.Dict[str, t.Any] = body if body is not None else {} if end is not None: __query["end"] = end if error_trace is not None: @@ -2473,12 +2551,15 @@ def preview_datafeed( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if job_config is not None: - __body["job_config"] = job_config if pretty is not None: __query["pretty"] = pretty if start is not None: __query["start"] = start + if not __body: + if datafeed_config is not None: + __body["datafeed_config"] = datafeed_config + if job_config is not None: + __body["job_config"] = job_config if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2489,7 +2570,7 @@ def preview_datafeed( ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "job_ids"), ) def put_calendar( self, @@ -2501,6 +2582,7 @@ def put_calendar( human: t.Optional[bool] = None, job_ids: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a calendar. @@ -2514,20 +2596,21 @@ def put_calendar( if calendar_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'calendar_id'") __path = f"/_ml/calendars/{_quote(calendar_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if job_ids is not None: - __body["job_ids"] = job_ids if pretty is not None: __query["pretty"] = pretty + if not __body: + if description is not None: + __body["description"] = description + if job_ids is not None: + __body["job_ids"] = job_ids if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2577,16 +2660,27 @@ def put_calendar_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis", + "dest", + "source", + "allow_lazy_start", + "analyzed_fields", + "description", + "headers", + "max_num_threads", + "model_memory_limit", + "version", + ), ignore_deprecated_options={"headers"}, ) def put_data_frame_analytics( self, *, id: str, - analysis: t.Mapping[str, t.Any], - dest: t.Mapping[str, t.Any], - source: t.Mapping[str, t.Any], + analysis: t.Optional[t.Mapping[str, t.Any]] = None, + dest: t.Optional[t.Mapping[str, t.Any]] = None, + source: t.Optional[t.Mapping[str, t.Any]] = None, allow_lazy_start: t.Optional[bool] = None, analyzed_fields: t.Optional[t.Mapping[str, t.Any]] = None, description: t.Optional[str] = None, @@ -2598,6 +2692,7 @@ def put_data_frame_analytics( model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, version: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a data frame analytics job. @@ -2661,50 +2756,67 @@ def put_data_frame_analytics( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if analysis is None: + if analysis is None and body is None: raise ValueError("Empty value passed for parameter 'analysis'") - if dest is None: + if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") - if source is None: + if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path = f"/_ml/data_frame/analytics/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis is not None: - __body["analysis"] = analysis - if dest is not None: - __body["dest"] = dest - if source is not None: - __body["source"] = source - if allow_lazy_start is not None: - __body["allow_lazy_start"] = allow_lazy_start - if analyzed_fields is not None: - __body["analyzed_fields"] = analyzed_fields - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if headers is not None: - __body["headers"] = headers if human is not None: __query["human"] = human - if max_num_threads is not None: - __body["max_num_threads"] = max_num_threads - if model_memory_limit is not None: - __body["model_memory_limit"] = model_memory_limit if pretty is not None: __query["pretty"] = pretty - if version is not None: - __body["version"] = version + if not __body: + if analysis is not None: + __body["analysis"] = analysis + if dest is not None: + __body["dest"] = dest + if source is not None: + __body["source"] = source + if allow_lazy_start is not None: + __body["allow_lazy_start"] = allow_lazy_start + if analyzed_fields is not None: + __body["analyzed_fields"] = analyzed_fields + if description is not None: + __body["description"] = description + if headers is not None: + __body["headers"] = headers + if max_num_threads is not None: + __body["max_num_threads"] = max_num_threads + if model_memory_limit is not None: + __body["model_memory_limit"] = model_memory_limit + if version is not None: + __body["version"] = version __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "chunking_config", + "delayed_data_check_config", + "frequency", + "headers", + "indexes", + "indices", + "indices_options", + "job_id", + "max_empty_searches", + "query", + "query_delay", + "runtime_mappings", + "script_fields", + "scroll_size", + ), ignore_deprecated_options={"headers"}, ) def put_datafeed( @@ -2741,6 +2853,7 @@ def put_datafeed( runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, script_fields: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, scroll_size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a datafeed. @@ -2819,61 +2932,62 @@ def put_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aggregations is not None: - __body["aggregations"] = aggregations + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if chunking_config is not None: - __body["chunking_config"] = chunking_config - if delayed_data_check_config is not None: - __body["delayed_data_check_config"] = delayed_data_check_config if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency - if headers is not None: - __body["headers"] = headers if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indexes is not None: - __body["indexes"] = indexes - if indices is not None: - __body["indices"] = indices - if indices_options is not None: - __body["indices_options"] = indices_options - if job_id is not None: - __body["job_id"] = job_id - if max_empty_searches is not None: - __body["max_empty_searches"] = max_empty_searches if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if query_delay is not None: - __body["query_delay"] = query_delay - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields - if scroll_size is not None: - __body["scroll_size"] = scroll_size + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if chunking_config is not None: + __body["chunking_config"] = chunking_config + if delayed_data_check_config is not None: + __body["delayed_data_check_config"] = delayed_data_check_config + if frequency is not None: + __body["frequency"] = frequency + if headers is not None: + __body["headers"] = headers + if indexes is not None: + __body["indexes"] = indexes + if indices is not None: + __body["indices"] = indices + if indices_options is not None: + __body["indices_options"] = indices_options + if job_id is not None: + __body["job_id"] = job_id + if max_empty_searches is not None: + __body["max_empty_searches"] = max_empty_searches + if query is not None: + __body["query"] = query + if query_delay is not None: + __body["query_delay"] = query_delay + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if scroll_size is not None: + __body["scroll_size"] = scroll_size __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "items"), ) def put_filter( self, @@ -2885,6 +2999,7 @@ def put_filter( human: t.Optional[bool] = None, items: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a filter. @@ -2899,34 +3014,51 @@ def put_filter( if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path = f"/_ml/filters/{_quote(filter_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if items is not None: - __body["items"] = items if pretty is not None: __query["pretty"] = pretty + if not __body: + if description is not None: + __body["description"] = description + if items is not None: + __body["items"] = items __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis_config", + "data_description", + "allow_lazy_open", + "analysis_limits", + "background_persist_interval", + "custom_settings", + "daily_model_snapshot_retention_after_days", + "datafeed_config", + "description", + "groups", + "model_plot_config", + "model_snapshot_retention_days", + "renormalization_window_days", + "results_index_name", + "results_retention_days", + ), ) def put_job( self, *, job_id: str, - analysis_config: t.Mapping[str, t.Any], - data_description: t.Mapping[str, t.Any], + analysis_config: t.Optional[t.Mapping[str, t.Any]] = None, + data_description: t.Optional[t.Mapping[str, t.Any]] = None, allow_lazy_open: t.Optional[bool] = None, analysis_limits: t.Optional[t.Mapping[str, t.Any]] = None, background_persist_interval: t.Optional[ @@ -2946,6 +3078,7 @@ def put_job( renormalization_window_days: t.Optional[int] = None, results_index_name: t.Optional[str] = None, results_retention_days: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates an anomaly detection job. @@ -3027,60 +3160,72 @@ def put_job( """ if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") - if analysis_config is None: + if analysis_config is None and body is None: raise ValueError("Empty value passed for parameter 'analysis_config'") - if data_description is None: + if data_description is None and body is None: raise ValueError("Empty value passed for parameter 'data_description'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis_config is not None: - __body["analysis_config"] = analysis_config - if data_description is not None: - __body["data_description"] = data_description - if allow_lazy_open is not None: - __body["allow_lazy_open"] = allow_lazy_open - if analysis_limits is not None: - __body["analysis_limits"] = analysis_limits - if background_persist_interval is not None: - __body["background_persist_interval"] = background_persist_interval - if custom_settings is not None: - __body["custom_settings"] = custom_settings - if daily_model_snapshot_retention_after_days is not None: - __body[ - "daily_model_snapshot_retention_after_days" - ] = daily_model_snapshot_retention_after_days - if datafeed_config is not None: - __body["datafeed_config"] = datafeed_config - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if groups is not None: - __body["groups"] = groups if human is not None: __query["human"] = human - if model_plot_config is not None: - __body["model_plot_config"] = model_plot_config - if model_snapshot_retention_days is not None: - __body["model_snapshot_retention_days"] = model_snapshot_retention_days if pretty is not None: __query["pretty"] = pretty - if renormalization_window_days is not None: - __body["renormalization_window_days"] = renormalization_window_days - if results_index_name is not None: - __body["results_index_name"] = results_index_name - if results_retention_days is not None: - __body["results_retention_days"] = results_retention_days + if not __body: + if analysis_config is not None: + __body["analysis_config"] = analysis_config + if data_description is not None: + __body["data_description"] = data_description + if allow_lazy_open is not None: + __body["allow_lazy_open"] = allow_lazy_open + if analysis_limits is not None: + __body["analysis_limits"] = analysis_limits + if background_persist_interval is not None: + __body["background_persist_interval"] = background_persist_interval + if custom_settings is not None: + __body["custom_settings"] = custom_settings + if daily_model_snapshot_retention_after_days is not None: + __body[ + "daily_model_snapshot_retention_after_days" + ] = daily_model_snapshot_retention_after_days + if datafeed_config is not None: + __body["datafeed_config"] = datafeed_config + if description is not None: + __body["description"] = description + if groups is not None: + __body["groups"] = groups + if model_plot_config is not None: + __body["model_plot_config"] = model_plot_config + if model_snapshot_retention_days is not None: + __body["model_snapshot_retention_days"] = model_snapshot_retention_days + if renormalization_window_days is not None: + __body["renormalization_window_days"] = renormalization_window_days + if results_index_name is not None: + __body["results_index_name"] = results_index_name + if results_retention_days is not None: + __body["results_retention_days"] = results_retention_days __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "compressed_definition", + "definition", + "description", + "inference_config", + "input", + "metadata", + "model_size_bytes", + "model_type", + "platform_architecture", + "tags", + ), ) def put_trained_model( self, @@ -3103,6 +3248,7 @@ def put_trained_model( platform_architecture: t.Optional[str] = None, pretty: t.Optional[bool] = None, tags: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an inference trained model. @@ -3142,38 +3288,39 @@ def put_trained_model( if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") __path = f"/_ml/trained_models/{_quote(model_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if compressed_definition is not None: - __body["compressed_definition"] = compressed_definition + __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_definition_decompression is not None: __query["defer_definition_decompression"] = defer_definition_decompression - if definition is not None: - __body["definition"] = definition - if description is not None: - __body["description"] = description if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if inference_config is not None: - __body["inference_config"] = inference_config - if input is not None: - __body["input"] = input - if metadata is not None: - __body["metadata"] = metadata - if model_size_bytes is not None: - __body["model_size_bytes"] = model_size_bytes - if model_type is not None: - __body["model_type"] = model_type - if platform_architecture is not None: - __body["platform_architecture"] = platform_architecture if pretty is not None: __query["pretty"] = pretty - if tags is not None: - __body["tags"] = tags + if not __body: + if compressed_definition is not None: + __body["compressed_definition"] = compressed_definition + if definition is not None: + __body["definition"] = definition + if description is not None: + __body["description"] = description + if inference_config is not None: + __body["inference_config"] = inference_config + if input is not None: + __body["input"] = input + if metadata is not None: + __body["metadata"] = metadata + if model_size_bytes is not None: + __body["model_size_bytes"] = model_size_bytes + if model_type is not None: + __body["model_type"] = model_type + if platform_architecture is not None: + __body["platform_architecture"] = platform_architecture + if tags is not None: + __body["tags"] = tags __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -3225,20 +3372,21 @@ def put_trained_model_alias( ) @_rewrite_parameters( - body_fields=True, + body_fields=("definition", "total_definition_length", "total_parts"), ) def put_trained_model_definition_part( self, *, model_id: str, part: int, - definition: str, - total_definition_length: int, - total_parts: int, + definition: t.Optional[str] = None, + total_definition_length: t.Optional[int] = None, + total_parts: t.Optional[int] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates part of a trained model definition @@ -3260,23 +3408,17 @@ def put_trained_model_definition_part( raise ValueError("Empty value passed for parameter 'model_id'") if part in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'part'") - if definition is None: + if definition is None and body is None: raise ValueError("Empty value passed for parameter 'definition'") - if total_definition_length is None: + if total_definition_length is None and body is None: raise ValueError( "Empty value passed for parameter 'total_definition_length'" ) - if total_parts is None: + if total_parts is None and body is None: raise ValueError("Empty value passed for parameter 'total_parts'") __path = f"/_ml/trained_models/{_quote(model_id)}/definition/{_quote(part)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if definition is not None: - __body["definition"] = definition - if total_definition_length is not None: - __body["total_definition_length"] = total_definition_length - if total_parts is not None: - __body["total_parts"] = total_parts + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3285,25 +3427,33 @@ def put_trained_model_definition_part( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if definition is not None: + __body["definition"] = definition + if total_definition_length is not None: + __body["total_definition_length"] = total_definition_length + if total_parts is not None: + __body["total_parts"] = total_parts __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("vocabulary", "merges", "scores"), ) def put_trained_model_vocabulary( self, *, model_id: str, - vocabulary: t.Sequence[str], + vocabulary: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, merges: t.Optional[t.Sequence[str]] = None, pretty: t.Optional[bool] = None, scores: t.Optional[t.Sequence[float]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a trained model vocabulary @@ -3317,25 +3467,26 @@ def put_trained_model_vocabulary( """ if model_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'model_id'") - if vocabulary is None: + if vocabulary is None and body is None: raise ValueError("Empty value passed for parameter 'vocabulary'") __path = f"/_ml/trained_models/{_quote(model_id)}/vocabulary" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if vocabulary is not None: - __body["vocabulary"] = vocabulary + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if merges is not None: - __body["merges"] = merges if pretty is not None: __query["pretty"] = pretty - if scores is not None: - __body["scores"] = scores + if not __body: + if vocabulary is not None: + __body["vocabulary"] = vocabulary + if merges is not None: + __body["merges"] = merges + if scores is not None: + __body["scores"] = scores __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -3387,7 +3538,7 @@ def reset_job( ) @_rewrite_parameters( - body_fields=True, + body_fields=("delete_intervening_results",), ) def revert_model_snapshot( self, @@ -3399,6 +3550,7 @@ def revert_model_snapshot( filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Reverts to a specific snapshot. @@ -3417,10 +3569,8 @@ def revert_model_snapshot( if snapshot_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'snapshot_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/model_snapshots/{_quote(snapshot_id)}/_revert" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if delete_intervening_results is not None: - __body["delete_intervening_results"] = delete_intervening_results + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3429,6 +3579,9 @@ def revert_model_snapshot( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if delete_intervening_results is not None: + __body["delete_intervening_results"] = delete_intervening_results if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3521,7 +3674,7 @@ def start_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("end", "start", "timeout"), ) def start_datafeed( self, @@ -3534,6 +3687,7 @@ def start_datafeed( pretty: t.Optional[bool] = None, start: t.Optional[t.Union[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Starts one or more datafeeds. @@ -3551,10 +3705,8 @@ def start_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_start" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if end is not None: - __body["end"] = end + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -3563,10 +3715,13 @@ def start_datafeed( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if start is not None: - __body["start"] = start - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if end is not None: + __body["end"] = end + if start is not None: + __body["start"] = start + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3717,7 +3872,7 @@ def stop_data_frame_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("allow_no_match", "force", "timeout"), ) def stop_datafeed( self, @@ -3730,6 +3885,7 @@ def stop_datafeed( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Stops one or more datafeeds. @@ -3748,22 +3904,23 @@ def stop_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_stop" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_no_match is not None: - __body["allow_no_match"] = allow_no_match + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if force is not None: - __body["force"] = force if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if allow_no_match is not None: + __body["allow_no_match"] = allow_no_match + if force is not None: + __body["force"] = force + if timeout is not None: + __body["timeout"] = timeout if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -3823,7 +3980,12 @@ def stop_trained_model_deployment( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_lazy_start", + "description", + "max_num_threads", + "model_memory_limit", + ), ) def update_data_frame_analytics( self, @@ -3837,6 +3999,7 @@ def update_data_frame_analytics( max_num_threads: t.Optional[int] = None, model_memory_limit: t.Optional[str] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a data frame analytics job. @@ -3862,31 +4025,47 @@ def update_data_frame_analytics( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/_ml/data_frame/analytics/{_quote(id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_lazy_start is not None: - __body["allow_lazy_start"] = allow_lazy_start - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if max_num_threads is not None: - __body["max_num_threads"] = max_num_threads - if model_memory_limit is not None: - __body["model_memory_limit"] = model_memory_limit if pretty is not None: __query["pretty"] = pretty + if not __body: + if allow_lazy_start is not None: + __body["allow_lazy_start"] = allow_lazy_start + if description is not None: + __body["description"] = description + if max_num_threads is not None: + __body["max_num_threads"] = max_num_threads + if model_memory_limit is not None: + __body["model_memory_limit"] = model_memory_limit __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "aggregations", + "chunking_config", + "delayed_data_check_config", + "frequency", + "indexes", + "indices", + "indices_options", + "job_id", + "max_empty_searches", + "query", + "query_delay", + "runtime_mappings", + "script_fields", + "scroll_size", + ), ) def update_datafeed( self, @@ -3921,6 +4100,7 @@ def update_datafeed( runtime_mappings: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, script_fields: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, scroll_size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a datafeed. @@ -4010,59 +4190,60 @@ def update_datafeed( if datafeed_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'datafeed_id'") __path = f"/_ml/datafeeds/{_quote(datafeed_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aggregations is not None: - __body["aggregations"] = aggregations + __body: t.Dict[str, t.Any] = body if body is not None else {} if allow_no_indices is not None: __query["allow_no_indices"] = allow_no_indices - if chunking_config is not None: - __body["chunking_config"] = chunking_config - if delayed_data_check_config is not None: - __body["delayed_data_check_config"] = delayed_data_check_config if error_trace is not None: __query["error_trace"] = error_trace if expand_wildcards is not None: __query["expand_wildcards"] = expand_wildcards if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human if ignore_throttled is not None: __query["ignore_throttled"] = ignore_throttled if ignore_unavailable is not None: __query["ignore_unavailable"] = ignore_unavailable - if indexes is not None: - __body["indexes"] = indexes - if indices is not None: - __body["indices"] = indices - if indices_options is not None: - __body["indices_options"] = indices_options - if job_id is not None: - __body["job_id"] = job_id - if max_empty_searches is not None: - __body["max_empty_searches"] = max_empty_searches if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if query_delay is not None: - __body["query_delay"] = query_delay - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if script_fields is not None: - __body["script_fields"] = script_fields - if scroll_size is not None: - __body["scroll_size"] = scroll_size + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if chunking_config is not None: + __body["chunking_config"] = chunking_config + if delayed_data_check_config is not None: + __body["delayed_data_check_config"] = delayed_data_check_config + if frequency is not None: + __body["frequency"] = frequency + if indexes is not None: + __body["indexes"] = indexes + if indices is not None: + __body["indices"] = indices + if indices_options is not None: + __body["indices_options"] = indices_options + if job_id is not None: + __body["job_id"] = job_id + if max_empty_searches is not None: + __body["max_empty_searches"] = max_empty_searches + if query is not None: + __body["query"] = query + if query_delay is not None: + __body["query_delay"] = query_delay + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if script_fields is not None: + __body["script_fields"] = script_fields + if scroll_size is not None: + __body["scroll_size"] = scroll_size __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("add_items", "description", "remove_items"), ) def update_filter( self, @@ -4075,6 +4256,7 @@ def update_filter( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, remove_items: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates the description of a filter, adds items, or removes items. @@ -4089,12 +4271,8 @@ def update_filter( if filter_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'filter_id'") __path = f"/_ml/filters/{_quote(filter_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if add_items is not None: - __body["add_items"] = add_items - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -4103,15 +4281,36 @@ def update_filter( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if remove_items is not None: - __body["remove_items"] = remove_items + if not __body: + if add_items is not None: + __body["add_items"] = add_items + if description is not None: + __body["description"] = description + if remove_items is not None: + __body["remove_items"] = remove_items __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "allow_lazy_open", + "analysis_limits", + "background_persist_interval", + "categorization_filters", + "custom_settings", + "daily_model_snapshot_retention_after_days", + "description", + "detectors", + "groups", + "model_plot_config", + "model_prune_window", + "model_snapshot_retention_days", + "per_partition_categorization", + "renormalization_window_days", + "results_retention_days", + ), ) def update_job( self, @@ -4140,6 +4339,7 @@ def update_job( pretty: t.Optional[bool] = None, renormalization_window_days: t.Optional[int] = None, results_retention_days: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of an anomaly detection job. @@ -4198,55 +4398,56 @@ def update_job( if job_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'job_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if allow_lazy_open is not None: - __body["allow_lazy_open"] = allow_lazy_open - if analysis_limits is not None: - __body["analysis_limits"] = analysis_limits - if background_persist_interval is not None: - __body["background_persist_interval"] = background_persist_interval - if categorization_filters is not None: - __body["categorization_filters"] = categorization_filters - if custom_settings is not None: - __body["custom_settings"] = custom_settings - if daily_model_snapshot_retention_after_days is not None: - __body[ - "daily_model_snapshot_retention_after_days" - ] = daily_model_snapshot_retention_after_days - if description is not None: - __body["description"] = description - if detectors is not None: - __body["detectors"] = detectors + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if groups is not None: - __body["groups"] = groups if human is not None: __query["human"] = human - if model_plot_config is not None: - __body["model_plot_config"] = model_plot_config - if model_prune_window is not None: - __body["model_prune_window"] = model_prune_window - if model_snapshot_retention_days is not None: - __body["model_snapshot_retention_days"] = model_snapshot_retention_days - if per_partition_categorization is not None: - __body["per_partition_categorization"] = per_partition_categorization if pretty is not None: __query["pretty"] = pretty - if renormalization_window_days is not None: - __body["renormalization_window_days"] = renormalization_window_days - if results_retention_days is not None: - __body["results_retention_days"] = results_retention_days + if not __body: + if allow_lazy_open is not None: + __body["allow_lazy_open"] = allow_lazy_open + if analysis_limits is not None: + __body["analysis_limits"] = analysis_limits + if background_persist_interval is not None: + __body["background_persist_interval"] = background_persist_interval + if categorization_filters is not None: + __body["categorization_filters"] = categorization_filters + if custom_settings is not None: + __body["custom_settings"] = custom_settings + if daily_model_snapshot_retention_after_days is not None: + __body[ + "daily_model_snapshot_retention_after_days" + ] = daily_model_snapshot_retention_after_days + if description is not None: + __body["description"] = description + if detectors is not None: + __body["detectors"] = detectors + if groups is not None: + __body["groups"] = groups + if model_plot_config is not None: + __body["model_plot_config"] = model_plot_config + if model_prune_window is not None: + __body["model_prune_window"] = model_prune_window + if model_snapshot_retention_days is not None: + __body["model_snapshot_retention_days"] = model_snapshot_retention_days + if per_partition_categorization is not None: + __body["per_partition_categorization"] = per_partition_categorization + if renormalization_window_days is not None: + __body["renormalization_window_days"] = renormalization_window_days + if results_retention_days is not None: + __body["results_retention_days"] = results_retention_days __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("description", "retain"), ) def update_model_snapshot( self, @@ -4259,6 +4460,7 @@ def update_model_snapshot( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, retain: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a snapshot. @@ -4277,10 +4479,8 @@ def update_model_snapshot( if snapshot_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'snapshot_id'") __path = f"/_ml/anomaly_detectors/{_quote(job_id)}/model_snapshots/{_quote(snapshot_id)}/_update" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -4289,8 +4489,11 @@ def update_model_snapshot( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if retain is not None: - __body["retain"] = retain + if not __body: + if description is not None: + __body["description"] = description + if retain is not None: + __body["retain"] = retain __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -4346,7 +4549,17 @@ def upgrade_job_snapshot( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "analysis_config", + "analysis_limits", + "data_description", + "description", + "job_id", + "model_plot", + "model_snapshot_id", + "model_snapshot_retention_days", + "results_index_name", + ), ) def validate( self, @@ -4364,6 +4577,7 @@ def validate( model_snapshot_retention_days: t.Optional[int] = None, pretty: t.Optional[bool] = None, results_index_name: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Validates an anomaly detection job. @@ -4381,34 +4595,35 @@ def validate( :param results_index_name: """ __path = "/_ml/anomaly_detectors/_validate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if analysis_config is not None: - __body["analysis_config"] = analysis_config - if analysis_limits is not None: - __body["analysis_limits"] = analysis_limits - if data_description is not None: - __body["data_description"] = data_description - if description is not None: - __body["description"] = description + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if job_id is not None: - __body["job_id"] = job_id - if model_plot is not None: - __body["model_plot"] = model_plot - if model_snapshot_id is not None: - __body["model_snapshot_id"] = model_snapshot_id - if model_snapshot_retention_days is not None: - __body["model_snapshot_retention_days"] = model_snapshot_retention_days if pretty is not None: __query["pretty"] = pretty - if results_index_name is not None: - __body["results_index_name"] = results_index_name + if not __body: + if analysis_config is not None: + __body["analysis_config"] = analysis_config + if analysis_limits is not None: + __body["analysis_limits"] = analysis_limits + if data_description is not None: + __body["data_description"] = data_description + if description is not None: + __body["description"] = description + if job_id is not None: + __body["job_id"] = job_id + if model_plot is not None: + __body["model_plot"] = model_plot + if model_snapshot_id is not None: + __body["model_snapshot_id"] = model_snapshot_id + if model_snapshot_retention_days is not None: + __body["model_snapshot_retention_days"] = model_snapshot_retention_days + if results_index_name is not None: + __body["results_index_name"] = results_index_name __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -4420,7 +4635,8 @@ def validate( def validate_detector( self, *, - detector: t.Mapping[str, t.Any], + detector: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -4433,8 +4649,12 @@ def validate_detector( :param detector: """ - if detector is None: - raise ValueError("Empty value passed for parameter 'detector'") + if detector is None and body is None: + raise ValueError( + "Empty value passed for parameters 'detector' and 'body', one of them should be set." + ) + elif detector is not None and body is not None: + raise ValueError("Cannot set both 'detector' and 'body'") __path = "/_ml/anomaly_detectors/_validate/detector" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -4445,7 +4665,7 @@ def validate_detector( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = detector + __body = detector if detector is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/monitoring.py b/elasticsearch/_sync/client/monitoring.py --- a/elasticsearch/_sync/client/monitoring.py +++ b/elasticsearch/_sync/client/monitoring.py @@ -31,7 +31,8 @@ def bulk( self, *, interval: t.Union["t.Literal[-1]", "t.Literal[0]", str], - operations: t.Sequence[t.Mapping[str, t.Any]], + operations: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, system_api_version: str, system_id: str, error_trace: t.Optional[bool] = None, @@ -51,8 +52,12 @@ def bulk( """ if interval is None: raise ValueError("Empty value passed for parameter 'interval'") - if operations is None: - raise ValueError("Empty value passed for parameter 'operations'") + if operations is None and body is None: + raise ValueError( + "Empty value passed for parameters 'operations' and 'body', one of them should be set." + ) + elif operations is not None and body is not None: + raise ValueError("Cannot set both 'operations' and 'body'") if system_api_version is None: raise ValueError("Empty value passed for parameter 'system_api_version'") if system_id is None: @@ -73,7 +78,7 @@ def bulk( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = operations + __body = operations if operations is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", diff --git a/elasticsearch/_sync/client/nodes.py b/elasticsearch/_sync/client/nodes.py --- a/elasticsearch/_sync/client/nodes.py +++ b/elasticsearch/_sync/client/nodes.py @@ -237,7 +237,7 @@ def info( ) @_rewrite_parameters( - body_fields=True, + body_fields=("secure_settings_password",), ) def reload_secure_settings( self, @@ -249,6 +249,7 @@ def reload_secure_settings( pretty: t.Optional[bool] = None, secure_settings_password: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Reloads secure settings. @@ -265,7 +266,7 @@ def reload_secure_settings( else: __path = "/_nodes/reload_secure_settings" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -274,10 +275,11 @@ def reload_secure_settings( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if secure_settings_password is not None: - __body["secure_settings_password"] = secure_settings_password if timeout is not None: __query["timeout"] = timeout + if not __body: + if secure_settings_password is not None: + __body["secure_settings_password"] = secure_settings_password if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/query_ruleset.py b/elasticsearch/_sync/client/query_ruleset.py --- a/elasticsearch/_sync/client/query_ruleset.py +++ b/elasticsearch/_sync/client/query_ruleset.py @@ -133,17 +133,18 @@ def list( ) @_rewrite_parameters( - body_fields=True, + body_fields=("rules",), ) def put( self, *, ruleset_id: str, - rules: t.Sequence[t.Mapping[str, t.Any]], + rules: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a query ruleset. @@ -156,13 +157,11 @@ def put( """ if ruleset_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'ruleset_id'") - if rules is None: + if rules is None and body is None: raise ValueError("Empty value passed for parameter 'rules'") __path = f"/_query_rules/{_quote(ruleset_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if rules is not None: - __body["rules"] = rules + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -171,6 +170,9 @@ def put( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if rules is not None: + __body["rules"] = rules __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/rollup.py b/elasticsearch/_sync/client/rollup.py --- a/elasticsearch/_sync/client/rollup.py +++ b/elasticsearch/_sync/client/rollup.py @@ -168,18 +168,27 @@ def get_rollup_index_caps( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "cron", + "groups", + "index_pattern", + "page_size", + "rollup_index", + "headers", + "metrics", + "timeout", + ), ignore_deprecated_options={"headers"}, ) def put_job( self, *, id: str, - cron: str, - groups: t.Mapping[str, t.Any], - index_pattern: str, - page_size: int, - rollup_index: str, + cron: t.Optional[str] = None, + groups: t.Optional[t.Mapping[str, t.Any]] = None, + index_pattern: t.Optional[str] = None, + page_size: t.Optional[int] = None, + rollup_index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, headers: t.Optional[t.Mapping[str, t.Union[str, t.Sequence[str]]]] = None, @@ -187,6 +196,7 @@ def put_job( metrics: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a rollup job. @@ -235,50 +245,51 @@ def put_job( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if cron is None: + if cron is None and body is None: raise ValueError("Empty value passed for parameter 'cron'") - if groups is None: + if groups is None and body is None: raise ValueError("Empty value passed for parameter 'groups'") - if index_pattern is None: + if index_pattern is None and body is None: raise ValueError("Empty value passed for parameter 'index_pattern'") - if page_size is None: + if page_size is None and body is None: raise ValueError("Empty value passed for parameter 'page_size'") - if rollup_index is None: + if rollup_index is None and body is None: raise ValueError("Empty value passed for parameter 'rollup_index'") __path = f"/_rollup/job/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if cron is not None: - __body["cron"] = cron - if groups is not None: - __body["groups"] = groups - if index_pattern is not None: - __body["index_pattern"] = index_pattern - if page_size is not None: - __body["page_size"] = page_size - if rollup_index is not None: - __body["rollup_index"] = rollup_index + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if headers is not None: - __body["headers"] = headers if human is not None: __query["human"] = human - if metrics is not None: - __body["metrics"] = metrics if pretty is not None: __query["pretty"] = pretty - if timeout is not None: - __body["timeout"] = timeout + if not __body: + if cron is not None: + __body["cron"] = cron + if groups is not None: + __body["groups"] = groups + if index_pattern is not None: + __body["index_pattern"] = index_pattern + if page_size is not None: + __body["page_size"] = page_size + if rollup_index is not None: + __body["rollup_index"] = rollup_index + if headers is not None: + __body["headers"] = headers + if metrics is not None: + __body["metrics"] = metrics + if timeout is not None: + __body["timeout"] = timeout __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("aggregations", "aggs", "query", "size"), ) def rollup_search( self, @@ -294,6 +305,7 @@ def rollup_search( rest_total_hits_as_int: t.Optional[bool] = None, size: t.Optional[int] = None, typed_keys: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Enables searching rolled-up data using the standard query DSL. @@ -313,12 +325,8 @@ def rollup_search( if index in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'index'") __path = f"/{_quote(index)}/_rollup_search" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if aggregations is not None: - __body["aggregations"] = aggregations - if aggs is not None: - __body["aggs"] = aggs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -327,14 +335,19 @@ def rollup_search( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query if rest_total_hits_as_int is not None: __query["rest_total_hits_as_int"] = rest_total_hits_as_int - if size is not None: - __body["size"] = size if typed_keys is not None: __query["typed_keys"] = typed_keys + if not __body: + if aggregations is not None: + __body["aggregations"] = aggregations + if aggs is not None: + __body["aggs"] = aggs + if query is not None: + __body["query"] = query + if size is not None: + __body["size"] = size __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/search_application.py b/elasticsearch/_sync/client/search_application.py --- a/elasticsearch/_sync/client/search_application.py +++ b/elasticsearch/_sync/client/search_application.py @@ -212,7 +212,8 @@ def put( self, *, name: str, - search_application: t.Mapping[str, t.Any], + search_application: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Mapping[str, t.Any]] = None, create: t.Optional[bool] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -231,8 +232,12 @@ def put( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if search_application is None: - raise ValueError("Empty value passed for parameter 'search_application'") + if search_application is None and body is None: + raise ValueError( + "Empty value passed for parameters 'search_application' and 'body', one of them should be set." + ) + elif search_application is not None and body is not None: + raise ValueError("Cannot set both 'search_application' and 'body'") __path = f"/_application/search_application/{_quote(name)}" __query: t.Dict[str, t.Any] = {} if create is not None: @@ -245,7 +250,7 @@ def put( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - __body = search_application + __body = search_application if search_application is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -286,7 +291,7 @@ def put_behavioral_analytics( ) @_rewrite_parameters( - body_fields=True, + body_fields=("params",), ignore_deprecated_options={"params"}, ) def search( @@ -298,6 +303,7 @@ def search( human: t.Optional[bool] = None, params: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Perform a search against a search application @@ -312,17 +318,18 @@ def search( raise ValueError("Empty value passed for parameter 'name'") __path = f"/_application/search_application/{_quote(name)}/_search" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if params is not None: - __body["params"] = params if pretty is not None: __query["pretty"] = pretty + if not __body: + if params is not None: + __body["params"] = params if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/searchable_snapshots.py b/elasticsearch/_sync/client/searchable_snapshots.py --- a/elasticsearch/_sync/client/searchable_snapshots.py +++ b/elasticsearch/_sync/client/searchable_snapshots.py @@ -126,14 +126,19 @@ def clear_cache( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "index", + "ignore_index_settings", + "index_settings", + "renamed_index", + ), ) def mount( self, *, repository: str, snapshot: str, - index: str, + index: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -146,6 +151,7 @@ def mount( renamed_index: t.Optional[str] = None, storage: t.Optional[str] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Mount a snapshot as a searchable index. @@ -169,33 +175,34 @@ def mount( raise ValueError("Empty value passed for parameter 'repository'") if snapshot in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'snapshot'") - if index is None: + if index is None and body is None: raise ValueError("Empty value passed for parameter 'index'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}/_mount" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if index is not None: - __body["index"] = index + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_index_settings is not None: - __body["ignore_index_settings"] = ignore_index_settings - if index_settings is not None: - __body["index_settings"] = index_settings if master_timeout is not None: __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if renamed_index is not None: - __body["renamed_index"] = renamed_index if storage is not None: __query["storage"] = storage if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if index is not None: + __body["index"] = index + if ignore_index_settings is not None: + __body["ignore_index_settings"] = ignore_index_settings + if index_settings is not None: + __body["index_settings"] = index_settings + if renamed_index is not None: + __body["renamed_index"] = renamed_index __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/security.py b/elasticsearch/_sync/client/security.py --- a/elasticsearch/_sync/client/security.py +++ b/elasticsearch/_sync/client/security.py @@ -25,12 +25,14 @@ class SecurityClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("grant_type", "access_token", "password", "username"), ) def activate_user_profile( self, *, - grant_type: t.Union["t.Literal['access_token', 'password']", str], + grant_type: t.Optional[ + t.Union["t.Literal['access_token', 'password']", str] + ] = None, access_token: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -38,6 +40,7 @@ def activate_user_profile( password: t.Optional[str] = None, pretty: t.Optional[bool] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates the user profile on behalf of another user. @@ -49,27 +52,28 @@ def activate_user_profile( :param password: :param username: """ - if grant_type is None: + if grant_type is None and body is None: raise ValueError("Empty value passed for parameter 'grant_type'") __path = "/_security/profile/_activate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if grant_type is not None: - __body["grant_type"] = grant_type - if access_token is not None: - __body["access_token"] = access_token + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if password is not None: - __body["password"] = password if pretty is not None: __query["pretty"] = pretty - if username is not None: - __body["username"] = username + if not __body: + if grant_type is not None: + __body["grant_type"] = grant_type + if access_token is not None: + __body["access_token"] = access_token + if password is not None: + __body["password"] = password + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -106,7 +110,7 @@ def authenticate( ) @_rewrite_parameters( - body_fields=True, + body_fields=("password", "password_hash"), ) def change_password( self, @@ -121,6 +125,7 @@ def change_password( refresh: t.Optional[ t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Changes the passwords of users in the native realm and built-in users. @@ -144,21 +149,22 @@ def change_password( else: __path = "/_security/user/_password" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if password is not None: - __body["password"] = password - if password_hash is not None: - __body["password_hash"] = password_hash if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh + if not __body: + if password is not None: + __body["password"] = password + if password_hash is not None: + __body["password_hash"] = password_hash __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -349,7 +355,7 @@ def clear_cached_service_tokens( ) @_rewrite_parameters( - body_fields=True, + body_fields=("expiration", "metadata", "name", "role_descriptors"), ) def create_api_key( self, @@ -365,6 +371,7 @@ def create_api_key( t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, role_descriptors: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an API key for access without requiring basic authentication. @@ -391,25 +398,26 @@ def create_api_key( """ __path = "/_security/api_key" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if expiration is not None: - __body["expiration"] = expiration if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata - if name is not None: - __body["name"] = name if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if role_descriptors is not None: - __body["role_descriptors"] = role_descriptors + if not __body: + if expiration is not None: + __body["expiration"] = expiration + if metadata is not None: + __body["metadata"] = metadata + if name is not None: + __body["name"] = name + if role_descriptors is not None: + __body["role_descriptors"] = role_descriptors __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -1212,7 +1220,14 @@ def get_service_credentials( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "grant_type", + "kerberos_ticket", + "password", + "refresh_token", + "scope", + "username", + ), ) def get_token( self, @@ -1232,6 +1247,7 @@ def get_token( refresh_token: t.Optional[str] = None, scope: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a bearer token for access without requiring basic authentication. @@ -1247,27 +1263,28 @@ def get_token( """ __path = "/_security/oauth2/token" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if grant_type is not None: - __body["grant_type"] = grant_type if human is not None: __query["human"] = human - if kerberos_ticket is not None: - __body["kerberos_ticket"] = kerberos_ticket - if password is not None: - __body["password"] = password if pretty is not None: __query["pretty"] = pretty - if refresh_token is not None: - __body["refresh_token"] = refresh_token - if scope is not None: - __body["scope"] = scope - if username is not None: - __body["username"] = username + if not __body: + if grant_type is not None: + __body["grant_type"] = grant_type + if kerberos_ticket is not None: + __body["kerberos_ticket"] = kerberos_ticket + if password is not None: + __body["password"] = password + if refresh_token is not None: + __body["refresh_token"] = refresh_token + if scope is not None: + __body["scope"] = scope + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -1402,14 +1419,23 @@ def get_user_profile( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "api_key", + "grant_type", + "access_token", + "password", + "run_as", + "username", + ), ignore_deprecated_options={"api_key"}, ) def grant_api_key( self, *, - api_key: t.Mapping[str, t.Any], - grant_type: t.Union["t.Literal['access_token', 'password']", str], + api_key: t.Optional[t.Mapping[str, t.Any]] = None, + grant_type: t.Optional[ + t.Union["t.Literal['access_token', 'password']", str] + ] = None, access_token: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -1418,6 +1444,7 @@ def grant_api_key( pretty: t.Optional[bool] = None, run_as: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates an API key on behalf of another user. @@ -1437,40 +1464,41 @@ def grant_api_key( grant type, this parameter is required. It is not valid with other grant types. """ - if api_key is None: + if api_key is None and body is None: raise ValueError("Empty value passed for parameter 'api_key'") - if grant_type is None: + if grant_type is None and body is None: raise ValueError("Empty value passed for parameter 'grant_type'") __path = "/_security/api_key/grant" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if api_key is not None: - __body["api_key"] = api_key - if grant_type is not None: - __body["grant_type"] = grant_type - if access_token is not None: - __body["access_token"] = access_token + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if password is not None: - __body["password"] = password if pretty is not None: __query["pretty"] = pretty - if run_as is not None: - __body["run_as"] = run_as - if username is not None: - __body["username"] = username + if not __body: + if api_key is not None: + __body["api_key"] = api_key + if grant_type is not None: + __body["grant_type"] = grant_type + if access_token is not None: + __body["access_token"] = access_token + if password is not None: + __body["password"] = password + if run_as is not None: + __body["run_as"] = run_as + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("application", "cluster", "index"), ) def has_privileges( self, @@ -1490,6 +1518,7 @@ def has_privileges( human: t.Optional[bool] = None, index: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Determines whether the specified user has a specified list of privileges. @@ -1505,39 +1534,41 @@ def has_privileges( __path = f"/_security/user/{_quote(user)}/_has_privileges" else: __path = "/_security/user/_has_privileges" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if application is not None: - __body["application"] = application - if cluster is not None: - __body["cluster"] = cluster + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if index is not None: - __body["index"] = index if pretty is not None: __query["pretty"] = pretty + if not __body: + if application is not None: + __body["application"] = application + if cluster is not None: + __body["cluster"] = cluster + if index is not None: + __body["index"] = index __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("privileges", "uids"), ) def has_privileges_user_profile( self, *, - privileges: t.Mapping[str, t.Any], - uids: t.Sequence[str], + privileges: t.Optional[t.Mapping[str, t.Any]] = None, + uids: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Determines whether the users associated with the specified profile IDs have all @@ -1549,17 +1580,13 @@ def has_privileges_user_profile( :param uids: A list of profile IDs. The privileges are checked for associated users of the profiles. """ - if privileges is None: + if privileges is None and body is None: raise ValueError("Empty value passed for parameter 'privileges'") - if uids is None: + if uids is None and body is None: raise ValueError("Empty value passed for parameter 'uids'") __path = "/_security/profile/_has_privileges" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if privileges is not None: - __body["privileges"] = privileges - if uids is not None: - __body["uids"] = uids + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1568,13 +1595,18 @@ def has_privileges_user_profile( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if privileges is not None: + __body["privileges"] = privileges + if uids is not None: + __body["uids"] = uids __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("id", "ids", "name", "owner", "realm_name", "username"), ) def invalidate_api_key( self, @@ -1589,6 +1621,7 @@ def invalidate_api_key( pretty: t.Optional[bool] = None, realm_name: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Invalidates one or more API keys. @@ -1611,34 +1644,35 @@ def invalidate_api_key( """ __path = "/_security/api_key" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if id is not None: - __body["id"] = id - if ids is not None: - __body["ids"] = ids - if name is not None: - __body["name"] = name - if owner is not None: - __body["owner"] = owner if pretty is not None: __query["pretty"] = pretty - if realm_name is not None: - __body["realm_name"] = realm_name - if username is not None: - __body["username"] = username + if not __body: + if id is not None: + __body["id"] = id + if ids is not None: + __body["ids"] = ids + if name is not None: + __body["name"] = name + if owner is not None: + __body["owner"] = owner + if realm_name is not None: + __body["realm_name"] = realm_name + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("realm_name", "refresh_token", "token", "username"), ) def invalidate_token( self, @@ -1651,6 +1685,7 @@ def invalidate_token( refresh_token: t.Optional[str] = None, token: t.Optional[str] = None, username: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Invalidates one or more access tokens or refresh tokens. @@ -1664,7 +1699,7 @@ def invalidate_token( """ __path = "/_security/oauth2/token" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -1673,14 +1708,15 @@ def invalidate_token( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm_name is not None: - __body["realm_name"] = realm_name - if refresh_token is not None: - __body["refresh_token"] = refresh_token - if token is not None: - __body["token"] = token - if username is not None: - __body["username"] = username + if not __body: + if realm_name is not None: + __body["realm_name"] = realm_name + if refresh_token is not None: + __body["refresh_token"] = refresh_token + if token is not None: + __body["token"] = token + if username is not None: + __body["username"] = username __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "DELETE", __path, params=__query, headers=__headers, body=__body @@ -1692,7 +1728,10 @@ def invalidate_token( def put_privileges( self, *, - privileges: t.Mapping[str, t.Mapping[str, t.Mapping[str, t.Any]]], + privileges: t.Optional[ + t.Mapping[str, t.Mapping[str, t.Mapping[str, t.Any]]] + ] = None, + body: t.Optional[t.Mapping[str, t.Mapping[str, t.Mapping[str, t.Any]]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -1711,8 +1750,12 @@ def put_privileges( this operation visible to search, if `wait_for` then wait for a refresh to make this operation visible to search, if `false` then do nothing with refreshes. """ - if privileges is None: - raise ValueError("Empty value passed for parameter 'privileges'") + if privileges is None and body is None: + raise ValueError( + "Empty value passed for parameters 'privileges' and 'body', one of them should be set." + ) + elif privileges is not None and body is not None: + raise ValueError("Cannot set both 'privileges' and 'body'") __path = "/_security/privilege" __query: t.Dict[str, t.Any] = {} if error_trace is not None: @@ -1725,14 +1768,22 @@ def put_privileges( __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - __body = privileges + __body = privileges if privileges is not None else body __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "applications", + "cluster", + "global_", + "indices", + "metadata", + "run_as", + "transient_metadata", + ), parameter_aliases={"global": "global_"}, ) def put_role( @@ -1760,6 +1811,7 @@ def put_role( ] = None, run_as: t.Optional[t.Sequence[str]] = None, transient_metadata: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Adds and updates roles in the native realm. @@ -1790,39 +1842,40 @@ def put_role( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_security/role/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if applications is not None: - __body["applications"] = applications - if cluster is not None: - __body["cluster"] = cluster + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if global_ is not None: - __body["global"] = global_ if human is not None: __query["human"] = human - if indices is not None: - __body["indices"] = indices - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if run_as is not None: - __body["run_as"] = run_as - if transient_metadata is not None: - __body["transient_metadata"] = transient_metadata + if not __body: + if applications is not None: + __body["applications"] = applications + if cluster is not None: + __body["cluster"] = cluster + if global_ is not None: + __body["global"] = global_ + if indices is not None: + __body["indices"] = indices + if metadata is not None: + __body["metadata"] = metadata + if run_as is not None: + __body["run_as"] = run_as + if transient_metadata is not None: + __body["transient_metadata"] = transient_metadata __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("enabled", "metadata", "roles", "rules", "run_as"), ) def put_role_mapping( self, @@ -1840,6 +1893,7 @@ def put_role_mapping( roles: t.Optional[t.Sequence[str]] = None, rules: t.Optional[t.Mapping[str, t.Any]] = None, run_as: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates and updates role mappings. @@ -1859,35 +1913,44 @@ def put_role_mapping( if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") __path = f"/_security/role_mapping/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if enabled is not None: - __body["enabled"] = enabled + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if roles is not None: - __body["roles"] = roles - if rules is not None: - __body["rules"] = rules - if run_as is not None: - __body["run_as"] = run_as + if not __body: + if enabled is not None: + __body["enabled"] = enabled + if metadata is not None: + __body["metadata"] = metadata + if roles is not None: + __body["roles"] = roles + if rules is not None: + __body["rules"] = rules + if run_as is not None: + __body["run_as"] = run_as __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "email", + "enabled", + "full_name", + "metadata", + "password", + "password_hash", + "roles", + ), ) def put_user( self, @@ -1907,6 +1970,7 @@ def put_user( t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, roles: t.Optional[t.Sequence[str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Adds and updates users in the native realm. These users are commonly referred @@ -1929,39 +1993,40 @@ def put_user( if username in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'username'") __path = f"/_security/user/{_quote(username)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if email is not None: - __body["email"] = email - if enabled is not None: - __body["enabled"] = enabled + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if full_name is not None: - __body["full_name"] = full_name if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata - if password is not None: - __body["password"] = password - if password_hash is not None: - __body["password_hash"] = password_hash if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh - if roles is not None: - __body["roles"] = roles + if not __body: + if email is not None: + __body["email"] = email + if enabled is not None: + __body["enabled"] = enabled + if full_name is not None: + __body["full_name"] = full_name + if metadata is not None: + __body["metadata"] = metadata + if password is not None: + __body["password"] = password + if password_hash is not None: + __body["password_hash"] = password_hash + if roles is not None: + __body["roles"] = roles __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("from_", "query", "search_after", "size", "sort"), parameter_aliases={"from": "from_"}, ) def query_api_keys( @@ -1984,6 +2049,7 @@ def query_api_keys( ] ] = None, with_limited_by: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves information for API keys using a subset of query DSL @@ -2010,7 +2076,7 @@ def query_api_keys( """ __path = "/_security/_query/api_key" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -2026,22 +2092,23 @@ def query_api_keys( __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if search_after is not None: - __body["search_after"] = search_after - if size is not None: - __body["size"] = size - if sort is not None: - __body["sort"] = sort if with_limited_by is not None: __query["with_limited_by"] = with_limited_by + if not __body: + if from_ is not None: + __body["from"] = from_ + if query is not None: + __body["query"] = query + if search_after is not None: + __body["search_after"] = search_after + if size is not None: + __body["size"] = size + if sort is not None: + __body["sort"] = sort if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2052,18 +2119,19 @@ def query_api_keys( ) @_rewrite_parameters( - body_fields=True, + body_fields=("content", "ids", "realm"), ) def saml_authenticate( self, *, - content: str, - ids: t.Union[str, t.Sequence[str]], + content: t.Optional[str] = None, + ids: t.Optional[t.Union[str, t.Sequence[str]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, realm: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Exchanges a SAML Response message for an Elasticsearch access token and refresh @@ -2078,17 +2146,13 @@ def saml_authenticate( :param realm: The name of the realm that should authenticate the SAML response. Useful in cases where many SAML realms are defined. """ - if content is None: + if content is None and body is None: raise ValueError("Empty value passed for parameter 'content'") - if ids is None: + if ids is None and body is None: raise ValueError("Empty value passed for parameter 'ids'") __path = "/_security/saml/authenticate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if content is not None: - __body["content"] = content - if ids is not None: - __body["ids"] = ids + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2097,27 +2161,33 @@ def saml_authenticate( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm is not None: - __body["realm"] = realm + if not __body: + if content is not None: + __body["content"] = content + if ids is not None: + __body["ids"] = ids + if realm is not None: + __body["realm"] = realm __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("ids", "realm", "content", "query_string"), ) def saml_complete_logout( self, *, - ids: t.Union[str, t.Sequence[str]], - realm: str, + ids: t.Optional[t.Union[str, t.Sequence[str]]] = None, + realm: t.Optional[str] = None, content: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, query_string: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Verifies the logout response sent from the SAML IdP @@ -2134,19 +2204,13 @@ def saml_complete_logout( :param query_string: If the SAML IdP sends the logout response with the HTTP-Redirect binding, this field must be set to the query string of the redirect URI. """ - if ids is None: + if ids is None and body is None: raise ValueError("Empty value passed for parameter 'ids'") - if realm is None: + if realm is None and body is None: raise ValueError("Empty value passed for parameter 'realm'") __path = "/_security/saml/complete_logout" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if ids is not None: - __body["ids"] = ids - if realm is not None: - __body["realm"] = realm - if content is not None: - __body["content"] = content + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2155,26 +2219,34 @@ def saml_complete_logout( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query_string is not None: - __body["query_string"] = query_string + if not __body: + if ids is not None: + __body["ids"] = ids + if realm is not None: + __body["realm"] = realm + if content is not None: + __body["content"] = content + if query_string is not None: + __body["query_string"] = query_string __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("query_string", "acs", "realm"), ) def saml_invalidate( self, *, - query_string: str, + query_string: t.Optional[str] = None, acs: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, realm: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Consumes a SAML LogoutRequest @@ -2197,15 +2269,11 @@ def saml_invalidate( :param realm: The name of the SAML realm in Elasticsearch the configuration. You must specify either this parameter or the acs parameter. """ - if query_string is None: + if query_string is None and body is None: raise ValueError("Empty value passed for parameter 'query_string'") __path = "/_security/saml/invalidate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if query_string is not None: - __body["query_string"] = query_string - if acs is not None: - __body["acs"] = acs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2214,25 +2282,31 @@ def saml_invalidate( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm is not None: - __body["realm"] = realm + if not __body: + if query_string is not None: + __body["query_string"] = query_string + if acs is not None: + __body["acs"] = acs + if realm is not None: + __body["realm"] = realm __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("token", "refresh_token"), ) def saml_logout( self, *, - token: str, + token: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, refresh_token: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Invalidates an access token and a refresh token that were generated via the SAML @@ -2247,13 +2321,11 @@ def saml_logout( the SAML authenticate API. Alternatively, the most recent refresh token that was received after refreshing the original access token. """ - if token is None: + if token is None and body is None: raise ValueError("Empty value passed for parameter 'token'") __path = "/_security/saml/logout" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if token is not None: - __body["token"] = token + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2262,15 +2334,18 @@ def saml_logout( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if refresh_token is not None: - __body["refresh_token"] = refresh_token + if not __body: + if token is not None: + __body["token"] = token + if refresh_token is not None: + __body["refresh_token"] = refresh_token __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("acs", "realm", "relay_state"), ) def saml_prepare_authentication( self, @@ -2282,6 +2357,7 @@ def saml_prepare_authentication( pretty: t.Optional[bool] = None, realm: t.Optional[str] = None, relay_state: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a SAML authentication request @@ -2299,10 +2375,8 @@ def saml_prepare_authentication( is signed, this value is used as part of the signature computation. """ __path = "/_security/saml/prepare" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if acs is not None: - __body["acs"] = acs + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2311,10 +2385,13 @@ def saml_prepare_authentication( __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if realm is not None: - __body["realm"] = realm - if relay_state is not None: - __body["relay_state"] = relay_state + if not __body: + if acs is not None: + __body["acs"] = acs + if realm is not None: + __body["realm"] = realm + if relay_state is not None: + __body["relay_state"] = relay_state __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -2355,7 +2432,7 @@ def saml_service_provider_metadata( ) @_rewrite_parameters( - body_fields=True, + body_fields=("data", "hint", "name", "size"), ) def suggest_user_profiles( self, @@ -2368,6 +2445,7 @@ def suggest_user_profiles( name: t.Optional[str] = None, pretty: t.Optional[bool] = None, size: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Get suggestions for user profiles that match specified search criteria. @@ -2387,24 +2465,25 @@ def suggest_user_profiles( :param size: Number of profiles to return. """ __path = "/_security/profile/_suggest" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if data is not None: - __body["data"] = data + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if hint is not None: - __body["hint"] = hint if human is not None: __query["human"] = human - if name is not None: - __body["name"] = name if pretty is not None: __query["pretty"] = pretty - if size is not None: - __body["size"] = size + if not __body: + if data is not None: + __body["data"] = data + if hint is not None: + __body["hint"] = hint + if name is not None: + __body["name"] = name + if size is not None: + __body["size"] = size if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2415,7 +2494,7 @@ def suggest_user_profiles( ) @_rewrite_parameters( - body_fields=True, + body_fields=("metadata", "role_descriptors"), ) def update_api_key( self, @@ -2427,6 +2506,7 @@ def update_api_key( metadata: t.Optional[t.Mapping[str, t.Any]] = None, pretty: t.Optional[bool] = None, role_descriptors: t.Optional[t.Mapping[str, t.Mapping[str, t.Any]]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates attributes of an existing API key. @@ -2450,19 +2530,20 @@ def update_api_key( raise ValueError("Empty value passed for parameter 'id'") __path = f"/_security/api_key/{_quote(id)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty - if role_descriptors is not None: - __body["role_descriptors"] = role_descriptors + if not __body: + if metadata is not None: + __body["metadata"] = metadata + if role_descriptors is not None: + __body["role_descriptors"] = role_descriptors if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -2473,7 +2554,7 @@ def update_api_key( ) @_rewrite_parameters( - body_fields=True, + body_fields=("data", "labels"), ) def update_user_profile_data( self, @@ -2490,6 +2571,7 @@ def update_user_profile_data( refresh: t.Optional[ t.Union["t.Literal['false', 'true', 'wait_for']", bool, str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Update application specific data for the user profile of the given unique ID. @@ -2512,10 +2594,8 @@ def update_user_profile_data( if uid in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'uid'") __path = f"/_security/profile/{_quote(uid)}/_data" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if data is not None: - __body["data"] = data + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -2526,12 +2606,15 @@ def update_user_profile_data( __query["if_primary_term"] = if_primary_term if if_seq_no is not None: __query["if_seq_no"] = if_seq_no - if labels is not None: - __body["labels"] = labels if pretty is not None: __query["pretty"] = pretty if refresh is not None: __query["refresh"] = refresh + if not __body: + if data is not None: + __body["data"] = data + if labels is not None: + __body["labels"] = labels __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/shutdown.py b/elasticsearch/_sync/client/shutdown.py --- a/elasticsearch/_sync/client/shutdown.py +++ b/elasticsearch/_sync/client/shutdown.py @@ -126,14 +126,16 @@ def get_node( ) @_rewrite_parameters( - body_fields=True, + body_fields=("reason", "type", "allocation_delay", "target_node_name"), ) def put_node( self, *, node_id: str, - reason: str, - type: t.Union["t.Literal['remove', 'replace', 'restart']", str], + reason: t.Optional[str] = None, + type: t.Optional[ + t.Union["t.Literal['remove', 'replace', 'restart']", str] + ] = None, allocation_delay: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, @@ -146,6 +148,7 @@ def put_node( timeout: t.Optional[ t.Union["t.Literal['d', 'h', 'm', 'micros', 'ms', 'nanos', 's']", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Adds a node to be shut down. Designed for indirect use by ECE/ESS and ECK. Direct @@ -188,19 +191,13 @@ def put_node( """ if node_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'node_id'") - if reason is None: + if reason is None and body is None: raise ValueError("Empty value passed for parameter 'reason'") - if type is None: + if type is None and body is None: raise ValueError("Empty value passed for parameter 'type'") __path = f"/_nodes/{_quote(node_id)}/shutdown" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if reason is not None: - __body["reason"] = reason - if type is not None: - __body["type"] = type - if allocation_delay is not None: - __body["allocation_delay"] = allocation_delay + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -211,10 +208,17 @@ def put_node( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if target_node_name is not None: - __body["target_node_name"] = target_node_name if timeout is not None: __query["timeout"] = timeout + if not __body: + if reason is not None: + __body["reason"] = reason + if type is not None: + __body["type"] = type + if allocation_delay is not None: + __body["allocation_delay"] = allocation_delay + if target_node_name is not None: + __body["target_node_name"] = target_node_name __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/slm.py b/elasticsearch/_sync/client/slm.py --- a/elasticsearch/_sync/client/slm.py +++ b/elasticsearch/_sync/client/slm.py @@ -218,7 +218,7 @@ def get_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=("config", "name", "repository", "retention", "schedule"), ) def put_lifecycle( self, @@ -237,6 +237,7 @@ def put_lifecycle( retention: t.Optional[t.Mapping[str, t.Any]] = None, schedule: t.Optional[str] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a snapshot lifecycle policy. @@ -265,10 +266,8 @@ def put_lifecycle( if policy_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'policy_id'") __path = f"/_slm/policy/{_quote(policy_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if config is not None: - __body["config"] = config + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -277,18 +276,21 @@ def put_lifecycle( __query["human"] = human if master_timeout is not None: __query["master_timeout"] = master_timeout - if name is not None: - __body["name"] = name if pretty is not None: __query["pretty"] = pretty - if repository is not None: - __body["repository"] = repository - if retention is not None: - __body["retention"] = retention - if schedule is not None: - __body["schedule"] = schedule if timeout is not None: __query["timeout"] = timeout + if not __body: + if config is not None: + __body["config"] = config + if name is not None: + __body["name"] = name + if repository is not None: + __body["repository"] = repository + if retention is not None: + __body["retention"] = retention + if schedule is not None: + __body["schedule"] = schedule if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/snapshot.py b/elasticsearch/_sync/client/snapshot.py --- a/elasticsearch/_sync/client/snapshot.py +++ b/elasticsearch/_sync/client/snapshot.py @@ -69,7 +69,7 @@ def cleanup_repository( ) @_rewrite_parameters( - body_fields=True, + body_fields=("indices",), ) def clone( self, @@ -77,7 +77,7 @@ def clone( repository: str, snapshot: str, target_snapshot: str, - indices: str, + indices: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -86,6 +86,7 @@ def clone( ] = None, pretty: t.Optional[bool] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Clones indices from one snapshot into another snapshot in the same repository. @@ -105,13 +106,11 @@ def clone( raise ValueError("Empty value passed for parameter 'snapshot'") if target_snapshot in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'target_snapshot'") - if indices is None: + if indices is None and body is None: raise ValueError("Empty value passed for parameter 'indices'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}/_clone/{_quote(target_snapshot)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if indices is not None: - __body["indices"] = indices + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -124,13 +123,23 @@ def clone( __query["pretty"] = pretty if timeout is not None: __query["timeout"] = timeout + if not __body: + if indices is not None: + __body["indices"] = indices __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "feature_states", + "ignore_unavailable", + "include_global_state", + "indices", + "metadata", + "partial", + ), ) def create( self, @@ -151,6 +160,7 @@ def create( partial: t.Optional[bool] = None, pretty: t.Optional[bool] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a snapshot in a repository. @@ -194,31 +204,32 @@ def create( raise ValueError("Empty value passed for parameter 'snapshot'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if feature_states is not None: - __body["feature_states"] = feature_states if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_unavailable is not None: - __body["ignore_unavailable"] = ignore_unavailable - if include_global_state is not None: - __body["include_global_state"] = include_global_state - if indices is not None: - __body["indices"] = indices if master_timeout is not None: __query["master_timeout"] = master_timeout - if metadata is not None: - __body["metadata"] = metadata - if partial is not None: - __body["partial"] = partial if pretty is not None: __query["pretty"] = pretty if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if feature_states is not None: + __body["feature_states"] = feature_states + if ignore_unavailable is not None: + __body["ignore_unavailable"] = ignore_unavailable + if include_global_state is not None: + __body["include_global_state"] = include_global_state + if indices is not None: + __body["indices"] = indices + if metadata is not None: + __body["metadata"] = metadata + if partial is not None: + __body["partial"] = partial if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -229,14 +240,14 @@ def create( ) @_rewrite_parameters( - body_fields=True, + body_fields=("settings", "type", "repository"), ) def create_repository( self, *, name: str, - settings: t.Mapping[str, t.Any], - type: str, + settings: t.Optional[t.Mapping[str, t.Any]] = None, + type: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, @@ -247,6 +258,7 @@ def create_repository( repository: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, verify: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a repository. @@ -263,17 +275,13 @@ def create_repository( """ if name in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'name'") - if settings is None: + if settings is None and body is None: raise ValueError("Empty value passed for parameter 'settings'") - if type is None: + if type is None and body is None: raise ValueError("Empty value passed for parameter 'type'") __path = f"/_snapshot/{_quote(name)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if settings is not None: - __body["settings"] = settings - if type is not None: - __body["type"] = type + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -284,12 +292,17 @@ def create_repository( __query["master_timeout"] = master_timeout if pretty is not None: __query["pretty"] = pretty - if repository is not None: - __body["repository"] = repository if timeout is not None: __query["timeout"] = timeout if verify is not None: __query["verify"] = verify + if not __body: + if settings is not None: + __body["settings"] = settings + if type is not None: + __body["type"] = type + if repository is not None: + __body["repository"] = repository __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -553,7 +566,18 @@ def get_repository( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "feature_states", + "ignore_index_settings", + "ignore_unavailable", + "include_aliases", + "include_global_state", + "index_settings", + "indices", + "partial", + "rename_pattern", + "rename_replacement", + ), ) def restore( self, @@ -578,6 +602,7 @@ def restore( rename_pattern: t.Optional[str] = None, rename_replacement: t.Optional[str] = None, wait_for_completion: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Restores a snapshot. @@ -606,39 +631,40 @@ def restore( raise ValueError("Empty value passed for parameter 'snapshot'") __path = f"/_snapshot/{_quote(repository)}/{_quote(snapshot)}/_restore" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if feature_states is not None: - __body["feature_states"] = feature_states if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_index_settings is not None: - __body["ignore_index_settings"] = ignore_index_settings - if ignore_unavailable is not None: - __body["ignore_unavailable"] = ignore_unavailable - if include_aliases is not None: - __body["include_aliases"] = include_aliases - if include_global_state is not None: - __body["include_global_state"] = include_global_state - if index_settings is not None: - __body["index_settings"] = index_settings - if indices is not None: - __body["indices"] = indices if master_timeout is not None: __query["master_timeout"] = master_timeout - if partial is not None: - __body["partial"] = partial if pretty is not None: __query["pretty"] = pretty - if rename_pattern is not None: - __body["rename_pattern"] = rename_pattern - if rename_replacement is not None: - __body["rename_replacement"] = rename_replacement if wait_for_completion is not None: __query["wait_for_completion"] = wait_for_completion + if not __body: + if feature_states is not None: + __body["feature_states"] = feature_states + if ignore_index_settings is not None: + __body["ignore_index_settings"] = ignore_index_settings + if ignore_unavailable is not None: + __body["ignore_unavailable"] = ignore_unavailable + if include_aliases is not None: + __body["include_aliases"] = include_aliases + if include_global_state is not None: + __body["include_global_state"] = include_global_state + if index_settings is not None: + __body["index_settings"] = index_settings + if indices is not None: + __body["indices"] = indices + if partial is not None: + __body["partial"] = partial + if rename_pattern is not None: + __body["rename_pattern"] = rename_pattern + if rename_replacement is not None: + __body["rename_replacement"] = rename_replacement if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/_sync/client/sql.py b/elasticsearch/_sync/client/sql.py --- a/elasticsearch/_sync/client/sql.py +++ b/elasticsearch/_sync/client/sql.py @@ -25,16 +25,17 @@ class SqlClient(NamespacedClient): @_rewrite_parameters( - body_fields=True, + body_fields=("cursor",), ) def clear_cursor( self, *, - cursor: str, + cursor: t.Optional[str] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Clears the SQL cursor @@ -43,13 +44,11 @@ def clear_cursor( :param cursor: Cursor to clear. """ - if cursor is None: + if cursor is None and body is None: raise ValueError("Empty value passed for parameter 'cursor'") __path = "/_sql/close" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if cursor is not None: - __body["cursor"] = cursor + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -58,6 +57,9 @@ def clear_cursor( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if cursor is not None: + __body["cursor"] = cursor __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body @@ -192,7 +194,24 @@ def get_async_status( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "catalog", + "columnar", + "cursor", + "fetch_size", + "field_multi_value_leniency", + "filter", + "index_using_frozen", + "keep_alive", + "keep_on_completion", + "page_timeout", + "params", + "query", + "request_timeout", + "runtime_mappings", + "time_zone", + "wait_for_completion_timeout", + ), ignore_deprecated_options={"params", "request_timeout"}, ) def query( @@ -223,6 +242,7 @@ def query( wait_for_completion_timeout: t.Optional[ t.Union["t.Literal[-1]", "t.Literal[0]", str] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Executes a SQL request @@ -261,62 +281,63 @@ def query( the search doesn’t finish within this period, the search becomes async. """ __path = "/_sql" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if catalog is not None: - __body["catalog"] = catalog - if columnar is not None: - __body["columnar"] = columnar - if cursor is not None: - __body["cursor"] = cursor + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if fetch_size is not None: - __body["fetch_size"] = fetch_size - if field_multi_value_leniency is not None: - __body["field_multi_value_leniency"] = field_multi_value_leniency - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if format is not None: __query["format"] = format if human is not None: __query["human"] = human - if index_using_frozen is not None: - __body["index_using_frozen"] = index_using_frozen - if keep_alive is not None: - __body["keep_alive"] = keep_alive - if keep_on_completion is not None: - __body["keep_on_completion"] = keep_on_completion - if page_timeout is not None: - __body["page_timeout"] = page_timeout - if params is not None: - __body["params"] = params if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if request_timeout is not None: - __body["request_timeout"] = request_timeout - if runtime_mappings is not None: - __body["runtime_mappings"] = runtime_mappings - if time_zone is not None: - __body["time_zone"] = time_zone - if wait_for_completion_timeout is not None: - __body["wait_for_completion_timeout"] = wait_for_completion_timeout + if not __body: + if catalog is not None: + __body["catalog"] = catalog + if columnar is not None: + __body["columnar"] = columnar + if cursor is not None: + __body["cursor"] = cursor + if fetch_size is not None: + __body["fetch_size"] = fetch_size + if field_multi_value_leniency is not None: + __body["field_multi_value_leniency"] = field_multi_value_leniency + if filter is not None: + __body["filter"] = filter + if index_using_frozen is not None: + __body["index_using_frozen"] = index_using_frozen + if keep_alive is not None: + __body["keep_alive"] = keep_alive + if keep_on_completion is not None: + __body["keep_on_completion"] = keep_on_completion + if page_timeout is not None: + __body["page_timeout"] = page_timeout + if params is not None: + __body["params"] = params + if query is not None: + __body["query"] = query + if request_timeout is not None: + __body["request_timeout"] = request_timeout + if runtime_mappings is not None: + __body["runtime_mappings"] = runtime_mappings + if time_zone is not None: + __body["time_zone"] = time_zone + if wait_for_completion_timeout is not None: + __body["wait_for_completion_timeout"] = wait_for_completion_timeout __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("query", "fetch_size", "filter", "time_zone"), ) def translate( self, *, - query: str, + query: t.Optional[str] = None, error_trace: t.Optional[bool] = None, fetch_size: t.Optional[int] = None, filter: t.Optional[t.Mapping[str, t.Any]] = None, @@ -324,6 +345,7 @@ def translate( human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, time_zone: t.Optional[str] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Translates SQL into Elasticsearch queries @@ -335,27 +357,28 @@ def translate( :param filter: Elasticsearch query DSL for additional filtering. :param time_zone: ISO-8601 time zone ID for the search. """ - if query is None: + if query is None and body is None: raise ValueError("Empty value passed for parameter 'query'") __path = "/_sql/translate" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if query is not None: - __body["query"] = query + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace - if fetch_size is not None: - __body["fetch_size"] = fetch_size - if filter is not None: - __body["filter"] = filter if filter_path is not None: __query["filter_path"] = filter_path if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if time_zone is not None: - __body["time_zone"] = time_zone + if not __body: + if query is not None: + __body["query"] = query + if fetch_size is not None: + __body["fetch_size"] = fetch_size + if filter is not None: + __body["filter"] = filter + if time_zone is not None: + __body["time_zone"] = time_zone __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/synonyms.py b/elasticsearch/_sync/client/synonyms.py --- a/elasticsearch/_sync/client/synonyms.py +++ b/elasticsearch/_sync/client/synonyms.py @@ -219,17 +219,18 @@ def get_synonyms_sets( ) @_rewrite_parameters( - body_fields=True, + body_fields=("synonyms_set",), ) def put_synonym( self, *, id: str, - synonyms_set: t.Sequence[t.Mapping[str, t.Any]], + synonyms_set: t.Optional[t.Sequence[t.Mapping[str, t.Any]]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a synonyms set @@ -241,13 +242,11 @@ def put_synonym( """ if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") - if synonyms_set is None: + if synonyms_set is None and body is None: raise ValueError("Empty value passed for parameter 'synonyms_set'") __path = f"/_synonyms/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if synonyms_set is not None: - __body["synonyms_set"] = synonyms_set + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -256,24 +255,28 @@ def put_synonym( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if synonyms_set is not None: + __body["synonyms_set"] = synonyms_set __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body ) @_rewrite_parameters( - body_fields=True, + body_fields=("synonyms",), ) def put_synonym_rule( self, *, set_id: str, rule_id: str, - synonyms: t.Sequence[str], + synonyms: t.Optional[t.Sequence[str]] = None, error_trace: t.Optional[bool] = None, filter_path: t.Optional[t.Union[str, t.Sequence[str]]] = None, human: t.Optional[bool] = None, pretty: t.Optional[bool] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates or updates a synonym rule in a synonym set @@ -288,13 +291,11 @@ def put_synonym_rule( raise ValueError("Empty value passed for parameter 'set_id'") if rule_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'rule_id'") - if synonyms is None: + if synonyms is None and body is None: raise ValueError("Empty value passed for parameter 'synonyms'") __path = f"/_synonyms/{_quote(set_id)}/{_quote(rule_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if synonyms is not None: - __body["synonyms"] = synonyms + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -303,6 +304,9 @@ def put_synonym_rule( __query["human"] = human if pretty is not None: __query["pretty"] = pretty + if not __body: + if synonyms is not None: + __body["synonyms"] = synonyms __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/text_structure.py b/elasticsearch/_sync/client/text_structure.py --- a/elasticsearch/_sync/client/text_structure.py +++ b/elasticsearch/_sync/client/text_structure.py @@ -30,7 +30,8 @@ class TextStructureClient(NamespacedClient): def find_structure( self, *, - text_files: t.Sequence[t.Any], + text_files: t.Optional[t.Sequence[t.Any]] = None, + body: t.Optional[t.Sequence[t.Any]] = None, charset: t.Optional[str] = None, column_names: t.Optional[str] = None, delimiter: t.Optional[str] = None, @@ -116,8 +117,12 @@ def find_structure( the file :param timestamp_format: The Java time format of the timestamp field in the text. """ - if text_files is None: - raise ValueError("Empty value passed for parameter 'text_files'") + if text_files is None and body is None: + raise ValueError( + "Empty value passed for parameters 'text_files' and 'body', one of them should be set." + ) + elif text_files is not None and body is not None: + raise ValueError("Cannot set both 'text_files' and 'body'") __path = "/_text_structure/find_structure" __query: t.Dict[str, t.Any] = {} if charset is not None: @@ -148,7 +153,7 @@ def find_structure( __query["timestamp_field"] = timestamp_field if timestamp_format is not None: __query["timestamp_format"] = timestamp_format - __body = text_files + __body = text_files if text_files is not None else body __headers = { "accept": "application/json", "content-type": "application/x-ndjson", diff --git a/elasticsearch/_sync/client/transform.py b/elasticsearch/_sync/client/transform.py --- a/elasticsearch/_sync/client/transform.py +++ b/elasticsearch/_sync/client/transform.py @@ -195,7 +195,17 @@ def get_transform_stats( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "description", + "dest", + "frequency", + "latest", + "pivot", + "retention_policy", + "settings", + "source", + "sync", + ), ) def preview_transform( self, @@ -215,6 +225,7 @@ def preview_transform( source: t.Optional[t.Mapping[str, t.Any]] = None, sync: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Previews a transform. @@ -247,36 +258,37 @@ def preview_transform( __path = f"/_transform/{_quote(transform_id)}/_preview" else: __path = "/_transform/_preview" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if description is not None: - __body["description"] = description - if dest is not None: - __body["dest"] = dest + __body: t.Dict[str, t.Any] = body if body is not None else {} if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human - if latest is not None: - __body["latest"] = latest - if pivot is not None: - __body["pivot"] = pivot if pretty is not None: __query["pretty"] = pretty - if retention_policy is not None: - __body["retention_policy"] = retention_policy - if settings is not None: - __body["settings"] = settings - if source is not None: - __body["source"] = source - if sync is not None: - __body["sync"] = sync if timeout is not None: __query["timeout"] = timeout + if not __body: + if description is not None: + __body["description"] = description + if dest is not None: + __body["dest"] = dest + if frequency is not None: + __body["frequency"] = frequency + if latest is not None: + __body["latest"] = latest + if pivot is not None: + __body["pivot"] = pivot + if retention_policy is not None: + __body["retention_policy"] = retention_policy + if settings is not None: + __body["settings"] = settings + if source is not None: + __body["source"] = source + if sync is not None: + __body["sync"] = sync if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -287,15 +299,26 @@ def preview_transform( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "dest", + "source", + "description", + "frequency", + "latest", + "meta", + "pivot", + "retention_policy", + "settings", + "sync", + ), parameter_aliases={"_meta": "meta"}, ) def put_transform( self, *, transform_id: str, - dest: t.Mapping[str, t.Any], - source: t.Mapping[str, t.Any], + dest: t.Optional[t.Mapping[str, t.Any]] = None, + source: t.Optional[t.Mapping[str, t.Any]] = None, defer_validation: t.Optional[bool] = None, description: t.Optional[str] = None, error_trace: t.Optional[bool] = None, @@ -310,6 +333,7 @@ def put_transform( settings: t.Optional[t.Mapping[str, t.Any]] = None, sync: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Instantiates a transform. @@ -348,45 +372,46 @@ def put_transform( """ if transform_id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'transform_id'") - if dest is None: + if dest is None and body is None: raise ValueError("Empty value passed for parameter 'dest'") - if source is None: + if source is None and body is None: raise ValueError("Empty value passed for parameter 'source'") __path = f"/_transform/{_quote(transform_id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if dest is not None: - __body["dest"] = dest - if source is not None: - __body["source"] = source + __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_validation is not None: __query["defer_validation"] = defer_validation - if description is not None: - __body["description"] = description if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human - if latest is not None: - __body["latest"] = latest - if meta is not None: - __body["_meta"] = meta - if pivot is not None: - __body["pivot"] = pivot if pretty is not None: __query["pretty"] = pretty - if retention_policy is not None: - __body["retention_policy"] = retention_policy - if settings is not None: - __body["settings"] = settings - if sync is not None: - __body["sync"] = sync if timeout is not None: __query["timeout"] = timeout + if not __body: + if dest is not None: + __body["dest"] = dest + if source is not None: + __body["source"] = source + if description is not None: + __body["description"] = description + if frequency is not None: + __body["frequency"] = frequency + if latest is not None: + __body["latest"] = latest + if meta is not None: + __body["_meta"] = meta + if pivot is not None: + __body["pivot"] = pivot + if retention_policy is not None: + __body["retention_policy"] = retention_policy + if settings is not None: + __body["settings"] = settings + if sync is not None: + __body["sync"] = sync __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "PUT", __path, params=__query, headers=__headers, body=__body @@ -589,7 +614,16 @@ def stop_transform( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "description", + "dest", + "frequency", + "meta", + "retention_policy", + "settings", + "source", + "sync", + ), parameter_aliases={"_meta": "meta"}, ) def update_transform( @@ -610,6 +644,7 @@ def update_transform( source: t.Optional[t.Mapping[str, t.Any]] = None, sync: t.Optional[t.Mapping[str, t.Any]] = None, timeout: t.Optional[t.Union["t.Literal[-1]", "t.Literal[0]", str]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Updates certain properties of a transform. @@ -639,35 +674,36 @@ def update_transform( raise ValueError("Empty value passed for parameter 'transform_id'") __path = f"/_transform/{_quote(transform_id)}/_update" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} if defer_validation is not None: __query["defer_validation"] = defer_validation - if description is not None: - __body["description"] = description - if dest is not None: - __body["dest"] = dest if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if frequency is not None: - __body["frequency"] = frequency if human is not None: __query["human"] = human - if meta is not None: - __body["_meta"] = meta if pretty is not None: __query["pretty"] = pretty - if retention_policy is not None: - __body["retention_policy"] = retention_policy - if settings is not None: - __body["settings"] = settings - if source is not None: - __body["source"] = source - if sync is not None: - __body["sync"] = sync if timeout is not None: __query["timeout"] = timeout + if not __body: + if description is not None: + __body["description"] = description + if dest is not None: + __body["dest"] = dest + if frequency is not None: + __body["frequency"] = frequency + if meta is not None: + __body["_meta"] = meta + if retention_policy is not None: + __body["retention_policy"] = retention_policy + if settings is not None: + __body["settings"] = settings + if source is not None: + __body["source"] = source + if sync is not None: + __body["sync"] = sync __headers = {"accept": "application/json", "content-type": "application/json"} return self.perform_request( # type: ignore[return-value] "POST", __path, params=__query, headers=__headers, body=__body diff --git a/elasticsearch/_sync/client/utils.py b/elasticsearch/_sync/client/utils.py --- a/elasticsearch/_sync/client/utils.py +++ b/elasticsearch/_sync/client/utils.py @@ -74,6 +74,8 @@ str, Sequence[Union[str, Mapping[str, Union[str, int]], NodeConfig]] ] +_TYPE_BODY = Union[bytes, str, Dict[str, Any]] + _TYPE_ASYNC_SNIFF_CALLBACK = Callable[ [AsyncTransport, SniffOptions], Awaitable[List[NodeConfig]] ] @@ -289,14 +291,40 @@ def _merge_kwargs_no_duplicates(kwargs: Dict[str, Any], values: Dict[str, Any]) if key in kwargs: raise ValueError( f"Received multiple values for '{key}', specify parameters " - "directly instead of using 'body' or 'params'" + "directly instead of using 'params'" ) kwargs[key] = val +def _merge_body_fields_no_duplicates( + body: _TYPE_BODY, kwargs: Dict[str, Any], body_fields: Tuple[str, ...] +) -> None: + for key in list(kwargs.keys()): + if key in body_fields: + if isinstance(body, (str, bytes)): + raise ValueError( + "Couldn't merge 'body' with other parameters as it wasn't a mapping." + ) + + if key in body: + raise ValueError( + f"Received multiple values for '{key}', specify parameters " + "using either body or parameters, not both." + ) + + warnings.warn( + f"Received '{key}' via a specific parameter in the presence of a " + "'body' parameter, which is deprecated and will be removed in a future " + "version. Instead, use only 'body' or only specific paremeters.", + category=DeprecationWarning, + stacklevel=warn_stacklevel(), + ) + body[key] = kwargs.pop(key) + + def _rewrite_parameters( body_name: Optional[str] = None, - body_fields: bool = False, + body_fields: Optional[Tuple[str, ...]] = None, parameter_aliases: Optional[Dict[str, str]] = None, ignore_deprecated_options: Optional[Set[str]] = None, ) -> Callable[[F], F]: @@ -372,7 +400,7 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: if "body" in kwargs and ( not ignore_deprecated_options or "body" not in ignore_deprecated_options ): - body = kwargs.pop("body") + body: Optional[_TYPE_BODY] = kwargs.pop("body") if body is not None: if body_name: if body_name in kwargs: @@ -384,13 +412,9 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: ) kwargs[body_name] = body - elif body_fields: - if not hasattr(body, "items"): - raise ValueError( - "Couldn't merge 'body' with other parameters as it wasn't a mapping. " - "Instead of using 'body' use individual API parameters" - ) - _merge_kwargs_no_duplicates(kwargs, body) + elif body_fields is not None: + _merge_body_fields_no_duplicates(body, kwargs, body_fields) + kwargs["body"] = body if parameter_aliases: for alias, rename_to in parameter_aliases.items(): diff --git a/elasticsearch/_sync/client/watcher.py b/elasticsearch/_sync/client/watcher.py --- a/elasticsearch/_sync/client/watcher.py +++ b/elasticsearch/_sync/client/watcher.py @@ -168,7 +168,15 @@ def delete_watch( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "action_modes", + "alternative_input", + "ignore_condition", + "record_execution", + "simulated_actions", + "trigger_data", + "watch", + ), ) def execute_watch( self, @@ -194,6 +202,7 @@ def execute_watch( simulated_actions: t.Optional[t.Mapping[str, t.Any]] = None, trigger_data: t.Optional[t.Mapping[str, t.Any]] = None, watch: t.Optional[t.Mapping[str, t.Any]] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Forces the execution of a stored watch. @@ -223,12 +232,8 @@ def execute_watch( __path = f"/_watcher/watch/{_quote(id)}/_execute" else: __path = "/_watcher/watch/_execute" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if action_modes is not None: - __body["action_modes"] = action_modes - if alternative_input is not None: - __body["alternative_input"] = alternative_input + __body: t.Dict[str, t.Any] = body if body is not None else {} if debug is not None: __query["debug"] = debug if error_trace is not None: @@ -237,18 +242,23 @@ def execute_watch( __query["filter_path"] = filter_path if human is not None: __query["human"] = human - if ignore_condition is not None: - __body["ignore_condition"] = ignore_condition if pretty is not None: __query["pretty"] = pretty - if record_execution is not None: - __body["record_execution"] = record_execution - if simulated_actions is not None: - __body["simulated_actions"] = simulated_actions - if trigger_data is not None: - __body["trigger_data"] = trigger_data - if watch is not None: - __body["watch"] = watch + if not __body: + if action_modes is not None: + __body["action_modes"] = action_modes + if alternative_input is not None: + __body["alternative_input"] = alternative_input + if ignore_condition is not None: + __body["ignore_condition"] = ignore_condition + if record_execution is not None: + __body["record_execution"] = record_execution + if simulated_actions is not None: + __body["simulated_actions"] = simulated_actions + if trigger_data is not None: + __body["trigger_data"] = trigger_data + if watch is not None: + __body["watch"] = watch if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -293,7 +303,15 @@ def get_watch( ) @_rewrite_parameters( - body_fields=True, + body_fields=( + "actions", + "condition", + "input", + "metadata", + "throttle_period", + "transform", + "trigger", + ), ) def put_watch( self, @@ -314,6 +332,7 @@ def put_watch( transform: t.Optional[t.Mapping[str, t.Any]] = None, trigger: t.Optional[t.Mapping[str, t.Any]] = None, version: t.Optional[int] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Creates a new watch, or updates an existing one. @@ -338,14 +357,10 @@ def put_watch( if id in SKIP_IN_PATH: raise ValueError("Empty value passed for parameter 'id'") __path = f"/_watcher/watch/{_quote(id)}" - __body: t.Dict[str, t.Any] = {} __query: t.Dict[str, t.Any] = {} - if actions is not None: - __body["actions"] = actions + __body: t.Dict[str, t.Any] = body if body is not None else {} if active is not None: __query["active"] = active - if condition is not None: - __body["condition"] = condition if error_trace is not None: __query["error_trace"] = error_trace if filter_path is not None: @@ -356,20 +371,25 @@ def put_watch( __query["if_primary_term"] = if_primary_term if if_seq_no is not None: __query["if_seq_no"] = if_seq_no - if input is not None: - __body["input"] = input - if metadata is not None: - __body["metadata"] = metadata if pretty is not None: __query["pretty"] = pretty - if throttle_period is not None: - __body["throttle_period"] = throttle_period - if transform is not None: - __body["transform"] = transform - if trigger is not None: - __body["trigger"] = trigger if version is not None: __query["version"] = version + if not __body: + if actions is not None: + __body["actions"] = actions + if condition is not None: + __body["condition"] = condition + if input is not None: + __body["input"] = input + if metadata is not None: + __body["metadata"] = metadata + if throttle_period is not None: + __body["throttle_period"] = throttle_period + if transform is not None: + __body["transform"] = transform + if trigger is not None: + __body["trigger"] = trigger if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} @@ -380,7 +400,7 @@ def put_watch( ) @_rewrite_parameters( - body_fields=True, + body_fields=("from_", "query", "search_after", "size", "sort"), parameter_aliases={"from": "from_"}, ) def query_watches( @@ -402,6 +422,7 @@ def query_watches( t.Union[str, t.Mapping[str, t.Any]], ] ] = None, + body: t.Optional[t.Dict[str, t.Any]] = None, ) -> ObjectApiResponse[t.Any]: """ Retrieves stored watches. @@ -417,7 +438,7 @@ def query_watches( """ __path = "/_watcher/_query/watches" __query: t.Dict[str, t.Any] = {} - __body: t.Dict[str, t.Any] = {} + __body: t.Dict[str, t.Any] = body if body is not None else {} # The 'sort' parameter with a colon can't be encoded to the body. if sort is not None and ( (isinstance(sort, str) and ":" in sort) @@ -433,20 +454,21 @@ def query_watches( __query["error_trace"] = error_trace if filter_path is not None: __query["filter_path"] = filter_path - if from_ is not None: - __body["from"] = from_ if human is not None: __query["human"] = human if pretty is not None: __query["pretty"] = pretty - if query is not None: - __body["query"] = query - if search_after is not None: - __body["search_after"] = search_after - if size is not None: - __body["size"] = size - if sort is not None: - __body["sort"] = sort + if not __body: + if from_ is not None: + __body["from"] = from_ + if query is not None: + __body["query"] = query + if search_after is not None: + __body["search_after"] = search_after + if size is not None: + __body["size"] = size + if sort is not None: + __body["sort"] = sort if not __body: __body = None # type: ignore[assignment] __headers = {"accept": "application/json"} diff --git a/elasticsearch/helpers/actions.py b/elasticsearch/helpers/actions.py --- a/elasticsearch/helpers/actions.py +++ b/elasticsearch/helpers/actions.py @@ -714,7 +714,7 @@ def normalize_from_keyword(kw: MutableMapping[str, Any]) -> None: search_kwargs = kwargs.copy() search_kwargs["scroll"] = scroll search_kwargs["size"] = size - resp = client.search(body=query, **search_kwargs) # type: ignore[call-arg] + resp = client.search(body=query, **search_kwargs) scroll_id = resp.get("_scroll_id") scroll_transport_kwargs = pop_transport_kwargs(scroll_kwargs)

diff --git a/test_elasticsearch/test_client/test_rewrite_parameters.py b/test_elasticsearch/test_client/test_rewrite_parameters.py --- a/test_elasticsearch/test_client/test_rewrite_parameters.py +++ b/test_elasticsearch/test_client/test_rewrite_parameters.py @@ -42,17 +42,19 @@ def wrapped_func_default(self, *args, **kwargs): def wrapped_func_body_name(self, *args, **kwargs): self.calls.append((args, kwargs)) - @_rewrite_parameters(body_fields=True) + @_rewrite_parameters(body_fields=("query", "source")) def wrapped_func_body_fields(self, *args, **kwargs): self.calls.append((args, kwargs)) @_rewrite_parameters( - body_fields=True, ignore_deprecated_options={"api_key", "body", "params"} + body_fields=("query",), ignore_deprecated_options={"api_key", "body", "params"} ) def wrapped_func_ignore(self, *args, **kwargs): self.calls.append((args, kwargs)) - @_rewrite_parameters(body_fields=True, parameter_aliases={"_source": "source"}) + @_rewrite_parameters( + body_fields=("source",), parameter_aliases={"_source": "source"} + ) def wrapped_func_aliases(self, *args, **kwargs): self.calls.append((args, kwargs)) @@ -81,6 +83,16 @@ def test_default(self): ((), {"query": {"match_all": {}}, "key": "value"}), ] + def test_default_params_conflict(self): + with pytest.raises(ValueError) as e: + self.wrapped_func_default( + query={"match_all": {}}, + params={"query": {"match_all": {}}}, + ) + assert str(e.value) == ( + "Received multiple values for 'query', specify parameters directly instead of using 'params'" + ) + def test_body_name_using_body(self): with warnings.catch_warnings(record=True) as w: self.wrapped_func_body_name( @@ -142,18 +154,21 @@ def test_body_fields(self): assert self.calls == [ ((), {"api_key": ("id", "api_key")}), - ((), {"query": {"match_all": {}}}), + ((), {"body": {"query": {"match_all": {}}}}), ] @pytest.mark.parametrize( - "body", ['{"query": {"match_all": {}}}', b'{"query": {"match_all": {}}}'] + "body, kwargs", + [ + ('{"query": {"match_all": {}}}', {"query": {"match_all": {}}}), + (b'{"query": {"match_all": {}}}', {"query": {"match_all": {}}}), + ], ) - def test_error_on_body_merge(self, body): + def test_error_on_body_merge(self, body, kwargs): with pytest.raises(ValueError) as e: - self.wrapped_func_body_fields(body=body) + self.wrapped_func_body_fields(body=body, **kwargs) assert str(e.value) == ( - "Couldn't merge 'body' with other parameters as it wasn't a mapping. Instead of " - "using 'body' use individual API parameters" + "Couldn't merge 'body' with other parameters as it wasn't a mapping." ) @pytest.mark.parametrize( @@ -167,6 +182,25 @@ def test_error_on_params_merge(self, params): "using 'params' use individual API parameters" ) + def test_body_fields_merge(self): + with warnings.catch_warnings(record=True) as w: + self.wrapped_func_body_fields(source=False, body={"query": {}}) + + assert len(w) == 1 + assert w[0].category == DeprecationWarning + assert str(w[0].message) == ( + "Received 'source' via a specific parameter in the presence of a " + "'body' parameter, which is deprecated and will be removed in a future " + "version. Instead, use only 'body' or only specific paremeters." + ) + + def test_body_fields_conflict(self): + with pytest.raises(ValueError) as e: + self.wrapped_func_body_fields(query={"match_all": {}}, body={"query": {}}) + assert str(e.value) == ( + "Received multiple values for 'query', specify parameters using either body or parameters, not both." + ) + def test_ignore_deprecated_options(self): with warnings.catch_warnings(record=True) as w: self.wrapped_func_ignore(

Please bring back `body` parameter to API calls **Describe the feature**: Currently, when the documentation or implementation is out of sync (see #2179 , #2180 for examples) the API becomes unusable and users have to resort to using the underlying `perform_request` method eliminating some of the benefits of having a dedicated API (parameter checking, URL composition, type hinting, ...). It would be useful to still be able to pass in the entire `body` in those cases. It would also be great for the common use case of getting an object from Elasticsearch, modifying it, and reapplying it to the cluster, for example index templates, ILM policies, and other configurations. This cannot be now easily done with `**body` since there are some manually introduced changes like `_meta` in the JSON body as opposed to `meta` in the python API. This makes the API extremely brittle for such cases.

Another case is point is inconsistencies like `put_settings` which doesn't follow the patttern of accepting a kwarg for every key in body but instead accepts a `body` parameter that is undocumented and renamed to `settings`. This is incredibly confusing (I'm aware you know most of the history here, want to give context to other readers) In the past our clients had the `body` key because we had no specification for HTTP request and response bodies so we necessarily had to be generic with the request body parameter from v1.x to v7.x of the client. We had information about path and query parameters so we were able to give helpful parameters for those parts of the API, but not the request body. This is unfortunate because this meant parameters that were from the Elasticsearch application layer (`_source`, `track_total_hits`) were mixed in with a parameter that is from the HTTP/transport layer (`body`). Starting in late 7.x timeframe we have an API spec for request bodies and are able to distinguish between a request body which is a set of parameters (ie `indices.create` has `mappings`, `settings`, etc) and where the entire request body is a parameter (the "create document" API has `document={...}`). All 8.x Elasticsearch clients use this API spec and give semantic names to the parts of a request body. > Currently, when the documentation or implementation is out of sync the API becomes unusable and users have to resort to using the underlying `perform_request` method I think we should aim to fix the API specification in these cases since the fixes there bubble down to all other language clients, docs, and other consumers. > It would also be great for the common use case of getting an object from Elasticsearch, modifying it, and reapplying it to the cluster, for example index templates, ILM policies, and other configurations. This cannot be now easily done with **body since there are some manually introduced changes like _meta in the JSON body as opposed to meta in the python API. This makes the API extremely brittle for such cases. The Python client automatically handles both `_meta=` and `meta=` in order to support this use-case since leading underscores have a special meaning in Python. I know there's another common case of having a request body in a JSON file, I've recommended using `**` for these cases as well. Semantically then the entire API request is being encoded as JSON rather than a single piece of the API request. You're giving us all the parameters and values and we figure out where best to serialize them. > Another case is point is inconsistencies like put_settings which doesn't follow the pattern of accepting a kwarg for every key in body but instead accepts a body parameter that is undocumented and renamed to settings. We can't hard-code all parameters in this case because index settings can be user-controlled/custom due to plugins. On the undocumented front (I'm assuming here you're talking about RTD) I'm confused why the `settings` parameter is not appearing in the autodocs since it's clearly a part of the function signature. Will look at why that's not getting populated. Happy to discuss more about any of the above. > On the undocumented front (I'm assuming here you're talking about RTD)... I am talking *anywhere* - currently there are well over 300 undocumented parameters. The change from `body` to various other keys (be it just a simple alias like `put_settings`, `index`, or decomposition like with `put_index_template`) has not been documented and realistically the only way to find out how to translate the documentation examples in the Elasticsearch documentation to a functioning Python code is to read the code of the client and understanding the mechanism of elasticsearch's API. I understand the arguments you are making (though I do not share the same view) but to make those changes without documentation, without supplying Python examples in the API documentation (despite having the UI suggesting there would be examples in Python) renders the client de facto undocumented. Keeping the `body` argument would at least preserve the option of using the official elasticsearch documentation to create a functioning Python code. btw another reason to have the `body` is performance - in the past I passed in already json-encoded string to `body` which provided significant performance benefits in niche cases Ultimately this is of course your decision but please consider adding the `body` parameter back as an addition to your approach. What I would suggest is adding it and then raising an exception if it is used together with any of the decomposed arguments - that way you can provide your more granular API as well as an easy escape hatch to be used for backwards compatibility and/or any above mentioned use cases. It would not increase the complexity of the code, or the documentation, in any meaningful way and would allow people to use the client more effectively. Discussed this with the team and only the Python client doesn't have some way to load JSON into a request. We also discussed the common use-case of people iterating on and refining their query in Kibana Dev Tools before wanting to put that request into use with a client library (and potentially further refining their query). If folks have to translate between the two the Python client not supporting `body` would be a point of confusion. For these reasons we've elected to un-deprecate `body` support in the Python client. elasticsearch-py 8.10.1 removed the deprecation warning! https://github.com/elastic/elasticsearch-py/releases/tag/v8.10.1. What is left to do is allowing any `body`, not just the ones we think are valid according to https://github.com/elastic/elasticsearch-specification/ (which is not always fully up-to-date and does not account for custom plugins). The current workaround is to call `client.perform_request`.

2023-11-23T12:00:06Z

elastic/elasticsearch-py

elastic__elasticsearch-py-2427

7d4a34b6e3ceceb82396be62682b7e8623c72665

diff --git a/elasticsearch/_sync/client/utils.py b/elasticsearch/_sync/client/utils.py --- a/elasticsearch/_sync/client/utils.py +++ b/elasticsearch/_sync/client/utils.py @@ -298,7 +298,8 @@ def _merge_kwargs_no_duplicates(kwargs: Dict[str, Any], values: Dict[str, Any]) def _merge_body_fields_no_duplicates( body: _TYPE_BODY, kwargs: Dict[str, Any], body_fields: Tuple[str, ...] -) -> None: +) -> bool: + mixed_body_and_params = False for key in list(kwargs.keys()): if key in body_fields: if isinstance(body, (str, bytes)): @@ -315,11 +316,13 @@ def _merge_body_fields_no_duplicates( warnings.warn( f"Received '{key}' via a specific parameter in the presence of a " "'body' parameter, which is deprecated and will be removed in a future " - "version. Instead, use only 'body' or only specific paremeters.", + "version. Instead, use only 'body' or only specific parameters.", category=DeprecationWarning, stacklevel=warn_stacklevel(), ) body[key] = kwargs.pop(key) + mixed_body_and_params = True + return mixed_body_and_params def _rewrite_parameters( @@ -401,6 +404,7 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: not ignore_deprecated_options or "body" not in ignore_deprecated_options ): body: Optional[_TYPE_BODY] = kwargs.pop("body") + mixed_body_and_params = False if body is not None: if body_name: if body_name in kwargs: @@ -411,11 +415,27 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: "issues/1698 for more information" ) kwargs[body_name] = body - elif body_fields is not None: - _merge_body_fields_no_duplicates(body, kwargs, body_fields) + mixed_body_and_params = _merge_body_fields_no_duplicates( + body, kwargs, body_fields + ) kwargs["body"] = body + if parameter_aliases and not isinstance(body, (str, bytes)): + for alias, rename_to in parameter_aliases.items(): + if rename_to in body: + body[alias] = body.pop(rename_to) + # If body and params are mixed, the alias may come from a param, + # in which case the warning below will not make sense. + if not mixed_body_and_params: + warnings.warn( + f"Using '{rename_to}' alias in 'body' is deprecated and will be removed " + f"in a future version of elasticsearch-py. Use '{alias}' directly instead. " + "See https://github.com/elastic/elasticsearch-py/issues/1698 for more information", + category=DeprecationWarning, + stacklevel=2, + ) + if parameter_aliases: for alias, rename_to in parameter_aliases.items(): try:

diff --git a/test_elasticsearch/test_client/test_rewrite_parameters.py b/test_elasticsearch/test_client/test_rewrite_parameters.py --- a/test_elasticsearch/test_client/test_rewrite_parameters.py +++ b/test_elasticsearch/test_client/test_rewrite_parameters.py @@ -191,7 +191,7 @@ def test_body_fields_merge(self): assert str(w[0].message) == ( "Received 'source' via a specific parameter in the presence of a " "'body' parameter, which is deprecated and will be removed in a future " - "version. Instead, use only 'body' or only specific paremeters." + "version. Instead, use only 'body' or only specific parameters." ) def test_body_fields_conflict(self): @@ -238,6 +238,41 @@ def test_parameter_aliases(self): self.wrapped_func_aliases(source=["key3"]) assert self.calls[-1] == ((), {"source": ["key3"]}) + def test_parameter_aliases_body(self): + with pytest.warns( + DeprecationWarning, + match=( + "Using 'source' alias in 'body' is deprecated and will be removed in a future version of elasticsearch-py. " + "Use '_source' directly instead." + ), + ): + self.wrapped_func_aliases(body={"source": ["key4"]}) + + # using the correct name does not warn + with warnings.catch_warnings(): + warnings.simplefilter("error") + self.wrapped_func_aliases(body={"_source": ["key4"]}) + + def test_parameter_aliases_body_param(self): + with pytest.warns( + DeprecationWarning, + match=( + "Received 'source' via a specific parameter in the presence of a " + "'body' parameter, which is deprecated and will be removed in a future " + "version. Instead, use only 'body' or only specific parameters." + ), + ): + self.wrapped_func_aliases( + source=["key4"], body={"query": {"match_all": {}}} + ) + + # using the correct name does not warn + with warnings.catch_warnings(): + warnings.simplefilter("error") + self.wrapped_func_aliases( + body={"query": {"match_all": {}}, "_source": ["key4"]} + ) + @pytest.mark.parametrize("client_cls", [Elasticsearch, AsyncElasticsearch]) def test_positional_argument_error(self, client_cls): client = client_cls("https://localhost:9200")

elasticsearch-py 8.12 breaks the use of `from_` in body parameters This bug was initially reported in the [Elastic Community Slack](https://www.elastic.co/blog/join-our-elastic-stack-workspace-on-slack). But first, come context. ## Context Since the early days of the Elasticsearch Python client, [back in July 2013](https://github.com/elastic/elasticsearch-py/commit/48ec1ab4bbc0b49ac5dfcdd39fb341d93c7f4538), the `body` parameter is the way to specify the request body for requests that accept it. API calls using body look like this: ```python es.search(index="bonsais", body={"query": {"match_all": {}}, "size": 50}) ``` However, this parameter is an untyped Python dictionary which is not validated by the client. That said, thanks to the [Elasticsearch specification](https://github.com/elastic/elasticsearch-specification/) which provides the full types of each Elasticsearch API, we can provide a better experience. elasticsearch-py 8.0 did just that, introducing this new way of calling APIs, where the first level of body keys can be specified using Python parameters: ```python es.search(index="bonsais", query={"match_all": {}}, size=50) ``` This has various advantages, including better autocompletion and type checks. For example, mypy will raise an error if size is not an integer. And since we realized we could [unpack](https://docs.python.org/3/tutorial/controlflow.html#unpacking-argument-lists) body to typed parameters like this: ```python es.search(index="bonsais", **{"query": {"match_all": {}}, "size": 50}) ``` We decided to deprecate the body API altogether. However, deprecating body has the following downsides: * A lot of code written in the past decade was now triggering a deprecation warning * Unknown parameters such as `sub_searches` or unintentional omissions from the Elasticsearch specification were rejected, causing queries to outright fail, unnecessarily forcing the use of raw requests. * Optimizations such as passing an already encoded body to avoid paying the cost of serializing JSON were no longer possible. The original author of the client, Honza Král, [pointed out those issues](https://github.com/elastic/elasticsearch-py/issues/2181), and we decided to allow `body` to work as before, without any warnings, [alongside the new API](https://github.com/elastic/elasticsearch-py/pull/2383). This is available elasticsearch-py 8.12.0. ## The case of Python keywords, like `from` One subtlety with the above is that some identifiers are [reserved by Python](https://docs.python.org/3/reference/lexical_analysis.html#keywords) and can't be used as parameters. This is the case of `from`, for example. As such, `es.search(index="bonsais", query={"match_all": {}}, from=100, size=50)`, is invalid Python code. For this reason, parameter aliases were introduced, and the correct way to write that query was to use `from_`, eg. `es.search(index="bonsais", query={"match_all": {}}, from_=100, size=50)`. And then, under the hood, `from` is actually sent to Elasticsearch: https://github.com/elastic/elasticsearch-py/blob/5014ce5337594f66040c81a2610220b1e8c0527e/elasticsearch/_sync/client/__init__.py#L1280-L1281 However, when the `body` parameter was deprecated in elasticsearch-py 8.12, it was deprecated by converting all `body` subfields to Python parameters internally, and *then* updated parameter aliases like `from_` to `from`. This means it was possible to write: ```python es.search(index="bonsais", body={"query": {"match_all": {}}, "from_": 100, "size": 50}) ``` which was then converted as if we had called: ```python es.search(index="bonsais", query={"match_all": {}, from_=100, size=50) ``` to finally send `{"query": {"match_all": {}}, "from": 100, "size": 50}` as the body to Elasticsearch. This no longer works with elasticsearch-py 8.12.0. The body is used as is, without any inspection, and the correct way to use `from` with the `body` parameter is the one that always worked: ```python es.search( index="*", body={ "query": {"match_all": {}}, "from": 10, "size": 10, }, ) ``` I'm still not sure what the solution is here.

2024-01-31T12:21:36Z

elastic/elasticsearch-py

elastic__elasticsearch-py-2551

f32878e62290f133d0957b271a87a97fad45e3e2

diff --git a/elasticsearch/serializer.py b/elasticsearch/serializer.py --- a/elasticsearch/serializer.py +++ b/elasticsearch/serializer.py @@ -171,7 +171,6 @@ def _attempt_serialize_numpy(data: Any) -> Tuple[bool, Any]: elif isinstance( data, ( - np.float_, np.float16, np.float32, np.float64,

diff --git a/test_elasticsearch/test_serializer.py b/test_elasticsearch/test_serializer.py --- a/test_elasticsearch/test_serializer.py +++ b/test_elasticsearch/test_serializer.py @@ -89,7 +89,6 @@ def test_serializes_numpy_integers(json_serializer): @requires_numpy_and_pandas def test_serializes_numpy_floats(json_serializer): for np_type in ( - np.float_, np.float32, np.float64, ):

Test failures against NumPy 2.0.0rc1 **Elasticsearch version** (`bin/elasticsearch --version`): n/a **`elasticsearch-py` version (`elasticsearch.__versionstr__`)**: 8.13.1, tested with main @ f32878e62290f133d0957b271a87a97fad45e3e2 as well Please make sure the major version matches the Elasticsearch server you are running. **Description of the problem including expected versus actual behavior**: When using NumPy 2.0.0rc1, some of the tests fail: ```pytb ======================================================= short test summary info ======================================================= FAILED test_elasticsearch/test_serializer.py::test_serializes_numpy_bool[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_numpy_floats[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_numpy_floats[OrjsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_numpy_datetime[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_numpy_ndarray[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_pandas_series[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_pandas_series[OrjsonSerializer] - elastic_transport.SerializationError: Unable to serialize to JSON: {'d': 0 a 1 b 2 c 3 d dtype: object} (type: dict) FAILED test_elasticsearch/test_serializer.py::test_serializes_pandas_na[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_pandas_na[OrjsonSerializer] - elastic_transport.SerializationError: Unable to serialize to JSON: {'d': <NA>} (type: dict) FAILED test_elasticsearch/test_serializer.py::test_raises_serialization_error_pandas_nat[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_pandas_category[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. FAILED test_elasticsearch/test_serializer.py::test_serializes_pandas_category[OrjsonSerializer] - elastic_transport.SerializationError: Unable to serialize to JSON: {'d': ['a', 'c', 'b', 'a'] Categories (3, object): ['a', 'b', 'c']} (type: dict) FAILED test_elasticsearch/test_serializer.py::test_json_raises_serialization_error_on_dump_error[JsonSerializer] - AttributeError: `np.float_` was removed in the NumPy 2.0 release. Use `np.float64` instead. ====================================== 13 failed, 245 passed, 140 skipped, 30 warnings in 8.74s ======================================= ``` Full log with tracebacks (200k): [test-log.txt](https://github.com/elastic/elasticsearch-py/files/15206657/test-log.txt) **Steps to reproduce**: ``` nox -s test-3.11 . .nox/test-3-11/bin/activate pip install -U --pre numpy python -m pytest ```

2024-05-04T09:12:44Z

lk-geimfari/mimesis

lk-geimfari__mimesis-262

031b237857cc99e770111e8ab5b82e46aba2bce8

diff --git a/setup.py b/setup.py --- a/setup.py +++ b/setup.py @@ -22,13 +22,8 @@ here = abspath(dirname(__file__)) -tests_requirements = [ - 'pytest', - 'flake8-builtins', - 'flake8-commas', - 'flake8-quotes', - 'pytest-flake8', -] +with open('dev_requirements.txt') as f: + tests_requirements = f.read().splitlines() # Long description. with open('PYPI_README.rst', 'r', encoding='utf-8') as f:

diff --git a/tests/test_builtins/en/test_usa_spec.py b/tests/test_builtins/en/test_usa_spec.py --- a/tests/test_builtins/en/test_usa_spec.py +++ b/tests/test_builtins/en/test_usa_spec.py @@ -39,9 +39,14 @@ def test_personality(usa): def test_ssn(usa): result = usa.ssn() assert result is not None - # todo fix so this actually checks that 666 prefix can never be returned assert '666' != result[:3] assert re.match('^\d{3}-\d{2}-\d{4}$', result) assert result.replace('-', '').isdigit() assert len(result.replace('-', '')) == 9 + + +def test_cpf_with_666_prefix(mocker, usa): + with mocker.patch.object(usa.random, 'randint', return_value=666): + result = usa.ssn() + assert '665' == result[:3] diff --git a/tests/test_data/test_code.py b/tests/test_data/test_code.py --- a/tests/test_data/test_code.py +++ b/tests/test_data/test_code.py @@ -14,15 +14,10 @@ def test_str(code): def test_custom_code(code): result = code.custom_code( - mask='@###', char='@', digit='#') - assert len(result) == 4 - - -def test_custom_code_args(code): - result = code.custom_code( - mask='@@@-###-@@@').split('-') + mask='@@@-###-@@@', char='@', digit='#') + assert len(result) == 11 - a, b, c = result + a, b, c = result.split('-') assert a.isalpha() assert b.isdigit() assert c.isalpha() diff --git a/tests/test_data/test_cryptographic.py b/tests/test_data/test_cryptographic.py --- a/tests/test_data/test_cryptographic.py +++ b/tests/test_data/test_cryptographic.py @@ -22,21 +22,25 @@ def test_uuid(crypto): ], ) def test_hash(crypto, algorithm, length): - assert len(crypto.hash(algorithm=algorithm)) == length + result = crypto.hash(algorithm=algorithm) + assert len(result) == length with pytest.raises(UnsupportedAlgorithm): crypto.hash(algorithm='mimesis') def test_bytes(crypto): - assert crypto.bytes(entropy=64) is not None - assert isinstance(crypto.bytes(entropy=64), bytes) + result = crypto.bytes(entropy=64) + assert result is not None + assert isinstance(result, bytes) def test_token(crypto): + result = crypto.token(entropy=16) + # Each byte converted to two hex digits. - assert len(crypto.token(entropy=16)) == 32 - assert isinstance(crypto.token(), str) + assert len(result) == 32 + assert isinstance(result, str) def test_salt(crypto): diff --git a/tests/test_data/test_development.py b/tests/test_data/test_development.py --- a/tests/test_data/test_development.py +++ b/tests/test_data/test_development.py @@ -39,8 +39,8 @@ def test_database(dev): result = dev.database() assert result in SQL - _result = dev.database(nosql=True) - assert _result in NOSQL + result = dev.database(nosql=True) + assert result in NOSQL def test_other(dev): diff --git a/tests/test_data/test_games.py b/tests/test_data/test_games.py --- a/tests/test_data/test_games.py +++ b/tests/test_data/test_games.py @@ -7,30 +7,31 @@ def test_gaming_platform(games): def test_score(games): - score = games.score(minimum=5.5, maximum=10) - assert isinstance(score, float) - assert (score >= 5.5) and (score <= 10) + result = games.score(minimum=5.5, maximum=10) + assert isinstance(result, float) + assert (result >= 5.5) and (result <= 10) def test_pegi_rating(games): - rating = games.pegi_rating().split(' ')[1] + result = games.pegi_rating().split(' ')[1] standard = [3, 7, 12, 16, 18] - assert int(rating) in standard + assert int(result) in standard - rating_pt = games.pegi_rating(pt=True).split(' ')[1] - assert int(rating_pt) <= 18 + result = games.pegi_rating(pt=True).split(' ')[1] + assert int(result) <= 18 def test_genre(games): - genre = games.genre() - assert genre in GENRES + result = games.genre() + assert result in GENRES def test_score_phrase(games): - phrase = games.score_phrase() - assert phrase in SCORE_PHRASES + result = games.score_phrase() + assert result in SCORE_PHRASES def test_game(games): - assert games.game() in GAMES + result = games.game() + assert result in GAMES diff --git a/tests/test_data/test_path.py b/tests/test_data/test_path.py --- a/tests/test_data/test_path.py +++ b/tests/test_data/test_path.py @@ -4,11 +4,13 @@ def test_root(path): - assert 'C:\\', '/' == path.root() + result = path.root() + assert 'C:\\', '/' == result def test_home(path): - assert 'С:\\Users\\', '/home/' == path.home() + result = path.home() + assert 'С:\\Users\\', '/home/' == result def test_user(path): diff --git a/tests/test_data/test_personal.py b/tests/test_data/test_personal.py --- a/tests/test_data/test_personal.py +++ b/tests/test_data/test_personal.py @@ -87,11 +87,11 @@ def test_paypal(_personal): ], ) def test_password(_personal, algorithm, length): - plain_password = _personal.password(length=15) - assert len(plain_password) == 15 + result = _personal.password(length=15) + assert len(result) == 15 - encrypted_password = _personal.password(algorithm=algorithm) - assert len(encrypted_password) == length + result = _personal.password(algorithm=algorithm) + assert len(result) == length with pytest.raises(UnsupportedAlgorithm): _personal.password(algorithm='sha42') @@ -109,8 +109,8 @@ def test_password(_personal, algorithm, length): ], ) def test_username(_personal, template): - username = _personal.username(template=template) - assert re.match(USERNAME_REGEX, username) + result = _personal.username(template=template) + assert re.match(USERNAME_REGEX, result) with pytest.raises(WrongArgument): _personal.username(template=':D') @@ -252,8 +252,8 @@ def test_telephone(personal): assert result is not None mask = '+5 (###)-###-##-##' - result2 = personal.telephone(mask=mask) - head = result2.split(' ')[0] + result = personal.telephone(mask=mask) + head = result.split(' ')[0] assert head == '+5' @@ -303,8 +303,8 @@ def test_gender(personal): result = personal.gender() assert result in personal.data['gender'] - symbol = personal.gender(symbol=True) - assert symbol in GENDER_SYMBOLS + result = personal.gender(symbol=True) + assert result in GENDER_SYMBOLS # The four codes specified in ISO/IEC 5218 are: # 0 = not known, 1 = male, 2 = female, 9 = not applicable. @@ -377,8 +377,8 @@ def test_title(personal, gender, title_type): assert result is not None assert isinstance(result, str) - result_by_gender = personal.title(gender=gender) - assert result_by_gender is not None + result = personal.title(gender=gender) + assert result is not None with pytest.raises(WrongArgument): personal.title(gender='other', title_type='religious') diff --git a/tests/test_data/test_structured.py b/tests/test_data/test_structured.py --- a/tests/test_data/test_structured.py +++ b/tests/test_data/test_structured.py @@ -58,6 +58,6 @@ def test_json(structured): # Recursive returns python object, not JSON and # maximum depth of three elements - r = structured.json(items=3, max_depth=4, recursive=True) - assert isinstance(r, (dict, list)) - assert depth(r) <= 4 + result = structured.json(items=3, max_depth=4, recursive=True) + assert isinstance(result, (dict, list)) + assert depth(result) <= 4 diff --git a/tests/test_generic.py b/tests/test_generic.py --- a/tests/test_generic.py +++ b/tests/test_generic.py @@ -56,36 +56,6 @@ def test_bad_argument(generic): _ = generic.bad_argument # noqa -def test_add_provider(generic): - class CustomProvider: - class Meta: - name = 'custom_provider' - - @staticmethod - def say(): - return 'Custom' - - @staticmethod - def number(): - return 1 - - generic.add_provider(CustomProvider) - assert generic.custom_provider.say() is not None - assert generic.custom_provider.number() == 1 - with pytest.raises(TypeError): - generic.add_provider(True) - - class UnnamedProvider(object): - @staticmethod - def nothing(): - return None - - generic.add_provider(UnnamedProvider) - assert generic.unnamedprovider.nothing() is None - - assert 'unnamedprovider' == UnnamedProvider.__name__.lower() - - def test_add_providers(generic): class Provider1(object): @staticmethod @@ -93,6 +63,9 @@ def one(): return 1 class Provider2(object): + class Meta: + name = 'custom_provider' + @staticmethod def two(): return 2 @@ -104,8 +77,16 @@ def three(): generic.add_providers(Provider1, Provider2, Provider3) assert generic.provider1.one() == 1 - assert generic.provider2.two() == 2 + assert generic.custom_provider.two() == 2 assert generic.provider3.three() == 3 with pytest.raises(TypeError): generic.add_providers(True) + + class UnnamedProvider(object): + @staticmethod + def nothing(): + return None + + generic.add_provider(UnnamedProvider) + assert generic.unnamedprovider.nothing() is None diff --git a/tests/test_helpers.py b/tests/test_helpers.py --- a/tests/test_helpers.py +++ b/tests/test_helpers.py @@ -14,11 +14,11 @@ def test_randints(random): # Length of default list is 3 assert len(result) == 3 - result_custom = random.randints(25, 1, 1) + result = random.randints(25, 1, 1) - assert len(result_custom) == 25 + assert len(result) == 25 # All elements in result_custom equals to 1. - assert result_custom[0] == 1 and result_custom[-1] == 1 + assert result[0] == 1 and result[-1] == 1 @pytest.mark.parametrize( diff --git a/tests/test_utils.py b/tests/test_utils.py --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,7 +1,6 @@ # -*- coding: utf-8 -*- import os -import socket import sys import pytest @@ -11,16 +10,6 @@ luhn_checksum, pull, update_dict) -def is_connected(): - try: - host = socket.gethostbyname('https://github.com/') - socket.create_connection((host, 80), 2) - return True - except: - pass - return False - - def test_luhn_checksum(): assert luhn_checksum('7992739871') == '3' @@ -45,14 +34,20 @@ def test_pull(): assert 'Melbourne' in data['city'] -def test_download_image(): +def test_download_image(mocker): result = download_image(url=None) assert result is None url = 'https://github.com/lk-geimfari/mimesis/' \ 'raw/master/media/mimesis.png' - if is_connected(): + def create_image(*args, **kwargs): + image_name = 'mimesis.png' + with open(image_name, 'w') as f: + f.write('testing image') + + with mocker.patch('mimesis.utils.request.urlretrieve', + side_effect=create_image): verified = download_image(url=url) assert verified == 'mimesis.png' os.remove(verified)

Check tests for duplicates. It is possible that some tests are repeated several times without any need. If so, then we need to fix it.

How does one run the tests? @pzelnip `make test` or `py.test .` I will work on this

2017-10-23T08:53:06Z