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ddboline/kaggle_imdb_sentiment_model | my_model.py | 1 | 1270 | #!/usr/bin/python
import os
import gzip
import cPickle as pickle
from load_data import load_data
#from load_data_bagofwords import load_data
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
import numpy as np
import pandas as pd
def score_model(model, xtrain, ytrain):
randint = reduce(lambda x,y: x|y, [ord(x)<<(n*8) for (n,x) in
enumerate(os.urandom(4))])
xTrain, xTest, yTrain, yTest = train_test_split(xtrain, ytrain,
test_size=0.4,
random_state=randint)
model.fit(xTrain, yTrain)
print model
print model.score(xTest, yTest)
return
def prepare_submission(model, xtrain, ytrain, xtest, ytest):
model.fit(xtrain, ytrain)
ytest_pred = model.predict(xtest)
output = pd.DataFrame(data={'id': ytest, 'sentiment': ytest_pred})
output.to_csv('submission.csv', index=False, quoting=3)
if __name__ == '__main__':
xtrain, ytrain, xtest, ytest = load_data()
model = RandomForestClassifier(n_estimators=400, n_jobs=-1)
score_model(model, xtrain, ytrain)
prepare_submission(model, xtrain, ytrain, xtest, ytest)
| mit |
liangz0707/scikit-learn | examples/cluster/plot_birch_vs_minibatchkmeans.py | 333 | 3694 | """
=================================
Compare BIRCH and MiniBatchKMeans
=================================
This example compares the timing of Birch (with and without the global
clustering step) and MiniBatchKMeans on a synthetic dataset having
100,000 samples and 2 features generated using make_blobs.
If ``n_clusters`` is set to None, the data is reduced from 100,000
samples to a set of 158 clusters. This can be viewed as a preprocessing
step before the final (global) clustering step that further reduces these
158 clusters to 100 clusters.
"""
# Authors: Manoj Kumar <[email protected]
# Alexandre Gramfort <[email protected]>
# License: BSD 3 clause
print(__doc__)
from itertools import cycle
from time import time
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import Birch, MiniBatchKMeans
from sklearn.datasets.samples_generator import make_blobs
# Generate centers for the blobs so that it forms a 10 X 10 grid.
xx = np.linspace(-22, 22, 10)
yy = np.linspace(-22, 22, 10)
xx, yy = np.meshgrid(xx, yy)
n_centres = np.hstack((np.ravel(xx)[:, np.newaxis],
np.ravel(yy)[:, np.newaxis]))
# Generate blobs to do a comparison between MiniBatchKMeans and Birch.
X, y = make_blobs(n_samples=100000, centers=n_centres, random_state=0)
# Use all colors that matplotlib provides by default.
colors_ = cycle(colors.cnames.keys())
fig = plt.figure(figsize=(12, 4))
fig.subplots_adjust(left=0.04, right=0.98, bottom=0.1, top=0.9)
# Compute clustering with Birch with and without the final clustering step
# and plot.
birch_models = [Birch(threshold=1.7, n_clusters=None),
Birch(threshold=1.7, n_clusters=100)]
final_step = ['without global clustering', 'with global clustering']
for ind, (birch_model, info) in enumerate(zip(birch_models, final_step)):
t = time()
birch_model.fit(X)
time_ = time() - t
print("Birch %s as the final step took %0.2f seconds" % (
info, (time() - t)))
# Plot result
labels = birch_model.labels_
centroids = birch_model.subcluster_centers_
n_clusters = np.unique(labels).size
print("n_clusters : %d" % n_clusters)
ax = fig.add_subplot(1, 3, ind + 1)
for this_centroid, k, col in zip(centroids, range(n_clusters), colors_):
mask = labels == k
ax.plot(X[mask, 0], X[mask, 1], 'w',
markerfacecolor=col, marker='.')
if birch_model.n_clusters is None:
ax.plot(this_centroid[0], this_centroid[1], '+', markerfacecolor=col,
markeredgecolor='k', markersize=5)
ax.set_ylim([-25, 25])
ax.set_xlim([-25, 25])
ax.set_autoscaley_on(False)
ax.set_title('Birch %s' % info)
# Compute clustering with MiniBatchKMeans.
mbk = MiniBatchKMeans(init='k-means++', n_clusters=100, batch_size=100,
n_init=10, max_no_improvement=10, verbose=0,
random_state=0)
t0 = time()
mbk.fit(X)
t_mini_batch = time() - t0
print("Time taken to run MiniBatchKMeans %0.2f seconds" % t_mini_batch)
mbk_means_labels_unique = np.unique(mbk.labels_)
ax = fig.add_subplot(1, 3, 3)
for this_centroid, k, col in zip(mbk.cluster_centers_,
range(n_clusters), colors_):
mask = mbk.labels_ == k
ax.plot(X[mask, 0], X[mask, 1], 'w', markerfacecolor=col, marker='.')
ax.plot(this_centroid[0], this_centroid[1], '+', markeredgecolor='k',
markersize=5)
ax.set_xlim([-25, 25])
ax.set_ylim([-25, 25])
ax.set_title("MiniBatchKMeans")
ax.set_autoscaley_on(False)
plt.show()
| bsd-3-clause |
sergeyk/vislab | vislab/gg.py | 4 | 5652 | """
All credit for rstyle, rhist, rbox goes to [messymind.net][1].
(With some additions from the comments section.)
Additional credit (husl_gen, rbar) goes to [Rob Story][2].
[1]: http://messymind.net/2012/07/making-matplotlib-look-like-ggplot/
[2]: http://nbviewer.ipython.org/urls/raw.github.com/\
wrobstory/climatic/master/examples/ggplot_styling_for_matplotlib.ipynb
"""
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import husl
import pylab
try:
import mpltools.style
mpltools.style.use('ggplot')
# Colors from http://mbostock.github.io/protovis/docs/color.html
matplotlib.rcParams['axes.color_cycle'] = [
"#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b",
"#e377c2", "#7f7f7f", "#bcbd22", "#17becf"]
except:
pass
def husl_gen():
"""
Generate random set of HUSL colors, one dark, one light.
"""
hue = np.random.randint(0, 360)
saturation, lightness = np.random.randint(0, 100, 2)
husl_dark = husl.husl_to_hex(hue, saturation, lightness / 3)
husl_light = husl.husl_to_hex(hue, saturation, lightness)
return str(husl_dark), str(husl_light)
def rstyle(ax, xlog=False, ylog=False):
"""
Styles x,y axes to appear like ggplot2.
Must be called after all plot and axis manipulation operations have been
carried out, as it needs to know the final tick spacing.
"""
#Set the style of the major and minor grid lines, filled blocks
ax.grid(True, 'major', color='w', linestyle='-', linewidth=1.4)
ax.grid(True, 'minor', color='0.99', linestyle='-', linewidth=0.7)
ax.patch.set_facecolor('#f3f3f3')
ax.set_axisbelow(True)
# #Set minor tick spacing to 1/2 of the major ticks
# if not xlog:
# ax.xaxis.set_minor_locator((pylab.MultipleLocator((
# plt.xticks()[0][1] - plt.xticks()[0][0]) / 2.0)))
# if not ylog:
# ax.yaxis.set_minor_locator((pylab.MultipleLocator((
# plt.yticks()[0][1] - plt.yticks()[0][0]) / 2.0)))
#Remove axis border
for child in ax.get_children():
if isinstance(child, matplotlib.spines.Spine):
child.set_alpha(0)
#Restyle the tick lines
for line in ax.get_xticklines() + ax.get_yticklines():
line.set_markersize(5)
line.set_color("lightgray")
line.set_markeredgewidth(1.4)
#Remove the minor tick lines
for line in (ax.xaxis.get_ticklines(minor=True) +
ax.yaxis.get_ticklines(minor=True)):
line.set_markersize(0)
#Only show bottom left ticks, pointing out of axis
plt.rcParams['xtick.direction'] = 'out'
plt.rcParams['ytick.direction'] = 'out'
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.legend()
legend = ax.get_legend()
if legend:
frame = legend.get_frame()
frame.set_facecolor('#f3f3f3')
def rbar(ax, left, height, **kwargs):
"""
Create a bar plot with default style parameters to look like ggplot2.
kwargs can be passed to changed other parameters
"""
defaults = {'facecolor': '0.15',
'edgecolor': '0.28',
'linewidth': 1,
'width': 1}
for x, y in defaults.iteritems():
kwargs.setdefault(x, y)
return ax.bar(left, height, **kwargs)
def rfill(ax, x_range, dist, **kwargs):
"""
Create a density plot to resemble ggplot2.
kwargs can be passed to change other parameters.
"""
defaults = {'linewidth': 2.0,
'alpha': 0.4}
for x, y in defaults.iteritems():
kwargs.setdefault(x, y)
# Make edge color a darker shade of facecolor.
patches = ax.fill(x_range, dist, **kwargs)
for patch in patches:
fc = patch.get_facecolor()
patch.set_edgecolor(tuple(x * 0.5 for x in fc[:3]) + (fc[3],))
return ax
def rhist(ax, data, **kwargs):
"""
Create a hist plot with default style parameters to look like ggplot2.
kwargs can be passed to changed other parameters.
"""
defaults = {'facecolor': '0.3',
'edgecolor': '0.36',
'linewidth': 1,
'rwidth': 1}
for x, y in defaults.iteritems():
kwargs.setdefault(x, y)
return ax.hist(data, **kwargs)
def rbox(ax, data, **keywords):
"""
Create a ggplot2 style boxplot, is eqivalent to calling ax.boxplot
with the following additions:
Keyword arguments:
colors -- array-like collection of colours for box fills
names -- array-like collection of box names which are passed on as
tick labels
"""
hasColors = 'colors' in keywords
if hasColors:
colors = keywords['colors']
keywords.pop('colors')
if 'names' in keywords:
ax.tickNames = plt.setp(ax, xticklabels=keywords['names'])
keywords.pop('names')
bp = ax.boxplot(data, **keywords)
pylab.setp(bp['boxes'], color='black')
pylab.setp(bp['whiskers'], color='black', linestyle='solid')
pylab.setp(bp['fliers'], color='black', alpha=.9, marker='o', markersize=3)
pylab.setp(bp['medians'], color='black')
numBoxes = len(data)
for i in range(numBoxes):
box = bp['boxes'][i]
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = zip(boxX, boxY)
if hasColors:
boxPolygon = pylab.Polygon(
boxCoords, facecolor=colors[i % len(colors)])
else:
boxPolygon = pylab.Polygon(boxCoords, facecolor='0.95')
ax.add_patch(boxPolygon)
return bp
| bsd-2-clause |
mhvk/numpy | numpy/lib/histograms.py | 8 | 40215 | """
Histogram-related functions
"""
import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
__all__ = ['histogram', 'histogramdd', 'histogram_bin_edges']
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
# range is a keyword argument to many functions, so save the builtin so they can
# use it.
_range = range
def _ptp(x):
"""Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array's data type.
This function returns an unsigned value for signed integer arrays.
"""
return _unsigned_subtract(x.max(), x.min())
def _hist_bin_sqrt(x, range):
"""
Square root histogram bin estimator.
Bin width is inversely proportional to the data size. Used by many
programs for its simplicity.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return _ptp(x) / np.sqrt(x.size)
def _hist_bin_sturges(x, range):
"""
Sturges histogram bin estimator.
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.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return _ptp(x) / (np.log2(x.size) + 1.0)
def _hist_bin_rice(x, range):
"""
Rice histogram bin estimator.
Another simple estimator with no normality assumption. It has better
performance for large data than Sturges, but tends to overestimate
the number of bins. The number of bins is proportional to the cube
root of data size (asymptotically optimal). The estimate depends
only on size of the data.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return _ptp(x) / (2.0 * x.size ** (1.0 / 3))
def _hist_bin_scott(x, range):
"""
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
def _hist_bin_stone(x, range):
"""
Histogram bin estimator based on minimizing the estimated integrated squared error (ISE).
The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution.
The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule.
https://en.wikipedia.org/wiki/Histogram#Scott.27s_normal_reference_rule
This paper by Stone appears to be the origination of this rule.
http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/34.pdf
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
range : (float, float)
The lower and upper range of the bins.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
n = x.size
ptp_x = _ptp(x)
if n <= 1 or ptp_x == 0:
return 0
def jhat(nbins):
hh = ptp_x / nbins
p_k = np.histogram(x, bins=nbins, range=range)[0] / n
return (2 - (n + 1) * p_k.dot(p_k)) / hh
nbins_upper_bound = max(100, int(np.sqrt(n)))
nbins = min(_range(1, nbins_upper_bound + 1), key=jhat)
if nbins == nbins_upper_bound:
warnings.warn("The number of bins estimated may be suboptimal.",
RuntimeWarning, stacklevel=3)
return ptp_x / nbins
def _hist_bin_doane(x, range):
"""
Doane's histogram bin estimator.
Improved version of Sturges' formula which works better for
non-normal data. See
stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
if x.size > 2:
sg1 = np.sqrt(6.0 * (x.size - 2) / ((x.size + 1.0) * (x.size + 3)))
sigma = np.std(x)
if sigma > 0.0:
# These three operations add up to
# g1 = np.mean(((x - np.mean(x)) / sigma)**3)
# but use only one temp array instead of three
temp = x - np.mean(x)
np.true_divide(temp, sigma, temp)
np.power(temp, 3, temp)
g1 = np.mean(temp)
return _ptp(x) / (1.0 + np.log2(x.size) +
np.log2(1.0 + np.absolute(g1) / sg1))
return 0.0
def _hist_bin_fd(x, range):
"""
The Freedman-Diaconis histogram bin estimator.
The Freedman-Diaconis rule uses interquartile range (IQR) to
estimate binwidth. It is considered a variation of the Scott rule
with more robustness 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.
If the IQR is 0, this function returns 0 for the bin width.
Binwidth is inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
iqr = np.subtract(*np.percentile(x, [75, 25]))
return 2.0 * iqr * x.size ** (-1.0 / 3.0)
def _hist_bin_auto(x, range):
"""
Histogram bin estimator that uses the minimum width of the
Freedman-Diaconis and Sturges estimators if the FD bin width is non-zero.
If the bin width from the FD estimator is 0, the Sturges estimator is used.
The FD estimator is usually the most robust method, but its width
estimate tends to be too large for small `x` and bad for data with limited
variance. The Sturges estimator is quite good for small (<1000) datasets
and is the default in the R language. This method gives good off-the-shelf
behaviour.
.. versionchanged:: 1.15.0
If there is limited variance the IQR can be 0, which results in the
FD bin width being 0 too. This is not a valid bin width, so
``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal.
If the IQR is 0, it's unlikely any variance-based estimators will be of
use, so we revert to the Sturges estimator, which only uses the size of the
dataset in its calculation.
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
See Also
--------
_hist_bin_fd, _hist_bin_sturges
"""
fd_bw = _hist_bin_fd(x, range)
sturges_bw = _hist_bin_sturges(x, range)
del range # unused
if fd_bw:
return min(fd_bw, sturges_bw)
else:
# limited variance, so we return a len dependent bw estimator
return sturges_bw
# Private dict initialized at module load time
_hist_bin_selectors = {'stone': _hist_bin_stone,
'auto': _hist_bin_auto,
'doane': _hist_bin_doane,
'fd': _hist_bin_fd,
'rice': _hist_bin_rice,
'scott': _hist_bin_scott,
'sqrt': _hist_bin_sqrt,
'sturges': _hist_bin_sturges}
def _ravel_and_check_weights(a, weights):
""" Check a and weights have matching shapes, and ravel both """
a = np.asarray(a)
# Ensure that the array is a "subtractable" dtype
if a.dtype == np.bool_:
warnings.warn("Converting input from {} to {} for compatibility."
.format(a.dtype, np.uint8),
RuntimeWarning, stacklevel=3)
a = a.astype(np.uint8)
if weights is not None:
weights = np.asarray(weights)
if weights.shape != a.shape:
raise ValueError(
'weights should have the same shape as a.')
weights = weights.ravel()
a = a.ravel()
return a, weights
def _get_outer_edges(a, range):
"""
Determine the outer bin edges to use, from either the data or the range
argument
"""
if range is not None:
first_edge, last_edge = range
if first_edge > last_edge:
raise ValueError(
'max must be larger than min in range parameter.')
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
"supplied range of [{}, {}] is not finite".format(first_edge, last_edge))
elif a.size == 0:
# handle empty arrays. Can't determine range, so use 0-1.
first_edge, last_edge = 0, 1
else:
first_edge, last_edge = a.min(), a.max()
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
"autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
# expand empty range to avoid divide by zero
if first_edge == last_edge:
first_edge = first_edge - 0.5
last_edge = last_edge + 0.5
return first_edge, last_edge
def _unsigned_subtract(a, b):
"""
Subtract two values where a >= b, and produce an unsigned result
This is needed when finding the difference between the upper and lower
bound of an int16 histogram
"""
# coerce to a single type
signed_to_unsigned = {
np.byte: np.ubyte,
np.short: np.ushort,
np.intc: np.uintc,
np.int_: np.uint,
np.longlong: np.ulonglong
}
dt = np.result_type(a, b)
try:
dt = signed_to_unsigned[dt.type]
except KeyError:
return np.subtract(a, b, dtype=dt)
else:
# we know the inputs are integers, and we are deliberately casting
# signed to unsigned
return np.subtract(a, b, casting='unsafe', dtype=dt)
def _get_bin_edges(a, bins, range, weights):
"""
Computes the bins used internally by `histogram`.
Parameters
==========
a : ndarray
Ravelled data array
bins, range
Forwarded arguments from `histogram`.
weights : ndarray, optional
Ravelled weights array, or None
Returns
=======
bin_edges : ndarray
Array of bin edges
uniform_bins : (Number, Number, int):
The upper bound, lowerbound, and number of bins, used in the optimized
implementation of `histogram` that works on uniform bins.
"""
# parse the overloaded bins argument
n_equal_bins = None
bin_edges = None
if isinstance(bins, str):
bin_name = bins
# if `bins` is a string for an automatic method,
# this will replace it with the number of bins calculated
if bin_name not in _hist_bin_selectors:
raise ValueError(
"{!r} is not a valid estimator for `bins`".format(bin_name))
if weights is not None:
raise TypeError("Automated estimation of the number of "
"bins is not supported for weighted data")
first_edge, last_edge = _get_outer_edges(a, range)
# truncate the range if needed
if range is not None:
keep = (a >= first_edge)
keep &= (a <= last_edge)
if not np.logical_and.reduce(keep):
a = a[keep]
if a.size == 0:
n_equal_bins = 1
else:
# Do not call selectors on empty arrays
width = _hist_bin_selectors[bin_name](a, (first_edge, last_edge))
if width:
n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width))
else:
# Width can be zero for some estimators, e.g. FD when
# the IQR of the data is zero.
n_equal_bins = 1
elif np.ndim(bins) == 0:
try:
n_equal_bins = operator.index(bins)
except TypeError as e:
raise TypeError(
'`bins` must be an integer, a string, or an array') from e
if n_equal_bins < 1:
raise ValueError('`bins` must be positive, when an integer')
first_edge, last_edge = _get_outer_edges(a, range)
elif np.ndim(bins) == 1:
bin_edges = np.asarray(bins)
if np.any(bin_edges[:-1] > bin_edges[1:]):
raise ValueError(
'`bins` must increase monotonically, when an array')
else:
raise ValueError('`bins` must be 1d, when an array')
if n_equal_bins is not None:
# gh-10322 means that type resolution rules are dependent on array
# shapes. To avoid this causing problems, we pick a type now and stick
# with it throughout.
bin_type = np.result_type(first_edge, last_edge, a)
if np.issubdtype(bin_type, np.integer):
bin_type = np.result_type(bin_type, float)
# bin edges must be computed
bin_edges = np.linspace(
first_edge, last_edge, n_equal_bins + 1,
endpoint=True, dtype=bin_type)
return bin_edges, (first_edge, last_edge, n_equal_bins)
else:
return bin_edges, None
def _search_sorted_inclusive(a, v):
"""
Like `searchsorted`, but where the last item in `v` is placed on the right.
In the context of a histogram, this makes the last bin edge inclusive
"""
return np.concatenate((
a.searchsorted(v[:-1], 'left'),
a.searchsorted(v[-1:], 'right')
))
def _histogram_bin_edges_dispatcher(a, bins=None, range=None, weights=None):
return (a, bins, weights)
@array_function_dispatch(_histogram_bin_edges_dispatcher)
def histogram_bin_edges(a, bins=10, range=None, weights=None):
r"""
Function to calculate only the edges of the bins used by the `histogram`
function.
Parameters
----------
a : array_like
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optional
If `bins` is an int, it defines the number of equal-width
bins in the given range (10, by default). If `bins` is a
sequence, it defines the bin edges, including the rightmost
edge, allowing for non-uniform bin widths.
If `bins` is a string from the list below, `histogram_bin_edges` will use
the method chosen to calculate the optimal bin width and
consequently the number of bins (see `Notes` for more detail on
the estimators) from the data that falls within the requested
range. While the bin width will be optimal for the actual data
in the range, the number of bins will be computed to fill the
entire range, including the empty portions. For visualisation,
using the 'auto' option is suggested. Weighted data is not
supported for automated bin size selection.
'auto'
Maximum of the 'sturges' and 'fd' estimators. Provides good
all around performance.
'fd' (Freedman Diaconis Estimator)
Robust (resilient to outliers) estimator that takes into
account data variability and data size.
'doane'
An improved version of Sturges' estimator that works better
with non-normal datasets.
'scott'
Less robust estimator that that takes into account data
variability and data size.
'stone'
Estimator based on leave-one-out cross-validation estimate of
the integrated squared error. Can be regarded as a generalization
of Scott's rule.
'rice'
Estimator does not take variability into account, only data
size. Commonly overestimates number of bins required.
'sturges'
R's default method, only accounts for data size. Only
optimal for gaussian data and underestimates number of bins
for large non-gaussian datasets.
'sqrt'
Square root (of data size) estimator, used by Excel and
other programs for its speed and simplicity.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range
is simply ``(a.min(), a.max())``. Values outside the range are
ignored. The first element of the range must be less than or
equal to the second. `range` affects the automatic bin
computation as well. While bin width is computed to be optimal
based on the actual data within `range`, the bin count will fill
the entire range including portions containing no data.
weights : array_like, optional
An array of weights, of the same shape as `a`. Each value in
`a` only contributes its associated weight towards the bin count
(instead of 1). This is currently not used by any of the bin estimators,
but may be in the future.
Returns
-------
bin_edges : array of dtype float
The edges to pass into `histogram`
See Also
--------
histogram
Notes
-----
The methods to estimate the optimal number of bins are well founded
in literature, and are inspired by the choices R provides for
histogram visualisation. Note that having the number of bins
proportional to :math:`n^{1/3}` is asymptotically optimal, which is
why it appears in most estimators. These are simply plug-in methods
that give good starting points for number of bins. In the equations
below, :math:`h` is the binwidth and :math:`n_h` is the number of
bins. All estimators that compute bin counts are recast to bin width
using the `ptp` of the data. The final bin count is obtained from
``np.round(np.ceil(range / h))``. The final bin width is often less
than what is returned by the estimators below.
'auto' (maximum of the 'sturges' and 'fd' estimators)
A compromise to get a good value. For small datasets the Sturges
value will usually be chosen, while larger datasets will usually
default to FD. Avoids the overly conservative behaviour of FD
and Sturges for small and large datasets respectively.
Switchover point is usually :math:`a.size \approx 1000`.
'fd' (Freedman Diaconis Estimator)
.. math:: h = 2 \frac{IQR}{n^{1/3}}
The binwidth is proportional to the interquartile range (IQR)
and inversely proportional to cube root of a.size. Can be too
conservative for small datasets, but is quite good for large
datasets. The IQR is very robust to outliers.
'scott'
.. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}}
The binwidth is proportional to the standard deviation of the
data and inversely proportional to cube root of ``x.size``. Can
be too conservative for small datasets, but is quite good for
large datasets. The standard deviation is not very robust to
outliers. Values are very similar to the Freedman-Diaconis
estimator in the absence of outliers.
'rice'
.. math:: n_h = 2n^{1/3}
The number of bins is only proportional to cube root of
``a.size``. It tends to overestimate the number of bins and it
does not take into account data variability.
'sturges'
.. math:: n_h = \log _{2}n+1
The number of bins is the base 2 log of ``a.size``. This
estimator assumes normality of data and is too conservative for
larger, non-normal datasets. This is the default method in R's
``hist`` method.
'doane'
.. math:: n_h = 1 + \log_{2}(n) +
\log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}})
g_1 = mean[(\frac{x - \mu}{\sigma})^3]
\sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}}
An improved version of Sturges' formula that produces better
estimates for non-normal datasets. This estimator attempts to
account for the skew of the data.
'sqrt'
.. math:: n_h = \sqrt n
The simplest and fastest estimator. Only takes into account the
data size.
Examples
--------
>>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5])
>>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1))
array([0. , 0.25, 0.5 , 0.75, 1. ])
>>> np.histogram_bin_edges(arr, bins=2)
array([0. , 2.5, 5. ])
For consistency with histogram, an array of pre-computed bins is
passed through unmodified:
>>> np.histogram_bin_edges(arr, [1, 2])
array([1, 2])
This function allows one set of bins to be computed, and reused across
multiple histograms:
>>> shared_bins = np.histogram_bin_edges(arr, bins='auto')
>>> shared_bins
array([0., 1., 2., 3., 4., 5.])
>>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1])
>>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins)
>>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins)
>>> hist_0; hist_1
array([1, 1, 0, 1, 0])
array([2, 0, 1, 1, 2])
Which gives more easily comparable results than using separate bins for
each histogram:
>>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto')
>>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto')
>>> hist_0; hist_1
array([1, 1, 1])
array([2, 1, 1, 2])
>>> bins_0; bins_1
array([0., 1., 2., 3.])
array([0. , 1.25, 2.5 , 3.75, 5. ])
"""
a, weights = _ravel_and_check_weights(a, weights)
bin_edges, _ = _get_bin_edges(a, bins, range, weights)
return bin_edges
def _histogram_dispatcher(
a, bins=None, range=None, normed=None, weights=None, density=None):
return (a, bins, weights)
@array_function_dispatch(_histogram_dispatcher)
def histogram(a, bins=10, range=None, normed=None, weights=None,
density=None):
r"""
Compute the histogram of a dataset.
Parameters
----------
a : array_like
Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optional
If `bins` is an int, it defines the number of equal-width
bins in the given range (10, by default). If `bins` is a
sequence, it defines a monotonically increasing array of bin edges,
including the rightmost edge, allowing for non-uniform bin widths.
.. versionadded:: 1.11.0
If `bins` is a string, it defines the method used to calculate the
optimal bin width, as defined by `histogram_bin_edges`.
range : (float, float), optional
The lower and upper range of the bins. If not provided, range
is simply ``(a.min(), a.max())``. Values outside the range are
ignored. The first element of the range must be less than or
equal to the second. `range` affects the automatic bin
computation as well. While bin width is computed to be optimal
based on the actual data within `range`, the bin count will fill
the entire range including portions containing no data.
normed : bool, optional
.. deprecated:: 1.6.0
This is equivalent to the `density` argument, but produces incorrect
results for unequal bin widths. It should not be used.
.. versionchanged:: 1.15.0
DeprecationWarnings are actually emitted.
weights : array_like, optional
An array of weights, of the same shape as `a`. Each value in
`a` only contributes its associated weight towards the bin count
(instead of 1). If `density` is True, the weights are
normalized, so that the integral of the density over the range
remains 1.
density : bool, optional
If ``False``, the result will contain the number of samples in
each bin. If ``True``, the result is the value of the
probability *density* function at the bin, normalized such that
the *integral* over the range is 1. Note that the sum of the
histogram values will not be equal to 1 unless bins of unity
width are chosen; it is not a probability *mass* function.
Overrides the ``normed`` keyword if given.
Returns
-------
hist : array
The values of the histogram. See `density` and `weights` for a
description of the possible semantics.
bin_edges : array of dtype float
Return the bin edges ``(length(hist)+1)``.
See Also
--------
histogramdd, bincount, searchsorted, digitize, histogram_bin_edges
Notes
-----
All but the last (righthand-most) bin is half-open. In other words,
if `bins` is::
[1, 2, 3, 4]
then the first bin is ``[1, 2)`` (including 1, but excluding 2) and
the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which
*includes* 4.
Examples
--------
>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
(array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))
>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, density=True)
>>> hist
array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
>>> hist.sum()
2.4999999999999996
>>> np.sum(hist * np.diff(bin_edges))
1.0
.. versionadded:: 1.11.0
Automated Bin Selection Methods example, using 2 peak random data
with 2000 points:
>>> import matplotlib.pyplot as plt
>>> rng = np.random.RandomState(10) # deterministic random data
>>> a = np.hstack((rng.normal(size=1000),
... rng.normal(loc=5, scale=2, size=1000)))
>>> _ = plt.hist(a, bins='auto') # arguments are passed to np.histogram
>>> plt.title("Histogram with 'auto' bins")
Text(0.5, 1.0, "Histogram with 'auto' bins")
>>> plt.show()
"""
a, weights = _ravel_and_check_weights(a, weights)
bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)
# Histogram is an integer or a float array depending on the weights.
if weights is None:
ntype = np.dtype(np.intp)
else:
ntype = weights.dtype
# We set a block size, as this allows us to iterate over chunks when
# computing histograms, to minimize memory usage.
BLOCK = 65536
# The fast path uses bincount, but that only works for certain types
# of weight
simple_weights = (
weights is None or
np.can_cast(weights.dtype, np.double) or
np.can_cast(weights.dtype, complex)
)
if uniform_bins is not None and simple_weights:
# Fast algorithm for equal bins
# We now convert values of a to bin indices, under the assumption of
# equal bin widths (which is valid here).
first_edge, last_edge, n_equal_bins = uniform_bins
# Initialize empty histogram
n = np.zeros(n_equal_bins, ntype)
# Pre-compute histogram scaling factor
norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge)
# We iterate over blocks here for two reasons: the first is that for
# large arrays, it is actually faster (for example for a 10^8 array it
# is 2x as fast) and it results in a memory footprint 3x lower in the
# limit of large arrays.
for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
if weights is None:
tmp_w = None
else:
tmp_w = weights[i:i + BLOCK]
# Only include values in the right range
keep = (tmp_a >= first_edge)
keep &= (tmp_a <= last_edge)
if not np.logical_and.reduce(keep):
tmp_a = tmp_a[keep]
if tmp_w is not None:
tmp_w = tmp_w[keep]
# This cast ensures no type promotions occur below, which gh-10322
# make unpredictable. Getting it wrong leads to precision errors
# like gh-8123.
tmp_a = tmp_a.astype(bin_edges.dtype, copy=False)
# Compute the bin indices, and for values that lie exactly on
# last_edge we need to subtract one
f_indices = _unsigned_subtract(tmp_a, first_edge) * norm
indices = f_indices.astype(np.intp)
indices[indices == n_equal_bins] -= 1
# The index computation is not guaranteed to give exactly
# consistent results within ~1 ULP of the bin edges.
decrement = tmp_a < bin_edges[indices]
indices[decrement] -= 1
# The last bin includes the right edge. The other bins do not.
increment = ((tmp_a >= bin_edges[indices + 1])
& (indices != n_equal_bins - 1))
indices[increment] += 1
# We now compute the histogram using bincount
if ntype.kind == 'c':
n.real += np.bincount(indices, weights=tmp_w.real,
minlength=n_equal_bins)
n.imag += np.bincount(indices, weights=tmp_w.imag,
minlength=n_equal_bins)
else:
n += np.bincount(indices, weights=tmp_w,
minlength=n_equal_bins).astype(ntype)
else:
# Compute via cumulative histogram
cum_n = np.zeros(bin_edges.shape, ntype)
if weights is None:
for i in _range(0, len(a), BLOCK):
sa = np.sort(a[i:i+BLOCK])
cum_n += _search_sorted_inclusive(sa, bin_edges)
else:
zero = np.zeros(1, dtype=ntype)
for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
tmp_w = weights[i:i+BLOCK]
sorting_index = np.argsort(tmp_a)
sa = tmp_a[sorting_index]
sw = tmp_w[sorting_index]
cw = np.concatenate((zero, sw.cumsum()))
bin_index = _search_sorted_inclusive(sa, bin_edges)
cum_n += cw[bin_index]
n = np.diff(cum_n)
# density overrides the normed keyword
if density is not None:
if normed is not None:
# 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
warnings.warn(
"The normed argument is ignored when density is provided. "
"In future passing both will result in an error.",
DeprecationWarning, stacklevel=3)
normed = None
if density:
db = np.array(np.diff(bin_edges), float)
return n/db/n.sum(), bin_edges
elif normed:
# 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
warnings.warn(
"Passing `normed=True` on non-uniform bins has always been "
"broken, and computes neither the probability density "
"function nor the probability mass function. "
"The result is only correct if the bins are uniform, when "
"density=True will produce the same result anyway. "
"The argument will be removed in a future version of "
"numpy.",
np.VisibleDeprecationWarning, stacklevel=3)
# this normalization is incorrect, but
db = np.array(np.diff(bin_edges), float)
return n/(n*db).sum(), bin_edges
else:
if normed is not None:
# 2018-06-13, numpy 1.15.0 (this was not noisily deprecated in 1.6)
warnings.warn(
"Passing normed=False is deprecated, and has no effect. "
"Consider passing the density argument instead.",
DeprecationWarning, stacklevel=3)
return n, bin_edges
def _histogramdd_dispatcher(sample, bins=None, range=None, normed=None,
weights=None, density=None):
if hasattr(sample, 'shape'): # same condition as used in histogramdd
yield sample
else:
yield from sample
with contextlib.suppress(TypeError):
yield from bins
yield weights
@array_function_dispatch(_histogramdd_dispatcher)
def histogramdd(sample, bins=10, range=None, normed=None, weights=None,
density=None):
"""
Compute the multidimensional histogram of some data.
Parameters
----------
sample : (N, D) array, or (D, N) array_like
The data to be histogrammed.
Note the unusual interpretation of sample when an array_like:
* When an array, each row is a coordinate in a D-dimensional space -
such as ``histogramdd(np.array([p1, p2, p3]))``.
* When an array_like, each element is the list of values for single
coordinate - such as ``histogramdd((X, Y, Z))``.
The first form should be preferred.
bins : sequence or int, optional
The bin specification:
* A sequence of arrays describing the monotonically increasing bin
edges along each dimension.
* The number of bins for each dimension (nx, ny, ... =bins)
* The number of bins for all dimensions (nx=ny=...=bins).
range : sequence, optional
A sequence of length D, each an optional (lower, upper) tuple giving
the outer bin edges to be used if the edges are not given explicitly in
`bins`.
An entry of None in the sequence results in the minimum and maximum
values being used for the corresponding dimension.
The default, None, is equivalent to passing a tuple of D None values.
density : bool, optional
If False, the default, returns the number of samples in each bin.
If True, returns the probability *density* function at the bin,
``bin_count / sample_count / bin_volume``.
normed : bool, optional
An alias for the density argument that behaves identically. To avoid
confusion with the broken normed argument to `histogram`, `density`
should be preferred.
weights : (N,) array_like, optional
An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
Weights are normalized to 1 if normed is True. If normed is False,
the values of the returned histogram are equal to the sum of the
weights belonging to the samples falling into each bin.
Returns
-------
H : ndarray
The multidimensional histogram of sample x. See normed and weights
for the different possible semantics.
edges : list
A list of D arrays describing the bin edges for each dimension.
See Also
--------
histogram: 1-D histogram
histogram2d: 2-D histogram
Examples
--------
>>> r = np.random.randn(100,3)
>>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
>>> H.shape, edges[0].size, edges[1].size, edges[2].size
((5, 8, 4), 6, 9, 5)
"""
try:
# Sample is an ND-array.
N, D = sample.shape
except (AttributeError, ValueError):
# Sample is a sequence of 1D arrays.
sample = np.atleast_2d(sample).T
N, D = sample.shape
nbin = np.empty(D, int)
edges = D*[None]
dedges = D*[None]
if weights is not None:
weights = np.asarray(weights)
try:
M = len(bins)
if M != D:
raise ValueError(
'The dimension of bins must be equal to the dimension of the '
' sample x.')
except TypeError:
# bins is an integer
bins = D*[bins]
# normalize the range argument
if range is None:
range = (None,) * D
elif len(range) != D:
raise ValueError('range argument must have one entry per dimension')
# Create edge arrays
for i in _range(D):
if np.ndim(bins[i]) == 0:
if bins[i] < 1:
raise ValueError(
'`bins[{}]` must be positive, when an integer'.format(i))
smin, smax = _get_outer_edges(sample[:,i], range[i])
try:
n = operator.index(bins[i])
except TypeError as e:
raise TypeError(
"`bins[{}]` must be an integer, when a scalar".format(i)
) from e
edges[i] = np.linspace(smin, smax, n + 1)
elif np.ndim(bins[i]) == 1:
edges[i] = np.asarray(bins[i])
if np.any(edges[i][:-1] > edges[i][1:]):
raise ValueError(
'`bins[{}]` must be monotonically increasing, when an array'
.format(i))
else:
raise ValueError(
'`bins[{}]` must be a scalar or 1d array'.format(i))
nbin[i] = len(edges[i]) + 1 # includes an outlier on each end
dedges[i] = np.diff(edges[i])
# Compute the bin number each sample falls into.
Ncount = tuple(
# avoid np.digitize to work around gh-11022
np.searchsorted(edges[i], sample[:, i], side='right')
for i in _range(D)
)
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
# counted in the last bin, and not as an outlier.
for i in _range(D):
# Find which points are on the rightmost edge.
on_edge = (sample[:, i] == edges[i][-1])
# Shift these points one bin to the left.
Ncount[i][on_edge] -= 1
# Compute the sample indices in the flattened histogram matrix.
# This raises an error if the array is too large.
xy = np.ravel_multi_index(Ncount, nbin)
# Compute the number of repetitions in xy and assign it to the
# flattened histmat.
hist = np.bincount(xy, weights, minlength=nbin.prod())
# Shape into a proper matrix
hist = hist.reshape(nbin)
# This preserves the (bad) behavior observed in gh-7845, for now.
hist = hist.astype(float, casting='safe')
# Remove outliers (indices 0 and -1 for each dimension).
core = D*(slice(1, -1),)
hist = hist[core]
# handle the aliasing normed argument
if normed is None:
if density is None:
density = False
elif density is None:
# an explicit normed argument was passed, alias it to the new name
density = normed
else:
raise TypeError("Cannot specify both 'normed' and 'density'")
if density:
# calculate the probability density function
s = hist.sum()
for i in _range(D):
shape = np.ones(D, int)
shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
if (hist.shape != nbin - 2).any():
raise RuntimeError(
"Internal Shape Error")
return hist, edges
| bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/matplotlib/delaunay/interpolate.py | 8 | 7288 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import numpy as np
from matplotlib._delaunay import compute_planes, linear_interpolate_grid
from matplotlib._delaunay import nn_interpolate_grid
from matplotlib._delaunay import nn_interpolate_unstructured
__all__ = ['LinearInterpolator', 'NNInterpolator']
def slice2gridspec(key):
"""Convert a 2-tuple of slices to start,stop,steps for x and y.
key -- (slice(ystart,ystop,ystep), slice(xtart, xstop, xstep))
For now, the only accepted step values are imaginary integers (interpreted
in the same way numpy.mgrid, etc. do).
"""
if ((len(key) != 2) or
(not isinstance(key[0], slice)) or
(not isinstance(key[1], slice))):
raise ValueError("only 2-D slices, please")
x0 = key[1].start
x1 = key[1].stop
xstep = key[1].step
if not isinstance(xstep, complex) or int(xstep.real) != xstep.real:
raise ValueError("only the [start:stop:numsteps*1j] form supported")
xstep = int(xstep.imag)
y0 = key[0].start
y1 = key[0].stop
ystep = key[0].step
if not isinstance(ystep, complex) or int(ystep.real) != ystep.real:
raise ValueError("only the [start:stop:numsteps*1j] form supported")
ystep = int(ystep.imag)
return x0, x1, xstep, y0, y1, ystep
class LinearInterpolator(object):
"""Interpolate a function defined on the nodes of a triangulation by
using the planes defined by the three function values at each corner of
the triangles.
LinearInterpolator(triangulation, z, default_value=numpy.nan)
triangulation -- Triangulation instance
z -- the function values at each node of the triangulation
default_value -- a float giving the default value should the interpolating
point happen to fall outside of the convex hull of the triangulation
At the moment, the only regular rectangular grids are supported for
interpolation.
vals = interp[ystart:ystop:ysteps*1j, xstart:xstop:xsteps*1j]
vals would then be a (ysteps, xsteps) array containing the interpolated
values. These arguments are interpreted the same way as numpy.mgrid.
Attributes:
planes -- (ntriangles, 3) array of floats specifying the plane for each
triangle.
Linear Interpolation
--------------------
Given the Delauany triangulation (or indeed *any* complete triangulation)
we can interpolate values inside the convex hull by locating the enclosing
triangle of the interpolation point and returning the value at that point
of the plane defined by the three node values.
f = planes[tri,0]*x + planes[tri,1]*y + planes[tri,2]
The interpolated function is C0 continuous across the convex hull of the
input points. It is C1 continuous across the convex hull except for the
nodes and the edges of the triangulation.
"""
def __init__(self, triangulation, z, default_value=np.nan):
self.triangulation = triangulation
self.z = np.asarray(z, dtype=np.float64)
self.default_value = default_value
self.planes = compute_planes(triangulation.x, triangulation.y, self.z,
triangulation.triangle_nodes)
def __getitem__(self, key):
x0, x1, xstep, y0, y1, ystep = slice2gridspec(key)
grid = linear_interpolate_grid(
x0, x1, xstep, y0, y1, ystep, self.default_value,
self.planes, self.triangulation.x, self.triangulation.y,
self.triangulation.triangle_nodes,
self.triangulation.triangle_neighbors)
return grid
class NNInterpolator(object):
"""Interpolate a function defined on the nodes of a triangulation by
the natural neighbors method.
NNInterpolator(triangulation, z, default_value=numpy.nan)
triangulation -- Triangulation instance
z -- the function values at each node of the triangulation
default_value -- a float giving the default value should the interpolating
point happen to fall outside of the convex hull of the triangulation
At the moment, the only regular rectangular grids are supported for
interpolation.
vals = interp[ystart:ystop:ysteps*1j, xstart:xstop:xsteps*1j]
vals would then be a (ysteps, xsteps) array containing the interpolated
values. These arguments are interpreted the same way as numpy.mgrid.
Natural Neighbors Interpolation
-------------------------------
One feature of the Delaunay triangulation is that for each triangle, its
circumcircle contains no other point (although in degenerate cases, like
squares, other points may be *on* the circumcircle). One can also
construct what is called the Voronoi diagram from a Delaunay triangulation
by connecting the circumcenters of the triangles to those of their
neighbors to form a tesselation of irregular polygons covering the plane
and containing only one node from the triangulation. Each point in one
node's Voronoi polygon is closer to that node than any other node.
To compute the Natural Neighbors interpolant, we consider adding the
interpolation point to the triangulation. We define the natural neighbors
of this point as the set of nodes participating in Delaunay triangles
whose circumcircles contain the point. To restore the Delaunay-ness of the
triangulation, one would only have to alter those triangles and Voronoi
polygons. The new Voronoi diagram would have a polygon around the
inserted point. This polygon would "steal" area from the original Voronoi
polygons. For each node i in the natural neighbors set, we compute the
area stolen from its original Voronoi polygon, stolen[i]. We define the
natural neighbors coordinates
phi[i] = stolen[i] / sum(stolen,axis=0)
We then use these phi[i] to weight the corresponding function values from
the input data z to compute the interpolated value.
The interpolated surface is C1-continuous except at the nodes themselves
across the convex hull of the input points. One can find the set of points
that a given node will affect by computing the union of the areas covered
by the circumcircles of each Delaunay triangle that node participates in.
"""
def __init__(self, triangulation, z, default_value=np.nan):
self.triangulation = triangulation
self.z = np.asarray(z, dtype=np.float64)
self.default_value = default_value
def __getitem__(self, key):
x0, x1, xstep, y0, y1, ystep = slice2gridspec(key)
grid = nn_interpolate_grid(
x0, x1, xstep, y0, y1, ystep, self.default_value,
self.triangulation.x, self.triangulation.y, self.z,
self.triangulation.circumcenters,
self.triangulation.triangle_nodes,
self.triangulation.triangle_neighbors)
return grid
def __call__(self, intx, inty):
intz = nn_interpolate_unstructured(intx, inty, self.default_value,
self.triangulation.x, self.triangulation.y, self.z,
self.triangulation.circumcenters,
self.triangulation.triangle_nodes,
self.triangulation.triangle_neighbors)
return intz
| mit |
rohit21122012/DCASE2013 | runs/2016/dnn2016med_traps/traps15/src/evaluation.py | 56 | 43426 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import numpy
import sys
from sklearn import metrics
class DCASE2016_SceneClassification_Metrics():
"""DCASE 2016 scene classification metrics
Examples
--------
>>> dcase2016_scene_metric = DCASE2016_SceneClassification_Metrics(class_list=dataset.scene_labels)
>>> for fold in dataset.folds(mode=dataset_evaluation_mode):
>>> results = []
>>> result_filename = get_result_filename(fold=fold, path=result_path)
>>>
>>> if os.path.isfile(result_filename):
>>> with open(result_filename, 'rt') as f:
>>> for row in csv.reader(f, delimiter='\t'):
>>> results.append(row)
>>>
>>> y_true = []
>>> y_pred = []
>>> for result in results:
>>> y_true.append(dataset.file_meta(result[0])[0]['scene_label'])
>>> y_pred.append(result[1])
>>>
>>> dcase2016_scene_metric.evaluate(system_output=y_pred, annotated_ground_truth=y_true)
>>>
>>> results = dcase2016_scene_metric.results()
"""
def __init__(self, class_list):
"""__init__ method.
Parameters
----------
class_list : list
Evaluated scene labels in the list
"""
self.accuracies_per_class = None
self.Nsys = None
self.Nref = None
self.class_list = class_list
self.eps = numpy.spacing(1)
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
return self.results()
def accuracies(self, y_true, y_pred, labels):
"""Calculate accuracy
Parameters
----------
y_true : numpy.array
Ground truth array, list of scene labels
y_pred : numpy.array
System output array, list of scene labels
labels : list
list of scene labels
Returns
-------
array : numpy.array [shape=(number of scene labels,)]
Accuracy per scene label class
"""
confusion_matrix = metrics.confusion_matrix(y_true=y_true, y_pred=y_pred, labels=labels).astype(float)
return numpy.divide(numpy.diag(confusion_matrix), numpy.sum(confusion_matrix, 1) + self.eps)
def evaluate(self, annotated_ground_truth, system_output):
"""Evaluate system output and annotated ground truth pair.
Use results method to get results.
Parameters
----------
annotated_ground_truth : numpy.array
Ground truth array, list of scene labels
system_output : numpy.array
System output array, list of scene labels
Returns
-------
nothing
"""
accuracies_per_class = self.accuracies(y_pred=system_output, y_true=annotated_ground_truth,
labels=self.class_list)
if self.accuracies_per_class is None:
self.accuracies_per_class = accuracies_per_class
else:
self.accuracies_per_class = numpy.vstack((self.accuracies_per_class, accuracies_per_class))
Nref = numpy.zeros(len(self.class_list))
Nsys = numpy.zeros(len(self.class_list))
for class_id, class_label in enumerate(self.class_list):
for item in system_output:
if item == class_label:
Nsys[class_id] += 1
for item in annotated_ground_truth:
if item == class_label:
Nref[class_id] += 1
if self.Nref is None:
self.Nref = Nref
else:
self.Nref = numpy.vstack((self.Nref, Nref))
if self.Nsys is None:
self.Nsys = Nsys
else:
self.Nsys = numpy.vstack((self.Nsys, Nsys))
def results(self):
"""Get results
Outputs results in dict, format:
{
'class_wise_data':
{
'office': {
'Nsys': 10,
'Nref': 7,
},
}
'class_wise_accuracy':
{
'office': 0.6,
'home': 0.4,
}
'overall_accuracy': numpy.mean(self.accuracies_per_class)
'Nsys': 100,
'Nref': 100,
}
Parameters
----------
nothing
Returns
-------
results : dict
Results dict
"""
results = {
'class_wise_data': {},
'class_wise_accuracy': {},
'overall_accuracy': numpy.mean(self.accuracies_per_class)
}
if len(self.Nsys.shape) == 2:
results['Nsys'] = int(sum(sum(self.Nsys)))
results['Nref'] = int(sum(sum(self.Nref)))
else:
results['Nsys'] = int(sum(self.Nsys))
results['Nref'] = int(sum(self.Nref))
for class_id, class_label in enumerate(self.class_list):
if len(self.accuracies_per_class.shape) == 2:
results['class_wise_accuracy'][class_label] = numpy.mean(self.accuracies_per_class[:, class_id])
results['class_wise_data'][class_label] = {
'Nsys': int(sum(self.Nsys[:, class_id])),
'Nref': int(sum(self.Nref[:, class_id])),
}
else:
results['class_wise_accuracy'][class_label] = numpy.mean(self.accuracies_per_class[class_id])
results['class_wise_data'][class_label] = {
'Nsys': int(self.Nsys[class_id]),
'Nref': int(self.Nref[class_id]),
}
return results
class EventDetectionMetrics(object):
"""Baseclass for sound event metric classes.
"""
def __init__(self, class_list):
"""__init__ method.
Parameters
----------
class_list : list
List of class labels to be evaluated.
"""
self.class_list = class_list
self.eps = numpy.spacing(1)
def max_event_offset(self, data):
"""Get maximum event offset from event list
Parameters
----------
data : list
Event list, list of event dicts
Returns
-------
max : float > 0
Maximum event offset
"""
max = 0
for event in data:
if event['event_offset'] > max:
max = event['event_offset']
return max
def list_to_roll(self, data, time_resolution=0.01):
"""Convert event list into event roll.
Event roll is binary matrix indicating event activity withing time segment defined by time_resolution.
Parameters
----------
data : list
Event list, list of event dicts
time_resolution : float > 0
Time resolution used when converting event into event roll.
Returns
-------
event_roll : numpy.ndarray [shape=(math.ceil(data_length * 1 / time_resolution) + 1, amount of classes)]
Event roll
"""
# Initialize
data_length = self.max_event_offset(data)
event_roll = numpy.zeros((math.ceil(data_length * 1 / time_resolution) + 1, len(self.class_list)))
# Fill-in event_roll
for event in data:
pos = self.class_list.index(event['event_label'].rstrip())
onset = math.floor(event['event_onset'] * 1 / time_resolution)
offset = math.ceil(event['event_offset'] * 1 / time_resolution) + 1
event_roll[onset:offset, pos] = 1
return event_roll
class DCASE2016_EventDetection_SegmentBasedMetrics(EventDetectionMetrics):
"""DCASE2016 Segment based metrics for sound event detection
Supported metrics:
- Overall
- Error rate (ER), Substitutions (S), Insertions (I), Deletions (D)
- F-score (F1)
- Class-wise
- Error rate (ER), Insertions (I), Deletions (D)
- F-score (F1)
Examples
--------
>>> overall_metrics_per_scene = {}
>>> for scene_id, scene_label in enumerate(dataset.scene_labels):
>>> dcase2016_segment_based_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))
>>> for fold in dataset.folds(mode=dataset_evaluation_mode):
>>> results = []
>>> result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)
>>>
>>> if os.path.isfile(result_filename):
>>> with open(result_filename, 'rt') as f:
>>> for row in csv.reader(f, delimiter='\t'):
>>> results.append(row)
>>>
>>> for file_id, item in enumerate(dataset.test(fold,scene_label=scene_label)):
>>> current_file_results = []
>>> for result_line in results:
>>> if result_line[0] == dataset.absolute_to_relative(item['file']):
>>> current_file_results.append(
>>> {'file': result_line[0],
>>> 'event_onset': float(result_line[1]),
>>> 'event_offset': float(result_line[2]),
>>> 'event_label': result_line[3]
>>> }
>>> )
>>> meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))
>>> dcase2016_segment_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)
>>> overall_metrics_per_scene[scene_label]['segment_based_metrics'] = dcase2016_segment_based_metric.results()
"""
def __init__(self, class_list, time_resolution=1.0):
"""__init__ method.
Parameters
----------
class_list : list
List of class labels to be evaluated.
time_resolution : float > 0
Time resolution used when converting event into event roll.
(Default value = 1.0)
"""
self.time_resolution = time_resolution
self.overall = {
'Ntp': 0.0,
'Ntn': 0.0,
'Nfp': 0.0,
'Nfn': 0.0,
'Nref': 0.0,
'Nsys': 0.0,
'ER': 0.0,
'S': 0.0,
'D': 0.0,
'I': 0.0,
}
self.class_wise = {}
for class_label in class_list:
self.class_wise[class_label] = {
'Ntp': 0.0,
'Ntn': 0.0,
'Nfp': 0.0,
'Nfn': 0.0,
'Nref': 0.0,
'Nsys': 0.0,
}
EventDetectionMetrics.__init__(self, class_list=class_list)
def __enter__(self):
# Initialize class and return it
return self
def __exit__(self, type, value, traceback):
# Finalize evaluation and return results
return self.results()
def evaluate(self, annotated_ground_truth, system_output):
"""Evaluate system output and annotated ground truth pair.
Use results method to get results.
Parameters
----------
annotated_ground_truth : numpy.array
Ground truth array, list of scene labels
system_output : numpy.array
System output array, list of scene labels
Returns
-------
nothing
"""
# Convert event list into frame-based representation
system_event_roll = self.list_to_roll(data=system_output, time_resolution=self.time_resolution)
annotated_event_roll = self.list_to_roll(data=annotated_ground_truth, time_resolution=self.time_resolution)
# Fix durations of both event_rolls to be equal
if annotated_event_roll.shape[0] > system_event_roll.shape[0]:
padding = numpy.zeros((annotated_event_roll.shape[0] - system_event_roll.shape[0], len(self.class_list)))
system_event_roll = numpy.vstack((system_event_roll, padding))
if system_event_roll.shape[0] > annotated_event_roll.shape[0]:
padding = numpy.zeros((system_event_roll.shape[0] - annotated_event_roll.shape[0], len(self.class_list)))
annotated_event_roll = numpy.vstack((annotated_event_roll, padding))
# Compute segment-based overall metrics
for segment_id in range(0, annotated_event_roll.shape[0]):
annotated_segment = annotated_event_roll[segment_id, :]
system_segment = system_event_roll[segment_id, :]
Ntp = sum(system_segment + annotated_segment > 1)
Ntn = sum(system_segment + annotated_segment == 0)
Nfp = sum(system_segment - annotated_segment > 0)
Nfn = sum(annotated_segment - system_segment > 0)
Nref = sum(annotated_segment)
Nsys = sum(system_segment)
S = min(Nref, Nsys) - Ntp
D = max(0, Nref - Nsys)
I = max(0, Nsys - Nref)
ER = max(Nref, Nsys) - Ntp
self.overall['Ntp'] += Ntp
self.overall['Ntn'] += Ntn
self.overall['Nfp'] += Nfp
self.overall['Nfn'] += Nfn
self.overall['Nref'] += Nref
self.overall['Nsys'] += Nsys
self.overall['S'] += S
self.overall['D'] += D
self.overall['I'] += I
self.overall['ER'] += ER
for class_id, class_label in enumerate(self.class_list):
annotated_segment = annotated_event_roll[:, class_id]
system_segment = system_event_roll[:, class_id]
Ntp = sum(system_segment + annotated_segment > 1)
Ntn = sum(system_segment + annotated_segment == 0)
Nfp = sum(system_segment - annotated_segment > 0)
Nfn = sum(annotated_segment - system_segment > 0)
Nref = sum(annotated_segment)
Nsys = sum(system_segment)
self.class_wise[class_label]['Ntp'] += Ntp
self.class_wise[class_label]['Ntn'] += Ntn
self.class_wise[class_label]['Nfp'] += Nfp
self.class_wise[class_label]['Nfn'] += Nfn
self.class_wise[class_label]['Nref'] += Nref
self.class_wise[class_label]['Nsys'] += Nsys
return self
def results(self):
"""Get results
Outputs results in dict, format:
{
'overall':
{
'Pre':
'Rec':
'F':
'ER':
'S':
'D':
'I':
}
'class_wise':
{
'office': {
'Pre':
'Rec':
'F':
'ER':
'D':
'I':
'Nref':
'Nsys':
'Ntp':
'Nfn':
'Nfp':
},
}
'class_wise_average':
{
'F':
'ER':
}
}
Parameters
----------
nothing
Returns
-------
results : dict
Results dict
"""
results = {'overall': {},
'class_wise': {},
'class_wise_average': {},
}
# Overall metrics
results['overall']['Pre'] = self.overall['Ntp'] / (self.overall['Nsys'] + self.eps)
results['overall']['Rec'] = self.overall['Ntp'] / self.overall['Nref']
results['overall']['F'] = 2 * ((results['overall']['Pre'] * results['overall']['Rec']) / (
results['overall']['Pre'] + results['overall']['Rec'] + self.eps))
results['overall']['ER'] = self.overall['ER'] / self.overall['Nref']
results['overall']['S'] = self.overall['S'] / self.overall['Nref']
results['overall']['D'] = self.overall['D'] / self.overall['Nref']
results['overall']['I'] = self.overall['I'] / self.overall['Nref']
# Class-wise metrics
class_wise_F = []
class_wise_ER = []
for class_id, class_label in enumerate(self.class_list):
if class_label not in results['class_wise']:
results['class_wise'][class_label] = {}
results['class_wise'][class_label]['Pre'] = self.class_wise[class_label]['Ntp'] / (
self.class_wise[class_label]['Nsys'] + self.eps)
results['class_wise'][class_label]['Rec'] = self.class_wise[class_label]['Ntp'] / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['F'] = 2 * (
(results['class_wise'][class_label]['Pre'] * results['class_wise'][class_label]['Rec']) / (
results['class_wise'][class_label]['Pre'] + results['class_wise'][class_label]['Rec'] + self.eps))
results['class_wise'][class_label]['ER'] = (self.class_wise[class_label]['Nfn'] +
self.class_wise[class_label]['Nfp']) / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['D'] = self.class_wise[class_label]['Nfn'] / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['I'] = self.class_wise[class_label]['Nfp'] / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['Nref'] = self.class_wise[class_label]['Nref']
results['class_wise'][class_label]['Nsys'] = self.class_wise[class_label]['Nsys']
results['class_wise'][class_label]['Ntp'] = self.class_wise[class_label]['Ntp']
results['class_wise'][class_label]['Nfn'] = self.class_wise[class_label]['Nfn']
results['class_wise'][class_label]['Nfp'] = self.class_wise[class_label]['Nfp']
class_wise_F.append(results['class_wise'][class_label]['F'])
class_wise_ER.append(results['class_wise'][class_label]['ER'])
results['class_wise_average']['F'] = numpy.mean(class_wise_F)
results['class_wise_average']['ER'] = numpy.mean(class_wise_ER)
return results
class DCASE2016_EventDetection_EventBasedMetrics(EventDetectionMetrics):
"""DCASE2016 Event based metrics for sound event detection
Supported metrics:
- Overall
- Error rate (ER), Substitutions (S), Insertions (I), Deletions (D)
- F-score (F1)
- Class-wise
- Error rate (ER), Insertions (I), Deletions (D)
- F-score (F1)
Examples
--------
>>> overall_metrics_per_scene = {}
>>> for scene_id, scene_label in enumerate(dataset.scene_labels):
>>> dcase2016_event_based_metric = DCASE2016_EventDetection_EventBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))
>>> for fold in dataset.folds(mode=dataset_evaluation_mode):
>>> results = []
>>> result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)
>>>
>>> if os.path.isfile(result_filename):
>>> with open(result_filename, 'rt') as f:
>>> for row in csv.reader(f, delimiter='\t'):
>>> results.append(row)
>>>
>>> for file_id, item in enumerate(dataset.test(fold,scene_label=scene_label)):
>>> current_file_results = []
>>> for result_line in results:
>>> if result_line[0] == dataset.absolute_to_relative(item['file']):
>>> current_file_results.append(
>>> {'file': result_line[0],
>>> 'event_onset': float(result_line[1]),
>>> 'event_offset': float(result_line[2]),
>>> 'event_label': result_line[3]
>>> }
>>> )
>>> meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))
>>> dcase2016_event_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)
>>> overall_metrics_per_scene[scene_label]['event_based_metrics'] = dcase2016_event_based_metric.results()
"""
def __init__(self, class_list, time_resolution=1.0, t_collar=0.2):
"""__init__ method.
Parameters
----------
class_list : list
List of class labels to be evaluated.
time_resolution : float > 0
Time resolution used when converting event into event roll.
(Default value = 1.0)
t_collar : float > 0
Time collar for event onset and offset condition
(Default value = 0.2)
"""
self.time_resolution = time_resolution
self.t_collar = t_collar
self.overall = {
'Nref': 0.0,
'Nsys': 0.0,
'Nsubs': 0.0,
'Ntp': 0.0,
'Nfp': 0.0,
'Nfn': 0.0,
}
self.class_wise = {}
for class_label in class_list:
self.class_wise[class_label] = {
'Nref': 0.0,
'Nsys': 0.0,
'Ntp': 0.0,
'Ntn': 0.0,
'Nfp': 0.0,
'Nfn': 0.0,
}
EventDetectionMetrics.__init__(self, class_list=class_list)
def __enter__(self):
# Initialize class and return it
return self
def __exit__(self, type, value, traceback):
# Finalize evaluation and return results
return self.results()
def evaluate(self, annotated_ground_truth, system_output):
"""Evaluate system output and annotated ground truth pair.
Use results method to get results.
Parameters
----------
annotated_ground_truth : numpy.array
Ground truth array, list of scene labels
system_output : numpy.array
System output array, list of scene labels
Returns
-------
nothing
"""
# Overall metrics
# Total number of detected and reference events
Nsys = len(system_output)
Nref = len(annotated_ground_truth)
sys_correct = numpy.zeros(Nsys, dtype=bool)
ref_correct = numpy.zeros(Nref, dtype=bool)
# Number of correctly transcribed events, onset/offset within a t_collar range
for j in range(0, len(annotated_ground_truth)):
for i in range(0, len(system_output)):
label_condition = annotated_ground_truth[j]['event_label'] == system_output[i]['event_label']
onset_condition = self.onset_condition(annotated_event=annotated_ground_truth[j],
system_event=system_output[i],
t_collar=self.t_collar)
offset_condition = self.offset_condition(annotated_event=annotated_ground_truth[j],
system_event=system_output[i],
t_collar=self.t_collar)
if label_condition and onset_condition and offset_condition:
ref_correct[j] = True
sys_correct[i] = True
break
Ntp = numpy.sum(sys_correct)
sys_leftover = numpy.nonzero(numpy.negative(sys_correct))[0]
ref_leftover = numpy.nonzero(numpy.negative(ref_correct))[0]
# Substitutions
Nsubs = 0
for j in ref_leftover:
for i in sys_leftover:
onset_condition = self.onset_condition(annotated_event=annotated_ground_truth[j],
system_event=system_output[i],
t_collar=self.t_collar)
offset_condition = self.offset_condition(annotated_event=annotated_ground_truth[j],
system_event=system_output[i],
t_collar=self.t_collar)
if onset_condition and offset_condition:
Nsubs += 1
break
Nfp = Nsys - Ntp - Nsubs
Nfn = Nref - Ntp - Nsubs
self.overall['Nref'] += Nref
self.overall['Nsys'] += Nsys
self.overall['Ntp'] += Ntp
self.overall['Nsubs'] += Nsubs
self.overall['Nfp'] += Nfp
self.overall['Nfn'] += Nfn
# Class-wise metrics
for class_id, class_label in enumerate(self.class_list):
Nref = 0.0
Nsys = 0.0
Ntp = 0.0
# Count event frequencies in the ground truth
for i in range(0, len(annotated_ground_truth)):
if annotated_ground_truth[i]['event_label'] == class_label:
Nref += 1
# Count event frequencies in the system output
for i in range(0, len(system_output)):
if system_output[i]['event_label'] == class_label:
Nsys += 1
for j in range(0, len(annotated_ground_truth)):
for i in range(0, len(system_output)):
if annotated_ground_truth[j]['event_label'] == class_label and system_output[i][
'event_label'] == class_label:
onset_condition = self.onset_condition(annotated_event=annotated_ground_truth[j],
system_event=system_output[i],
t_collar=self.t_collar)
offset_condition = self.offset_condition(annotated_event=annotated_ground_truth[j],
system_event=system_output[i],
t_collar=self.t_collar)
if onset_condition and offset_condition:
Ntp += 1
break
Nfp = Nsys - Ntp
Nfn = Nref - Ntp
self.class_wise[class_label]['Nref'] += Nref
self.class_wise[class_label]['Nsys'] += Nsys
self.class_wise[class_label]['Ntp'] += Ntp
self.class_wise[class_label]['Nfp'] += Nfp
self.class_wise[class_label]['Nfn'] += Nfn
def onset_condition(self, annotated_event, system_event, t_collar=0.200):
"""Onset condition, checked does the event pair fulfill condition
Condition:
- event onsets are within t_collar each other
Parameters
----------
annotated_event : dict
Event dict
system_event : dict
Event dict
t_collar : float > 0
Defines how close event onsets have to be in order to be considered match. In seconds.
(Default value = 0.2)
Returns
-------
result : bool
Condition result
"""
return math.fabs(annotated_event['event_onset'] - system_event['event_onset']) <= t_collar
def offset_condition(self, annotated_event, system_event, t_collar=0.200, percentage_of_length=0.5):
"""Offset condition, checking does the event pair fulfill condition
Condition:
- event offsets are within t_collar each other
or
- system event offset is within the percentage_of_length*annotated event_length
Parameters
----------
annotated_event : dict
Event dict
system_event : dict
Event dict
t_collar : float > 0
Defines how close event onsets have to be in order to be considered match. In seconds.
(Default value = 0.2)
percentage_of_length : float [0-1]
Returns
-------
result : bool
Condition result
"""
annotated_length = annotated_event['event_offset'] - annotated_event['event_onset']
return math.fabs(annotated_event['event_offset'] - system_event['event_offset']) <= max(t_collar,
percentage_of_length * annotated_length)
def results(self):
"""Get results
Outputs results in dict, format:
{
'overall':
{
'Pre':
'Rec':
'F':
'ER':
'S':
'D':
'I':
}
'class_wise':
{
'office': {
'Pre':
'Rec':
'F':
'ER':
'D':
'I':
'Nref':
'Nsys':
'Ntp':
'Nfn':
'Nfp':
},
}
'class_wise_average':
{
'F':
'ER':
}
}
Parameters
----------
nothing
Returns
-------
results : dict
Results dict
"""
results = {
'overall': {},
'class_wise': {},
'class_wise_average': {},
}
# Overall metrics
results['overall']['Pre'] = self.overall['Ntp'] / (self.overall['Nsys'] + self.eps)
results['overall']['Rec'] = self.overall['Ntp'] / self.overall['Nref']
results['overall']['F'] = 2 * ((results['overall']['Pre'] * results['overall']['Rec']) / (
results['overall']['Pre'] + results['overall']['Rec'] + self.eps))
results['overall']['ER'] = (self.overall['Nfn'] + self.overall['Nfp'] + self.overall['Nsubs']) / self.overall[
'Nref']
results['overall']['S'] = self.overall['Nsubs'] / self.overall['Nref']
results['overall']['D'] = self.overall['Nfn'] / self.overall['Nref']
results['overall']['I'] = self.overall['Nfp'] / self.overall['Nref']
# Class-wise metrics
class_wise_F = []
class_wise_ER = []
for class_label in self.class_list:
if class_label not in results['class_wise']:
results['class_wise'][class_label] = {}
results['class_wise'][class_label]['Pre'] = self.class_wise[class_label]['Ntp'] / (
self.class_wise[class_label]['Nsys'] + self.eps)
results['class_wise'][class_label]['Rec'] = self.class_wise[class_label]['Ntp'] / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['F'] = 2 * (
(results['class_wise'][class_label]['Pre'] * results['class_wise'][class_label]['Rec']) / (
results['class_wise'][class_label]['Pre'] + results['class_wise'][class_label]['Rec'] + self.eps))
results['class_wise'][class_label]['ER'] = (self.class_wise[class_label]['Nfn'] +
self.class_wise[class_label]['Nfp']) / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['D'] = self.class_wise[class_label]['Nfn'] / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['I'] = self.class_wise[class_label]['Nfp'] / (
self.class_wise[class_label]['Nref'] + self.eps)
results['class_wise'][class_label]['Nref'] = self.class_wise[class_label]['Nref']
results['class_wise'][class_label]['Nsys'] = self.class_wise[class_label]['Nsys']
results['class_wise'][class_label]['Ntp'] = self.class_wise[class_label]['Ntp']
results['class_wise'][class_label]['Nfn'] = self.class_wise[class_label]['Nfn']
results['class_wise'][class_label]['Nfp'] = self.class_wise[class_label]['Nfp']
class_wise_F.append(results['class_wise'][class_label]['F'])
class_wise_ER.append(results['class_wise'][class_label]['ER'])
# Class-wise average
results['class_wise_average']['F'] = numpy.mean(class_wise_F)
results['class_wise_average']['ER'] = numpy.mean(class_wise_ER)
return results
class DCASE2013_EventDetection_Metrics(EventDetectionMetrics):
"""Lecagy DCASE2013 metrics, converted from the provided Matlab implementation
Supported metrics:
- Frame based
- F-score (F)
- AEER
- Event based
- Onset
- F-Score (F)
- AEER
- Onset-offset
- F-Score (F)
- AEER
- Class based
- Onset
- F-Score (F)
- AEER
- Onset-offset
- F-Score (F)
- AEER
"""
#
def frame_based(self, annotated_ground_truth, system_output, resolution=0.01):
# Convert event list into frame-based representation
system_event_roll = self.list_to_roll(data=system_output, time_resolution=resolution)
annotated_event_roll = self.list_to_roll(data=annotated_ground_truth, time_resolution=resolution)
# Fix durations of both event_rolls to be equal
if annotated_event_roll.shape[0] > system_event_roll.shape[0]:
padding = numpy.zeros((annotated_event_roll.shape[0] - system_event_roll.shape[0], len(self.class_list)))
system_event_roll = numpy.vstack((system_event_roll, padding))
if system_event_roll.shape[0] > annotated_event_roll.shape[0]:
padding = numpy.zeros((system_event_roll.shape[0] - annotated_event_roll.shape[0], len(self.class_list)))
annotated_event_roll = numpy.vstack((annotated_event_roll, padding))
# Compute frame-based metrics
Nref = sum(sum(annotated_event_roll))
Ntot = sum(sum(system_event_roll))
Ntp = sum(sum(system_event_roll + annotated_event_roll > 1))
Nfp = sum(sum(system_event_roll - annotated_event_roll > 0))
Nfn = sum(sum(annotated_event_roll - system_event_roll > 0))
Nsubs = min(Nfp, Nfn)
eps = numpy.spacing(1)
results = dict()
results['Rec'] = Ntp / (Nref + eps)
results['Pre'] = Ntp / (Ntot + eps)
results['F'] = 2 * ((results['Pre'] * results['Rec']) / (results['Pre'] + results['Rec'] + eps))
results['AEER'] = (Nfn + Nfp + Nsubs) / (Nref + eps)
return results
def event_based(self, annotated_ground_truth, system_output):
# Event-based evaluation for event detection task
# outputFile: the output of the event detection system
# GTFile: the ground truth list of events
# Total number of detected and reference events
Ntot = len(system_output)
Nref = len(annotated_ground_truth)
# Number of correctly transcribed events, onset within a +/-100 ms range
Ncorr = 0
NcorrOff = 0
for j in range(0, len(annotated_ground_truth)):
for i in range(0, len(system_output)):
if annotated_ground_truth[j]['event_label'] == system_output[i]['event_label'] and (
math.fabs(annotated_ground_truth[j]['event_onset'] - system_output[i]['event_onset']) <= 0.1):
Ncorr += 1
# If offset within a +/-100 ms range or within 50% of ground-truth event's duration
if math.fabs(annotated_ground_truth[j]['event_offset'] - system_output[i]['event_offset']) <= max(
0.1, 0.5 * (
annotated_ground_truth[j]['event_offset'] - annotated_ground_truth[j]['event_onset'])):
NcorrOff += 1
break # In order to not evaluate duplicates
# Compute onset-only event-based metrics
eps = numpy.spacing(1)
results = {
'onset': {},
'onset-offset': {},
}
Nfp = Ntot - Ncorr
Nfn = Nref - Ncorr
Nsubs = min(Nfp, Nfn)
results['onset']['Rec'] = Ncorr / (Nref + eps)
results['onset']['Pre'] = Ncorr / (Ntot + eps)
results['onset']['F'] = 2 * (
(results['onset']['Pre'] * results['onset']['Rec']) / (
results['onset']['Pre'] + results['onset']['Rec'] + eps))
results['onset']['AEER'] = (Nfn + Nfp + Nsubs) / (Nref + eps)
# Compute onset-offset event-based metrics
NfpOff = Ntot - NcorrOff
NfnOff = Nref - NcorrOff
NsubsOff = min(NfpOff, NfnOff)
results['onset-offset']['Rec'] = NcorrOff / (Nref + eps)
results['onset-offset']['Pre'] = NcorrOff / (Ntot + eps)
results['onset-offset']['F'] = 2 * ((results['onset-offset']['Pre'] * results['onset-offset']['Rec']) / (
results['onset-offset']['Pre'] + results['onset-offset']['Rec'] + eps))
results['onset-offset']['AEER'] = (NfnOff + NfpOff + NsubsOff) / (Nref + eps)
return results
def class_based(self, annotated_ground_truth, system_output):
# Class-wise event-based evaluation for event detection task
# outputFile: the output of the event detection system
# GTFile: the ground truth list of events
# Total number of detected and reference events per class
Ntot = numpy.zeros((len(self.class_list), 1))
for event in system_output:
pos = self.class_list.index(event['event_label'])
Ntot[pos] += 1
Nref = numpy.zeros((len(self.class_list), 1))
for event in annotated_ground_truth:
pos = self.class_list.index(event['event_label'])
Nref[pos] += 1
I = (Nref > 0).nonzero()[0] # index for classes present in ground-truth
# Number of correctly transcribed events per class, onset within a +/-100 ms range
Ncorr = numpy.zeros((len(self.class_list), 1))
NcorrOff = numpy.zeros((len(self.class_list), 1))
for j in range(0, len(annotated_ground_truth)):
for i in range(0, len(system_output)):
if annotated_ground_truth[j]['event_label'] == system_output[i]['event_label'] and (
math.fabs(
annotated_ground_truth[j]['event_onset'] - system_output[i]['event_onset']) <= 0.1):
pos = self.class_list.index(system_output[i]['event_label'])
Ncorr[pos] += 1
# If offset within a +/-100 ms range or within 50% of ground-truth event's duration
if math.fabs(annotated_ground_truth[j]['event_offset'] - system_output[i]['event_offset']) <= max(
0.1, 0.5 * (
annotated_ground_truth[j]['event_offset'] - annotated_ground_truth[j][
'event_onset'])):
pos = self.class_list.index(system_output[i]['event_label'])
NcorrOff[pos] += 1
break # In order to not evaluate duplicates
# Compute onset-only class-wise event-based metrics
eps = numpy.spacing(1)
results = {
'onset': {},
'onset-offset': {},
}
Nfp = Ntot - Ncorr
Nfn = Nref - Ncorr
Nsubs = numpy.minimum(Nfp, Nfn)
tempRec = Ncorr[I] / (Nref[I] + eps)
tempPre = Ncorr[I] / (Ntot[I] + eps)
results['onset']['Rec'] = numpy.mean(tempRec)
results['onset']['Pre'] = numpy.mean(tempPre)
tempF = 2 * ((tempPre * tempRec) / (tempPre + tempRec + eps))
results['onset']['F'] = numpy.mean(tempF)
tempAEER = (Nfn[I] + Nfp[I] + Nsubs[I]) / (Nref[I] + eps)
results['onset']['AEER'] = numpy.mean(tempAEER)
# Compute onset-offset class-wise event-based metrics
NfpOff = Ntot - NcorrOff
NfnOff = Nref - NcorrOff
NsubsOff = numpy.minimum(NfpOff, NfnOff)
tempRecOff = NcorrOff[I] / (Nref[I] + eps)
tempPreOff = NcorrOff[I] / (Ntot[I] + eps)
results['onset-offset']['Rec'] = numpy.mean(tempRecOff)
results['onset-offset']['Pre'] = numpy.mean(tempPreOff)
tempFOff = 2 * ((tempPreOff * tempRecOff) / (tempPreOff + tempRecOff + eps))
results['onset-offset']['F'] = numpy.mean(tempFOff)
tempAEEROff = (NfnOff[I] + NfpOff[I] + NsubsOff[I]) / (Nref[I] + eps)
results['onset-offset']['AEER'] = numpy.mean(tempAEEROff)
return results
def main(argv):
# Examples to show usage and required data structures
class_list = ['class1', 'class2', 'class3']
system_output = [
{
'event_label': 'class1',
'event_onset': 0.1,
'event_offset': 1.0
},
{
'event_label': 'class2',
'event_onset': 4.1,
'event_offset': 4.7
},
{
'event_label': 'class3',
'event_onset': 5.5,
'event_offset': 6.7
}
]
annotated_groundtruth = [
{
'event_label': 'class1',
'event_onset': 0.1,
'event_offset': 1.0
},
{
'event_label': 'class2',
'event_onset': 4.2,
'event_offset': 5.4
},
{
'event_label': 'class3',
'event_onset': 5.5,
'event_offset': 6.7
}
]
dcase2013metric = DCASE2013_EventDetection_Metrics(class_list=class_list)
print 'DCASE2013'
print 'Frame-based:', dcase2013metric.frame_based(system_output=system_output,
annotated_ground_truth=annotated_groundtruth)
print 'Event-based:', dcase2013metric.event_based(system_output=system_output,
annotated_ground_truth=annotated_groundtruth)
print 'Class-based:', dcase2013metric.class_based(system_output=system_output,
annotated_ground_truth=annotated_groundtruth)
dcase2016_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=class_list)
print 'DCASE2016'
print dcase2016_metric.evaluate(system_output=system_output, annotated_ground_truth=annotated_groundtruth).results()
if __name__ == "__main__":
sys.exit(main(sys.argv))
| mit |
datapythonista/pandas | versioneer.py | 4 | 70101 | # Version: 0.19
"""The Versioneer - like a rocketeer, but for versions.
The Versioneer
==============
* like a rocketeer, but for versions!
* https://github.com/python-versioneer/python-versioneer
* Brian Warner
* License: Public Domain
* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3
* [![Latest Version][pypi-image]][pypi-url]
* [![Build Status][travis-image]][travis-url]
This is a tool for managing a recorded version number in distutils-based
python projects. The goal is to remove the tedious and error-prone "update
the embedded version string" step from your release process. Making a new
release should be as easy as recording a new tag in your version-control
system, and maybe making new tarballs.
## Quick Install
* `pip install versioneer` to somewhere in your $PATH
* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md))
* run `versioneer install` in your source tree, commit the results
* Verify version information with `python setup.py version`
## Version Identifiers
Source trees come from a variety of places:
* a version-control system checkout (mostly used by developers)
* a nightly tarball, produced by build automation
* a snapshot tarball, produced by a web-based VCS browser, like github's
"tarball from tag" feature
* a release tarball, produced by "setup.py sdist", distributed through PyPI
Within each source tree, the version identifier (either a string or a number,
this tool is format-agnostic) can come from a variety of places:
* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
about recent "tags" and an absolute revision-id
* the name of the directory into which the tarball was unpacked
* an expanded VCS keyword ($Id$, etc)
* a `_version.py` created by some earlier build step
For released software, the version identifier is closely related to a VCS
tag. Some projects use tag names that include more than just the version
string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
needs to strip the tag prefix to extract the version identifier. For
unreleased software (between tags), the version identifier should provide
enough information to help developers recreate the same tree, while also
giving them an idea of roughly how old the tree is (after version 1.2, before
version 1.3). Many VCS systems can report a description that captures this,
for example `git describe --tags --dirty --always` reports things like
"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
uncommitted changes).
The version identifier is used for multiple purposes:
* to allow the module to self-identify its version: `myproject.__version__`
* to choose a name and prefix for a 'setup.py sdist' tarball
## Theory of Operation
Versioneer works by adding a special `_version.py` file into your source
tree, where your `__init__.py` can import it. This `_version.py` knows how to
dynamically ask the VCS tool for version information at import time.
`_version.py` also contains `$Revision$` markers, and the installation
process marks `_version.py` to have this marker rewritten with a tag name
during the `git archive` command. As a result, generated tarballs will
contain enough information to get the proper version.
To allow `setup.py` to compute a version too, a `versioneer.py` is added to
the top level of your source tree, next to `setup.py` and the `setup.cfg`
that configures it. This overrides several distutils/setuptools commands to
compute the version when invoked, and changes `setup.py build` and `setup.py
sdist` to replace `_version.py` with a small static file that contains just
the generated version data.
## Installation
See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
## Version-String Flavors
Code which uses Versioneer can learn about its version string at runtime by
importing `_version` from your main `__init__.py` file and running the
`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
import the top-level `versioneer.py` and run `get_versions()`.
Both functions return a dictionary with different flavors of version
information:
* `['version']`: A condensed version string, rendered using the selected
style. This is the most commonly used value for the project's version
string. The default "pep440" style yields strings like `0.11`,
`0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
below for alternative styles.
* `['full-revisionid']`: detailed revision identifier. For Git, this is the
full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
commit date in ISO 8601 format. This will be None if the date is not
available.
* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
this is only accurate if run in a VCS checkout, otherwise it is likely to
be False or None
* `['error']`: if the version string could not be computed, this will be set
to a string describing the problem, otherwise it will be None. It may be
useful to throw an exception in setup.py if this is set, to avoid e.g.
creating tarballs with a version string of "unknown".
Some variants are more useful than others. Including `full-revisionid` in a
bug report should allow developers to reconstruct the exact code being tested
(or indicate the presence of local changes that should be shared with the
developers). `version` is suitable for display in an "about" box or a CLI
`--version` output: it can be easily compared against release notes and lists
of bugs fixed in various releases.
The installer adds the following text to your `__init__.py` to place a basic
version in `YOURPROJECT.__version__`:
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
## Styles
The setup.cfg `style=` configuration controls how the VCS information is
rendered into a version string.
The default style, "pep440", produces a PEP440-compliant string, equal to the
un-prefixed tag name for actual releases, and containing an additional "local
version" section with more detail for in-between builds. For Git, this is
TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
that this commit is two revisions ("+2") beyond the "0.11" tag. For released
software (exactly equal to a known tag), the identifier will only contain the
stripped tag, e.g. "0.11".
Other styles are available. See [details.md](details.md) in the Versioneer
source tree for descriptions.
## Debugging
Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
to return a version of "0+unknown". To investigate the problem, run `setup.py
version`, which will run the version-lookup code in a verbose mode, and will
display the full contents of `get_versions()` (including the `error` string,
which may help identify what went wrong).
## Known Limitations
Some situations are known to cause problems for Versioneer. This details the
most significant ones. More can be found on Github
[issues page](https://github.com/python-versioneer/python-versioneer/issues).
### Subprojects
Versioneer has limited support for source trees in which `setup.py` is not in
the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
two common reasons why `setup.py` might not be in the root:
* Source trees which contain multiple subprojects, such as
[Buildbot](https://github.com/buildbot/buildbot), which contains both
"master" and "slave" subprojects, each with their own `setup.py`,
`setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
distributions (and upload multiple independently-installable tarballs).
* Source trees whose main purpose is to contain a C library, but which also
provide bindings to Python (and perhaps other languages) in subdirectories.
Versioneer will look for `.git` in parent directories, and most operations
should get the right version string. However `pip` and `setuptools` have bugs
and implementation details which frequently cause `pip install .` from a
subproject directory to fail to find a correct version string (so it usually
defaults to `0+unknown`).
`pip install --editable .` should work correctly. `setup.py install` might
work too.
Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
some later version.
[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
this issue. The discussion in
[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
issue from the Versioneer side in more detail.
[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
pip to let Versioneer work correctly.
Versioneer-0.16 and earlier only looked for a `.git` directory next to the
`setup.cfg`, so subprojects were completely unsupported with those releases.
### Editable installs with setuptools <= 18.5
`setup.py develop` and `pip install --editable .` allow you to install a
project into a virtualenv once, then continue editing the source code (and
test) without re-installing after every change.
"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
convenient way to specify executable scripts that should be installed along
with the python package.
These both work as expected when using modern setuptools. When using
setuptools-18.5 or earlier, however, certain operations will cause
`pkg_resources.DistributionNotFound` errors when running the entrypoint
script, which must be resolved by re-installing the package. This happens
when the install happens with one version, then the egg_info data is
regenerated while a different version is checked out. Many setup.py commands
cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
a different virtualenv), so this can be surprising.
[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
this one, but upgrading to a newer version of setuptools should probably
resolve it.
## Updating Versioneer
To upgrade your project to a new release of Versioneer, do the following:
* install the new Versioneer (`pip install -U versioneer` or equivalent)
* edit `setup.cfg`, if necessary, to include any new configuration settings
indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
* re-run `versioneer install` in your source tree, to replace
`SRC/_version.py`
* commit any changed files
## Future Directions
This tool is designed to make it easily extended to other version-control
systems: all VCS-specific components are in separate directories like
src/git/ . The top-level `versioneer.py` script is assembled from these
components by running make-versioneer.py . In the future, make-versioneer.py
will take a VCS name as an argument, and will construct a version of
`versioneer.py` that is specific to the given VCS. It might also take the
configuration arguments that are currently provided manually during
installation by editing setup.py . Alternatively, it might go the other
direction and include code from all supported VCS systems, reducing the
number of intermediate scripts.
## Similar projects
* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
dependency
* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
versioneer
## License
To make Versioneer easier to embed, all its code is dedicated to the public
domain. The `_version.py` that it creates is also in the public domain.
Specifically, both are released under the Creative Commons "Public Domain
Dedication" license (CC0-1.0), as described in
https://creativecommons.org/publicdomain/zero/1.0/ .
[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
[pypi-url]: https://pypi.python.org/pypi/versioneer/
[travis-image]:
https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
"""
import configparser
import errno
import json
import os
import re
import subprocess
import sys
class VersioneerConfig:
"""Container for Versioneer configuration parameters."""
def get_root():
"""Get the project root directory.
We require that all commands are run from the project root, i.e. the
directory that contains setup.py, setup.cfg, and versioneer.py .
"""
root = os.path.realpath(os.path.abspath(os.getcwd()))
setup_py = os.path.join(root, "setup.py")
versioneer_py = os.path.join(root, "versioneer.py")
if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
# allow 'python path/to/setup.py COMMAND'
root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
setup_py = os.path.join(root, "setup.py")
versioneer_py = os.path.join(root, "versioneer.py")
if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
err = (
"Versioneer was unable to run the project root directory. "
"Versioneer requires setup.py to be executed from "
"its immediate directory (like 'python setup.py COMMAND'), "
"or in a way that lets it use sys.argv[0] to find the root "
"(like 'python path/to/setup.py COMMAND')."
)
raise VersioneerBadRootError(err)
try:
# Certain runtime workflows (setup.py install/develop in a setuptools
# tree) execute all dependencies in a single python process, so
# "versioneer" may be imported multiple times, and python's shared
# module-import table will cache the first one. So we can't use
# os.path.dirname(__file__), as that will find whichever
# versioneer.py was first imported, even in later projects.
me = os.path.realpath(os.path.abspath(__file__))
me_dir = os.path.normcase(os.path.splitext(me)[0])
vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
if me_dir != vsr_dir:
print(
"Warning: build in %s is using versioneer.py from %s"
% (os.path.dirname(me), versioneer_py)
)
except NameError:
pass
return root
def get_config_from_root(root):
"""Read the project setup.cfg file to determine Versioneer config."""
# This might raise EnvironmentError (if setup.cfg is missing), or
# configparser.NoSectionError (if it lacks a [versioneer] section), or
# configparser.NoOptionError (if it lacks "VCS="). See the docstring at
# the top of versioneer.py for instructions on writing your setup.cfg .
setup_cfg = os.path.join(root, "setup.cfg")
parser = configparser.ConfigParser()
with open(setup_cfg) as f:
parser.read_file(f)
VCS = parser.get("versioneer", "VCS") # mandatory
def get(parser, name):
if parser.has_option("versioneer", name):
return parser.get("versioneer", name)
return None
cfg = VersioneerConfig()
cfg.VCS = VCS
cfg.style = get(parser, "style") or ""
cfg.versionfile_source = get(parser, "versionfile_source")
cfg.versionfile_build = get(parser, "versionfile_build")
cfg.tag_prefix = get(parser, "tag_prefix")
if cfg.tag_prefix in ("''", '""'):
cfg.tag_prefix = ""
cfg.parentdir_prefix = get(parser, "parentdir_prefix")
cfg.verbose = get(parser, "verbose")
return cfg
class NotThisMethod(Exception):
"""Exception raised if a method is not valid for the current scenario."""
# these dictionaries contain VCS-specific tools
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
"""Create decorator to mark a method as the handler of a VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
p = None
for c in commands:
try:
dispcmd = str([c] + args)
# remember shell=False, so use git.cmd on windows, not just git
p = subprocess.Popen(
[c] + args,
cwd=cwd,
env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr else None),
)
break
except OSError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %s" % dispcmd)
print(e)
return None, None
else:
if verbose:
print(f"unable to find command, tried {commands}")
return None, None
stdout = p.communicate()[0].strip().decode()
if p.returncode != 0:
if verbose:
print("unable to run %s (error)" % dispcmd)
print("stdout was %s" % stdout)
return None, p.returncode
return stdout, p.returncode
LONG_VERSION_PY[
"git"
] = r'''
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains the computed version number.
# This file is released into the public domain. Generated by
# versioneer-0.19 (https://github.com/python-versioneer/python-versioneer)
"""Git implementation of _version.py."""
import errno
import os
import re
import subprocess
import sys
def get_keywords():
"""Get the keywords needed to look up the version information."""
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
class VersioneerConfig:
"""Container for Versioneer configuration parameters."""
def get_config():
"""Create, populate and return the VersioneerConfig() object."""
# these strings are filled in when 'setup.py versioneer' creates
# _version.py
cfg = VersioneerConfig()
cfg.VCS = "git"
cfg.style = "%(STYLE)s"
cfg.tag_prefix = "%(TAG_PREFIX)s"
cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
cfg.verbose = False
return cfg
class NotThisMethod(Exception):
"""Exception raised if a method is not valid for the current scenario."""
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
"""Create decorator to mark a method as the handler of a VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
p = None
for c in commands:
try:
dispcmd = str([c] + args)
# remember shell=False, so use git.cmd on windows, not just git
p = subprocess.Popen([c] + args, cwd=cwd, env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
else None))
break
except EnvironmentError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %%s" %% dispcmd)
print(e)
return None, None
else:
if verbose:
print("unable to find command, tried %%s" %% (commands,))
return None, None
stdout = p.communicate()[0].strip().decode()
if p.returncode != 0:
if verbose:
print("unable to run %%s (error)" %% dispcmd)
print("stdout was %%s" %% stdout)
return None, p.returncode
return stdout, p.returncode
def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for i in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
else:
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %%s but none started with prefix %%s" %%
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
f = open(versionfile_abs, "r")
for line in f.readlines():
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
f.close()
except EnvironmentError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if not keywords:
raise NotThisMethod("no keywords at all, weird")
date = keywords.get("date")
if date is not None:
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
# git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = set([r.strip() for r in refnames.strip("()").split(",")])
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %%d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = set([r for r in refs if re.search(r'\d', r)])
if verbose:
print("discarding '%%s', no digits" %% ",".join(refs - tags))
if verbose:
print("likely tags: %%s" %% ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix):]
if verbose:
print("picking %%s" %% r)
return {"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": None,
"date": date}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": "no suitable tags", "date": None}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %%s not under git control" %% root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
"--always", "--long",
"--match", "%%s*" %% tag_prefix],
cwd=root)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[:git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = ("unable to parse git-describe output: '%%s'"
%% describe_out)
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%%s' doesn't start with prefix '%%s'"
print(fmt %% (full_tag, tag_prefix))
pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
%% (full_tag, tag_prefix))
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix):]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = run_command(GITS, ["show", "-s", "--format=%%ci", "HEAD"],
cwd=root)[0].strip()
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
"""TAG[.post0.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post0.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post0.dev%%d" %% pieces["distance"]
else:
# exception #1
rendered = "0.post0.dev%%d" %% pieces["distance"]
return rendered
def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%%s" %% pieces["short"]
else:
# exception #1
rendered = "0.post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%%s" %% pieces["short"]
return rendered
def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%%s'" %% style)
return {"version": rendered, "full-revisionid": pieces["long"],
"dirty": pieces["dirty"], "error": None,
"date": pieces.get("date")}
def get_versions():
"""Get version information or return default if unable to do so."""
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
# __file__, we can work backwards from there to the root. Some
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
# case we can only use expanded keywords.
cfg = get_config()
verbose = cfg.verbose
try:
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
verbose)
except NotThisMethod:
pass
try:
root = os.path.realpath(__file__)
# versionfile_source is the relative path from the top of the source
# tree (where the .git directory might live) to this file. Invert
# this to find the root from __file__.
for i in cfg.versionfile_source.split('/'):
root = os.path.dirname(root)
except NameError:
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to find root of source tree",
"date": None}
try:
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
return render(pieces, cfg.style)
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
except NotThisMethod:
pass
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version", "date": None}
'''
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
f = open(versionfile_abs)
for line in f.readlines():
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
f.close()
except OSError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if not keywords:
raise NotThisMethod("no keywords at all, weird")
date = keywords.get("date")
if date is not None:
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
# git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = {r.strip() for r in refnames.strip("()").split(",")}
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = {r for r in refs if re.search(r"\d", r)}
if verbose:
print("discarding '%s', no digits" % ",".join(refs - tags))
if verbose:
print("likely tags: %s" % ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix) :]
if verbose:
print("picking %s" % r)
return {
"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False,
"error": None,
"date": date,
}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {
"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False,
"error": "no suitable tags",
"date": None,
}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %s not under git control" % root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = run_command(
GITS,
[
"describe",
"--tags",
"--dirty",
"--always",
"--long",
"--match",
"%s*" % tag_prefix,
],
cwd=root,
)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[: git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%s' doesn't start with prefix '%s'"
print(fmt % (full_tag, tag_prefix))
pieces["error"] = "tag '{}' doesn't start with prefix '{}'".format(
full_tag,
tag_prefix,
)
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix) :]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[
0
].strip()
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces
def do_vcs_install(manifest_in, versionfile_source, ipy):
"""Git-specific installation logic for Versioneer.
For Git, this means creating/changing .gitattributes to mark _version.py
for export-subst keyword substitution.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
files = [manifest_in, versionfile_source]
if ipy:
files.append(ipy)
try:
me = __file__
if me.endswith(".pyc") or me.endswith(".pyo"):
me = os.path.splitext(me)[0] + ".py"
versioneer_file = os.path.relpath(me)
except NameError:
versioneer_file = "versioneer.py"
files.append(versioneer_file)
present = False
try:
f = open(".gitattributes")
for line in f.readlines():
if line.strip().startswith(versionfile_source):
if "export-subst" in line.strip().split()[1:]:
present = True
f.close()
except OSError:
pass
if not present:
f = open(".gitattributes", "a+")
f.write("%s export-subst\n" % versionfile_source)
f.close()
files.append(".gitattributes")
run_command(GITS, ["add", "--"] + files)
def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for i in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {
"version": dirname[len(parentdir_prefix) :],
"full-revisionid": None,
"dirty": False,
"error": None,
"date": None,
}
else:
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print(
"Tried directories %s but none started with prefix %s"
% (str(rootdirs), parentdir_prefix)
)
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
SHORT_VERSION_PY = """
# This file was generated by 'versioneer.py' (0.19) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.
import json
version_json = '''
%s
''' # END VERSION_JSON
def get_versions():
return json.loads(version_json)
"""
def versions_from_file(filename):
"""Try to determine the version from _version.py if present."""
try:
with open(filename) as f:
contents = f.read()
except OSError:
raise NotThisMethod("unable to read _version.py")
mo = re.search(
r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S
)
if not mo:
mo = re.search(
r"version_json = '''\r\n(.*)''' # END VERSION_JSON", contents, re.M | re.S
)
if not mo:
raise NotThisMethod("no version_json in _version.py")
return json.loads(mo.group(1))
def write_to_version_file(filename, versions):
"""Write the given version number to the given _version.py file."""
os.unlink(filename)
contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": "))
with open(filename, "w") as f:
f.write(SHORT_VERSION_PY % contents)
print("set {} to '{}'".format(filename, versions["version"]))
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
"""TAG[.post0.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post0.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post0.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post0.dev%d" % pieces["distance"]
return rendered
def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
return rendered
def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {
"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None,
}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%s'" % style)
return {
"version": rendered,
"full-revisionid": pieces["long"],
"dirty": pieces["dirty"],
"error": None,
"date": pieces.get("date"),
}
class VersioneerBadRootError(Exception):
"""The project root directory is unknown or missing key files."""
def get_versions(verbose=False):
"""Get the project version from whatever source is available.
Returns dict with two keys: 'version' and 'full'.
"""
if "versioneer" in sys.modules:
# see the discussion in cmdclass.py:get_cmdclass()
del sys.modules["versioneer"]
root = get_root()
cfg = get_config_from_root(root)
assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
handlers = HANDLERS.get(cfg.VCS)
assert handlers, "unrecognized VCS '%s'" % cfg.VCS
verbose = verbose or cfg.verbose
assert (
cfg.versionfile_source is not None
), "please set versioneer.versionfile_source"
assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
versionfile_abs = os.path.join(root, cfg.versionfile_source)
# extract version from first of: _version.py, VCS command (e.g. 'git
# describe'), parentdir. This is meant to work for developers using a
# source checkout, for users of a tarball created by 'setup.py sdist',
# and for users of a tarball/zipball created by 'git archive' or github's
# download-from-tag feature or the equivalent in other VCSes.
get_keywords_f = handlers.get("get_keywords")
from_keywords_f = handlers.get("keywords")
if get_keywords_f and from_keywords_f:
try:
keywords = get_keywords_f(versionfile_abs)
ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
if verbose:
print("got version from expanded keyword %s" % ver)
return ver
except NotThisMethod:
pass
try:
ver = versions_from_file(versionfile_abs)
if verbose:
print(f"got version from file {versionfile_abs} {ver}")
return ver
except NotThisMethod:
pass
from_vcs_f = handlers.get("pieces_from_vcs")
if from_vcs_f:
try:
pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
ver = render(pieces, cfg.style)
if verbose:
print("got version from VCS %s" % ver)
return ver
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
if verbose:
print("got version from parentdir %s" % ver)
return ver
except NotThisMethod:
pass
if verbose:
print("unable to compute version")
return {
"version": "0+unknown",
"full-revisionid": None,
"dirty": None,
"error": "unable to compute version",
"date": None,
}
def get_version():
"""Get the short version string for this project."""
return get_versions()["version"]
def get_cmdclass(cmdclass=None):
"""Get the custom setuptools/distutils subclasses used by Versioneer.
If the package uses a different cmdclass (e.g. one from numpy), it
should be provide as an argument.
"""
if "versioneer" in sys.modules:
del sys.modules["versioneer"]
# this fixes the "python setup.py develop" case (also 'install' and
# 'easy_install .'), in which subdependencies of the main project are
# built (using setup.py bdist_egg) in the same python process. Assume
# a main project A and a dependency B, which use different versions
# of Versioneer. A's setup.py imports A's Versioneer, leaving it in
# sys.modules by the time B's setup.py is executed, causing B to run
# with the wrong versioneer. Setuptools wraps the sub-dep builds in a
# sandbox that restores sys.modules to its pre-build state, so the
# parent is protected against the child's "import versioneer". By
# removing ourselves from sys.modules here, before the child build
# happens, we protect the child from the parent's versioneer too.
# Also see https://github.com/python-versioneer/python-versioneer/issues/52
cmds = {} if cmdclass is None else cmdclass.copy()
# we add "version" to both distutils and setuptools
from distutils.core import Command
class cmd_version(Command):
description = "report generated version string"
user_options = []
boolean_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
vers = get_versions(verbose=True)
print("Version: %s" % vers["version"])
print(" full-revisionid: %s" % vers.get("full-revisionid"))
print(" dirty: %s" % vers.get("dirty"))
print(" date: %s" % vers.get("date"))
if vers["error"]:
print(" error: %s" % vers["error"])
cmds["version"] = cmd_version
# we override "build_py" in both distutils and setuptools
#
# most invocation pathways end up running build_py:
# distutils/build -> build_py
# distutils/install -> distutils/build ->..
# setuptools/bdist_wheel -> distutils/install ->..
# setuptools/bdist_egg -> distutils/install_lib -> build_py
# setuptools/install -> bdist_egg ->..
# setuptools/develop -> ?
# pip install:
# copies source tree to a tempdir before running egg_info/etc
# if .git isn't copied too, 'git describe' will fail
# then does setup.py bdist_wheel, or sometimes setup.py install
# setup.py egg_info -> ?
# we override different "build_py" commands for both environments
if "build_py" in cmds:
_build_py = cmds["build_py"]
elif "setuptools" in sys.modules:
from setuptools.command.build_py import build_py as _build_py
else:
from distutils.command.build_py import build_py as _build_py
class cmd_build_py(_build_py):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
_build_py.run(self)
# now locate _version.py in the new build/ directory and replace
# it with an updated value
if cfg.versionfile_build:
target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
cmds["build_py"] = cmd_build_py
if "setuptools" in sys.modules:
from setuptools.command.build_ext import build_ext as _build_ext
else:
from distutils.command.build_ext import build_ext as _build_ext
class cmd_build_ext(_build_ext):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
_build_ext.run(self)
if self.inplace:
# build_ext --inplace will only build extensions in
# build/lib<..> dir with no _version.py to write to.
# As in place builds will already have a _version.py
# in the module dir, we do not need to write one.
return
# now locate _version.py in the new build/ directory and replace
# it with an updated value
target_versionfile = os.path.join(self.build_lib, cfg.versionfile_source)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
cmds["build_ext"] = cmd_build_ext
if "cx_Freeze" in sys.modules: # cx_freeze enabled?
from cx_Freeze.dist import build_exe as _build_exe
# nczeczulin reports that py2exe won't like the pep440-style string
# as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
# setup(console=[{
# "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
# "product_version": versioneer.get_version(),
# ...
class cmd_build_exe(_build_exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_build_exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
cmds["build_exe"] = cmd_build_exe
del cmds["build_py"]
if "py2exe" in sys.modules: # py2exe enabled?
from py2exe.distutils_buildexe import py2exe as _py2exe
class cmd_py2exe(_py2exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_py2exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
cmds["py2exe"] = cmd_py2exe
# we override different "sdist" commands for both environments
if "sdist" in cmds:
_sdist = cmds["sdist"]
elif "setuptools" in sys.modules:
from setuptools.command.sdist import sdist as _sdist
else:
from distutils.command.sdist import sdist as _sdist
class cmd_sdist(_sdist):
def run(self):
versions = get_versions()
self._versioneer_generated_versions = versions
# unless we update this, the command will keep using the old
# version
self.distribution.metadata.version = versions["version"]
return _sdist.run(self)
def make_release_tree(self, base_dir, files):
root = get_root()
cfg = get_config_from_root(root)
_sdist.make_release_tree(self, base_dir, files)
# now locate _version.py in the new base_dir directory
# (remembering that it may be a hardlink) and replace it with an
# updated value
target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
print("UPDATING %s" % target_versionfile)
write_to_version_file(
target_versionfile, self._versioneer_generated_versions
)
cmds["sdist"] = cmd_sdist
return cmds
CONFIG_ERROR = """
setup.cfg is missing the necessary Versioneer configuration. You need
a section like:
[versioneer]
VCS = git
style = pep440
versionfile_source = src/myproject/_version.py
versionfile_build = myproject/_version.py
tag_prefix =
parentdir_prefix = myproject-
You will also need to edit your setup.py to use the results:
import versioneer
setup(version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(), ...)
Please read the docstring in ./versioneer.py for configuration instructions,
edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
"""
SAMPLE_CONFIG = """
# See the docstring in versioneer.py for instructions. Note that you must
# re-run 'versioneer.py setup' after changing this section, and commit the
# resulting files.
[versioneer]
#VCS = git
#style = pep440
#versionfile_source =
#versionfile_build =
#tag_prefix =
#parentdir_prefix =
"""
INIT_PY_SNIPPET = """
from pandas._version import get_versions
__version__ = get_versions()['version']
del get_versions
"""
def do_setup():
"""Do main VCS-independent setup function for installing Versioneer."""
root = get_root()
try:
cfg = get_config_from_root(root)
except (OSError, configparser.NoSectionError, configparser.NoOptionError) as e:
if isinstance(e, (EnvironmentError, configparser.NoSectionError)):
print("Adding sample versioneer config to setup.cfg", file=sys.stderr)
with open(os.path.join(root, "setup.cfg"), "a") as f:
f.write(SAMPLE_CONFIG)
print(CONFIG_ERROR, file=sys.stderr)
return 1
print(" creating %s" % cfg.versionfile_source)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(
LONG
% {
"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
}
)
ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py")
if os.path.exists(ipy):
try:
with open(ipy) as f:
old = f.read()
except OSError:
old = ""
if INIT_PY_SNIPPET not in old:
print(" appending to %s" % ipy)
with open(ipy, "a") as f:
f.write(INIT_PY_SNIPPET)
else:
print(" %s unmodified" % ipy)
else:
print(" %s doesn't exist, ok" % ipy)
ipy = None
# Make sure both the top-level "versioneer.py" and versionfile_source
# (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
# they'll be copied into source distributions. Pip won't be able to
# install the package without this.
manifest_in = os.path.join(root, "MANIFEST.in")
simple_includes = set()
try:
with open(manifest_in) as f:
for line in f:
if line.startswith("include "):
for include in line.split()[1:]:
simple_includes.add(include)
except OSError:
pass
# That doesn't cover everything MANIFEST.in can do
# (http://docs.python.org/2/distutils/sourcedist.html#commands), so
# it might give some false negatives. Appending redundant 'include'
# lines is safe, though.
if "versioneer.py" not in simple_includes:
print(" appending 'versioneer.py' to MANIFEST.in")
with open(manifest_in, "a") as f:
f.write("include versioneer.py\n")
else:
print(" 'versioneer.py' already in MANIFEST.in")
if cfg.versionfile_source not in simple_includes:
print(
" appending versionfile_source ('%s') to MANIFEST.in"
% cfg.versionfile_source
)
with open(manifest_in, "a") as f:
f.write("include %s\n" % cfg.versionfile_source)
else:
print(" versionfile_source already in MANIFEST.in")
# Make VCS-specific changes. For git, this means creating/changing
# .gitattributes to mark _version.py for export-subst keyword
# substitution.
do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
return 0
def scan_setup_py():
"""Validate the contents of setup.py against Versioneer's expectations."""
found = set()
setters = False
errors = 0
with open("setup.py") as f:
for line in f.readlines():
if "import versioneer" in line:
found.add("import")
if "versioneer.get_cmdclass()" in line:
found.add("cmdclass")
if "versioneer.get_version()" in line:
found.add("get_version")
if "versioneer.VCS" in line:
setters = True
if "versioneer.versionfile_source" in line:
setters = True
if len(found) != 3:
print("")
print("Your setup.py appears to be missing some important items")
print("(but I might be wrong). Please make sure it has something")
print("roughly like the following:")
print("")
print(" import versioneer")
print(" setup( version=versioneer.get_version(),")
print(" cmdclass=versioneer.get_cmdclass(), ...)")
print("")
errors += 1
if setters:
print("You should remove lines like 'versioneer.VCS = ' and")
print("'versioneer.versionfile_source = ' . This configuration")
print("now lives in setup.cfg, and should be removed from setup.py")
print("")
errors += 1
return errors
if __name__ == "__main__":
cmd = sys.argv[1]
if cmd == "setup":
errors = do_setup()
errors += scan_setup_py()
if errors:
sys.exit(1)
| bsd-3-clause |
RomainBrault/scikit-learn | examples/linear_model/plot_iris_logistic.py | 119 | 1679 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The
datapoints are colored according to their labels.
"""
print(__doc__)
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
h = .02 # step size in the mesh
logreg = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(X, Y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.show()
| bsd-3-clause |
Jul13/wepy | examples/learn-pandas.py | 1 | 1692 | # ==============================================================================
# title : learn-pandas
# description :
# author : Julien Reynier
# date : 02.04.17
# version :
# IDE : PyCharm Community Edition
# ==============================================================================
# http://pandas.pydata.org/pandas-docs/stable/10min.html
# http://pandas.pydata.org/pandas-docs/stable/tutorials.html
# SCRIPTS FROM 10 MINUTES TO PANDAS
# IMPORTS:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# CREATE OBJECT
s = pd.Series([1, 2, 3, np.nan, 6, 8])
s
dates = pd.date_range('20170101', periods=6)
dates
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
df
df2 = pd.DataFrame({'A': 1.,
'B': pd.Timestamp('20130102'),
'C': pd.Series(1, index=list(range(4)), dtype='float32'),
'D': np.array([3] * 4, dtype='int32'),
'E': pd.Categorical(["test", "train", "test", "train"]),
'F': 'foo'})
df2
df2.dtypes
# VIEW DATA INSIDE
df.head() # default n is 5
df.tail(3)
df.index # we defined the index at df creation in that case
df.describe() # some summary statistics about the data frame
# SIMPLE OPERATIONS
df.T # dataframe transpose
df.sort_index(axis=1, ascending=False)
df.sort_values(by='B')
# SELECTION INDEXING SLICING OPERATIONS
# I quote:
# "we recommend the optimized pandas data access methods, .at, .iat, .loc,
# .iloc and .ix."
# SIMPLE SELECTION
df['A']
df[2::-1]
df['20170103':'20170101':-1]
# SELECTION BY LABEL
df.loc[[dates[0], dates[4], dates[1]]]
df.loc[:,['A','B']]
| apache-2.0 |
mschmittfull/nbodykit | contrib/ClosePairsBias.py | 2 | 6240 | from nbodykit.extensionpoints import DataSource
import numpy
from nbodykit.utils import selectionlanguage
from scipy.spatial import cKDTree as KDTree
import mpsort
def append_fields(data, dict):
def guessdtype(data):
return (data.dtype, data.shape[1:])
names1 = data.dtype.names
names2 = [name for name in dict]
dtype = [(name, guessdtype(data[name])) for name in data.dtype.names] \
+ [(name, guessdtype(dict[name])) for name in dict]
newdata = numpy.empty(len(data), dtype=dtype)
for name in data.dtype.names:
newdata[name] = data[name]
for name in dict:
newdata[name] = dict[name]
return newdata
class ClosePairBiasing(DataSource):
"""
Reading in nbodykit hdf5 halo catalogue, and filter the
results by proximity to massive halos.
Notes
-----
* `h5py` must be installed to use this data source.
Parameters
----------
path : str
the path of the file to read the data from
dataset: list of str
For text files, one or more strings specifying the names of the data
columns. Shape must be equal to number of columns
in the field, otherwise, behavior is undefined.
For hdf5 files, the name of the pandas data group.
BoxSize : float or array_like (3,)
the box size, either provided as a single float (isotropic)
or an array of the sizes of the three dimensions
"""
plugin_name = "ClosePairBias"
def __init__(self, path, dataset, BoxSize, m0, massive,
rsd=None, select1=None, select2=None):
pass
@classmethod
def register(cls):
s = cls.schema
s.add_argument("path", help="path to file")
s.add_argument("dataset", help="name of dataset in HDF5 file")
s.add_argument("BoxSize", type=cls.BoxSizeParser,
help="the size of the isotropic box, or the sizes of the 3 box dimensions.")
s.add_argument("m0", type=float, help="mass of a particle")
s.add_argument("massive", type=selectionlanguage.Query,
help="selection that defines the 'massive halo'; it can also be 'less massive halo' ")
s.add_argument("sd", choices="xyz", help="direction to do redshift distortion")
s.add_argument("select1", type=selectionlanguage.Query,
help='row selection based on conditions specified as string')
s.add_argument("select2", type=selectionlanguage.Query,
help='row selection based on conditions specified as string')
def parallel_read(self, columns, full=False):
if self.comm.rank == 0:
try:
import h5py
except:
raise ImportError("h5py must be installed to use HDF5 reader")
dataset = h5py.File(self.path, mode='r')[self.dataset]
data = dataset[...]
nobj = len(data)
data['Position'] *= self.BoxSize
data['Velocity'] *= self.BoxSize
data = append_fields(data,
dict(Mass=data['Length'] * self.m0,
LogMass=numpy.log10(data['Length'] * self.m0),
Proximity=numpy.zeros(len(data)))
)
massive = data[self.massive.get_mask(data)]
self.logger.info("Selected %d 'massive halos'" % len(massive))
if len(massive) == 0:
raise ValueError("too few massive halos. Check the 'massive' selection clause.")
data = numpy.array_split(data, self.comm.size)
else:
massive = None
data = None
if self.comm.rank == 0:
self.logger.info("load balancing ")
data = self.comm.scatter(data)
massive = self.comm.bcast(massive)
if self.comm.rank == 0:
self.logger.info("Querying KDTree")
tree = KDTree(massive['Position'])
nobjs = self.comm.allreduce(len(data))
if self.comm.rank == 0:
self.logger.info("total number of objects is %d" % nobjs)
# select based on input conditions
if self.select1 is not None:
mask = self.select1.get_mask(data)
data = data[mask]
nobjs1 = self.comm.allreduce(len(data))
if self.comm.rank == 0:
self.logger.info("selected (1) number of objects is %d" % (nobjs1 ))
d, i = tree.query(data['Position'], k=2)
d[d == 0] = numpy.inf
data['Proximity'][:] = d.min(axis=-1)
if len(data) > 0:
mymax = data['Proximity'].max()
else:
mymax = 0
pbins = numpy.linspace(0, numpy.max(self.comm.allgather(mymax)), 10)
h = self.comm.allreduce(numpy.histogram(data['Proximity'], bins=pbins)[0])
if self.comm.rank == 0:
for p1, p2, h in zip(list(pbins), list(pbins[1:]) + [numpy.inf], h):
self.logger.info("Proximity: [%g - %g] Halos %d" % (p1, p2, h))
if self.select2 is not None:
mask = self.select2.get_mask(data)
data = data[mask]
nobjs2 = self.comm.allreduce(len(data))
if self.comm.rank == 0:
self.logger.info("selected (2) number of objects is %d (%g %%)" % (nobjs2, 100.0 * nobjs2 / nobjs1))
meanmass = self.comm.allreduce(data['Mass'].sum(dtype='f8')) \
/ self.comm.allreduce(len(data))
if self.comm.rank == 0:
self.logger.info("mean mass of selected objects is %g (log10 = %g)"
% (meanmass, numpy.log10(meanmass)))
pos = data['Position']
vel = data['Velocity']
mass = None
P = {}
if 'Position' in columns:
P['Position'] = pos
if 'Velocity' in columns or self.rsd is not None:
P['Velocity'] = vel
if 'Mass' in columns:
P['Mass'] = mass
P['Weight'] = numpy.ones(len(pos))
if self.rsd is not None:
dir = "xyz".index(self.rsd)
P['Position'][:, dir] += P['Velocity'][:, dir]
P['Position'][:, dir] %= self.BoxSize[dir]
yield [P.get(key, None) for key in columns]
| gpl-3.0 |
wzbozon/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessarily use all five components
while the DP model will effectively only use as many as are needed for
a good fit. This is a property of the Dirichlet Process prior. Here we
can see that the EM model splits some components arbitrarily, because it
is trying to fit too many components, while the Dirichlet Process model
adapts it number of state automatically.
This example doesn't show it, as we're in a low-dimensional space, but
another advantage of the Dirichlet process model is that it can fit
full covariance matrices effectively even when there are less examples
per cluster than there are dimensions in the data, due to
regularization properties of the inference algorithm.
"""
import itertools
import numpy as np
from scipy import linalg
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import mixture
# Number of samples per component
n_samples = 500
# Generate random sample, two components
np.random.seed(0)
C = np.array([[0., -0.1], [1.7, .4]])
X = np.r_[np.dot(np.random.randn(n_samples, 2), C),
.7 * np.random.randn(n_samples, 2) + np.array([-6, 3])]
# Fit a mixture of Gaussians with EM using five components
gmm = mixture.GMM(n_components=5, covariance_type='full')
gmm.fit(X)
# Fit a Dirichlet process mixture of Gaussians using five components
dpgmm = mixture.DPGMM(n_components=5, covariance_type='full')
dpgmm.fit(X)
color_iter = itertools.cycle(['r', 'g', 'b', 'c', 'm'])
for i, (clf, title) in enumerate([(gmm, 'GMM'),
(dpgmm, 'Dirichlet Process GMM')]):
splot = plt.subplot(2, 1, 1 + i)
Y_ = clf.predict(X)
for i, (mean, covar, color) in enumerate(zip(
clf.means_, clf._get_covars(), color_iter)):
v, w = linalg.eigh(covar)
u = w[0] / linalg.norm(w[0])
# as the DP will not use every component it has access to
# unless it needs it, we shouldn't plot the redundant
# components.
if not np.any(Y_ == i):
continue
plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color)
# Plot an ellipse to show the Gaussian component
angle = np.arctan(u[1] / u[0])
angle = 180 * angle / np.pi # convert to degrees
ell = mpl.patches.Ellipse(mean, v[0], v[1], 180 + angle, color=color)
ell.set_clip_box(splot.bbox)
ell.set_alpha(0.5)
splot.add_artist(ell)
plt.xlim(-10, 10)
plt.ylim(-3, 6)
plt.xticks(())
plt.yticks(())
plt.title(title)
plt.show()
| bsd-3-clause |
sid88in/incubator-airflow | airflow/www/views.py | 1 | 113062 | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF 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.
#
import ast
import codecs
import copy
import datetime as dt
import itertools
import json
import logging
import math
import os
import traceback
from collections import defaultdict
from datetime import timedelta
from functools import wraps
from textwrap import dedent
import bleach
import markdown
import nvd3
import pendulum
import pkg_resources
import sqlalchemy as sqla
from flask import (
abort, jsonify, redirect, url_for, request, Markup, Response,
current_app, render_template, make_response)
from flask import flash
from flask._compat import PY2
from flask_admin import BaseView, expose, AdminIndexView
from flask_admin.actions import action
from flask_admin.babel import lazy_gettext
from flask_admin.contrib.sqla import ModelView
from flask_admin.form.fields import DateTimeField
from flask_admin.tools import iterdecode
from jinja2 import escape
from jinja2.sandbox import ImmutableSandboxedEnvironment
from past.builtins import basestring, unicode
from pygments import highlight, lexers
from pygments.formatters import HtmlFormatter
from sqlalchemy import or_, desc, and_, union_all
from wtforms import (
Form, SelectField, TextAreaField, PasswordField,
StringField, validators)
import airflow
from airflow import configuration as conf
from airflow import models
from airflow import settings
from airflow.api.common.experimental.mark_tasks import (set_dag_run_state_to_running,
set_dag_run_state_to_success,
set_dag_run_state_to_failed)
from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.models import XCom, DagRun
from airflow.operators.subdag_operator import SubDagOperator
from airflow.ti_deps.dep_context import DepContext, QUEUE_DEPS, SCHEDULER_DEPS
from airflow.utils import timezone
from airflow.utils.dates import infer_time_unit, scale_time_units, parse_execution_date
from airflow.utils.db import create_session, provide_session
from airflow.utils.helpers import alchemy_to_dict
from airflow.utils.json import json_ser
from airflow.utils.net import get_hostname
from airflow.utils.state import State
from airflow.utils.timezone import datetime
from airflow.www import utils as wwwutils
from airflow.www.forms import (DateTimeForm, DateTimeWithNumRunsForm,
DateTimeWithNumRunsWithDagRunsForm)
from airflow.www.validators import GreaterEqualThan
QUERY_LIMIT = 100000
CHART_LIMIT = 200000
UTF8_READER = codecs.getreader('utf-8')
dagbag = models.DagBag(settings.DAGS_FOLDER)
login_required = airflow.login.login_required
current_user = airflow.login.current_user
logout_user = airflow.login.logout_user
FILTER_BY_OWNER = False
PAGE_SIZE = conf.getint('webserver', 'page_size')
if conf.getboolean('webserver', 'FILTER_BY_OWNER'):
# filter_by_owner if authentication is enabled and filter_by_owner is true
FILTER_BY_OWNER = not current_app.config['LOGIN_DISABLED']
def dag_link(v, c, m, p):
if m.dag_id is None:
return Markup()
dag_id = bleach.clean(m.dag_id)
url = url_for(
'airflow.graph',
dag_id=dag_id,
execution_date=m.execution_date)
return Markup(
'<a href="{}">{}</a>'.format(url, dag_id))
def log_url_formatter(v, c, m, p):
return Markup(
'<a href="{m.log_url}">'
' <span class="glyphicon glyphicon-book" aria-hidden="true">'
'</span></a>').format(**locals())
def dag_run_link(v, c, m, p):
dag_id = bleach.clean(m.dag_id)
url = url_for(
'airflow.graph',
dag_id=m.dag_id,
run_id=m.run_id,
execution_date=m.execution_date)
return Markup('<a href="{url}">{m.run_id}</a>'.format(**locals()))
def task_instance_link(v, c, m, p):
dag_id = bleach.clean(m.dag_id)
task_id = bleach.clean(m.task_id)
url = url_for(
'airflow.task',
dag_id=dag_id,
task_id=task_id,
execution_date=m.execution_date.isoformat())
url_root = url_for(
'airflow.graph',
dag_id=dag_id,
root=task_id,
execution_date=m.execution_date.isoformat())
return Markup(
"""
<span style="white-space: nowrap;">
<a href="{url}">{task_id}</a>
<a href="{url_root}" title="Filter on this task and upstream">
<span class="glyphicon glyphicon-filter" style="margin-left: 0px;"
aria-hidden="true"></span>
</a>
</span>
""".format(**locals()))
def state_token(state):
color = State.color(state)
return Markup(
'<span class="label" style="background-color:{color};">'
'{state}</span>'.format(**locals()))
def parse_datetime_f(value):
if not isinstance(value, dt.datetime):
return value
return timezone.make_aware(value)
def state_f(v, c, m, p):
return state_token(m.state)
def duration_f(v, c, m, p):
if m.end_date and m.duration:
return timedelta(seconds=m.duration)
def datetime_f(v, c, m, p):
attr = getattr(m, p)
dttm = attr.isoformat() if attr else ''
if timezone.utcnow().isoformat()[:4] == dttm[:4]:
dttm = dttm[5:]
return Markup("<nobr>{}</nobr>".format(dttm))
def nobr_f(v, c, m, p):
return Markup("<nobr>{}</nobr>".format(getattr(m, p)))
def label_link(v, c, m, p):
try:
default_params = ast.literal_eval(m.default_params)
except Exception:
default_params = {}
url = url_for(
'airflow.chart', chart_id=m.id, iteration_no=m.iteration_no,
**default_params)
return Markup("<a href='{url}'>{m.label}</a>".format(**locals()))
def pool_link(v, c, m, p):
url = '/admin/taskinstance/?flt1_pool_equals=' + m.pool
return Markup("<a href='{url}'>{m.pool}</a>".format(**locals()))
def pygment_html_render(s, lexer=lexers.TextLexer):
return highlight(
s,
lexer(),
HtmlFormatter(linenos=True),
)
def render(obj, lexer):
out = ""
if isinstance(obj, basestring):
out += pygment_html_render(obj, lexer)
elif isinstance(obj, (tuple, list)):
for i, s in enumerate(obj):
out += "<div>List item #{}</div>".format(i)
out += "<div>" + pygment_html_render(s, lexer) + "</div>"
elif isinstance(obj, dict):
for k, v in obj.items():
out += '<div>Dict item "{}"</div>'.format(k)
out += "<div>" + pygment_html_render(v, lexer) + "</div>"
return out
def wrapped_markdown(s):
return '<div class="rich_doc">' + markdown.markdown(s) + "</div>"
attr_renderer = {
'bash_command': lambda x: render(x, lexers.BashLexer),
'hql': lambda x: render(x, lexers.SqlLexer),
'sql': lambda x: render(x, lexers.SqlLexer),
'doc': lambda x: render(x, lexers.TextLexer),
'doc_json': lambda x: render(x, lexers.JsonLexer),
'doc_rst': lambda x: render(x, lexers.RstLexer),
'doc_yaml': lambda x: render(x, lexers.YamlLexer),
'doc_md': wrapped_markdown,
'python_callable': lambda x: render(
wwwutils.get_python_source(x),
lexers.PythonLexer,
),
}
def data_profiling_required(f):
"""Decorator for views requiring data profiling access"""
@wraps(f)
def decorated_function(*args, **kwargs):
if (
current_app.config['LOGIN_DISABLED'] or
(not current_user.is_anonymous and current_user.data_profiling())
):
return f(*args, **kwargs)
else:
flash("This page requires data profiling privileges", "error")
return redirect(url_for('admin.index'))
return decorated_function
def fused_slots(v, c, m, p):
url = (
'/admin/taskinstance/' +
'?flt1_pool_equals=' + m.pool +
'&flt2_state_equals=running')
return Markup("<a href='{0}'>{1}</a>".format(url, m.used_slots()))
def fqueued_slots(v, c, m, p):
url = (
'/admin/taskinstance/' +
'?flt1_pool_equals=' + m.pool +
'&flt2_state_equals=queued&sort=10&desc=1')
return Markup("<a href='{0}'>{1}</a>".format(url, m.queued_slots()))
def recurse_tasks(tasks, task_ids, dag_ids, task_id_to_dag):
if isinstance(tasks, list):
for task in tasks:
recurse_tasks(task, task_ids, dag_ids, task_id_to_dag)
return
if isinstance(tasks, SubDagOperator):
subtasks = tasks.subdag.tasks
dag_ids.append(tasks.subdag.dag_id)
for subtask in subtasks:
if subtask.task_id not in task_ids:
task_ids.append(subtask.task_id)
task_id_to_dag[subtask.task_id] = tasks.subdag
recurse_tasks(subtasks, task_ids, dag_ids, task_id_to_dag)
if isinstance(tasks, BaseOperator):
task_id_to_dag[tasks.task_id] = tasks.dag
def get_chart_height(dag):
"""
TODO(aoen): See [AIRFLOW-1263] We use the number of tasks in the DAG as a heuristic to
approximate the size of generated chart (otherwise the charts are tiny and unreadable
when DAGs have a large number of tasks). Ideally nvd3 should allow for dynamic-height
charts, that is charts that take up space based on the size of the components within.
"""
return 600 + len(dag.tasks) * 10
def get_date_time_num_runs_dag_runs_form_data(request, session, dag):
dttm = request.args.get('execution_date')
if dttm:
dttm = pendulum.parse(dttm)
else:
dttm = dag.latest_execution_date or timezone.utcnow()
base_date = request.args.get('base_date')
if base_date:
base_date = timezone.parse(base_date)
else:
# The DateTimeField widget truncates milliseconds and would loose
# the first dag run. Round to next second.
base_date = (dttm + timedelta(seconds=1)).replace(microsecond=0)
default_dag_run = conf.getint('webserver', 'default_dag_run_display_number')
num_runs = request.args.get('num_runs')
num_runs = int(num_runs) if num_runs else default_dag_run
DR = models.DagRun
drs = (
session.query(DR)
.filter(
DR.dag_id == dag.dag_id,
DR.execution_date <= base_date)
.order_by(desc(DR.execution_date))
.limit(num_runs)
.all()
)
dr_choices = []
dr_state = None
for dr in drs:
dr_choices.append((dr.execution_date.isoformat(), dr.run_id))
if dttm == dr.execution_date:
dr_state = dr.state
# Happens if base_date was changed and the selected dag run is not in result
if not dr_state and drs:
dr = drs[0]
dttm = dr.execution_date
dr_state = dr.state
return {
'dttm': dttm,
'base_date': base_date,
'num_runs': num_runs,
'execution_date': dttm.isoformat(),
'dr_choices': dr_choices,
'dr_state': dr_state,
}
class Airflow(BaseView):
def is_visible(self):
return False
@expose('/')
@login_required
def index(self):
return self.render('airflow/dags.html')
@expose('/chart_data')
@data_profiling_required
@wwwutils.gzipped
# @cache.cached(timeout=3600, key_prefix=wwwutils.make_cache_key)
def chart_data(self):
from airflow import macros
import pandas as pd
if conf.getboolean('core', 'secure_mode'):
abort(404)
with create_session() as session:
chart_id = request.args.get('chart_id')
csv = request.args.get('csv') == "true"
chart = session.query(models.Chart).filter_by(id=chart_id).first()
db = session.query(
models.Connection).filter_by(conn_id=chart.conn_id).first()
payload = {
"state": "ERROR",
"error": ""
}
# Processing templated fields
try:
args = ast.literal_eval(chart.default_params)
if not isinstance(args, dict):
raise AirflowException('Not a dict')
except Exception:
args = {}
payload['error'] += (
"Default params is not valid, string has to evaluate as "
"a Python dictionary. ")
request_dict = {k: request.args.get(k) for k in request.args}
args.update(request_dict)
args['macros'] = macros
sandbox = ImmutableSandboxedEnvironment()
sql = sandbox.from_string(chart.sql).render(**args)
label = sandbox.from_string(chart.label).render(**args)
payload['sql_html'] = Markup(highlight(
sql,
lexers.SqlLexer(), # Lexer call
HtmlFormatter(noclasses=True))
)
payload['label'] = label
pd.set_option('display.max_colwidth', 100)
hook = db.get_hook()
try:
df = hook.get_pandas_df(
wwwutils.limit_sql(sql, CHART_LIMIT, conn_type=db.conn_type))
df = df.fillna(0)
except Exception as e:
payload['error'] += "SQL execution failed. Details: " + str(e)
if csv:
return Response(
response=df.to_csv(index=False),
status=200,
mimetype="application/text")
if not payload['error'] and len(df) == CHART_LIMIT:
payload['warning'] = (
"Data has been truncated to {0}"
" rows. Expect incomplete results.").format(CHART_LIMIT)
if not payload['error'] and len(df) == 0:
payload['error'] += "Empty result set. "
elif (
not payload['error'] and
chart.sql_layout == 'series' and
chart.chart_type != "datatable" and
len(df.columns) < 3):
payload['error'] += "SQL needs to return at least 3 columns. "
elif (
not payload['error'] and
chart.sql_layout == 'columns' and
len(df.columns) < 2):
payload['error'] += "SQL needs to return at least 2 columns. "
elif not payload['error']:
import numpy as np
chart_type = chart.chart_type
data = None
if chart.show_datatable or chart_type == "datatable":
data = df.to_dict(orient="split")
data['columns'] = [{'title': c} for c in data['columns']]
payload['data'] = data
# Trying to convert time to something Highcharts likes
x_col = 1 if chart.sql_layout == 'series' else 0
if chart.x_is_date:
try:
# From string to datetime
df[df.columns[x_col]] = pd.to_datetime(
df[df.columns[x_col]])
df[df.columns[x_col]] = df[df.columns[x_col]].apply(
lambda x: int(x.strftime("%s")) * 1000)
except Exception as e:
payload['error'] = "Time conversion failed"
if chart_type == 'datatable':
payload['state'] = 'SUCCESS'
return wwwutils.json_response(payload)
else:
if chart.sql_layout == 'series':
# User provides columns (series, x, y)
df[df.columns[2]] = df[df.columns[2]].astype(np.float)
df = df.pivot_table(
index=df.columns[1],
columns=df.columns[0],
values=df.columns[2], aggfunc=np.sum)
else:
# User provides columns (x, y, metric1, metric2, ...)
df.index = df[df.columns[0]]
df = df.sort(df.columns[0])
del df[df.columns[0]]
for col in df.columns:
df[col] = df[col].astype(np.float)
df = df.fillna(0)
NVd3ChartClass = chart_mapping.get(chart.chart_type)
NVd3ChartClass = getattr(nvd3, NVd3ChartClass)
nvd3_chart = NVd3ChartClass(x_is_date=chart.x_is_date)
for col in df.columns:
nvd3_chart.add_serie(name=col, y=df[col].tolist(), x=df[col].index.tolist())
try:
nvd3_chart.buildcontent()
payload['chart_type'] = nvd3_chart.__class__.__name__
payload['htmlcontent'] = nvd3_chart.htmlcontent
except Exception as e:
payload['error'] = str(e)
payload['state'] = 'SUCCESS'
payload['request_dict'] = request_dict
return wwwutils.json_response(payload)
@expose('/chart')
@data_profiling_required
def chart(self):
if conf.getboolean('core', 'secure_mode'):
abort(404)
with create_session() as session:
chart_id = request.args.get('chart_id')
embed = request.args.get('embed')
chart = session.query(models.Chart).filter_by(id=chart_id).first()
NVd3ChartClass = chart_mapping.get(chart.chart_type)
if not NVd3ChartClass:
flash(
"Not supported anymore as the license was incompatible, "
"sorry",
"danger")
redirect('/admin/chart/')
sql = ""
if chart.show_sql:
sql = Markup(highlight(
chart.sql,
lexers.SqlLexer(), # Lexer call
HtmlFormatter(noclasses=True))
)
return self.render(
'airflow/nvd3.html',
chart=chart,
title="Airflow - Chart",
sql=sql,
label=chart.label,
embed=embed)
@expose('/dag_stats')
@login_required
@provide_session
def dag_stats(self, session=None):
ds = models.DagStat
ds.update(
dag_ids=[dag.dag_id for dag in dagbag.dags.values() if not dag.is_subdag]
)
qry = (
session.query(ds.dag_id, ds.state, ds.count)
)
data = {}
for dag_id, state, count in qry:
if dag_id not in data:
data[dag_id] = {}
data[dag_id][state] = count
payload = {}
for dag in dagbag.dags.values():
payload[dag.safe_dag_id] = []
for state in State.dag_states:
try:
count = data[dag.dag_id][state]
except Exception:
count = 0
d = {
'state': state,
'count': count,
'dag_id': dag.dag_id,
'color': State.color(state)
}
payload[dag.safe_dag_id].append(d)
return wwwutils.json_response(payload)
@expose('/task_stats')
@login_required
@provide_session
def task_stats(self, session=None):
TI = models.TaskInstance
DagRun = models.DagRun
Dag = models.DagModel
LastDagRun = (
session.query(DagRun.dag_id, sqla.func.max(DagRun.execution_date).label('execution_date'))
.join(Dag, Dag.dag_id == DagRun.dag_id)
.filter(DagRun.state != State.RUNNING)
.filter(Dag.is_active == True) # noqa: E712
.filter(Dag.is_subdag == False) # noqa: E712
.group_by(DagRun.dag_id)
.subquery('last_dag_run')
)
RunningDagRun = (
session.query(DagRun.dag_id, DagRun.execution_date)
.join(Dag, Dag.dag_id == DagRun.dag_id)
.filter(DagRun.state == State.RUNNING)
.filter(Dag.is_active == True) # noqa: E712
.filter(Dag.is_subdag == False) # noqa: E712
.subquery('running_dag_run')
)
# Select all task_instances from active dag_runs.
# If no dag_run is active, return task instances from most recent dag_run.
LastTI = (
session.query(TI.dag_id.label('dag_id'), TI.state.label('state'))
.join(LastDagRun, and_(
LastDagRun.c.dag_id == TI.dag_id,
LastDagRun.c.execution_date == TI.execution_date))
)
RunningTI = (
session.query(TI.dag_id.label('dag_id'), TI.state.label('state'))
.join(RunningDagRun, and_(
RunningDagRun.c.dag_id == TI.dag_id,
RunningDagRun.c.execution_date == TI.execution_date))
)
UnionTI = union_all(LastTI, RunningTI).alias('union_ti')
qry = (
session.query(UnionTI.c.dag_id, UnionTI.c.state, sqla.func.count())
.group_by(UnionTI.c.dag_id, UnionTI.c.state)
)
data = {}
for dag_id, state, count in qry:
if dag_id not in data:
data[dag_id] = {}
data[dag_id][state] = count
session.commit()
payload = {}
for dag in dagbag.dags.values():
payload[dag.safe_dag_id] = []
for state in State.task_states:
try:
count = data[dag.dag_id][state]
except Exception:
count = 0
d = {
'state': state,
'count': count,
'dag_id': dag.dag_id,
'color': State.color(state)
}
payload[dag.safe_dag_id].append(d)
return wwwutils.json_response(payload)
@expose('/code')
@login_required
def code(self):
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
title = dag_id
try:
with wwwutils.open_maybe_zipped(dag.fileloc, 'r') as f:
code = f.read()
html_code = highlight(
code, lexers.PythonLexer(), HtmlFormatter(linenos=True))
except IOError as e:
html_code = str(e)
return self.render(
'airflow/dag_code.html', html_code=html_code, dag=dag, title=title,
root=request.args.get('root'),
demo_mode=conf.getboolean('webserver', 'demo_mode'))
@expose('/dag_details')
@login_required
@provide_session
def dag_details(self, session=None):
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
title = "DAG details"
TI = models.TaskInstance
states = session\
.query(TI.state, sqla.func.count(TI.dag_id))\
.filter(TI.dag_id == dag_id)\
.group_by(TI.state)\
.all()
return self.render(
'airflow/dag_details.html',
dag=dag, title=title, states=states, State=State)
@current_app.errorhandler(404)
def circles(self):
return render_template(
'airflow/circles.html', hostname=get_hostname()), 404
@current_app.errorhandler(500)
def show_traceback(self):
from airflow.utils import asciiart as ascii_
return render_template(
'airflow/traceback.html',
hostname=get_hostname(),
nukular=ascii_.nukular,
info=traceback.format_exc()), 500
@expose('/noaccess')
def noaccess(self):
return self.render('airflow/noaccess.html')
@expose('/pickle_info')
@login_required
def pickle_info(self):
d = {}
dag_id = request.args.get('dag_id')
dags = [dagbag.dags.get(dag_id)] if dag_id else dagbag.dags.values()
for dag in dags:
if not dag.is_subdag:
d[dag.dag_id] = dag.pickle_info()
return wwwutils.json_response(d)
@expose('/login', methods=['GET', 'POST'])
def login(self):
return airflow.login.login(self, request)
@expose('/logout')
def logout(self):
logout_user()
flash('You have been logged out.')
return redirect(url_for('admin.index'))
@expose('/rendered')
@login_required
@wwwutils.action_logging
def rendered(self):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
execution_date = request.args.get('execution_date')
dttm = pendulum.parse(execution_date)
form = DateTimeForm(data={'execution_date': dttm})
dag = dagbag.get_dag(dag_id)
task = copy.copy(dag.get_task(task_id))
ti = models.TaskInstance(task=task, execution_date=dttm)
try:
ti.render_templates()
except Exception as e:
flash("Error rendering template: " + str(e), "error")
title = "Rendered Template"
html_dict = {}
for template_field in task.__class__.template_fields:
content = getattr(task, template_field)
if template_field in attr_renderer:
html_dict[template_field] = attr_renderer[template_field](content)
else:
html_dict[template_field] = (
"<pre><code>" + str(content) + "</pre></code>")
return self.render(
'airflow/ti_code.html',
html_dict=html_dict,
dag=dag,
task_id=task_id,
execution_date=execution_date,
form=form,
title=title, )
@expose('/get_logs_with_metadata')
@login_required
@wwwutils.action_logging
@provide_session
def get_logs_with_metadata(self, session=None):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
execution_date = request.args.get('execution_date')
dttm = pendulum.parse(execution_date)
try_number = int(request.args.get('try_number'))
metadata = request.args.get('metadata')
metadata = json.loads(metadata)
# metadata may be null
if not metadata:
metadata = {}
# Convert string datetime into actual datetime
try:
execution_date = timezone.parse(execution_date)
except ValueError:
error_message = (
'Given execution date, {}, could not be identified '
'as a date. Example date format: 2015-11-16T14:34:15+00:00'.format(
execution_date))
response = jsonify({'error': error_message})
response.status_code = 400
return response
logger = logging.getLogger('airflow.task')
task_log_reader = conf.get('core', 'task_log_reader')
handler = next((handler for handler in logger.handlers
if handler.name == task_log_reader), None)
ti = session.query(models.TaskInstance).filter(
models.TaskInstance.dag_id == dag_id,
models.TaskInstance.task_id == task_id,
models.TaskInstance.execution_date == dttm).first()
try:
if ti is None:
logs = ["*** Task instance did not exist in the DB\n"]
metadata['end_of_log'] = True
else:
dag = dagbag.get_dag(dag_id)
ti.task = dag.get_task(ti.task_id)
logs, metadatas = handler.read(ti, try_number, metadata=metadata)
metadata = metadatas[0]
for i, log in enumerate(logs):
if PY2 and not isinstance(log, unicode):
logs[i] = log.decode('utf-8')
message = logs[0]
return jsonify(message=message, metadata=metadata)
except AttributeError as e:
error_message = ["Task log handler {} does not support read logs.\n{}\n"
.format(task_log_reader, str(e))]
metadata['end_of_log'] = True
return jsonify(message=error_message, error=True, metadata=metadata)
@expose('/log')
@login_required
@wwwutils.action_logging
@provide_session
def log(self, session=None):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
execution_date = request.args.get('execution_date')
dttm = pendulum.parse(execution_date)
form = DateTimeForm(data={'execution_date': dttm})
dag = dagbag.get_dag(dag_id)
ti = session.query(models.TaskInstance).filter(
models.TaskInstance.dag_id == dag_id,
models.TaskInstance.task_id == task_id,
models.TaskInstance.execution_date == dttm).first()
logs = [''] * (ti.next_try_number - 1 if ti is not None else 0)
return self.render(
'airflow/ti_log.html',
logs=logs, dag=dag, title="Log by attempts",
dag_id=dag.dag_id, task_id=task_id,
execution_date=execution_date, form=form)
@expose('/task')
@login_required
@wwwutils.action_logging
def task(self):
TI = models.TaskInstance
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
# Carrying execution_date through, even though it's irrelevant for
# this context
execution_date = request.args.get('execution_date')
dttm = pendulum.parse(execution_date)
form = DateTimeForm(data={'execution_date': dttm})
dag = dagbag.get_dag(dag_id)
if not dag or task_id not in dag.task_ids:
flash(
"Task [{}.{}] doesn't seem to exist"
" at the moment".format(dag_id, task_id),
"error")
return redirect('/admin/')
task = copy.copy(dag.get_task(task_id))
task.resolve_template_files()
ti = TI(task=task, execution_date=dttm)
ti.refresh_from_db()
ti_attrs = []
for attr_name in dir(ti):
if not attr_name.startswith('_'):
attr = getattr(ti, attr_name)
if type(attr) != type(self.task): # noqa: E721
ti_attrs.append((attr_name, str(attr)))
task_attrs = []
for attr_name in dir(task):
if not attr_name.startswith('_'):
attr = getattr(task, attr_name)
if type(attr) != type(self.task) and \
attr_name not in attr_renderer: # noqa: E721
task_attrs.append((attr_name, str(attr)))
# Color coding the special attributes that are code
special_attrs_rendered = {}
for attr_name in attr_renderer:
if hasattr(task, attr_name):
source = getattr(task, attr_name)
special_attrs_rendered[attr_name] = attr_renderer[attr_name](source)
no_failed_deps_result = [(
"Unknown",
dedent("""\
All dependencies are met but the task instance is not running.
In most cases this just means that the task will probably
be scheduled soon unless:<br/>
- The scheduler is down or under heavy load<br/>
- The following configuration values may be limiting the number
of queueable processes:
<code>parallelism</code>,
<code>dag_concurrency</code>,
<code>max_active_dag_runs_per_dag</code>,
<code>non_pooled_task_slot_count</code><br/>
{}
<br/>
If this task instance does not start soon please contact your Airflow """
"""administrator for assistance."""
.format(
"- This task instance already ran and had its state changed "
"manually (e.g. cleared in the UI)<br/>"
if ti.state == State.NONE else "")))]
# Use the scheduler's context to figure out which dependencies are not met
dep_context = DepContext(SCHEDULER_DEPS)
failed_dep_reasons = [(dep.dep_name, dep.reason) for dep in
ti.get_failed_dep_statuses(
dep_context=dep_context)]
title = "Task Instance Details"
return self.render(
'airflow/task.html',
task_attrs=task_attrs,
ti_attrs=ti_attrs,
failed_dep_reasons=failed_dep_reasons or no_failed_deps_result,
task_id=task_id,
execution_date=execution_date,
special_attrs_rendered=special_attrs_rendered,
form=form,
dag=dag, title=title)
@expose('/xcom')
@login_required
@wwwutils.action_logging
@provide_session
def xcom(self, session=None):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
# Carrying execution_date through, even though it's irrelevant for
# this context
execution_date = request.args.get('execution_date')
dttm = pendulum.parse(execution_date)
form = DateTimeForm(data={'execution_date': dttm})
dag = dagbag.get_dag(dag_id)
if not dag or task_id not in dag.task_ids:
flash(
"Task [{}.{}] doesn't seem to exist"
" at the moment".format(dag_id, task_id),
"error")
return redirect('/admin/')
xcomlist = session.query(XCom).filter(
XCom.dag_id == dag_id, XCom.task_id == task_id,
XCom.execution_date == dttm).all()
attributes = []
for xcom in xcomlist:
if not xcom.key.startswith('_'):
attributes.append((xcom.key, xcom.value))
title = "XCom"
return self.render(
'airflow/xcom.html',
attributes=attributes,
task_id=task_id,
execution_date=execution_date,
form=form,
dag=dag, title=title)
@expose('/run')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def run(self):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
origin = request.args.get('origin')
dag = dagbag.get_dag(dag_id)
task = dag.get_task(task_id)
execution_date = request.args.get('execution_date')
execution_date = pendulum.parse(execution_date)
ignore_all_deps = request.args.get('ignore_all_deps') == "true"
ignore_task_deps = request.args.get('ignore_task_deps') == "true"
ignore_ti_state = request.args.get('ignore_ti_state') == "true"
from airflow.executors import GetDefaultExecutor
executor = GetDefaultExecutor()
valid_celery_config = False
valid_kubernetes_config = False
try:
from airflow.executors.celery_executor import CeleryExecutor
valid_celery_config = isinstance(executor, CeleryExecutor)
except ImportError:
pass
try:
from airflow.contrib.executors.kubernetes_executor import KubernetesExecutor
valid_kubernetes_config = isinstance(executor, KubernetesExecutor)
except ImportError:
pass
if not valid_celery_config and not valid_kubernetes_config:
flash("Only works with the Celery or Kubernetes executors, sorry", "error")
return redirect(origin)
ti = models.TaskInstance(task=task, execution_date=execution_date)
ti.refresh_from_db()
# Make sure the task instance can be queued
dep_context = DepContext(
deps=QUEUE_DEPS,
ignore_all_deps=ignore_all_deps,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state)
failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
if failed_deps:
failed_deps_str = ", ".join(
["{}: {}".format(dep.dep_name, dep.reason) for dep in failed_deps])
flash("Could not queue task instance for execution, dependencies not met: "
"{}".format(failed_deps_str),
"error")
return redirect(origin)
executor.start()
executor.queue_task_instance(
ti,
ignore_all_deps=ignore_all_deps,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state)
executor.heartbeat()
flash(
"Sent {} to the message queue, "
"it should start any moment now.".format(ti))
return redirect(origin)
@expose('/delete')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def delete(self):
from airflow.api.common.experimental import delete_dag
from airflow.exceptions import DagNotFound, DagFileExists
dag_id = request.args.get('dag_id')
origin = request.args.get('origin') or "/admin/"
try:
delete_dag.delete_dag(dag_id)
except DagNotFound:
flash("DAG with id {} not found. Cannot delete".format(dag_id))
return redirect(request.referrer)
except DagFileExists:
flash("Dag id {} is still in DagBag. "
"Remove the DAG file first.".format(dag_id))
return redirect(request.referrer)
flash("Deleting DAG with id {}. May take a couple minutes to fully"
" disappear.".format(dag_id))
# Upon successful delete return to origin
return redirect(origin)
@expose('/trigger')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def trigger(self):
dag_id = request.args.get('dag_id')
origin = request.args.get('origin') or "/admin/"
dag = dagbag.get_dag(dag_id)
if not dag:
flash("Cannot find dag {}".format(dag_id))
return redirect(origin)
execution_date = timezone.utcnow()
run_id = "manual__{0}".format(execution_date.isoformat())
dr = DagRun.find(dag_id=dag_id, run_id=run_id)
if dr:
flash("This run_id {} already exists".format(run_id))
return redirect(origin)
run_conf = {}
dag.create_dagrun(
run_id=run_id,
execution_date=execution_date,
state=State.RUNNING,
conf=run_conf,
external_trigger=True
)
flash(
"Triggered {}, "
"it should start any moment now.".format(dag_id))
return redirect(origin)
def _clear_dag_tis(self, dag, start_date, end_date, origin,
recursive=False, confirmed=False):
if confirmed:
count = dag.clear(
start_date=start_date,
end_date=end_date,
include_subdags=recursive,
include_parentdag=recursive,
)
flash("{0} task instances have been cleared".format(count))
return redirect(origin)
tis = dag.clear(
start_date=start_date,
end_date=end_date,
include_subdags=recursive,
dry_run=True,
include_parentdag=recursive,
)
if not tis:
flash("No task instances to clear", 'error')
response = redirect(origin)
else:
details = "\n".join([str(t) for t in tis])
response = self.render(
'airflow/confirm.html',
message=("Here's the list of task instances you are about "
"to clear:"),
details=details)
return response
@expose('/clear')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def clear(self):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
origin = request.args.get('origin')
dag = dagbag.get_dag(dag_id)
execution_date = request.args.get('execution_date')
execution_date = pendulum.parse(execution_date)
confirmed = request.args.get('confirmed') == "true"
upstream = request.args.get('upstream') == "true"
downstream = request.args.get('downstream') == "true"
future = request.args.get('future') == "true"
past = request.args.get('past') == "true"
recursive = request.args.get('recursive') == "true"
dag = dag.sub_dag(
task_regex=r"^{0}$".format(task_id),
include_downstream=downstream,
include_upstream=upstream)
end_date = execution_date if not future else None
start_date = execution_date if not past else None
return self._clear_dag_tis(dag, start_date, end_date, origin,
recursive=recursive, confirmed=confirmed)
@expose('/dagrun_clear')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def dagrun_clear(self):
dag_id = request.args.get('dag_id')
origin = request.args.get('origin')
execution_date = request.args.get('execution_date')
confirmed = request.args.get('confirmed') == "true"
dag = dagbag.get_dag(dag_id)
execution_date = pendulum.parse(execution_date)
start_date = execution_date
end_date = execution_date
return self._clear_dag_tis(dag, start_date, end_date, origin,
recursive=True, confirmed=confirmed)
@expose('/blocked')
@login_required
@provide_session
def blocked(self, session=None):
DR = models.DagRun
dags = session\
.query(DR.dag_id, sqla.func.count(DR.id))\
.filter(DR.state == State.RUNNING)\
.group_by(DR.dag_id)\
.all()
payload = []
for dag_id, active_dag_runs in dags:
max_active_runs = 0
if dag_id in dagbag.dags:
max_active_runs = dagbag.dags[dag_id].max_active_runs
payload.append({
'dag_id': dag_id,
'active_dag_run': active_dag_runs,
'max_active_runs': max_active_runs,
})
return wwwutils.json_response(payload)
def _mark_dagrun_state_as_failed(self, dag_id, execution_date, confirmed, origin):
if not execution_date:
flash('Invalid execution date', 'error')
return redirect(origin)
execution_date = pendulum.parse(execution_date)
dag = dagbag.get_dag(dag_id)
if not dag:
flash('Cannot find DAG: {}'.format(dag_id), 'error')
return redirect(origin)
new_dag_state = set_dag_run_state_to_failed(dag, execution_date, commit=confirmed)
if confirmed:
flash('Marked failed on {} task instances'.format(len(new_dag_state)))
return redirect(origin)
else:
details = '\n'.join([str(t) for t in new_dag_state])
response = self.render('airflow/confirm.html',
message=("Here's the list of task instances you are "
"about to mark as failed"),
details=details)
return response
def _mark_dagrun_state_as_success(self, dag_id, execution_date, confirmed, origin):
if not execution_date:
flash('Invalid execution date', 'error')
return redirect(origin)
execution_date = pendulum.parse(execution_date)
dag = dagbag.get_dag(dag_id)
if not dag:
flash('Cannot find DAG: {}'.format(dag_id), 'error')
return redirect(origin)
new_dag_state = set_dag_run_state_to_success(dag, execution_date,
commit=confirmed)
if confirmed:
flash('Marked success on {} task instances'.format(len(new_dag_state)))
return redirect(origin)
else:
details = '\n'.join([str(t) for t in new_dag_state])
response = self.render('airflow/confirm.html',
message=("Here's the list of task instances you are "
"about to mark as success"),
details=details)
return response
@expose('/dagrun_failed')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def dagrun_failed(self):
dag_id = request.args.get('dag_id')
execution_date = request.args.get('execution_date')
confirmed = request.args.get('confirmed') == 'true'
origin = request.args.get('origin')
return self._mark_dagrun_state_as_failed(dag_id, execution_date,
confirmed, origin)
@expose('/dagrun_success')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def dagrun_success(self):
dag_id = request.args.get('dag_id')
execution_date = request.args.get('execution_date')
confirmed = request.args.get('confirmed') == 'true'
origin = request.args.get('origin')
return self._mark_dagrun_state_as_success(dag_id, execution_date,
confirmed, origin)
def _mark_task_instance_state(self, dag_id, task_id, origin, execution_date,
confirmed, upstream, downstream,
future, past, state):
dag = dagbag.get_dag(dag_id)
task = dag.get_task(task_id)
task.dag = dag
execution_date = pendulum.parse(execution_date)
if not dag:
flash("Cannot find DAG: {}".format(dag_id))
return redirect(origin)
if not task:
flash("Cannot find task {} in DAG {}".format(task_id, dag.dag_id))
return redirect(origin)
from airflow.api.common.experimental.mark_tasks import set_state
if confirmed:
altered = set_state(task=task, execution_date=execution_date,
upstream=upstream, downstream=downstream,
future=future, past=past, state=state,
commit=True)
flash("Marked {} on {} task instances".format(state, len(altered)))
return redirect(origin)
to_be_altered = set_state(task=task, execution_date=execution_date,
upstream=upstream, downstream=downstream,
future=future, past=past, state=state,
commit=False)
details = "\n".join([str(t) for t in to_be_altered])
response = self.render("airflow/confirm.html",
message=("Here's the list of task instances you are "
"about to mark as {}:".format(state)),
details=details)
return response
@expose('/failed')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def failed(self):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
origin = request.args.get('origin')
execution_date = request.args.get('execution_date')
confirmed = request.args.get('confirmed') == "true"
upstream = request.args.get('upstream') == "true"
downstream = request.args.get('downstream') == "true"
future = request.args.get('future') == "true"
past = request.args.get('past') == "true"
return self._mark_task_instance_state(dag_id, task_id, origin, execution_date,
confirmed, upstream, downstream,
future, past, State.FAILED)
@expose('/success')
@login_required
@wwwutils.action_logging
@wwwutils.notify_owner
def success(self):
dag_id = request.args.get('dag_id')
task_id = request.args.get('task_id')
origin = request.args.get('origin')
execution_date = request.args.get('execution_date')
confirmed = request.args.get('confirmed') == "true"
upstream = request.args.get('upstream') == "true"
downstream = request.args.get('downstream') == "true"
future = request.args.get('future') == "true"
past = request.args.get('past') == "true"
return self._mark_task_instance_state(dag_id, task_id, origin, execution_date,
confirmed, upstream, downstream,
future, past, State.SUCCESS)
@expose('/tree')
@login_required
@wwwutils.gzipped
@wwwutils.action_logging
@provide_session
def tree(self, session=None):
default_dag_run = conf.getint('webserver', 'default_dag_run_display_number')
dag_id = request.args.get('dag_id')
blur = conf.getboolean('webserver', 'demo_mode')
dag = dagbag.get_dag(dag_id)
if dag_id not in dagbag.dags:
flash('DAG "{0}" seems to be missing.'.format(dag_id), "error")
return redirect('/admin/')
root = request.args.get('root')
if root:
dag = dag.sub_dag(
task_regex=root,
include_downstream=False,
include_upstream=True)
base_date = request.args.get('base_date')
num_runs = request.args.get('num_runs')
num_runs = int(num_runs) if num_runs else default_dag_run
if base_date:
base_date = timezone.parse(base_date)
else:
base_date = dag.latest_execution_date or timezone.utcnow()
DR = models.DagRun
dag_runs = (
session.query(DR)
.filter(
DR.dag_id == dag.dag_id,
DR.execution_date <= base_date)
.order_by(DR.execution_date.desc())
.limit(num_runs)
.all()
)
dag_runs = {
dr.execution_date: alchemy_to_dict(dr) for dr in dag_runs}
dates = sorted(list(dag_runs.keys()))
max_date = max(dates) if dates else None
min_date = min(dates) if dates else None
tis = dag.get_task_instances(
session, start_date=min_date, end_date=base_date)
task_instances = {}
for ti in tis:
tid = alchemy_to_dict(ti)
dr = dag_runs.get(ti.execution_date)
tid['external_trigger'] = dr['external_trigger'] if dr else False
task_instances[(ti.task_id, ti.execution_date)] = tid
expanded = []
# The default recursion traces every path so that tree view has full
# expand/collapse functionality. After 5,000 nodes we stop and fall
# back on a quick DFS search for performance. See PR #320.
node_count = [0]
node_limit = 5000 / max(1, len(dag.roots))
def recurse_nodes(task, visited):
visited.add(task)
node_count[0] += 1
children = [
recurse_nodes(t, visited) for t in task.upstream_list
if node_count[0] < node_limit or t not in visited]
# D3 tree uses children vs _children to define what is
# expanded or not. The following block makes it such that
# repeated nodes are collapsed by default.
children_key = 'children'
if task.task_id not in expanded:
expanded.append(task.task_id)
elif children:
children_key = "_children"
def set_duration(tid):
if isinstance(tid, dict) and tid.get("state") == State.RUNNING \
and tid["start_date"] is not None:
d = timezone.utcnow() - pendulum.parse(tid["start_date"])
tid["duration"] = d.total_seconds()
return tid
return {
'name': task.task_id,
'instances': [
set_duration(task_instances.get((task.task_id, d))) or {
'execution_date': d.isoformat(),
'task_id': task.task_id
}
for d in dates],
children_key: children,
'num_dep': len(task.upstream_list),
'operator': task.task_type,
'retries': task.retries,
'owner': task.owner,
'start_date': task.start_date,
'end_date': task.end_date,
'depends_on_past': task.depends_on_past,
'ui_color': task.ui_color,
}
data = {
'name': '[DAG]',
'children': [recurse_nodes(t, set()) for t in dag.roots],
'instances': [dag_runs.get(d) or {'execution_date': d.isoformat()} for d in dates],
}
# minimize whitespace as this can be huge for bigger dags
data = json.dumps(data, default=json_ser, separators=(',', ':'))
session.commit()
form = DateTimeWithNumRunsForm(data={'base_date': max_date,
'num_runs': num_runs})
return self.render(
'airflow/tree.html',
operators=sorted(
list(set([op.__class__ for op in dag.tasks])),
key=lambda x: x.__name__
),
root=root,
form=form,
dag=dag, data=data, blur=blur, num_runs=num_runs)
@expose('/graph')
@login_required
@wwwutils.gzipped
@wwwutils.action_logging
@provide_session
def graph(self, session=None):
dag_id = request.args.get('dag_id')
blur = conf.getboolean('webserver', 'demo_mode')
dag = dagbag.get_dag(dag_id)
if dag_id not in dagbag.dags:
flash('DAG "{0}" seems to be missing.'.format(dag_id), "error")
return redirect('/admin/')
root = request.args.get('root')
if root:
dag = dag.sub_dag(
task_regex=root,
include_upstream=True,
include_downstream=False)
arrange = request.args.get('arrange', dag.orientation)
nodes = []
edges = []
for task in dag.tasks:
nodes.append({
'id': task.task_id,
'value': {
'label': task.task_id,
'labelStyle': "fill:{0};".format(task.ui_fgcolor),
'style': "fill:{0};".format(task.ui_color),
}
})
def get_upstream(task):
for t in task.upstream_list:
edge = {
'u': t.task_id,
'v': task.task_id,
}
if edge not in edges:
edges.append(edge)
get_upstream(t)
for t in dag.roots:
get_upstream(t)
dt_nr_dr_data = get_date_time_num_runs_dag_runs_form_data(request, session, dag)
dt_nr_dr_data['arrange'] = arrange
dttm = dt_nr_dr_data['dttm']
class GraphForm(DateTimeWithNumRunsWithDagRunsForm):
arrange = SelectField("Layout", choices=(
('LR', "Left->Right"),
('RL', "Right->Left"),
('TB', "Top->Bottom"),
('BT', "Bottom->Top"),
))
form = GraphForm(data=dt_nr_dr_data)
form.execution_date.choices = dt_nr_dr_data['dr_choices']
task_instances = {
ti.task_id: alchemy_to_dict(ti)
for ti in dag.get_task_instances(session, dttm, dttm)}
tasks = {
t.task_id: {
'dag_id': t.dag_id,
'task_type': t.task_type,
}
for t in dag.tasks}
if not tasks:
flash("No tasks found", "error")
session.commit()
doc_md = markdown.markdown(dag.doc_md) if hasattr(dag, 'doc_md') and dag.doc_md else ''
return self.render(
'airflow/graph.html',
dag=dag,
form=form,
width=request.args.get('width', "100%"),
height=request.args.get('height', "800"),
execution_date=dttm.isoformat(),
state_token=state_token(dt_nr_dr_data['dr_state']),
doc_md=doc_md,
arrange=arrange,
operators=sorted(
list(set([op.__class__ for op in dag.tasks])),
key=lambda x: x.__name__
),
blur=blur,
root=root or '',
task_instances=json.dumps(task_instances, indent=2),
tasks=json.dumps(tasks, indent=2),
nodes=json.dumps(nodes, indent=2),
edges=json.dumps(edges, indent=2), )
@expose('/duration')
@login_required
@wwwutils.action_logging
@provide_session
def duration(self, session=None):
default_dag_run = conf.getint('webserver', 'default_dag_run_display_number')
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
base_date = request.args.get('base_date')
num_runs = request.args.get('num_runs')
num_runs = int(num_runs) if num_runs else default_dag_run
if base_date:
base_date = pendulum.parse(base_date)
else:
base_date = dag.latest_execution_date or timezone.utcnow()
dates = dag.date_range(base_date, num=-abs(num_runs))
min_date = dates[0] if dates else datetime(2000, 1, 1)
root = request.args.get('root')
if root:
dag = dag.sub_dag(
task_regex=root,
include_upstream=True,
include_downstream=False)
chart_height = get_chart_height(dag)
chart = nvd3.lineChart(
name="lineChart", x_is_date=True, height=chart_height, width="1200")
cum_chart = nvd3.lineChart(
name="cumLineChart", x_is_date=True, height=chart_height, width="1200")
y = defaultdict(list)
x = defaultdict(list)
cum_y = defaultdict(list)
tis = dag.get_task_instances(
session, start_date=min_date, end_date=base_date)
TF = models.TaskFail
ti_fails = (
session
.query(TF)
.filter(
TF.dag_id == dag.dag_id,
TF.execution_date >= min_date,
TF.execution_date <= base_date,
TF.task_id.in_([t.task_id for t in dag.tasks]))
.all()
)
fails_totals = defaultdict(int)
for tf in ti_fails:
dict_key = (tf.dag_id, tf.task_id, tf.execution_date)
fails_totals[dict_key] += tf.duration
for ti in tis:
if ti.duration:
dttm = wwwutils.epoch(ti.execution_date)
x[ti.task_id].append(dttm)
y[ti.task_id].append(float(ti.duration))
fails_dict_key = (ti.dag_id, ti.task_id, ti.execution_date)
fails_total = fails_totals[fails_dict_key]
cum_y[ti.task_id].append(float(ti.duration + fails_total))
# determine the most relevant time unit for the set of task instance
# durations for the DAG
y_unit = infer_time_unit([d for t in y.values() for d in t])
cum_y_unit = infer_time_unit([d for t in cum_y.values() for d in t])
# update the y Axis on both charts to have the correct time units
chart.create_y_axis('yAxis', format='.02f', custom_format=False,
label='Duration ({})'.format(y_unit))
chart.axislist['yAxis']['axisLabelDistance'] = '40'
cum_chart.create_y_axis('yAxis', format='.02f', custom_format=False,
label='Duration ({})'.format(cum_y_unit))
cum_chart.axislist['yAxis']['axisLabelDistance'] = '40'
for task in dag.tasks:
if x[task.task_id]:
chart.add_serie(name=task.task_id, x=x[task.task_id],
y=scale_time_units(y[task.task_id], y_unit))
cum_chart.add_serie(name=task.task_id, x=x[task.task_id],
y=scale_time_units(cum_y[task.task_id],
cum_y_unit))
dates = sorted(list({ti.execution_date for ti in tis}))
max_date = max([ti.execution_date for ti in tis]) if dates else None
session.commit()
form = DateTimeWithNumRunsForm(data={'base_date': max_date,
'num_runs': num_runs})
chart.buildcontent()
cum_chart.buildcontent()
s_index = cum_chart.htmlcontent.rfind('});')
cum_chart.htmlcontent = (cum_chart.htmlcontent[:s_index] +
"$(function() {$( document ).trigger('chartload') })" +
cum_chart.htmlcontent[s_index:])
return self.render(
'airflow/duration_chart.html',
dag=dag,
demo_mode=conf.getboolean('webserver', 'demo_mode'),
root=root,
form=form,
chart=chart.htmlcontent,
cum_chart=cum_chart.htmlcontent
)
@expose('/tries')
@login_required
@wwwutils.action_logging
@provide_session
def tries(self, session=None):
default_dag_run = conf.getint('webserver', 'default_dag_run_display_number')
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
base_date = request.args.get('base_date')
num_runs = request.args.get('num_runs')
num_runs = int(num_runs) if num_runs else default_dag_run
if base_date:
base_date = pendulum.parse(base_date)
else:
base_date = dag.latest_execution_date or timezone.utcnow()
dates = dag.date_range(base_date, num=-abs(num_runs))
min_date = dates[0] if dates else datetime(2000, 1, 1)
root = request.args.get('root')
if root:
dag = dag.sub_dag(
task_regex=root,
include_upstream=True,
include_downstream=False)
chart_height = get_chart_height(dag)
chart = nvd3.lineChart(
name="lineChart", x_is_date=True, y_axis_format='d', height=chart_height,
width="1200")
for task in dag.tasks:
y = []
x = []
for ti in task.get_task_instances(session, start_date=min_date,
end_date=base_date):
dttm = wwwutils.epoch(ti.execution_date)
x.append(dttm)
y.append(ti.try_number)
if x:
chart.add_serie(name=task.task_id, x=x, y=y)
tis = dag.get_task_instances(
session, start_date=min_date, end_date=base_date)
tries = sorted(list({ti.try_number for ti in tis}))
max_date = max([ti.execution_date for ti in tis]) if tries else None
session.commit()
form = DateTimeWithNumRunsForm(data={'base_date': max_date,
'num_runs': num_runs})
chart.buildcontent()
return self.render(
'airflow/chart.html',
dag=dag,
demo_mode=conf.getboolean('webserver', 'demo_mode'),
root=root,
form=form,
chart=chart.htmlcontent
)
@expose('/landing_times')
@login_required
@wwwutils.action_logging
@provide_session
def landing_times(self, session=None):
default_dag_run = conf.getint('webserver', 'default_dag_run_display_number')
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
base_date = request.args.get('base_date')
num_runs = request.args.get('num_runs')
num_runs = int(num_runs) if num_runs else default_dag_run
if base_date:
base_date = pendulum.parse(base_date)
else:
base_date = dag.latest_execution_date or timezone.utcnow()
dates = dag.date_range(base_date, num=-abs(num_runs))
min_date = dates[0] if dates else datetime(2000, 1, 1)
root = request.args.get('root')
if root:
dag = dag.sub_dag(
task_regex=root,
include_upstream=True,
include_downstream=False)
chart_height = get_chart_height(dag)
chart = nvd3.lineChart(
name="lineChart", x_is_date=True, height=chart_height, width="1200")
y = {}
x = {}
for task in dag.tasks:
y[task.task_id] = []
x[task.task_id] = []
for ti in task.get_task_instances(session, start_date=min_date,
end_date=base_date):
if ti.end_date:
ts = ti.execution_date
following_schedule = dag.following_schedule(ts)
if dag.schedule_interval and following_schedule:
ts = following_schedule
dttm = wwwutils.epoch(ti.execution_date)
secs = (ti.end_date - ts).total_seconds()
x[ti.task_id].append(dttm)
y[ti.task_id].append(secs)
# determine the most relevant time unit for the set of landing times
# for the DAG
y_unit = infer_time_unit([d for t in y.values() for d in t])
# update the y Axis to have the correct time units
chart.create_y_axis('yAxis', format='.02f', custom_format=False,
label='Landing Time ({})'.format(y_unit))
chart.axislist['yAxis']['axisLabelDistance'] = '40'
for task in dag.tasks:
if x[task.task_id]:
chart.add_serie(name=task.task_id, x=x[task.task_id],
y=scale_time_units(y[task.task_id], y_unit))
tis = dag.get_task_instances(
session, start_date=min_date, end_date=base_date)
dates = sorted(list({ti.execution_date for ti in tis}))
max_date = max([ti.execution_date for ti in tis]) if dates else None
form = DateTimeWithNumRunsForm(data={'base_date': max_date,
'num_runs': num_runs})
chart.buildcontent()
return self.render(
'airflow/chart.html',
dag=dag,
chart=chart.htmlcontent,
height=str(chart_height + 100) + "px",
demo_mode=conf.getboolean('webserver', 'demo_mode'),
root=root,
form=form,
)
@expose('/paused', methods=['POST'])
@login_required
@wwwutils.action_logging
@provide_session
def paused(self, session=None):
DagModel = models.DagModel
dag_id = request.args.get('dag_id')
orm_dag = session.query(
DagModel).filter(DagModel.dag_id == dag_id).first()
if request.args.get('is_paused') == 'false':
orm_dag.is_paused = True
else:
orm_dag.is_paused = False
session.merge(orm_dag)
session.commit()
dagbag.get_dag(dag_id)
return "OK"
@expose('/refresh')
@login_required
@wwwutils.action_logging
@provide_session
def refresh(self, session=None):
DagModel = models.DagModel
dag_id = request.args.get('dag_id')
orm_dag = session.query(
DagModel).filter(DagModel.dag_id == dag_id).first()
if orm_dag:
orm_dag.last_expired = timezone.utcnow()
session.merge(orm_dag)
session.commit()
dagbag.get_dag(dag_id)
flash("DAG [{}] is now fresh as a daisy".format(dag_id))
return redirect(request.referrer)
@expose('/refresh_all')
@login_required
@wwwutils.action_logging
def refresh_all(self):
dagbag.collect_dags(only_if_updated=False)
flash("All DAGs are now up to date")
return redirect('/')
@expose('/gantt')
@login_required
@wwwutils.action_logging
@provide_session
def gantt(self, session=None):
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
demo_mode = conf.getboolean('webserver', 'demo_mode')
root = request.args.get('root')
if root:
dag = dag.sub_dag(
task_regex=root,
include_upstream=True,
include_downstream=False)
dt_nr_dr_data = get_date_time_num_runs_dag_runs_form_data(request, session, dag)
dttm = dt_nr_dr_data['dttm']
form = DateTimeWithNumRunsWithDagRunsForm(data=dt_nr_dr_data)
form.execution_date.choices = dt_nr_dr_data['dr_choices']
tis = [
ti for ti in dag.get_task_instances(session, dttm, dttm)
if ti.start_date]
tis = sorted(tis, key=lambda ti: ti.start_date)
TF = models.TaskFail
ti_fails = list(itertools.chain(*[(
session
.query(TF)
.filter(TF.dag_id == ti.dag_id,
TF.task_id == ti.task_id,
TF.execution_date == ti.execution_date)
.all()
) for ti in tis]))
TR = models.TaskReschedule
ti_reschedules = list(itertools.chain(*[(
session
.query(TR)
.filter(TR.dag_id == ti.dag_id,
TR.task_id == ti.task_id,
TR.execution_date == ti.execution_date)
.all()
) for ti in tis]))
# determine bars to show in the gantt chart
# all reschedules of one attempt are combinded into one bar
gantt_bar_items = []
for task_id, items in itertools.groupby(
sorted(tis + ti_fails + ti_reschedules, key=lambda ti: ti.task_id),
key=lambda ti: ti.task_id):
start_date = None
for i in sorted(items, key=lambda ti: ti.start_date):
start_date = start_date or i.start_date
end_date = i.end_date or timezone.utcnow()
if type(i) == models.TaskInstance:
gantt_bar_items.append((task_id, start_date, end_date, i.state))
start_date = None
elif type(i) == TF and (len(gantt_bar_items) == 0 or
end_date != gantt_bar_items[-1][2]):
gantt_bar_items.append((task_id, start_date, end_date, State.FAILED))
start_date = None
tasks = []
for gantt_bar_item in gantt_bar_items:
task_id = gantt_bar_item[0]
start_date = gantt_bar_item[1]
end_date = gantt_bar_item[2]
state = gantt_bar_item[3]
tasks.append({
'startDate': wwwutils.epoch(start_date),
'endDate': wwwutils.epoch(end_date),
'isoStart': start_date.isoformat()[:-4],
'isoEnd': end_date.isoformat()[:-4],
'taskName': task_id,
'duration': "{}".format(end_date - start_date)[:-4],
'status': state,
'executionDate': dttm.isoformat(),
})
states = {task['status']: task['status'] for task in tasks}
data = {
'taskNames': [ti.task_id for ti in tis],
'tasks': tasks,
'taskStatus': states,
'height': len(tis) * 25 + 25,
}
session.commit()
return self.render(
'airflow/gantt.html',
dag=dag,
execution_date=dttm.isoformat(),
form=form,
data=json.dumps(data, indent=2),
base_date='',
demo_mode=demo_mode,
root=root,
)
@expose('/object/task_instances')
@login_required
@wwwutils.action_logging
@provide_session
def task_instances(self, session=None):
dag_id = request.args.get('dag_id')
dag = dagbag.get_dag(dag_id)
dttm = request.args.get('execution_date')
if dttm:
dttm = pendulum.parse(dttm)
else:
return "Error: Invalid execution_date"
task_instances = {
ti.task_id: alchemy_to_dict(ti)
for ti in dag.get_task_instances(session, dttm, dttm)}
return json.dumps(task_instances)
@expose('/variables/<form>', methods=["GET", "POST"])
@login_required
@wwwutils.action_logging
def variables(self, form):
try:
if request.method == 'POST':
data = request.json
if data:
with create_session() as session:
var = models.Variable(key=form, val=json.dumps(data))
session.add(var)
session.commit()
return ""
else:
return self.render(
'airflow/variables/{}.html'.format(form)
)
except Exception:
# prevent XSS
form = escape(form)
return ("Error: form airflow/variables/{}.html "
"not found.").format(form), 404
@expose('/varimport', methods=["GET", "POST"])
@login_required
@wwwutils.action_logging
def varimport(self):
try:
d = json.load(UTF8_READER(request.files['file']))
except Exception as e:
flash("Missing file or syntax error: {}.".format(e))
else:
suc_count = fail_count = 0
for k, v in d.items():
try:
models.Variable.set(k, v, serialize_json=isinstance(v, dict))
except Exception as e:
logging.info('Variable import failed: {}'.format(repr(e)))
fail_count += 1
else:
suc_count += 1
flash("{} variable(s) successfully updated.".format(suc_count), 'info')
if fail_count:
flash(
"{} variables(s) failed to be updated.".format(fail_count), 'error')
return redirect('/admin/variable')
class HomeView(AdminIndexView):
@expose("/")
@login_required
@provide_session
def index(self, session=None):
DM = models.DagModel
# restrict the dags shown if filter_by_owner and current user is not superuser
do_filter = FILTER_BY_OWNER and (not current_user.is_superuser())
owner_mode = conf.get('webserver', 'OWNER_MODE').strip().lower()
hide_paused_dags_by_default = conf.getboolean('webserver',
'hide_paused_dags_by_default')
show_paused_arg = request.args.get('showPaused', 'None')
def get_int_arg(value, default=0):
try:
return int(value)
except ValueError:
return default
arg_current_page = request.args.get('page', '0')
arg_search_query = request.args.get('search', None)
dags_per_page = PAGE_SIZE
current_page = get_int_arg(arg_current_page, default=0)
if show_paused_arg.strip().lower() == 'false':
hide_paused = True
elif show_paused_arg.strip().lower() == 'true':
hide_paused = False
else:
hide_paused = hide_paused_dags_by_default
# read orm_dags from the db
sql_query = session.query(DM)
if do_filter and owner_mode == 'ldapgroup':
sql_query = sql_query.filter(
~DM.is_subdag,
DM.is_active,
DM.owners.in_(current_user.ldap_groups)
)
elif do_filter and owner_mode == 'user':
sql_query = sql_query.filter(
~DM.is_subdag, DM.is_active,
DM.owners == current_user.user.username
)
else:
sql_query = sql_query.filter(
~DM.is_subdag, DM.is_active
)
# optionally filter out "paused" dags
if hide_paused:
sql_query = sql_query.filter(~DM.is_paused)
orm_dags = {dag.dag_id: dag for dag
in sql_query
.all()}
import_errors = session.query(models.ImportError).all()
for ie in import_errors:
flash(
"Broken DAG: [{ie.filename}] {ie.stacktrace}".format(ie=ie),
"error")
# get a list of all non-subdag dags visible to everyone
# optionally filter out "paused" dags
if hide_paused:
unfiltered_webserver_dags = [dag for dag in dagbag.dags.values() if
not dag.parent_dag and not dag.is_paused]
else:
unfiltered_webserver_dags = [dag for dag in dagbag.dags.values() if
not dag.parent_dag]
# optionally filter to get only dags that the user should see
if do_filter and owner_mode == 'ldapgroup':
# only show dags owned by someone in @current_user.ldap_groups
webserver_dags = {
dag.dag_id: dag
for dag in unfiltered_webserver_dags
if dag.owner in current_user.ldap_groups
}
elif do_filter and owner_mode == 'user':
# only show dags owned by @current_user.user.username
webserver_dags = {
dag.dag_id: dag
for dag in unfiltered_webserver_dags
if dag.owner == current_user.user.username
}
else:
webserver_dags = {
dag.dag_id: dag
for dag in unfiltered_webserver_dags
}
if arg_search_query:
lower_search_query = arg_search_query.lower()
# filter by dag_id
webserver_dags_filtered = {
dag_id: dag
for dag_id, dag in webserver_dags.items()
if (lower_search_query in dag_id.lower() or
lower_search_query in dag.owner.lower())
}
all_dag_ids = (set([dag.dag_id for dag in orm_dags.values()
if lower_search_query in dag.dag_id.lower() or
lower_search_query in dag.owners.lower()]) |
set(webserver_dags_filtered.keys()))
sorted_dag_ids = sorted(all_dag_ids)
else:
webserver_dags_filtered = webserver_dags
sorted_dag_ids = sorted(set(orm_dags.keys()) | set(webserver_dags.keys()))
start = current_page * dags_per_page
end = start + dags_per_page
num_of_all_dags = len(sorted_dag_ids)
page_dag_ids = sorted_dag_ids[start:end]
num_of_pages = int(math.ceil(num_of_all_dags / float(dags_per_page)))
auto_complete_data = set()
for dag in webserver_dags_filtered.values():
auto_complete_data.add(dag.dag_id)
auto_complete_data.add(dag.owner)
for dag in orm_dags.values():
auto_complete_data.add(dag.dag_id)
auto_complete_data.add(dag.owners)
return self.render(
'airflow/dags.html',
webserver_dags=webserver_dags_filtered,
orm_dags=orm_dags,
hide_paused=hide_paused,
current_page=current_page,
search_query=arg_search_query if arg_search_query else '',
page_size=dags_per_page,
num_of_pages=num_of_pages,
num_dag_from=start + 1,
num_dag_to=min(end, num_of_all_dags),
num_of_all_dags=num_of_all_dags,
paging=wwwutils.generate_pages(current_page, num_of_pages,
search=arg_search_query,
showPaused=not hide_paused),
dag_ids_in_page=page_dag_ids,
auto_complete_data=auto_complete_data)
class QueryView(wwwutils.DataProfilingMixin, BaseView):
@expose('/', methods=['POST', 'GET'])
@wwwutils.gzipped
@provide_session
def query(self, session=None):
dbs = session.query(models.Connection).order_by(
models.Connection.conn_id).all()
session.expunge_all()
db_choices = list(
((db.conn_id, db.conn_id) for db in dbs if db.get_hook()))
conn_id_str = request.form.get('conn_id')
csv = request.form.get('csv') == "true"
sql = request.form.get('sql')
class QueryForm(Form):
conn_id = SelectField("Layout", choices=db_choices)
sql = TextAreaField("SQL", widget=wwwutils.AceEditorWidget())
data = {
'conn_id': conn_id_str,
'sql': sql,
}
results = None
has_data = False
error = False
if conn_id_str:
db = [db for db in dbs if db.conn_id == conn_id_str][0]
hook = db.get_hook()
try:
df = hook.get_pandas_df(wwwutils.limit_sql(sql, QUERY_LIMIT, conn_type=db.conn_type))
# df = hook.get_pandas_df(sql)
has_data = len(df) > 0
df = df.fillna('')
results = df.to_html(
classes=[
'table', 'table-bordered', 'table-striped', 'no-wrap'],
index=False,
na_rep='',
) if has_data else ''
except Exception as e:
flash(str(e), 'error')
error = True
if has_data and len(df) == QUERY_LIMIT:
flash(
"Query output truncated at " + str(QUERY_LIMIT) +
" rows", 'info')
if not has_data and error:
flash('No data', 'error')
if csv:
return Response(
response=df.to_csv(index=False),
status=200,
mimetype="application/text")
form = QueryForm(request.form, data=data)
session.commit()
return self.render(
'airflow/query.html', form=form,
title="Ad Hoc Query",
results=results or '',
has_data=has_data)
class AirflowModelView(ModelView):
list_template = 'airflow/model_list.html'
edit_template = 'airflow/model_edit.html'
create_template = 'airflow/model_create.html'
column_display_actions = True
page_size = PAGE_SIZE
class ModelViewOnly(wwwutils.LoginMixin, AirflowModelView):
"""
Modifying the base ModelView class for non edit, browse only operations
"""
named_filter_urls = True
can_create = False
can_edit = False
can_delete = False
column_display_pk = True
class PoolModelView(wwwutils.SuperUserMixin, AirflowModelView):
column_list = ('pool', 'slots', 'used_slots', 'queued_slots')
column_formatters = dict(
pool=pool_link, used_slots=fused_slots, queued_slots=fqueued_slots)
named_filter_urls = True
form_args = {
'pool': {
'validators': [
validators.DataRequired(),
]
}
}
class SlaMissModelView(wwwutils.SuperUserMixin, ModelViewOnly):
verbose_name_plural = "SLA misses"
verbose_name = "SLA miss"
column_list = (
'dag_id', 'task_id', 'execution_date', 'email_sent', 'timestamp')
column_formatters = dict(
task_id=task_instance_link,
execution_date=datetime_f,
timestamp=datetime_f,
dag_id=dag_link)
named_filter_urls = True
column_searchable_list = ('dag_id', 'task_id',)
column_filters = (
'dag_id', 'task_id', 'email_sent', 'timestamp', 'execution_date')
filter_converter = wwwutils.UtcFilterConverter()
form_widget_args = {
'email_sent': {'disabled': True},
'timestamp': {'disabled': True},
}
@provide_session
def _connection_ids(session=None):
return [(c.conn_id, c.conn_id) for c in (
session
.query(models.Connection.conn_id)
.group_by(models.Connection.conn_id))]
class ChartModelView(wwwutils.DataProfilingMixin, AirflowModelView):
verbose_name = "chart"
verbose_name_plural = "charts"
form_columns = (
'label',
'owner',
'conn_id',
'chart_type',
'show_datatable',
'x_is_date',
'y_log_scale',
'show_sql',
'height',
'sql_layout',
'sql',
'default_params',
)
column_list = (
'label',
'conn_id',
'chart_type',
'owner',
'last_modified',
)
column_sortable_list = (
'label',
'conn_id',
'chart_type',
('owner', 'owner.username'),
'last_modified',
)
column_formatters = dict(label=label_link, last_modified=datetime_f)
column_default_sort = ('last_modified', True)
create_template = 'airflow/chart/create.html'
edit_template = 'airflow/chart/edit.html'
column_filters = ('label', 'owner.username', 'conn_id')
column_searchable_list = ('owner.username', 'label', 'sql')
column_descriptions = {
'label': "Can include {{ templated_fields }} and {{ macros }}",
'chart_type': "The type of chart to be displayed",
'sql': "Can include {{ templated_fields }} and {{ macros }}.",
'height': "Height of the chart, in pixels.",
'conn_id': "Source database to run the query against",
'x_is_date': (
"Whether the X axis should be casted as a date field. Expect most "
"intelligible date formats to get casted properly."
),
'owner': (
"The chart's owner, mostly used for reference and filtering in "
"the list view."
),
'show_datatable':
"Whether to display an interactive data table under the chart.",
'default_params': (
'A dictionary of {"key": "values",} that define what the '
'templated fields (parameters) values should be by default. '
'To be valid, it needs to "eval" as a Python dict. '
'The key values will show up in the url\'s querystring '
'and can be altered there.'
),
'show_sql': "Whether to display the SQL statement as a collapsible "
"section in the chart page.",
'y_log_scale': "Whether to use a log scale for the Y axis.",
'sql_layout': (
"Defines the layout of the SQL that the application should "
"expect. Depending on the tables you are sourcing from, it may "
"make more sense to pivot / unpivot the metrics."
),
}
column_labels = {
'sql': "SQL",
'height': "Chart Height",
'sql_layout': "SQL Layout",
'show_sql': "Display the SQL Statement",
'default_params': "Default Parameters",
}
form_choices = {
'chart_type': [
('line', 'Line Chart'),
('spline', 'Spline Chart'),
('bar', 'Bar Chart'),
('column', 'Column Chart'),
('area', 'Overlapping Area Chart'),
('stacked_area', 'Stacked Area Chart'),
('percent_area', 'Percent Area Chart'),
('datatable', 'No chart, data table only'),
],
'sql_layout': [
('series', 'SELECT series, x, y FROM ...'),
('columns', 'SELECT x, y (series 1), y (series 2), ... FROM ...'),
],
'conn_id': _connection_ids()
}
def on_model_change(self, form, model, is_created=True):
if model.iteration_no is None:
model.iteration_no = 0
else:
model.iteration_no += 1
if not model.user_id and current_user and hasattr(current_user, 'id'):
model.user_id = current_user.id
model.last_modified = timezone.utcnow()
chart_mapping = (
('line', 'lineChart'),
('spline', 'lineChart'),
('bar', 'multiBarChart'),
('column', 'multiBarChart'),
('area', 'stackedAreaChart'),
('stacked_area', 'stackedAreaChart'),
('percent_area', 'stackedAreaChart'),
('datatable', 'datatable'),
)
chart_mapping = dict(chart_mapping)
class KnownEventView(wwwutils.DataProfilingMixin, AirflowModelView):
verbose_name = "known event"
verbose_name_plural = "known events"
form_columns = (
'label',
'event_type',
'start_date',
'end_date',
'reported_by',
'description',
)
form_args = {
'label': {
'validators': [
validators.DataRequired(),
],
},
'event_type': {
'validators': [
validators.DataRequired(),
],
},
'start_date': {
'validators': [
validators.DataRequired(),
],
'filters': [
parse_datetime_f,
],
},
'end_date': {
'validators': [
validators.DataRequired(),
GreaterEqualThan(fieldname='start_date'),
],
'filters': [
parse_datetime_f,
]
},
'reported_by': {
'validators': [
validators.DataRequired(),
],
}
}
column_list = (
'label',
'event_type',
'start_date',
'end_date',
'reported_by',
)
column_default_sort = ("start_date", True)
column_sortable_list = (
'label',
# todo: yes this has a spelling error
('event_type', 'event_type.know_event_type'),
'start_date',
'end_date',
('reported_by', 'reported_by.username'),
)
filter_converter = wwwutils.UtcFilterConverter()
form_overrides = dict(start_date=DateTimeField, end_date=DateTimeField)
class KnownEventTypeView(wwwutils.DataProfilingMixin, AirflowModelView):
pass
# NOTE: For debugging / troubleshooting
# mv = KnowEventTypeView(
# models.KnownEventType,
# Session, name="Known Event Types", category="Manage")
# admin.add_view(mv)
# class DagPickleView(SuperUserMixin, ModelView):
# pass
# mv = DagPickleView(
# models.DagPickle,
# Session, name="Pickles", category="Manage")
# admin.add_view(mv)
class VariableView(wwwutils.DataProfilingMixin, AirflowModelView):
verbose_name = "Variable"
verbose_name_plural = "Variables"
list_template = 'airflow/variable_list.html'
def hidden_field_formatter(view, context, model, name):
if wwwutils.should_hide_value_for_key(model.key):
return Markup('*' * 8)
val = getattr(model, name)
if val:
return val
else:
return Markup('<span class="label label-danger">Invalid</span>')
form_columns = (
'key',
'val',
)
column_list = ('key', 'val', 'is_encrypted',)
column_filters = ('key', 'val')
column_searchable_list = ('key', 'val', 'is_encrypted',)
column_default_sort = ('key', False)
form_widget_args = {
'is_encrypted': {'disabled': True},
'val': {
'rows': 20,
}
}
form_args = {
'key': {
'validators': {
validators.DataRequired(),
},
},
}
column_sortable_list = (
'key',
'val',
'is_encrypted',
)
column_formatters = {
'val': hidden_field_formatter,
}
# Default flask-admin export functionality doesn't handle serialized json
@action('varexport', 'Export', None)
@provide_session
def action_varexport(self, ids, session=None):
V = models.Variable
qry = session.query(V).filter(V.id.in_(ids)).all()
var_dict = {}
d = json.JSONDecoder()
for var in qry:
val = None
try:
val = d.decode(var.val)
except Exception:
val = var.val
var_dict[var.key] = val
response = make_response(json.dumps(var_dict, sort_keys=True, indent=4))
response.headers["Content-Disposition"] = "attachment; filename=variables.json"
return response
def on_form_prefill(self, form, id):
if wwwutils.should_hide_value_for_key(form.key.data):
form.val.data = '*' * 8
class XComView(wwwutils.SuperUserMixin, AirflowModelView):
verbose_name = "XCom"
verbose_name_plural = "XComs"
form_columns = (
'key',
'value',
'execution_date',
'task_id',
'dag_id',
)
form_extra_fields = {
'value': StringField('Value'),
}
form_args = {
'execution_date': {
'filters': [
parse_datetime_f,
]
}
}
column_filters = ('key', 'timestamp', 'execution_date', 'task_id', 'dag_id')
column_searchable_list = ('key', 'timestamp', 'execution_date', 'task_id', 'dag_id')
filter_converter = wwwutils.UtcFilterConverter()
form_overrides = dict(execution_date=DateTimeField)
class JobModelView(ModelViewOnly):
verbose_name_plural = "jobs"
verbose_name = "job"
column_display_actions = False
column_default_sort = ('start_date', True)
column_filters = (
'job_type', 'dag_id', 'state',
'unixname', 'hostname', 'start_date', 'end_date', 'latest_heartbeat')
column_formatters = dict(
start_date=datetime_f,
end_date=datetime_f,
hostname=nobr_f,
state=state_f,
latest_heartbeat=datetime_f)
filter_converter = wwwutils.UtcFilterConverter()
class DagRunModelView(ModelViewOnly):
verbose_name_plural = "DAG Runs"
can_edit = True
can_create = True
column_editable_list = ('state',)
verbose_name = "dag run"
column_default_sort = ('execution_date', True)
form_choices = {
'state': [
('success', 'success'),
('running', 'running'),
('failed', 'failed'),
],
}
form_args = dict(
dag_id=dict(validators=[validators.DataRequired()])
)
column_list = (
'state', 'dag_id', 'execution_date', 'run_id', 'external_trigger')
column_filters = column_list
filter_converter = wwwutils.UtcFilterConverter()
column_searchable_list = ('dag_id', 'state', 'run_id')
column_formatters = dict(
execution_date=datetime_f,
state=state_f,
start_date=datetime_f,
dag_id=dag_link,
run_id=dag_run_link
)
@action('new_delete', "Delete", "Are you sure you want to delete selected records?")
@provide_session
def action_new_delete(self, ids, session=None):
deleted = set(session.query(models.DagRun)
.filter(models.DagRun.id.in_(ids))
.all())
session.query(models.DagRun) \
.filter(models.DagRun.id.in_(ids)) \
.delete(synchronize_session='fetch')
session.commit()
dirty_ids = []
for row in deleted:
dirty_ids.append(row.dag_id)
models.DagStat.update(dirty_ids, dirty_only=False, session=session)
@action('set_running', "Set state to 'running'", None)
@provide_session
def action_set_running(self, ids, session=None):
try:
DR = models.DagRun
count = 0
dirty_ids = []
for dr in session.query(DR).filter(DR.id.in_(ids)).all():
dirty_ids.append(dr.dag_id)
count += 1
dr.state = State.RUNNING
dr.start_date = timezone.utcnow()
models.DagStat.update(dirty_ids, session=session)
flash(
"{count} dag runs were set to running".format(**locals()))
except Exception as ex:
if not self.handle_view_exception(ex):
raise Exception("Ooops")
flash('Failed to set state', 'error')
@action('set_failed', "Set state to 'failed'",
"All running task instances would also be marked as failed, are you sure?")
@provide_session
def action_set_failed(self, ids, session=None):
try:
DR = models.DagRun
count = 0
dirty_ids = []
altered_tis = []
for dr in session.query(DR).filter(DR.id.in_(ids)).all():
dirty_ids.append(dr.dag_id)
count += 1
altered_tis += \
set_dag_run_state_to_failed(dagbag.get_dag(dr.dag_id),
dr.execution_date,
commit=True,
session=session)
models.DagStat.update(dirty_ids, session=session)
altered_ti_count = len(altered_tis)
flash(
"{count} dag runs and {altered_ti_count} task instances "
"were set to failed".format(**locals()))
except Exception as ex:
if not self.handle_view_exception(ex):
raise Exception("Ooops")
flash('Failed to set state', 'error')
@action('set_success', "Set state to 'success'",
"All task instances would also be marked as success, are you sure?")
@provide_session
def action_set_success(self, ids, session=None):
try:
DR = models.DagRun
count = 0
dirty_ids = []
altered_tis = []
for dr in session.query(DR).filter(DR.id.in_(ids)).all():
dirty_ids.append(dr.dag_id)
count += 1
altered_tis += \
set_dag_run_state_to_success(dagbag.get_dag(dr.dag_id),
dr.execution_date,
commit=True,
session=session)
models.DagStat.update(dirty_ids, session=session)
altered_ti_count = len(altered_tis)
flash(
"{count} dag runs and {altered_ti_count} task instances "
"were set to success".format(**locals()))
except Exception as ex:
if not self.handle_view_exception(ex):
raise Exception("Ooops")
flash('Failed to set state', 'error')
# Called after editing DagRun model in the UI.
@provide_session
def after_model_change(self, form, dagrun, is_created, session=None):
altered_tis = []
if dagrun.state == State.SUCCESS:
altered_tis = set_dag_run_state_to_success(
dagbag.get_dag(dagrun.dag_id),
dagrun.execution_date,
commit=True,
session=session)
elif dagrun.state == State.FAILED:
altered_tis = set_dag_run_state_to_failed(
dagbag.get_dag(dagrun.dag_id),
dagrun.execution_date,
commit=True,
session=session)
elif dagrun.state == State.RUNNING:
altered_tis = set_dag_run_state_to_running(
dagbag.get_dag(dagrun.dag_id),
dagrun.execution_date,
commit=True,
session=session)
altered_ti_count = len(altered_tis)
models.DagStat.update([dagrun.dag_id], session=session)
flash(
"1 dag run and {altered_ti_count} task instances "
"were set to '{dagrun.state}'".format(**locals()))
class LogModelView(ModelViewOnly):
verbose_name_plural = "logs"
verbose_name = "log"
column_display_actions = False
column_default_sort = ('dttm', True)
column_filters = ('dag_id', 'task_id', 'execution_date', 'extra')
filter_converter = wwwutils.UtcFilterConverter()
column_formatters = dict(
dttm=datetime_f, execution_date=datetime_f, dag_id=dag_link)
class TaskInstanceModelView(ModelViewOnly):
verbose_name_plural = "task instances"
verbose_name = "task instance"
column_filters = (
'state', 'dag_id', 'task_id', 'execution_date', 'hostname',
'queue', 'pool', 'operator', 'start_date', 'end_date')
filter_converter = wwwutils.UtcFilterConverter()
named_filter_urls = True
column_formatters = dict(
log_url=log_url_formatter,
task_id=task_instance_link,
hostname=nobr_f,
state=state_f,
execution_date=datetime_f,
start_date=datetime_f,
end_date=datetime_f,
queued_dttm=datetime_f,
dag_id=dag_link,
run_id=dag_run_link,
duration=duration_f)
column_searchable_list = ('dag_id', 'task_id', 'state')
column_default_sort = ('job_id', True)
form_choices = {
'state': [
('success', 'success'),
('running', 'running'),
('failed', 'failed'),
],
}
column_list = (
'state', 'dag_id', 'task_id', 'execution_date', 'operator',
'start_date', 'end_date', 'duration', 'job_id', 'hostname',
'unixname', 'priority_weight', 'queue', 'queued_dttm', 'try_number',
'pool', 'log_url')
page_size = PAGE_SIZE
@action('set_running', "Set state to 'running'", None)
def action_set_running(self, ids):
self.set_task_instance_state(ids, State.RUNNING)
@action('set_failed', "Set state to 'failed'", None)
def action_set_failed(self, ids):
self.set_task_instance_state(ids, State.FAILED)
@action('set_success', "Set state to 'success'", None)
def action_set_success(self, ids):
self.set_task_instance_state(ids, State.SUCCESS)
@action('set_retry', "Set state to 'up_for_retry'", None)
def action_set_retry(self, ids):
self.set_task_instance_state(ids, State.UP_FOR_RETRY)
@provide_session
@action('clear',
lazy_gettext('Clear'),
lazy_gettext(
'Are you sure you want to clear the state of the selected task instance(s)'
' and set their dagruns to the running state?'))
def action_clear(self, ids, session=None):
try:
TI = models.TaskInstance
dag_to_task_details = {}
dag_to_tis = {}
# Collect dags upfront as dagbag.get_dag() will reset the session
for id_str in ids:
task_id, dag_id, execution_date = iterdecode(id_str)
dag = dagbag.get_dag(dag_id)
task_details = dag_to_task_details.setdefault(dag, [])
task_details.append((task_id, execution_date))
for dag, task_details in dag_to_task_details.items():
for task_id, execution_date in task_details:
execution_date = parse_execution_date(execution_date)
ti = session.query(TI).filter(TI.task_id == task_id,
TI.dag_id == dag.dag_id,
TI.execution_date == execution_date).one()
tis = dag_to_tis.setdefault(dag, [])
tis.append(ti)
for dag, tis in dag_to_tis.items():
models.clear_task_instances(tis, session, dag=dag)
session.commit()
flash("{0} task instances have been cleared".format(len(ids)))
except Exception as ex:
if not self.handle_view_exception(ex):
raise Exception("Ooops")
flash('Failed to clear task instances', 'error')
@provide_session
def set_task_instance_state(self, ids, target_state, session=None):
try:
TI = models.TaskInstance
count = len(ids)
for id in ids:
task_id, dag_id, execution_date = iterdecode(id)
execution_date = parse_execution_date(execution_date)
ti = session.query(TI).filter(TI.task_id == task_id,
TI.dag_id == dag_id,
TI.execution_date == execution_date).one()
ti.state = target_state
session.commit()
flash(
"{count} task instances were set to '{target_state}'".format(**locals()))
except Exception as ex:
if not self.handle_view_exception(ex):
raise Exception("Ooops")
flash('Failed to set state', 'error')
def get_one(self, id):
"""
As a workaround for AIRFLOW-252, this method overrides Flask-Admin's ModelView.get_one().
TODO: this method should be removed once the below bug is fixed on Flask-Admin side.
https://github.com/flask-admin/flask-admin/issues/1226
"""
task_id, dag_id, execution_date = iterdecode(id)
execution_date = pendulum.parse(execution_date)
return self.session.query(self.model).get((task_id, dag_id, execution_date))
class ConnectionModelView(wwwutils.SuperUserMixin, AirflowModelView):
create_template = 'airflow/conn_create.html'
edit_template = 'airflow/conn_edit.html'
list_template = 'airflow/conn_list.html'
form_columns = (
'conn_id',
'conn_type',
'host',
'schema',
'login',
'password',
'port',
'extra',
'extra__jdbc__drv_path',
'extra__jdbc__drv_clsname',
'extra__google_cloud_platform__project',
'extra__google_cloud_platform__key_path',
'extra__google_cloud_platform__keyfile_dict',
'extra__google_cloud_platform__scope',
)
verbose_name = "Connection"
verbose_name_plural = "Connections"
column_default_sort = ('conn_id', False)
column_list = ('conn_id', 'conn_type', 'host', 'port', 'is_encrypted', 'is_extra_encrypted',)
form_overrides = dict(_password=PasswordField, _extra=TextAreaField)
form_widget_args = {
'is_extra_encrypted': {'disabled': True},
'is_encrypted': {'disabled': True},
}
# Used to customized the form, the forms elements get rendered
# and results are stored in the extra field as json. All of these
# need to be prefixed with extra__ and then the conn_type ___ as in
# extra__{conn_type}__name. You can also hide form elements and rename
# others from the connection_form.js file
form_extra_fields = {
'extra__jdbc__drv_path': StringField('Driver Path'),
'extra__jdbc__drv_clsname': StringField('Driver Class'),
'extra__google_cloud_platform__project': StringField('Project Id'),
'extra__google_cloud_platform__key_path': StringField('Keyfile Path'),
'extra__google_cloud_platform__keyfile_dict': PasswordField('Keyfile JSON'),
'extra__google_cloud_platform__scope': StringField('Scopes (comma separated)'),
}
form_choices = {
'conn_type': models.Connection._types
}
def on_model_change(self, form, model, is_created):
formdata = form.data
if formdata['conn_type'] in ['jdbc', 'google_cloud_platform']:
extra = {
key: formdata[key]
for key in self.form_extra_fields.keys() if key in formdata}
model.extra = json.dumps(extra)
@classmethod
def alert_fernet_key(cls):
fk = None
try:
fk = conf.get('core', 'fernet_key')
except Exception:
pass
return fk is None
@classmethod
def is_secure(cls):
"""
Used to display a message in the Connection list view making it clear
that the passwords and `extra` field can't be encrypted.
"""
is_secure = False
try:
import cryptography # noqa F401
conf.get('core', 'fernet_key')
is_secure = True
except Exception:
pass
return is_secure
def on_form_prefill(self, form, id):
try:
d = json.loads(form.data.get('extra', '{}'))
except Exception:
d = {}
for field in list(self.form_extra_fields.keys()):
value = d.get(field, '')
if value:
field = getattr(form, field)
field.data = value
class UserModelView(wwwutils.SuperUserMixin, AirflowModelView):
verbose_name = "User"
verbose_name_plural = "Users"
column_default_sort = 'username'
class VersionView(wwwutils.SuperUserMixin, BaseView):
@expose('/')
def version(self):
# Look at the version from setup.py
try:
airflow_version = pkg_resources.require("apache-airflow")[0].version
except Exception as e:
airflow_version = None
logging.error(e)
# Get the Git repo and git hash
git_version = None
try:
with open(os.path.join(*[settings.AIRFLOW_HOME, 'airflow', 'git_version'])) as f:
git_version = f.readline()
except Exception as e:
logging.error(e)
# Render information
title = "Version Info"
return self.render('airflow/version.html',
title=title,
airflow_version=airflow_version,
git_version=git_version)
class ConfigurationView(wwwutils.SuperUserMixin, BaseView):
@expose('/')
def conf(self):
raw = request.args.get('raw') == "true"
title = "Airflow Configuration"
subtitle = conf.AIRFLOW_CONFIG
if conf.getboolean("webserver", "expose_config"):
with open(conf.AIRFLOW_CONFIG, 'r') as f:
config = f.read()
table = [(section, key, value, source)
for section, parameters in conf.as_dict(True, True).items()
for key, (value, source) in parameters.items()]
else:
config = (
"# Your Airflow administrator chose not to expose the "
"configuration, most likely for security reasons.")
table = None
if raw:
return Response(
response=config,
status=200,
mimetype="application/text")
else:
code_html = Markup(highlight(
config,
lexers.IniLexer(), # Lexer call
HtmlFormatter(noclasses=True))
)
return self.render(
'airflow/config.html',
pre_subtitle=settings.HEADER + " v" + airflow.__version__,
code_html=code_html, title=title, subtitle=subtitle,
table=table)
class DagModelView(wwwutils.SuperUserMixin, ModelView):
column_list = ('dag_id', 'owners')
column_editable_list = ('is_paused',)
form_excluded_columns = ('is_subdag', 'is_active')
column_searchable_list = ('dag_id',)
column_filters = (
'dag_id', 'owners', 'is_paused', 'is_active', 'is_subdag',
'last_scheduler_run', 'last_expired')
filter_converter = wwwutils.UtcFilterConverter()
form_widget_args = {
'last_scheduler_run': {'disabled': True},
'fileloc': {'disabled': True},
'is_paused': {'disabled': True},
'last_pickled': {'disabled': True},
'pickle_id': {'disabled': True},
'last_loaded': {'disabled': True},
'last_expired': {'disabled': True},
'pickle_size': {'disabled': True},
'scheduler_lock': {'disabled': True},
'owners': {'disabled': True},
}
column_formatters = dict(
dag_id=dag_link,
)
can_delete = False
can_create = False
page_size = PAGE_SIZE
list_template = 'airflow/list_dags.html'
named_filter_urls = True
def get_query(self):
"""
Default filters for model
"""
return super(DagModelView, self)\
.get_query()\
.filter(or_(models.DagModel.is_active, models.DagModel.is_paused))\
.filter(~models.DagModel.is_subdag)
def get_count_query(self):
"""
Default filters for model
"""
return super(DagModelView, self)\
.get_count_query()\
.filter(models.DagModel.is_active)\
.filter(~models.DagModel.is_subdag)
| apache-2.0 |
tejaskhot/deep-learning | conv_cifar/scripts/conv_destin.py | 6 | 9175 | """
@author: Tejas Khot
@contact: [email protected]
"""
__author__='tejas'
import os
from time import time
import cPickle as pickle
from destin.load_data import *
from destin.network import *
import nnet.datasets as ds
from sklearn import svm
t_0 = time()
# *****Define Parameters for the Network and nodes
# Network Params
num_layers = 4
patch_mode = 'Adjacent'
image_type = 'Color'
network_mode = True
cifar_stat=[]
# For a Node: specify Your Algorithm Choice and Corresponding parameters
# ******************************************************************************************
#
# Incremental Clustering
#
num_nodes_per_layer = [[8, 8], [4, 4], [2, 2], [1, 1]]
num_cents_per_layer = [50, 25, 25 ,50]
pool_size = [(16,1),(2,2),(2,2),(1,1)] #pooling size: The first number is the number of vector
#you want to pool. For example, (64,1) will pool all the
#vector in the first layer. (16,1) will divide the first layer
#in to 4 quarters and pool each of them. (4,1) will divide the
#first layer in to 16th pieces and pool each of them
print "Uniform DeSTIN with Clustering"
algorithm_choice = 'Clustering'
alg_params = {'mr': 0.01, 'vr': 0.01, 'sr': 0.001, 'DIMS': [],
'CENTS': [], 'node_id': [],
'num_cents_per_layer': num_cents_per_layer}
# ******************************************************************************************
#Load Data, 10 loads 5 batches in total 50,000
# 1 to 5 load batch_1 to batch_5training images, 1 to five
# Declare a Network Object and load Training Data
DESTIN = Network( num_layers, algorithm_choice, alg_params, num_nodes_per_layer, cifar_stat , patch_mode, image_type,)
#, , , , cifar_stat, patch_mode='Adjacent', image_type='Color'
DESTIN.setmode(network_mode)
DESTIN.set_lowest_layer(0)
# Load Data
# Modify the location of the training data in file "load_data.py"
# data = np.random.rand(5,32*32*3)
# Initialize Network; there is is also a layer-wise initialization option
DESTIN.init_network()
train_names=np.arange(0,476,25)
"""
#Train the Network
print "DeSTIN Training/with out Feature extraction"
for epoch in range(5):
counter=0
if epoch==0:
k=16
else:
k=0
for num in train_names[k:]:
data=load_train(num)
for I in range(data.shape[0]): # For Every image in the data set batch
if counter % 1000 == 0:
print("Training Iteration Image Number : %d" % counter)
for L in range(DESTIN.number_of_layers):
if L == 0:
img=data[I][:].reshape(50, 38, 38)
img=img.swapaxes(0,1).swapaxes(1,2) ## (38, 38, 50)
img=img[3:-3, 3:-3, :] ## (32, 32, 50)
# This is equivalent to sharing centroids or kernels
DESTIN.layers[0][L].load_input(img, [4, 4])
DESTIN.layers[0][L].do_layer_learning()
#DESTIN.layers[0][L].shared_learning()
else:
DESTIN.layers[0][L].load_input(DESTIN.layers[0][L - 1].nodes, [2, 2])
DESTIN.layers[0][L].do_layer_learning()
#DESTIN.layers[0][L].shared_learning()
if counter>0 and counter % 10000==0:
try:
pickle.dump( DESTIN, open( "DESTIN_conv_"+ str(epoch)+"_"+str(counter), "wb" ) )
print "Pickled DeSTIN till ", counter
except:
print "Could not pickle DeSTIN"
counter+=1
print "Epoch " + str(epoch+1) + " completed"
try:
pickle.dump( DESTIN, open( "DESTIN_conv", "wb" ) )
print "Pickled DeSTIN "
except:
print "Could not pickle DeSTIN"
print "done with destin training network"
"""
DESTIN=pickle.load( open( "DESTIN_conv", "rb" ) )
print("DeSTIN running | Feature Extraction over the Training Data")
network_mode = False
DESTIN.setmode(network_mode)
# Testing it over the training set
"""
if not os.path.exists('train'):
os.makedirs('train')
counter=29800
k=11
for num in train_names[k:]:
if num==train_names[11]:
data=load_train(num)[2300:]
else:
data=load_train(num)
for I in range(data.shape[0]): # For Every image in the data set
if counter % 1000 == 0:
print("Testing Iteration Number : Completed till Image: %d" % counter)
for L in range(DESTIN.number_of_layers):
if L == 0:
img=data[I][:].reshape(50, 38, 38)
img=img.swapaxes(0,1).swapaxes(1,2) ## (38, 38, 50)
img=img[3:-3, 3:-3, :] ## (32, 32, 50)
DESTIN.layers[0][L].load_input(img, [4, 4])
DESTIN.layers[0][L].do_layer_learning()
else:
DESTIN.layers[0][L].load_input(DESTIN.layers[0][L - 1].nodes, [2, 2])
DESTIN.layers[0][L].do_layer_learning()
DESTIN.update_belief_exporter(pool_size, True ,'average_exc_pad') #( maxpool_shape , ignore_border, mode)
if counter in range(199, 50999, 200):
Name = 'train/' + str(counter) + '.txt'
#file_id = open(Name, 'w')
np.savetxt(Name, np.array(DESTIN.network_belief['belief']))
#file_id.close()
# Get rid-off accumulated training beliefs
DESTIN.clean_belief_exporter()
counter+=1
print("Feature Extraction with the test set")
if not os.path.exists('test'):
os.makedirs('test')
test_names=np.arange(0,76,25)
counter=0
for num in test_names:
data=load_test(num)
for I in range(data.shape[0]): # For Every image in the data set
if counter % 1000 == 0:
print("Testing Iteration Number : Completed till Image: %d" % (counter))
for L in range(DESTIN.number_of_layers):
if L == 0:
img=data[I][:].reshape(50, 38, 38)
img=img.swapaxes(0,1).swapaxes(1,2) ## (38, 38, 50)
img=img[3:-3, 3:-3, :] ## (32, 32, 50)
DESTIN.layers[0][L].load_input(img, [4, 4])
DESTIN.layers[0][L].do_layer_learning() # Calculates belief for
else:
DESTIN.layers[0][L].load_input(DESTIN.layers[0][L - 1].nodes, [2, 2])
DESTIN.layers[0][L].do_layer_learning()
DESTIN.update_belief_exporter(pool_size, True ,'average_exc_pad')
if counter in range(199, 10199, 200):
Name = 'test/' + str(counter + 1) + '.txt'
np.savetxt(Name, np.array(DESTIN.network_belief['belief']))
# Get rid-off accumulated training beliefs
DESTIN.clean_belief_exporter()
counter+=1
del data
"""
print "Training With SVM"
print("Loading training and test labels")
trainData, trainLabel, testData, testLabel=ds.load_CIFAR10("/home/ubuntu/destin/cifar-10-batches-py")
del trainData
del testData
# Load Training and Test Data/Extracted from DeSTIN
# here we do not use the whole set of feature extracted from DeSTIN
# We use the features which are extracted from the top few layers
print("Loading training and testing features")
I = 199
Name = 'train/' + str(I) + '.txt'
trainData = np.ravel(np.loadtxt(Name))
for I in range(399, 50000, 200):
Name = 'train/' + str(I) + '.txt'
file_id = open(Name, 'r')
Temp = np.ravel(np.loadtxt(Name))
trainData = np.hstack((trainData, Temp))
del Temp
Len = np.shape(trainData)[0]
Size = np.size(trainData)
print "Training data shape is : ", trainData.shape
Width = Len/50000
print Len
print Width*50000
trainData = trainData.reshape((50000, Width))
# Training SVM
SVM = svm.LinearSVC(C=1)
# C=100, kernel='rbf')
print "Training the SVM"
trainLabel = np.squeeze(np.asarray(trainLabel).reshape(50000, 1))
#print trainData
SVM.fit(trainData, trainLabel)
print("Training Score = %f " % float(100 * SVM.score(trainData, trainLabel)))
#print("Training Accuracy = %f" % (SVM.score(trainData, trainLabel) * 100))
eff = {}
eff['train'] = SVM.score(trainData, trainLabel) * 100
del trainData
testData = np.array([])
print("Loading training and testing features")
I = 399
Name = 'test/' + str(I + 1) + '.txt'
testData = np.ravel(np.loadtxt(Name))
for I in range(599, 10000, 200):
Name = 'test/' + str(I + 1) + '.txt'
file_id = open(Name, 'r')
Temp = np.ravel(np.loadtxt(Name))
testData = np.hstack((testData, Temp))
del Temp
Len = np.shape(testData)[0]
Size = np.size(testData)
I = 399
Name = 'test/' + str(I + 1) + '.txt'
testData1 = np.ravel(np.loadtxt(Name))
print np.shape(testData1)[0]/200.0
Width = np.float(Len)/9800.0
print Len
print Size
testData = testData.reshape((9800, Width))
print "Predicting Test samples"
print("Test Score = %f" % float(100 * SVM.score(testData, testLabel[200:10000])))
#print("Training Accuracy = %f" % (SVM.score(testData, testLabel) * 100))
eff['test'] = SVM.score(testData, testLabel[200:10000]) * 100
io.savemat('accuracy.mat', eff)
print "Total time taken: ", time()-t_0
| gpl-2.0 |
jm-begon/scikit-learn | doc/sphinxext/gen_rst.py | 142 | 40026 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
import glob
import sys
import gzip
import posixpath
import subprocess
import warnings
from sklearn.externals import six
# Try Python 2 first, otherwise load from Python 3
try:
from StringIO import StringIO
import cPickle as pickle
import urllib2 as urllib
from urllib2 import HTTPError, URLError
except ImportError:
from io import StringIO
import pickle
import urllib.request
import urllib.error
import urllib.parse
from urllib.error import HTTPError, URLError
try:
# Python 2 built-in
execfile
except NameError:
def execfile(filename, global_vars=None, local_vars=None):
with open(filename, encoding='utf-8') as f:
code = compile(f.read(), filename, 'exec')
exec(code, global_vars, local_vars)
try:
basestring
except NameError:
basestring = str
import token
import tokenize
import numpy as np
try:
# make sure that the Agg backend is set before importing any
# matplotlib
import matplotlib
matplotlib.use('Agg')
except ImportError:
# this script can be imported by nosetest to find tests to run: we should not
# impose the matplotlib requirement in that case.
pass
from sklearn.externals import joblib
###############################################################################
# A tee object to redict streams to multiple outputs
class Tee(object):
def __init__(self, file1, file2):
self.file1 = file1
self.file2 = file2
def write(self, data):
self.file1.write(data)
self.file2.write(data)
def flush(self):
self.file1.flush()
self.file2.flush()
###############################################################################
# Documentation link resolver objects
def _get_data(url):
"""Helper function to get data over http or from a local file"""
if url.startswith('http://'):
# Try Python 2, use Python 3 on exception
try:
resp = urllib.urlopen(url)
encoding = resp.headers.dict.get('content-encoding', 'plain')
except AttributeError:
resp = urllib.request.urlopen(url)
encoding = resp.headers.get('content-encoding', 'plain')
data = resp.read()
if encoding == 'plain':
pass
elif encoding == 'gzip':
data = StringIO(data)
data = gzip.GzipFile(fileobj=data).read()
else:
raise RuntimeError('unknown encoding')
else:
with open(url, 'r') as fid:
data = fid.read()
fid.close()
return data
mem = joblib.Memory(cachedir='_build')
get_data = mem.cache(_get_data)
def parse_sphinx_searchindex(searchindex):
"""Parse a Sphinx search index
Parameters
----------
searchindex : str
The Sphinx search index (contents of searchindex.js)
Returns
-------
filenames : list of str
The file names parsed from the search index.
objects : dict
The objects parsed from the search index.
"""
def _select_block(str_in, start_tag, end_tag):
"""Select first block delimited by start_tag and end_tag"""
start_pos = str_in.find(start_tag)
if start_pos < 0:
raise ValueError('start_tag not found')
depth = 0
for pos in range(start_pos, len(str_in)):
if str_in[pos] == start_tag:
depth += 1
elif str_in[pos] == end_tag:
depth -= 1
if depth == 0:
break
sel = str_in[start_pos + 1:pos]
return sel
def _parse_dict_recursive(dict_str):
"""Parse a dictionary from the search index"""
dict_out = dict()
pos_last = 0
pos = dict_str.find(':')
while pos >= 0:
key = dict_str[pos_last:pos]
if dict_str[pos + 1] == '[':
# value is a list
pos_tmp = dict_str.find(']', pos + 1)
if pos_tmp < 0:
raise RuntimeError('error when parsing dict')
value = dict_str[pos + 2: pos_tmp].split(',')
# try to convert elements to int
for i in range(len(value)):
try:
value[i] = int(value[i])
except ValueError:
pass
elif dict_str[pos + 1] == '{':
# value is another dictionary
subdict_str = _select_block(dict_str[pos:], '{', '}')
value = _parse_dict_recursive(subdict_str)
pos_tmp = pos + len(subdict_str)
else:
raise ValueError('error when parsing dict: unknown elem')
key = key.strip('"')
if len(key) > 0:
dict_out[key] = value
pos_last = dict_str.find(',', pos_tmp)
if pos_last < 0:
break
pos_last += 1
pos = dict_str.find(':', pos_last)
return dict_out
# Make sure searchindex uses UTF-8 encoding
if hasattr(searchindex, 'decode'):
searchindex = searchindex.decode('UTF-8')
# parse objects
query = 'objects:'
pos = searchindex.find(query)
if pos < 0:
raise ValueError('"objects:" not found in search index')
sel = _select_block(searchindex[pos:], '{', '}')
objects = _parse_dict_recursive(sel)
# parse filenames
query = 'filenames:'
pos = searchindex.find(query)
if pos < 0:
raise ValueError('"filenames:" not found in search index')
filenames = searchindex[pos + len(query) + 1:]
filenames = filenames[:filenames.find(']')]
filenames = [f.strip('"') for f in filenames.split(',')]
return filenames, objects
class SphinxDocLinkResolver(object):
""" Resolve documentation links using searchindex.js generated by Sphinx
Parameters
----------
doc_url : str
The base URL of the project website.
searchindex : str
Filename of searchindex, relative to doc_url.
extra_modules_test : list of str
List of extra module names to test.
relative : bool
Return relative links (only useful for links to documentation of this
package).
"""
def __init__(self, doc_url, searchindex='searchindex.js',
extra_modules_test=None, relative=False):
self.doc_url = doc_url
self.relative = relative
self._link_cache = {}
self.extra_modules_test = extra_modules_test
self._page_cache = {}
if doc_url.startswith('http://'):
if relative:
raise ValueError('Relative links are only supported for local '
'URLs (doc_url cannot start with "http://)"')
searchindex_url = doc_url + '/' + searchindex
else:
searchindex_url = os.path.join(doc_url, searchindex)
# detect if we are using relative links on a Windows system
if os.name.lower() == 'nt' and not doc_url.startswith('http://'):
if not relative:
raise ValueError('You have to use relative=True for the local'
' package on a Windows system.')
self._is_windows = True
else:
self._is_windows = False
# download and initialize the search index
sindex = get_data(searchindex_url)
filenames, objects = parse_sphinx_searchindex(sindex)
self._searchindex = dict(filenames=filenames, objects=objects)
def _get_link(self, cobj):
"""Get a valid link, False if not found"""
fname_idx = None
full_name = cobj['module_short'] + '.' + cobj['name']
if full_name in self._searchindex['objects']:
value = self._searchindex['objects'][full_name]
if isinstance(value, dict):
value = value[next(iter(value.keys()))]
fname_idx = value[0]
elif cobj['module_short'] in self._searchindex['objects']:
value = self._searchindex['objects'][cobj['module_short']]
if cobj['name'] in value.keys():
fname_idx = value[cobj['name']][0]
if fname_idx is not None:
fname = self._searchindex['filenames'][fname_idx] + '.html'
if self._is_windows:
fname = fname.replace('/', '\\')
link = os.path.join(self.doc_url, fname)
else:
link = posixpath.join(self.doc_url, fname)
if hasattr(link, 'decode'):
link = link.decode('utf-8', 'replace')
if link in self._page_cache:
html = self._page_cache[link]
else:
html = get_data(link)
self._page_cache[link] = html
# test if cobj appears in page
comb_names = [cobj['module_short'] + '.' + cobj['name']]
if self.extra_modules_test is not None:
for mod in self.extra_modules_test:
comb_names.append(mod + '.' + cobj['name'])
url = False
if hasattr(html, 'decode'):
# Decode bytes under Python 3
html = html.decode('utf-8', 'replace')
for comb_name in comb_names:
if hasattr(comb_name, 'decode'):
# Decode bytes under Python 3
comb_name = comb_name.decode('utf-8', 'replace')
if comb_name in html:
url = link + u'#' + comb_name
link = url
else:
link = False
return link
def resolve(self, cobj, this_url):
"""Resolve the link to the documentation, returns None if not found
Parameters
----------
cobj : dict
Dict with information about the "code object" for which we are
resolving a link.
cobi['name'] : function or class name (str)
cobj['module_short'] : shortened module name (str)
cobj['module'] : module name (str)
this_url: str
URL of the current page. Needed to construct relative URLs
(only used if relative=True in constructor).
Returns
-------
link : str | None
The link (URL) to the documentation.
"""
full_name = cobj['module_short'] + '.' + cobj['name']
link = self._link_cache.get(full_name, None)
if link is None:
# we don't have it cached
link = self._get_link(cobj)
# cache it for the future
self._link_cache[full_name] = link
if link is False or link is None:
# failed to resolve
return None
if self.relative:
link = os.path.relpath(link, start=this_url)
if self._is_windows:
# replace '\' with '/' so it on the web
link = link.replace('\\', '/')
# for some reason, the relative link goes one directory too high up
link = link[3:]
return link
###############################################################################
rst_template = """
.. _example_%(short_fname)s:
%(docstring)s
**Python source code:** :download:`%(fname)s <%(fname)s>`
.. literalinclude:: %(fname)s
:lines: %(end_row)s-
"""
plot_rst_template = """
.. _example_%(short_fname)s:
%(docstring)s
%(image_list)s
%(stdout)s
**Python source code:** :download:`%(fname)s <%(fname)s>`
.. literalinclude:: %(fname)s
:lines: %(end_row)s-
**Total running time of the example:** %(time_elapsed) .2f seconds
(%(time_m) .0f minutes %(time_s) .2f seconds)
"""
# The following strings are used when we have several pictures: we use
# an html div tag that our CSS uses to turn the lists into horizontal
# lists.
HLIST_HEADER = """
.. rst-class:: horizontal
"""
HLIST_IMAGE_TEMPLATE = """
*
.. image:: images/%s
:scale: 47
"""
SINGLE_IMAGE = """
.. image:: images/%s
:align: center
"""
# The following dictionary contains the information used to create the
# thumbnails for the front page of the scikit-learn home page.
# key: first image in set
# values: (number of plot in set, height of thumbnail)
carousel_thumbs = {'plot_classifier_comparison_001.png': (1, 600),
'plot_outlier_detection_001.png': (3, 372),
'plot_gp_regression_001.png': (2, 250),
'plot_adaboost_twoclass_001.png': (1, 372),
'plot_compare_methods_001.png': (1, 349)}
def extract_docstring(filename, ignore_heading=False):
""" Extract a module-level docstring, if any
"""
if six.PY2:
lines = open(filename).readlines()
else:
lines = open(filename, encoding='utf-8').readlines()
start_row = 0
if lines[0].startswith('#!'):
lines.pop(0)
start_row = 1
docstring = ''
first_par = ''
line_iterator = iter(lines)
tokens = tokenize.generate_tokens(lambda: next(line_iterator))
for tok_type, tok_content, _, (erow, _), _ in tokens:
tok_type = token.tok_name[tok_type]
if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'):
continue
elif tok_type == 'STRING':
docstring = eval(tok_content)
# If the docstring is formatted with several paragraphs, extract
# the first one:
paragraphs = '\n'.join(
line.rstrip() for line
in docstring.split('\n')).split('\n\n')
if paragraphs:
if ignore_heading:
if len(paragraphs) > 1:
first_par = re.sub('\n', ' ', paragraphs[1])
first_par = ((first_par[:95] + '...')
if len(first_par) > 95 else first_par)
else:
raise ValueError("Docstring not found by gallery.\n"
"Please check the layout of your"
" example file:\n {}\n and make sure"
" it's correct".format(filename))
else:
first_par = paragraphs[0]
break
return docstring, first_par, erow + 1 + start_row
def generate_example_rst(app):
""" Generate the list of examples, as well as the contents of
examples.
"""
root_dir = os.path.join(app.builder.srcdir, 'auto_examples')
example_dir = os.path.abspath(os.path.join(app.builder.srcdir, '..',
'examples'))
generated_dir = os.path.abspath(os.path.join(app.builder.srcdir,
'modules', 'generated'))
try:
plot_gallery = eval(app.builder.config.plot_gallery)
except TypeError:
plot_gallery = bool(app.builder.config.plot_gallery)
if not os.path.exists(example_dir):
os.makedirs(example_dir)
if not os.path.exists(root_dir):
os.makedirs(root_dir)
if not os.path.exists(generated_dir):
os.makedirs(generated_dir)
# we create an index.rst with all examples
fhindex = open(os.path.join(root_dir, 'index.rst'), 'w')
# Note: The sidebar button has been removed from the examples page for now
# due to how it messes up the layout. Will be fixed at a later point
fhindex.write("""\
.. raw:: html
<style type="text/css">
div#sidebarbutton {
/* hide the sidebar collapser, while ensuring vertical arrangement */
display: none;
}
</style>
.. _examples-index:
Examples
========
""")
# Here we don't use an os.walk, but we recurse only twice: flat is
# better than nested.
seen_backrefs = set()
generate_dir_rst('.', fhindex, example_dir, root_dir, plot_gallery, seen_backrefs)
for directory in sorted(os.listdir(example_dir)):
if os.path.isdir(os.path.join(example_dir, directory)):
generate_dir_rst(directory, fhindex, example_dir, root_dir, plot_gallery, seen_backrefs)
fhindex.flush()
def extract_line_count(filename, target_dir):
# Extract the line count of a file
example_file = os.path.join(target_dir, filename)
if six.PY2:
lines = open(example_file).readlines()
else:
lines = open(example_file, encoding='utf-8').readlines()
start_row = 0
if lines and lines[0].startswith('#!'):
lines.pop(0)
start_row = 1
line_iterator = iter(lines)
tokens = tokenize.generate_tokens(lambda: next(line_iterator))
check_docstring = True
erow_docstring = 0
for tok_type, _, _, (erow, _), _ in tokens:
tok_type = token.tok_name[tok_type]
if tok_type in ('NEWLINE', 'COMMENT', 'NL', 'INDENT', 'DEDENT'):
continue
elif (tok_type == 'STRING') and check_docstring:
erow_docstring = erow
check_docstring = False
return erow_docstring+1+start_row, erow+1+start_row
def line_count_sort(file_list, target_dir):
# Sort the list of examples by line-count
new_list = [x for x in file_list if x.endswith('.py')]
unsorted = np.zeros(shape=(len(new_list), 2))
unsorted = unsorted.astype(np.object)
for count, exmpl in enumerate(new_list):
docstr_lines, total_lines = extract_line_count(exmpl, target_dir)
unsorted[count][1] = total_lines - docstr_lines
unsorted[count][0] = exmpl
index = np.lexsort((unsorted[:, 0].astype(np.str),
unsorted[:, 1].astype(np.float)))
if not len(unsorted):
return []
return np.array(unsorted[index][:, 0]).tolist()
def _thumbnail_div(subdir, full_dir, fname, snippet):
"""Generates RST to place a thumbnail in a gallery"""
thumb = os.path.join(full_dir, 'images', 'thumb', fname[:-3] + '.png')
link_name = os.path.join(full_dir, fname).replace(os.path.sep, '_')
ref_name = os.path.join(subdir, fname).replace(os.path.sep, '_')
if ref_name.startswith('._'):
ref_name = ref_name[2:]
out = []
out.append("""
.. raw:: html
<div class="thumbnailContainer" tooltip="{}">
""".format(snippet))
out.append('.. figure:: %s\n' % thumb)
if link_name.startswith('._'):
link_name = link_name[2:]
if full_dir != '.':
out.append(' :target: ./%s/%s.html\n\n' % (full_dir, fname[:-3]))
else:
out.append(' :target: ./%s.html\n\n' % link_name[:-3])
out.append(""" :ref:`example_%s`
.. raw:: html
</div>
""" % (ref_name))
return ''.join(out)
def generate_dir_rst(directory, fhindex, example_dir, root_dir, plot_gallery, seen_backrefs):
""" Generate the rst file for an example directory.
"""
if not directory == '.':
target_dir = os.path.join(root_dir, directory)
src_dir = os.path.join(example_dir, directory)
else:
target_dir = root_dir
src_dir = example_dir
if not os.path.exists(os.path.join(src_dir, 'README.txt')):
raise ValueError('Example directory %s does not have a README.txt' %
src_dir)
fhindex.write("""
%s
""" % open(os.path.join(src_dir, 'README.txt')).read())
if not os.path.exists(target_dir):
os.makedirs(target_dir)
sorted_listdir = line_count_sort(os.listdir(src_dir),
src_dir)
if not os.path.exists(os.path.join(directory, 'images', 'thumb')):
os.makedirs(os.path.join(directory, 'images', 'thumb'))
for fname in sorted_listdir:
if fname.endswith('py'):
backrefs = generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery)
new_fname = os.path.join(src_dir, fname)
_, snippet, _ = extract_docstring(new_fname, True)
fhindex.write(_thumbnail_div(directory, directory, fname, snippet))
fhindex.write("""
.. toctree::
:hidden:
%s/%s
""" % (directory, fname[:-3]))
for backref in backrefs:
include_path = os.path.join(root_dir, '../modules/generated/%s.examples' % backref)
seen = backref in seen_backrefs
with open(include_path, 'a' if seen else 'w') as ex_file:
if not seen:
# heading
print(file=ex_file)
print('Examples using ``%s``' % backref, file=ex_file)
print('-----------------%s--' % ('-' * len(backref)),
file=ex_file)
print(file=ex_file)
rel_dir = os.path.join('../../auto_examples', directory)
ex_file.write(_thumbnail_div(directory, rel_dir, fname, snippet))
seen_backrefs.add(backref)
fhindex.write("""
.. raw:: html
<div class="clearer"></div>
""") # clear at the end of the section
# modules for which we embed links into example code
DOCMODULES = ['sklearn', 'matplotlib', 'numpy', 'scipy']
def make_thumbnail(in_fname, out_fname, width, height):
"""Make a thumbnail with the same aspect ratio centered in an
image with a given width and height
"""
# local import to avoid testing dependency on PIL:
try:
from PIL import Image
except ImportError:
import Image
img = Image.open(in_fname)
width_in, height_in = img.size
scale_w = width / float(width_in)
scale_h = height / float(height_in)
if height_in * scale_w <= height:
scale = scale_w
else:
scale = scale_h
width_sc = int(round(scale * width_in))
height_sc = int(round(scale * height_in))
# resize the image
img.thumbnail((width_sc, height_sc), Image.ANTIALIAS)
# insert centered
thumb = Image.new('RGB', (width, height), (255, 255, 255))
pos_insert = ((width - width_sc) // 2, (height - height_sc) // 2)
thumb.paste(img, pos_insert)
thumb.save(out_fname)
# Use optipng to perform lossless compression on the resized image if
# software is installed
if os.environ.get('SKLEARN_DOC_OPTIPNG', False):
try:
subprocess.call(["optipng", "-quiet", "-o", "9", out_fname])
except Exception:
warnings.warn('Install optipng to reduce the size of the generated images')
def get_short_module_name(module_name, obj_name):
""" Get the shortest possible module name """
parts = module_name.split('.')
short_name = module_name
for i in range(len(parts) - 1, 0, -1):
short_name = '.'.join(parts[:i])
try:
exec('from %s import %s' % (short_name, obj_name))
except ImportError:
# get the last working module name
short_name = '.'.join(parts[:(i + 1)])
break
return short_name
class NameFinder(ast.NodeVisitor):
"""Finds the longest form of variable names and their imports in code
Only retains names from imported modules.
"""
def __init__(self):
super(NameFinder, self).__init__()
self.imported_names = {}
self.accessed_names = set()
def visit_Import(self, node, prefix=''):
for alias in node.names:
local_name = alias.asname or alias.name
self.imported_names[local_name] = prefix + alias.name
def visit_ImportFrom(self, node):
self.visit_Import(node, node.module + '.')
def visit_Name(self, node):
self.accessed_names.add(node.id)
def visit_Attribute(self, node):
attrs = []
while isinstance(node, ast.Attribute):
attrs.append(node.attr)
node = node.value
if isinstance(node, ast.Name):
# This is a.b, not e.g. a().b
attrs.append(node.id)
self.accessed_names.add('.'.join(reversed(attrs)))
else:
# need to get a in a().b
self.visit(node)
def get_mapping(self):
for name in self.accessed_names:
local_name = name.split('.', 1)[0]
remainder = name[len(local_name):]
if local_name in self.imported_names:
# Join import path to relative path
full_name = self.imported_names[local_name] + remainder
yield name, full_name
def identify_names(code):
"""Builds a codeobj summary by identifying and resovles used names
>>> code = '''
... from a.b import c
... import d as e
... print(c)
... e.HelloWorld().f.g
... '''
>>> for name, o in sorted(identify_names(code).items()):
... print(name, o['name'], o['module'], o['module_short'])
c c a.b a.b
e.HelloWorld HelloWorld d d
"""
finder = NameFinder()
finder.visit(ast.parse(code))
example_code_obj = {}
for name, full_name in finder.get_mapping():
# name is as written in file (e.g. np.asarray)
# full_name includes resolved import path (e.g. numpy.asarray)
module, attribute = full_name.rsplit('.', 1)
# get shortened module name
module_short = get_short_module_name(module, attribute)
cobj = {'name': attribute, 'module': module,
'module_short': module_short}
example_code_obj[name] = cobj
return example_code_obj
def generate_file_rst(fname, target_dir, src_dir, root_dir, plot_gallery):
""" Generate the rst file for a given example.
Returns the set of sklearn functions/classes imported in the example.
"""
base_image_name = os.path.splitext(fname)[0]
image_fname = '%s_%%03d.png' % base_image_name
this_template = rst_template
last_dir = os.path.split(src_dir)[-1]
# to avoid leading . in file names, and wrong names in links
if last_dir == '.' or last_dir == 'examples':
last_dir = ''
else:
last_dir += '_'
short_fname = last_dir + fname
src_file = os.path.join(src_dir, fname)
example_file = os.path.join(target_dir, fname)
shutil.copyfile(src_file, example_file)
# The following is a list containing all the figure names
figure_list = []
image_dir = os.path.join(target_dir, 'images')
thumb_dir = os.path.join(image_dir, 'thumb')
if not os.path.exists(image_dir):
os.makedirs(image_dir)
if not os.path.exists(thumb_dir):
os.makedirs(thumb_dir)
image_path = os.path.join(image_dir, image_fname)
stdout_path = os.path.join(image_dir,
'stdout_%s.txt' % base_image_name)
time_path = os.path.join(image_dir,
'time_%s.txt' % base_image_name)
thumb_file = os.path.join(thumb_dir, base_image_name + '.png')
time_elapsed = 0
if plot_gallery and fname.startswith('plot'):
# generate the plot as png image if file name
# starts with plot and if it is more recent than an
# existing image.
first_image_file = image_path % 1
if os.path.exists(stdout_path):
stdout = open(stdout_path).read()
else:
stdout = ''
if os.path.exists(time_path):
time_elapsed = float(open(time_path).read())
if not os.path.exists(first_image_file) or \
os.stat(first_image_file).st_mtime <= os.stat(src_file).st_mtime:
# We need to execute the code
print('plotting %s' % fname)
t0 = time()
import matplotlib.pyplot as plt
plt.close('all')
cwd = os.getcwd()
try:
# First CD in the original example dir, so that any file
# created by the example get created in this directory
orig_stdout = sys.stdout
os.chdir(os.path.dirname(src_file))
my_buffer = StringIO()
my_stdout = Tee(sys.stdout, my_buffer)
sys.stdout = my_stdout
my_globals = {'pl': plt}
execfile(os.path.basename(src_file), my_globals)
time_elapsed = time() - t0
sys.stdout = orig_stdout
my_stdout = my_buffer.getvalue()
if '__doc__' in my_globals:
# The __doc__ is often printed in the example, we
# don't with to echo it
my_stdout = my_stdout.replace(
my_globals['__doc__'],
'')
my_stdout = my_stdout.strip().expandtabs()
if my_stdout:
stdout = '**Script output**::\n\n %s\n\n' % (
'\n '.join(my_stdout.split('\n')))
open(stdout_path, 'w').write(stdout)
open(time_path, 'w').write('%f' % time_elapsed)
os.chdir(cwd)
# In order to save every figure we have two solutions :
# * iterate from 1 to infinity and call plt.fignum_exists(n)
# (this requires the figures to be numbered
# incrementally: 1, 2, 3 and not 1, 2, 5)
# * iterate over [fig_mngr.num for fig_mngr in
# matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]
fig_managers = matplotlib._pylab_helpers.Gcf.get_all_fig_managers()
for fig_mngr in fig_managers:
# Set the fig_num figure as the current figure as we can't
# save a figure that's not the current figure.
fig = plt.figure(fig_mngr.num)
kwargs = {}
to_rgba = matplotlib.colors.colorConverter.to_rgba
for attr in ['facecolor', 'edgecolor']:
fig_attr = getattr(fig, 'get_' + attr)()
default_attr = matplotlib.rcParams['figure.' + attr]
if to_rgba(fig_attr) != to_rgba(default_attr):
kwargs[attr] = fig_attr
fig.savefig(image_path % fig_mngr.num, **kwargs)
figure_list.append(image_fname % fig_mngr.num)
except:
print(80 * '_')
print('%s is not compiling:' % fname)
traceback.print_exc()
print(80 * '_')
finally:
os.chdir(cwd)
sys.stdout = orig_stdout
print(" - time elapsed : %.2g sec" % time_elapsed)
else:
figure_list = [f[len(image_dir):]
for f in glob.glob(image_path.replace("%03d",
'[0-9][0-9][0-9]'))]
figure_list.sort()
# generate thumb file
this_template = plot_rst_template
car_thumb_path = os.path.join(os.path.split(root_dir)[0], '_build/html/stable/_images/')
# Note: normaly, make_thumbnail is used to write to the path contained in `thumb_file`
# which is within `auto_examples/../images/thumbs` depending on the example.
# Because the carousel has different dimensions than those of the examples gallery,
# I did not simply reuse them all as some contained whitespace due to their default gallery
# thumbnail size. Below, for a few cases, seperate thumbnails are created (the originals can't
# just be overwritten with the carousel dimensions as it messes up the examples gallery layout).
# The special carousel thumbnails are written directly to _build/html/stable/_images/,
# as for some reason unknown to me, Sphinx refuses to copy my 'extra' thumbnails from the
# auto examples gallery to the _build folder. This works fine as is, but it would be cleaner to
# have it happen with the rest. Ideally the should be written to 'thumb_file' as well, and then
# copied to the _images folder during the `Copying Downloadable Files` step like the rest.
if not os.path.exists(car_thumb_path):
os.makedirs(car_thumb_path)
if os.path.exists(first_image_file):
# We generate extra special thumbnails for the carousel
carousel_tfile = os.path.join(car_thumb_path, base_image_name + '_carousel.png')
first_img = image_fname % 1
if first_img in carousel_thumbs:
make_thumbnail((image_path % carousel_thumbs[first_img][0]),
carousel_tfile, carousel_thumbs[first_img][1], 190)
make_thumbnail(first_image_file, thumb_file, 400, 280)
if not os.path.exists(thumb_file):
# create something to replace the thumbnail
make_thumbnail('images/no_image.png', thumb_file, 200, 140)
docstring, short_desc, end_row = extract_docstring(example_file)
# Depending on whether we have one or more figures, we're using a
# horizontal list or a single rst call to 'image'.
if len(figure_list) == 1:
figure_name = figure_list[0]
image_list = SINGLE_IMAGE % figure_name.lstrip('/')
else:
image_list = HLIST_HEADER
for figure_name in figure_list:
image_list += HLIST_IMAGE_TEMPLATE % figure_name.lstrip('/')
time_m, time_s = divmod(time_elapsed, 60)
f = open(os.path.join(target_dir, base_image_name + '.rst'), 'w')
f.write(this_template % locals())
f.flush()
# save variables so we can later add links to the documentation
if six.PY2:
example_code_obj = identify_names(open(example_file).read())
else:
example_code_obj = \
identify_names(open(example_file, encoding='utf-8').read())
if example_code_obj:
codeobj_fname = example_file[:-3] + '_codeobj.pickle'
with open(codeobj_fname, 'wb') as fid:
pickle.dump(example_code_obj, fid, pickle.HIGHEST_PROTOCOL)
backrefs = set('{module_short}.{name}'.format(**entry)
for entry in example_code_obj.values()
if entry['module'].startswith('sklearn'))
return backrefs
def embed_code_links(app, exception):
"""Embed hyperlinks to documentation into example code"""
if exception is not None:
return
print('Embedding documentation hyperlinks in examples..')
if app.builder.name == 'latex':
# Don't embed hyperlinks when a latex builder is used.
return
# Add resolvers for the packages for which we want to show links
doc_resolvers = {}
doc_resolvers['sklearn'] = SphinxDocLinkResolver(app.builder.outdir,
relative=True)
resolver_urls = {
'matplotlib': 'http://matplotlib.org',
'numpy': 'http://docs.scipy.org/doc/numpy-1.6.0',
'scipy': 'http://docs.scipy.org/doc/scipy-0.11.0/reference',
}
for this_module, url in resolver_urls.items():
try:
doc_resolvers[this_module] = SphinxDocLinkResolver(url)
except HTTPError as e:
print("The following HTTP Error has occurred:\n")
print(e.code)
except URLError as e:
print("\n...\n"
"Warning: Embedding the documentation hyperlinks requires "
"internet access.\nPlease check your network connection.\n"
"Unable to continue embedding `{0}` links due to a URL "
"Error:\n".format(this_module))
print(e.args)
example_dir = os.path.join(app.builder.srcdir, 'auto_examples')
html_example_dir = os.path.abspath(os.path.join(app.builder.outdir,
'auto_examples'))
# patterns for replacement
link_pattern = '<a href="%s">%s</a>'
orig_pattern = '<span class="n">%s</span>'
period = '<span class="o">.</span>'
for dirpath, _, filenames in os.walk(html_example_dir):
for fname in filenames:
print('\tprocessing: %s' % fname)
full_fname = os.path.join(html_example_dir, dirpath, fname)
subpath = dirpath[len(html_example_dir) + 1:]
pickle_fname = os.path.join(example_dir, subpath,
fname[:-5] + '_codeobj.pickle')
if os.path.exists(pickle_fname):
# we have a pickle file with the objects to embed links for
with open(pickle_fname, 'rb') as fid:
example_code_obj = pickle.load(fid)
fid.close()
str_repl = {}
# generate replacement strings with the links
for name, cobj in example_code_obj.items():
this_module = cobj['module'].split('.')[0]
if this_module not in doc_resolvers:
continue
try:
link = doc_resolvers[this_module].resolve(cobj,
full_fname)
except (HTTPError, URLError) as e:
print("The following error has occurred:\n")
print(repr(e))
continue
if link is not None:
parts = name.split('.')
name_html = period.join(orig_pattern % part
for part in parts)
str_repl[name_html] = link_pattern % (link, name_html)
# do the replacement in the html file
# ensure greediness
names = sorted(str_repl, key=len, reverse=True)
expr = re.compile(r'(?<!\.)\b' + # don't follow . or word
'|'.join(re.escape(name)
for name in names))
def substitute_link(match):
return str_repl[match.group()]
if len(str_repl) > 0:
with open(full_fname, 'rb') as fid:
lines_in = fid.readlines()
with open(full_fname, 'wb') as fid:
for line in lines_in:
line = line.decode('utf-8')
line = expr.sub(substitute_link, line)
fid.write(line.encode('utf-8'))
print('[done]')
def setup(app):
app.connect('builder-inited', generate_example_rst)
app.add_config_value('plot_gallery', True, 'html')
# embed links after build is finished
app.connect('build-finished', embed_code_links)
# Sphinx hack: sphinx copies generated images to the build directory
# each time the docs are made. If the desired image name already
# exists, it appends a digit to prevent overwrites. The problem is,
# the directory is never cleared. This means that each time you build
# the docs, the number of images in the directory grows.
#
# This question has been asked on the sphinx development list, but there
# was no response: http://osdir.com/ml/sphinx-dev/2011-02/msg00123.html
#
# The following is a hack that prevents this behavior by clearing the
# image build directory each time the docs are built. If sphinx
# changes their layout between versions, this will not work (though
# it should probably not cause a crash). Tested successfully
# on Sphinx 1.0.7
build_image_dir = '_build/html/_images'
if os.path.exists(build_image_dir):
filelist = os.listdir(build_image_dir)
for filename in filelist:
if filename.endswith('png'):
os.remove(os.path.join(build_image_dir, filename))
def setup_module():
# HACK: Stop nosetests running setup() above
pass
| bsd-3-clause |
ppegusii/cs689-mini2 | src/python/parse.py | 1 | 3752 | from __future__ import print_function
from collections import defaultdict
import numpy as np
import pandas as pd
import re
from sklearn import preprocessing as pp
import sys
# Returns training and testing data.
def trainTestData(fileNames, beginTestIdx=11, featureCnt=25):
train, test = separateTrainTest(fileNames, beginTestIdx)
trainXY = list(matrixXY(train, featureCnt))
# print('trainXY[0].shape = {:s}'.format(trainXY[0].shape))
# print('trainXY[1].shape = {:s}'.format(trainXY[1].shape))
testXY = list(matrixXY(test, featureCnt))
# print('trainXY = {:s}'.format(trainXY))
trainXY[0], testXY[0] = normalize(trainXY[0], testXY[0])
# print('trainXY = {:s}'.format(trainXY))
# randomize
ranIdx = np.random.permutation(trainXY[0].shape[0])
trainXY[0] = trainXY[0][ranIdx]
trainXY[1] = trainXY[1][ranIdx]
return tuple(trainXY), tuple(testXY)
def normalize(train, test):
scaler = pp.StandardScaler().fit(train)
train = scaler.transform(train)
test = scaler.transform(test)
return train, test
# Convert the files to:
# X = None size=[n_samples,n_features]
# Y = None size=[n_samples]
# featureCnt = n_features
def matrixXY(defDictFiles, featureCnt):
X = None # size=[n_samples,n_features]
Y = None # size=[n_samples]
for person, personFiles in defDictFiles.items():
for personFile in personFiles:
with open(personFile, 'rb') as f:
df = pd.read_table(f, sep=' ', header=None, engine='python')
df = df.iloc[:, 0:2] # only keep height and width
# Resample before extracting features.
df = resample(df, featureCnt)
if df is None:
continue
y = np.array([[classLabel(person)]])
Y = (y if Y is None else
np.append(Y, [[classLabel(person)]], axis=0))
x = extractFeatures(df)
X = ([x] if X is None else
np.append(X, [x], axis=0))
return X, Y
# Extract a size=[1,n_features] vector from the dataframe
# containing [[height,width],n_samples]
def extractFeatures(df):
v = df.values
# return v[:, 0]**2 / v[:, 1] # best for KNN
# return v[:, 0] / v[:, 1]**2
# return v[:, 0]**-1.0 * v[:, 1]**-1.0
# return v[:, 0] * v[:, 1] # best for SVM
# return v[:, 0]**2 * v[:, 1] # best for SVM
# If I were to resample assuming even robot sampling rate, I could:
# tIndex = pd.date_range('1/1/1970', periods=16, freq='S')
# ts = pd.Series(df.iloc[:,0].values,index=tIndex)
# But if I reample so that all vectors are the same length,
# then I throw away knowledge of velocity.
# I could truncate it to 15.
# Losing 7 samples with less than 15, while some samples have
# 25 entries.
def resample(df, length=15):
if length < 10 or length > 25:
print('Given length outside of the length of data [10,25]'.format(
length))
sys.exit()
# forward fill last value
fill = np.empty((25 - df.shape[0], df.shape[1],))
fill[:] = np.nan
df = df.append(pd.DataFrame(fill), ignore_index=True)
return df.fillna(method='ffill')
# truncating
if df.shape[0] < length:
return None
return df.iloc[0:length, :]
def classLabel(person):
# using ascii code
# return ord(person)
return person
def separateTrainTest(fns, beginTestIdx):
pattern = r'([a-g])([0-9]{1,2})\.dat'
train = defaultdict(list)
test = defaultdict(list)
for fn in fns:
match = re.search(pattern, fn)
if not match:
continue
if int(match.group(2)) < beginTestIdx:
train[match.group(1)].append(fn)
else:
test[match.group(1)].append(fn)
return train, test
| gpl-2.0 |
workflo/dxf2gcode | python_examples/Ellipse_BiArc/BIARC_TEST.py | 1 | 2624 | #!/usr/bin/python
# -*- coding: cp1252 -*-
#
# Abhängigkeit:
#
# BIARC_TEST.py
# +--> clsEllipse.py
# +--> clsBiArc.py
#matplotlib see: http://matplotlib.sourceforge.net/ and http://www.scipy.org/Cookbook/Matplotlib/
#numpy see: http://numpy.scipy.org/ and http://sourceforge.net/projects/numpy/
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.numerix import arange, sin, pi
from matplotlib.axes import Subplot
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from matplotlib.figure import Figure
from Tkconstants import TOP, BOTH, BOTTOM, LEFT, RIGHT,GROOVE
from Tkinter import Tk, Button, Frame
from math import radians, degrees, cos, sin, tan, atan, atan2, sqrt, pow, pi, atan
import sys
from clsPoint import PointClass
from clsEllipse import EllipseClass
if 1:
master = Tk()
figure = Figure(figsize=(8,8), dpi=100)
frame_c=Frame(relief = GROOVE,bd = 2)
frame_c.pack(fill=BOTH, expand=1,)
canvas = FigureCanvasTkAgg(figure, master=frame_c)
canvas.show()
canvas.get_tk_widget().pack(fill=BOTH, expand=1)
plot1 = figure.add_subplot(111)
plot1.axis('equal')
# Parameter der Ellipse
el = EllipseClass(PointClass(0,0), 200, 100, radians(30))
polyline_step = 10 # PolyLine - Winkel-Schritt, in dem wir um die Ellipse sausen
biarc_step = 35 # BiArc - Winkel-Schritt, in dem wir um die Ellipse sausen
# Ellipse als PolyLine
xC=[]; yC=[]; xP=[]; yP=[]
xC.append(el.Center.x)
yC.append(el.Center.y)
for i in range(0, 360, polyline_step):
P = el.EPoint(radians(i))
xP.append(P.x)
yP.append(P.y)
if (i < 360): # und noch kurzer Bogen, falls es nicht genau naus gegangen ist
P = el.EPoint(radians(360))
xP.append(P.x)
yP.append(P.y)
# zeichnen:
plot1.plot(xC,yC,'-.xr',xP,yP,'-xb')
# Test: Steigungsverlauf
#print "Steigungsverlauf:"
#for i in range(0, 361, 10):
# print "w= " + str(i) + "\t -> t= " + str(el.Tangente(radians(i)))
# Ellipse als PolyArc :-)
biarcs = []
w = 0
for w in range(biarc_step, 360, biarc_step):
biarcs.append(el.BiArc(radians(w - biarc_step), radians(w)))
if (w < 360): # und noch kurzer Bogen, falls es nicht genau naus gegangen ist
biarcs.append(el.BiArc(radians(w), radians(360)))
# alle BiArcs zeichnen:
plot2 = figure.add_subplot(111)
for biarc in biarcs:
for geo in biarc.geos:
geo.plot2plot(plot2)
master.mainloop()
| gpl-3.0 |
eclee25/flu-SDI-simulations-age | age_time_immunity_prop_viz.py | 1 | 7042 | #!/usr/bin/python
##############################################
###Python template
###Author: Elizabeth Lee
###Date: 7/16/14
###Function:
##### Visualize results of time-based epidemic simulations where pre-existing immunity exists and is heterogeneous within the adult population. Vary single average value of immunity for subpopulation of adults with any pre-existing immunity.
###Import data:
###Command Line: python age_time_immunity_single_viz.py
##############################################
### notes ###
# Ages:
# 1 = Infant, 2 = Toddler, 3 = Child, 4 = Adult, 5 = Senior, 6 = Elder (in nursing home)
# Places (edge attribute):
# F = household/family, S = school, H = hospital, M = shopping mall, W = workplace, D = daycare, E = elsehwere, P = preschool, O = nursing homes, N = neighbor
# T_critical = 0.0565868
### packages/modules ###
import matplotlib.pyplot as plt
import numpy as np
from collections import defaultdict
import zipfile
from time import clock
## local modules ##
import percolations as perc
import simulation_parameters as par
import pretty_print as pp
### plotting parameters ###
numsims = par.pp_numsims
size_epi = par.pp_size_epi
inf_period = par.pp_inf_period
g = par.pp_gamma
T = par.pp_T
b = par.pp_b
# specific to immunity params
imm_val = par.pp_immune_val
prop_ls = par.pp_prop_list
zstring = par.pp_pstr_range
zstring2 = par.pp_mstr_fixed
print "Params:", numsims, size_epi, inf_period, g, T, b, imm_val, prop_ls
### data structures ###
d_node_age = {} # d_node_age[nodenumber] = ageclass
### ziparchive to read and write results ###
zipname = '/home/elee/Dropbox/Elizabeth_Bansal_Lab/Age_Based_Simulations/Results/immunity_time_%ssims_beta%.3f_%s_%s.zip' %(numsims, b, zstring, zstring2)
#############################################
# age data processing
graph_ages = open('/home/elee/Dropbox/Elizabeth_Bansal_Lab/urban_network_added_with_info_May24_2014/urban_ages_N10k_Sept2012.txt') # node number and age class
for line in graph_ages:
new_line = line.strip().split(' ')
node, age = new_line
d_node_age[node] = age # node-ageclass dictionary
# define network size
N = len(d_node_age)
print "network size:", N
# create binary lists to indicate children and adults
ch = [1 if d_node_age[str(node)] == '3' else 0 for node in xrange(1, int(N) + 1)]
ad = [1 if d_node_age[str(node)] == '4' else 0 for node in xrange(1, int(N) + 1)]
##############################################
# data processing - convert tstep info into dictionaries
# declare dictionaries
# dict_epiincid[(code, simnumber, 'T', 'C' or 'A')] = [T, C or A incid at tstep 0, T, C or A incid at tstep 1...], where incidence is simply number of new cases (raw)
# dict_epiAR[(code, simnumber, 'T', 'C' or 'A')] = [T, C or A attack rate at tstep 0, T, C or A attack rate at tstep 1...], where attack rate is number of new cases per population size
# dict_epiOR[(code, simnumber)] = [OR at tstep0, OR at tstep1...]
# dict_epiOR_filt[(code, simnum)] = [OR for each time step for epidemics only where OR is nan when we want to exclude the time point due to small infected numbers]
# dict_epiresults[(code, simnumber)] = (episize, c_episize, a_episize)
d_epiincid, d_epiOR, d_epiresults, d_epiAR, d_epiOR_filt = defaultdict(list), defaultdict(list), {}, defaultdict(list), defaultdict(list)
for prop in prop_ls:
zstring3 = 'prop%s' %(prop) # string for filename disambiguation
processing = clock()
Itstep_file = 'Results/Itstep_immunity_time_%ssims_beta%.3f_%s_%s.txt' %(numsims, b, zstring3, zstring2)
Rtstep_file = 'Results/Rtstep_immunity_time_%ssims_beta%.3f_%s_%s.txt' %(numsims, b, zstring3, zstring2)
d_epiincid, d_epiOR, d_epiresults, d_epiAR, d_epiOR_filt = perc.recreate_epidata(Itstep_file, Rtstep_file, zipname, prop, size_epi, ch, ad, d_epiincid, d_epiOR, d_epiresults, d_epiAR, d_epiOR_filt)
print "processed", clock() - processing
# number of simulations that reached epidemic size
num_epi = sum([1 for key in d_epiresults if d_epiresults[key][0] > size_epi])
print prop, "number of epidemics", num_epi
# grab unique list of proportion values that produced at least one epidemic
prop_epi = list(set([key[0] for key in d_epiincid]))
#############################################
## draw plots
for prop in prop_epi:
pl_ls = [key for key in d_epiOR if key[0] == prop]
zstring3 = 'prop%s' %(prop) # string for filename disambiguation
##############################################
### plot OR by time ###
# each epidemic sim is one line
for key in pl_ls:
plt.plot(xrange(len(d_epiOR[key])), d_epiOR[key], marker = 'None', color = 'grey')
plt.plot(xrange(250), [1] * 250, marker = 'None', color = 'red', linewidth = 2)
plt.xlabel('time step')
plt.ylabel(par.pf_OR_lab)
figname = 'Figures/epiOR_immunity_time_%ssims_beta%.3f_%s_%s.png' %(numsims, b, zstring3, zstring2)
plt.savefig(figname)
plt.close()
pp.compress_to_ziparchive(zipname, figname)
# plt.show()
for prop in prop_epi:
pl_ls = [key for key in d_epiOR if key[0] == prop]
zstring3 = 'prop%s' %(prop) # string for filename disambiguation
##############################################
### plot filtered OR by time ###
# each sim is one line
for key in pl_ls:
plt.plot(xrange(len(d_epiOR_filt[key])), d_epiOR_filt[key], marker = 'None', color = 'grey')
plt.plot(xrange(250), [1] * 250, marker = 'None', color = 'red', linewidth = 2)
plt.xlabel('sim time step, 5-95% cum infections')
plt.ylabel(par.pf_OR_lab)
figname = 'Figures/epiORfilt_immunity_time_%ssims_beta%.3f_%s_%s.png' %(numsims, b, zstring3, zstring2)
plt.savefig(figname)
plt.close()
pp.compress_to_ziparchive(zipname, figname)
# plt.show()
for prop in prop_epi:
pl_ls = [key for key in d_epiOR if key[0] == prop]
zstring3 = 'prop%s' %(prop) # string for filename disambiguation
##############################################
### plot incidence by time ###
# each sim is one line
pl_ls = [key for key in d_epiincid if key[0] == prop and key[2] == 'T']
for key in pl_ls:
plt.plot(xrange(len(d_epiincid[key])), d_epiincid[key], marker = 'None', color = 'grey')
plt.xlabel('time step')
plt.ylabel('number of new cases')
figname = 'Figures/epiincid_immunity_time_%ssims_beta%.3f_%s_%s.png' %(numsims, b, zstring3, zstring2)
plt.savefig(figname)
plt.close()
pp.compress_to_ziparchive(zipname, figname)
# plt.show()
##############################################
### plot hist of episize by proportion of adults immune ###
d_episize = defaultdict(list)
for v in prop_epi:
d_episize[v] = [sum(d_epiincid[key]) for key in d_epiincid if key[0] == v and key[2] == 'T']
plt.errorbar(prop_epi, [np.mean(d_episize[v]) for v in prop_epi], yerr = [np.std(d_episize[v]) for v in prop_epi], marker = 'o', color = 'black', linestyle = 'None')
plt.xlim([-0.1, 1.1])
plt.xlabel('proportion of adults with any immunity')
plt.ylabel('epidemic size')
figname = 'Figures/episize_immunity_time_%ssims_beta%.3f_%s_%s.png' %(numsims, b, zstring, zstring2)
plt.savefig(figname)
plt.close()
pp.compress_to_ziparchive(zipname, figname)
# plt.show() | mit |
Heathckliff/cantera | interfaces/cython/cantera/examples/reactors/periodic_cstr.py | 4 | 3128 | """
Periodic CSTR
This example illustrates a CSTR with steady inputs but periodic interior state.
A stoichiometric hydrogen/oxygen mixture is introduced and reacts to produce
water. But since water has a large efficiency as a third body in the chain
termination reaction
H + O2 + M = HO2 + M
as soon as a significant amount of water is produced the reaction stops. After
enough time has passed that the water is exhausted from the reactor, the mixture
explodes again and the process repeats. This explanation can be verified by
decreasing the rate for reaction 7 in file 'h2o2.cti' and re-running the
example.
Acknowledgments: The idea for this example and an estimate of the conditions
needed to see the oscillations came from Bob Kee, Colorado School of Mines
"""
import cantera as ct
import numpy as np
# create the gas mixture
gas = ct.Solution('h2o2.cti')
# pressure = 60 Torr, T = 770 K
p = 60.0*133.3
t = 770.0
gas.TPX = t, p, 'H2:2, O2:1'
# create an upstream reservoir that will supply the reactor. The temperature,
# pressure, and composition of the upstream reservoir are set to those of the
# 'gas' object at the time the reservoir is created.
upstream = ct.Reservoir(gas)
# Now create the reactor object with the same initial state
cstr = ct.IdealGasReactor(gas)
# Set its volume to 10 cm^3. In this problem, the reactor volume is fixed, so
# the initial volume is the volume at all later times.
cstr.volume = 10.0*1.0e-6
# We need to have heat loss to see the oscillations. Create a reservoir to
# represent the environment, and initialize its temperature to the reactor
# temperature.
env = ct.Reservoir(gas)
# Create a heat-conducting wall between the reactor and the environment. Set its
# area, and its overall heat transfer coefficient. Larger U causes the reactor
# to be closer to isothermal. If U is too small, the gas ignites, and the
# temperature spikes and stays high.
w = ct.Wall(cstr, env, A=1.0, U=0.02)
# Connect the upstream reservoir to the reactor with a mass flow controller
# (constant mdot). Set the mass flow rate to 1.25 sccm.
sccm = 1.25
vdot = sccm * 1.0e-6/60.0 * ((ct.one_atm / gas.P) * ( gas.T / 273.15)) # m^3/s
mdot = gas.density * vdot # kg/s
mfc = ct.MassFlowController(upstream, cstr, mdot=mdot)
# now create a downstream reservoir to exhaust into.
downstream = ct.Reservoir(gas)
# connect the reactor to the downstream reservoir with a valve, and set the
# coefficient sufficiently large to keep the reactor pressure close to the
# downstream pressure of 60 Torr.
v = ct.Valve(cstr, downstream, K=1.0e-9)
# create the network
network = ct.ReactorNet([cstr])
# now integrate in time
t = 0.0
dt = 0.1
tm = []
y = []
while t < 300.0:
t += dt
network.advance(t)
tm.append(t)
y.append(cstr.thermo['H2','O2','H2O'].Y)
if __name__ == '__main__':
print(__doc__)
try:
import matplotlib.pyplot as plt
plt.figure(1)
plt.plot(tm, y)
plt.legend(['H2','O2','H2O'])
plt.title('Mass Fractions')
plt.show()
except ImportError:
print('Matplotlib not found. Unable to plot results.')
| bsd-3-clause |
jkarnows/scikit-learn | sklearn/kernel_ridge.py | 44 | 6504 | """Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
# Authors: Mathieu Blondel <[email protected]>
# Jan Hendrik Metzen <[email protected]>
# License: BSD 3 clause
import numpy as np
from .base import BaseEstimator, RegressorMixin
from .metrics.pairwise import pairwise_kernels
from .linear_model.ridge import _solve_cholesky_kernel
from .utils import check_X_y
from .utils.validation import check_is_fitted
class KernelRidge(BaseEstimator, RegressorMixin):
"""Kernel ridge regression.
Kernel ridge regression (KRR) combines ridge regression (linear least
squares with l2-norm regularization) with the kernel trick. It thus
learns a linear function in the space induced by the respective kernel and
the data. For non-linear kernels, this corresponds to a non-linear
function in the original space.
The form of the model learned by KRR is identical to support vector
regression (SVR). However, different loss functions are used: KRR uses
squared error loss while support vector regression uses epsilon-insensitive
loss, both combined with l2 regularization. In contrast to SVR, fitting a
KRR model can be done in closed-form and is typically faster for
medium-sized datasets. On the other hand, the learned model is non-sparse
and thus slower than SVR, which learns a sparse model for epsilon > 0, at
prediction-time.
This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the :ref:`User Guide <kernel_ridge>`.
Parameters
----------
alpha : {float, array-like}, shape = [n_targets]
Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
``(2*C)^-1`` in other linear models such as LogisticRegression or
LinearSVC. If an array is passed, penalties are assumed to be specific
to the targets. Hence they must correspond in number.
kernel : string or callable, default="linear"
Kernel mapping used internally. A callable should accept two arguments
and the keyword arguments passed to this object as kernel_params, and
should return a floating point number.
gamma : float, default=None
Gamma parameter for the RBF, polynomial, exponential chi2 and
sigmoid kernels. Interpretation of the default value is left to
the kernel; see the documentation for sklearn.metrics.pairwise.
Ignored by other kernels.
degree : float, default=3
Degree of the polynomial kernel. Ignored by other kernels.
coef0 : float, default=1
Zero coefficient for polynomial and sigmoid kernels.
Ignored by other kernels.
kernel_params : mapping of string to any, optional
Additional parameters (keyword arguments) for kernel function passed
as callable object.
Attributes
----------
dual_coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s) in kernel space
X_fit_ : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data, which is also required for prediction
References
----------
* Kevin P. Murphy
"Machine Learning: A Probabilistic Perspective", The MIT Press
chapter 14.4.3, pp. 492-493
See also
--------
Ridge
Linear ridge regression.
SVR
Support Vector Regression implemented using libsvm.
Examples
--------
>>> from sklearn.kernel_ridge import KernelRidge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> clf = KernelRidge(alpha=1.0)
>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear',
kernel_params=None)
"""
def __init__(self, alpha=1, kernel="linear", gamma=None, degree=3, coef0=1,
kernel_params=None):
self.alpha = alpha
self.kernel = kernel
self.gamma = gamma
self.degree = degree
self.coef0 = coef0
self.kernel_params = kernel_params
def _get_kernel(self, X, Y=None):
if callable(self.kernel):
params = self.kernel_params or {}
else:
params = {"gamma": self.gamma,
"degree": self.degree,
"coef0": self.coef0}
return pairwise_kernels(X, Y, metric=self.kernel,
filter_params=True, **params)
@property
def _pairwise(self):
return self.kernel == "precomputed"
def fit(self, X, y=None, sample_weight=None):
"""Fit Kernel Ridge regression model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample, ignored if None is passed.
Returns
-------
self : returns an instance of self.
"""
# Convert data
X, y = check_X_y(X, y, accept_sparse=("csr", "csc"), multi_output=True)
K = self._get_kernel(X)
alpha = np.atleast_1d(self.alpha)
ravel = False
if len(y.shape) == 1:
y = y.reshape(-1, 1)
ravel = True
copy = self.kernel == "precomputed"
self.dual_coef_ = _solve_cholesky_kernel(K, y, alpha,
sample_weight,
copy)
if ravel:
self.dual_coef_ = self.dual_coef_.ravel()
self.X_fit_ = X
return self
def predict(self, X):
"""Predict using the the kernel ridge model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Samples.
Returns
-------
C : array, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.
"""
check_is_fitted(self, ["X_fit_", "dual_coef_"])
K = self._get_kernel(X, self.X_fit_)
return np.dot(K, self.dual_coef_)
| bsd-3-clause |
keflavich/agpy | agpy/collapse_gaussfit.py | 6 | 20915 | """
-----------------
Collapse Gaussfit
-----------------
This was an early attempt to automate gaussian fitting over a data cube using
(multiple) gaussian decomposition for each spectrum. It's reasonably
effective, but the uses are somewhat minimal. I've tried shifting my
cube-related work to `pyspeckit <pyspeckit.bitbucket.org>`_.
"""
try:
import scipy
from scipy import optimize,sqrt
from scipy.optimize import leastsq
#from scipy.stats.stats import nanmedian,nanmean,_nanmedian
except ImportError:
print "Scipy cold not be loaded. Collapse_gaussfit may fail"
import numpy
from numpy import vectorize,zeros,exp,median,where,asarray,array,nonzero,ma,arange,square
import matplotlib
#matplotlib.use('Agg')
from pylab import indices,figure,clf,savefig,plot,legend,text,axes,title
import pickle
import pyfits
import time
from mad import MAD
from ratosexagesimal import ratos,dectos
def nanmedian(arr):
""" nanmedian - this version is NOT capable of broadcasting (operating along axes) """
return median(arr[arr==arr])
def nanmean(arr):
""" nanmean - this version is NOT capable of broadcasting (operating along axes) """
return (arr[arr==arr]).mean()
# read in file
# filename = sys.argv[1]
# fitsfile = pyfits.open(filename)
# cube = fitsfile[0].data
# def gaussian(dx,sigma):
# return lambda x: exp( - (x-dx)**2 / sigma**2 )
# def return_param(xarr,param):
# errorfunction = lambda p:gaussian(*p)(*indices(xarr.shape))-xarr
# pars, cov, infodict, errmsg, success = optimize.leastsq(errorfunction, [len(xarr)/2.,1], full_output=1)
# print errmsg
# if param == 'width':
# return pars[1]
# elif param == 'center':
# return pars[0]
# else:
# return
def gaussian(dx,sigma,a):
return lambda x: a*exp( - (x-dx)**2 / sigma**2 )
def double_gaussian(dx1,dx2,sigma1,sigma2,a1,a2):
return lambda x: a1*exp( - (x-dx1)**2 / sigma1**2 ) + a2*exp( - (x-dx2)**2 / sigma2**2 )
def triple_gaussian(dx1,dx2,dx3,sigma1,sigma2,sigma3,a1,a2,a3):
return lambda x: abs(a1)*exp( - (x-dx1)**2 / sigma1**2 ) + abs(a2)*exp( - (x-dx2)**2 / sigma2**2 ) + abs(a3)*exp( - (x-dx3)**2 / sigma3**2 )
def n_gaussian(dx,sigma,a):
def g(x):
v = zeros(len(x))
for i in range(len(dx)):
v += a[i] * exp( - ( x - dx[i] )**2 / sigma[i]**2 )
return v
return g
def gerr(xarr):
return lambda p:xarr-gaussian(*p)(*indices(xarr.shape))
def double_gerr(xarr):
return lambda p:xarr-double_gaussian(*p)(*indices(xarr.shape))
def triple_gerr(xarr):
return lambda p:xarr-triple_gaussian(*p)(*indices(xarr.shape))
def return_param(xarr,params=None,negamp=False):
if params == None:
if negamp:
params = [xarr.argmin(),5,xarr.min()]
else:
params = [xarr.argmax(),5,xarr.max()]
pars, cov, infodict, errmsg, success = optimize.leastsq(gerr(xarr), params, full_output=1)
return pars
def return_double_param(xarr,params=None):
if params == None:
params = [xarr.argmax(),xarr.argmax()+3,4.2,2.3,xarr.max(),xarr.max()/2]
pars, cov, infodict, errmsg, success = optimize.leastsq(double_gerr(xarr), params, full_output=1)
return pars
def return_triple_param(xarr,params=None):
"""
input parameters: center[1-3],width[1-3],amplitude[1-3]
"""
if params == None:
params = [xarr.argmax(),xarr.argmax()+3,xarr.argmax(),4.2,2.3,10,xarr.max(),xarr.max()/2.,xarr.max()/5.]
pars, cov, infodict, errmsg, success = optimize.leastsq(triple_gerr(xarr), params, full_output=1)
return pars
def adaptive_collapse_gaussfit(cube,axis=2,nsig=3,nrsig=4,prefix='interesting',
vconv=lambda x: x,xtora=lambda x: x,ytodec=lambda x: x,doplot=True):
"""
Attempts to fit one or two Gaussians to each spectrum in a data cube and returns the parameters of the fits.
Adaptively determines where to fit two Gaussian components based on residuals. Will fit 3 gaussians if a
two-gaussian fit is not better than a certain threshold (specified by nsig), and those fits will be output
to images with filename prefix+(coordinate).png. The 3-gaussian fit parameters will not be returned because
the automated fitting is very unlikely to get that part right.
inputs:
cube - a data cube with two spatial and one spectral dimensions
axis - the axis of the spectral dimension
nsig - number of sigma over the mean residual to trigger double-gaussian fitting
also, cutoff to do any fitting at all
prefix - the prefix (including directory name) of the output images from 3-gaussian fitting
doplot - option to turn off plotting of triple-gaussian fits
vconv,xtora,ytodec - functions to convert the axes from pixel coordinates to ra/dec/velocity coordinates
returns:
width_arr1,width_arr2,chi2_arr,offset_arr1,offset_arr2,amp_arr1,amp_arr2
The Gaussian widths, line centers (in pixel units), amplitudes, and the chi-squared value, not in that order
These returns are identical to the returns from double_gaussian, but all components will be zero for the second
gaussian in the case of a single-gaussian fit
the triple gaussian is guessed to be the double gaussian plus a broad, low-amplitude gaussian. Ideally this should
fit outflows reasonably well, but who knows if it really will.
Another option is to fit a negative-amplitude gaussian to account for self-absorption
"""
std_coll = cube.std(axis=axis) # standard deviation of each spectrum
# mad_coll = MAD(cube,axis=axis)
mean_std = median(std_coll.ravel()) # median standard deviation (to reject high-signal spectra that have high std)
if axis > 0: # force spectral axis to first axis
cube = cube.swapaxes(0,axis)
width_arr = zeros(cube.shape[1:]) # define gaussian param arrays
width_arr1 = zeros(cube.shape[1:]) # define gaussian param arrays
width_arr2 = zeros(cube.shape[1:]) # define gaussian param arrays
amp_arr = zeros(cube.shape[1:]) # define gaussian param arrays
amp_arr1 = zeros(cube.shape[1:]) # define gaussian param arrays
amp_arr2 = zeros(cube.shape[1:]) # define gaussian param arrays
chi2_arr = zeros(cube.shape[1:]) # define gaussian param arrays
resid_arr = zeros(cube.shape[1:]) # define gaussian param arrays
offset_arr = zeros(cube.shape[1:]) # define gaussian param arrays
offset_arr1 = zeros(cube.shape[1:]) # define gaussian param arrays
offset_arr2 = zeros(cube.shape[1:]) # define gaussian param arrays
ncarr = (cube.max(axis=0) > mean_std*nsig) # cutoff: don't fit no-signal spectra
starttime = time.time() # timing for output
print cube.shape
print "Fitting a total of %i spectra with peak signal above %f" % (ncarr.sum(),mean_std*nsig)
for i in xrange(cube.shape[1]): # Loop over all elements for
t0 = time.time()
nspec = (cube[:,i,:].max(axis=0) > mean_std*nsig).sum()
print "Working on row %d with %d spectra to fit" % (i,nspec) ,
for j in xrange(cube.shape[2]):
if cube[:,i,j].max() > mean_std*nsig:
# if cube[:,i,j].max() > MAD(cube[:,i,j]):
pars = return_param(cube[:,i,j])
width_arr[i,j] = pars[1]
width_arr1[i,j] = pars[1]
amp_arr[i,j] = pars[2]
amp_arr1[i,j] = pars[2]
# chi2_arr[i,j] = sum(( gerr(cube[:,i,j])(pars) )**2)
resid_arr[i,j] = (gerr(cube[:,i,j])(pars)).sum()
offset_arr[i,j] = pars[0]
offset_arr1[i,j] = pars[0]
else:
width_arr1[i,j] = numpy.nan
chi2_arr[i,j] = numpy.nan
resid_arr[i,j] = numpy.nan
offset_arr1[i,j] = numpy.nan
dt = time.time()-t0
if nspec > 0:
print "in %f seconds (average: %f)" % (dt,dt/float(nspec))
else:
print
chi2_arr = resid_arr**2
resids = ma.masked_where(numpy.isnan(chi2_arr),chi2_arr) # hide bad values
# residcut = (resids.mean() + (resids.std() * nrsig) ) # Old versino - used standard deviation and mean
residcut = (nanmedian(chi2_arr.ravel()) + (MAD(chi2_arr.ravel()) * nrsig) ) # New version: set cutoff by median + nrsig * MAD
to_refit = (resids > residcut).astype('bool')
# to_refit[numpy.isnan(to_refit)] = 0
inds = array(nonzero(to_refit)).transpose()
dgc,tgc = 0,0
print "Refitting a total of %i spectra with peak residual above %f" % (to_refit.sum(),residcut)
f=open("%s_triples.txt" % prefix,'w')
# vconv = lambda x: (x-p3+1)*dv+v0 # convert to velocity frame
vind = vconv(arange(cube[:,0,0].shape[0]))
xind = arange(cube[:,0,0].shape[0])
for ind in inds:
i,j = ind
doublepars = return_double_param(cube[:,i,j])
old_chi2 = chi2_arr[i,j]
new_chi2 = sum(square( double_gerr(cube[:,i,j])(doublepars) ))
if new_chi2 < old_chi2: # if 2 gaussians is an improvement, use it!
chi2_arr[i,j] = new_chi2
width_arr1[i,j] = doublepars[2]
width_arr2[i,j] = doublepars[3]
amp_arr1[i,j] = doublepars[4]
amp_arr2[i,j] = doublepars[5]
offset_arr1[i,j] = doublepars[0]
offset_arr2[i,j] = doublepars[1]
ncarr[i,j] += 1
if new_chi2 > residcut: # Even if double was better, see if a triple might be better yet [but don't store it in the params arrays!]
print >>f,"Triple-gaussian fitting at %i,%i (%i'th double, %i'th triple)" % (i,j,dgc,tgc)
if tgc % 100 == 0:
print "Triple-gaussian fitting at %i,%i (%i'th double, %i'th triple)" % (i,j,dgc,tgc)
tgc += 1
tpguess = [doublepars[0],doublepars[1],(doublepars[0]+doublepars[1])/2.,doublepars[2],doublepars[3],doublepars[2]*5.,doublepars[4],doublepars[5],doublepars[4]/5.]
triplepars = return_triple_param(cube[:,i,j],params=tpguess)
pars = [offset_arr[i,j],width_arr[i,j],amp_arr[i,j]]
if doplot: # if you don't, there's really no point in fitting at all...
ax = axes([.05,.05,.7,.9])
plot(vind,cube[:,i,j],color='black',linestyle='steps',linewidth='.5')
plot(vind,gaussian(*pars)(xind),'r-.',label="Single %f" % ( (gerr(cube[:,i,j])(pars)).sum() ) )
plot(vind,double_gaussian(*doublepars)(xind),'g--',label="Double %f" % ( (double_gerr(cube[:,i,j])(doublepars)).sum() ))
plot(vind,triple_gaussian(*triplepars)(xind),'b:',label="Triple %f" % ( (triple_gerr(cube[:,i,j])(triplepars)).sum() ),linewidth=2)
pars[0] = vconv(pars[0])
text(1.05,.8,"c1 %3.2f w1 %3.2f a1 %3.2f" % tuple(pars),transform=ax.transAxes,size='smaller')
dp = [ vconv(doublepars[0]) , doublepars[2], doublepars[4], vconv(doublepars[1]), doublepars[3], doublepars[5] ]
text(1.05,.6,"c1 %3.2f w1 %3.2f a1 %3.2f\nc2 %3.2f w2 %3.2f a2 %3.2f" % tuple(dp),transform=ax.transAxes,size='smaller')
tp = [ vconv(triplepars[0]) , triplepars[3], triplepars[6], vconv(triplepars[1]), triplepars[4], triplepars[7], vconv(triplepars[2]), triplepars[5], triplepars[8] ]
text(1.05,.4,"c1 %3.2f w1 %3.2f a1 %3.2f\nc2 %3.2f w2 %3.2f a2 %3.2f\nc3 %3.2f w3 %3.2f a3 %3.2f" % tuple(tp),transform=ax.transAxes,size='smaller')
title("Spectrum at %s %s" % (ratos(xtora(i)),dectos(ytodec(j))) )
legend(loc='best')
savefig("%s_%s.%s.png" % (prefix,i,j))
clf()
ncarr[i,j] += 1
print >>f,triplepars
dgc += 1
f.close()
print "Total time %f seconds for %i double and %i triple gaussians" % (time.time()-starttime,dgc,tgc)
return width_arr1,width_arr2,chi2_arr,offset_arr1,offset_arr2,amp_arr1,amp_arr2,ncarr
def collapse_gaussfit(cube,axis=2,negamp=False):
std_coll = cube.std(axis=axis)
mean_std = median(std_coll.ravel())
if axis > 0:
cube = cube.swapaxes(0,axis)
width_arr = zeros(cube.shape[1:])
amp_arr = zeros(cube.shape[1:])
chi2_arr = zeros(cube.shape[1:])
offset_arr = zeros(cube.shape[1:])
starttime = time.time()
print cube.shape
print "Fitting a total of %i spectra with peak signal above %f" % ((cube.max(axis=0) > mean_std).sum(),mean_std)
for i in xrange(cube.shape[1]):
t0 = time.time()
nspec = (cube[:,i,:].max(axis=0) > mean_std).sum()
print "Working on row %d with %d spectra to fit" % (i,nspec) ,
for j in xrange(cube.shape[2]):
if not negamp and cube[:,i,j].max() > mean_std:
pars = return_param(cube[:,i,j],negamp=negamp)
width_arr[i,j] = pars[1]
chi2_arr[i,j] = sum(( gerr(cube[:,i,j])(pars) )**2)
offset_arr[i,j] = pars[0]
amp_arr[i,j] = pars[2]
elif negamp and cube[:,i,j].min() < -1*mean_std:
pars = return_param(cube[:,i,j],negamp=negamp)
width_arr[i,j] = pars[1]
chi2_arr[i,j] = sum(( gerr(cube[:,i,j])(pars) )**2)
offset_arr[i,j] = pars[0]
amp_arr[i,j] = pars[2]
else:
width_arr[i,j] = numpy.nan
chi2_arr[i,j] = numpy.nan
offset_arr[i,j] = numpy.nan
amp_arr[i,j] = numpy.nan
dt = time.time()-t0
if nspec > 0:
print "in %f seconds (average: %f)" % (dt,dt/float(nspec))
print "Total time %f seconds" % (time.time()-starttime)
return width_arr,offset_arr,amp_arr,chi2_arr
# next step: find 2-gaussian fits
def collapse_double_gaussfit(cube,axis=2):
std_coll = cube.std(axis=axis)
mean_std = median(std_coll.ravel())
if axis > 0:
cube = cube.swapaxes(0,axis)
width_arr1 = zeros(cube.shape[1:])
width_arr2 = zeros(cube.shape[1:])
amp_arr1 = zeros(cube.shape[1:])
amp_arr2 = zeros(cube.shape[1:])
chi2_arr = zeros(cube.shape[1:])
offset_arr1 = zeros(cube.shape[1:])
offset_arr2 = zeros(cube.shape[1:])
starttime = time.time()
print cube.shape
print "Fitting a total of %i spectra with peak signal above %f" % ((cube.max(axis=0) > mean_std).sum(),mean_std)
for i in xrange(cube.shape[1]):
t0 = time.time()
nspec = (cube[:,i,:].max(axis=0) > mean_std).sum()
print "Working on row %d with %d spectra to fit" % (i,nspec) ,
for j in xrange(cube.shape[2]):
if cube[:,i,j].max() > mean_std:
pars = return_double_param(cube[:,i,j])
width_arr1[i,j] = pars[2]
width_arr2[i,j] = pars[3]
amp_arr1[i,j] = pars[4]
amp_arr2[i,j] = pars[5]
chi2_arr[i,j] = sum(( double_gerr(cube[:,i,j])(pars) )**2)
offset_arr1[i,j] = pars[0]
offset_arr2[i,j] = pars[1]
else:
width_arr1[i,j] = numpy.nan
width_arr2[i,j] = numpy.nan
chi2_arr[i,j] = numpy.nan
offset_arr1[i,j] = numpy.nan
offset_arr2[i,j] = numpy.nan
dt = time.time()-t0
if nspec > 0:
print "in %f seconds (average: %f)" % (dt,dt/float(nspec))
print "Total time %f seconds" % (time.time()-starttime)
return width_arr1,width_arr2,chi2_arr,offset_arr1,offset_arr2,amp_arr1,amp_arr2
def wrap_collapse_gauss(filename,outprefix,redo='no'):
"""
redo - if not equal to 'no', then...
if collapse_gaussfit succeeded (to the extent that the .pysav files were written),
but some part of the file writing or successive procedures failed, re-do those
procedures without redoing the whole collapse
"""
fitsfile = pyfits.open(filename)
dv,v0,p3 = fitsfile[0].header['CD3_3'],fitsfile[0].header['CRVAL3'],fitsfile[0].header['CRPIX3']
cube = fitsfile[0].data
cube = where(numpy.isnan(cube),0,cube)
if redo=='no':
doubleB = asarray(collapse_double_gaussfit(cube,axis=0))
doubleB[numpy.isnan(doubleB)] = 0
pickle.dump(doubleB,open('%s_doubleB.pysav' % outprefix,'w'))
else:
doubleB = pickle.load(open('%s_doubleB.pysav' % outprefix,'r'))
db = doubleB
gcd = double_gaussian(db[3],db[4],db[0],db[1],db[5],db[6])(indices(cube.shape)[0])
fitsfile[0].data = gcd
fitsfile.writeto('%s_doublegausscube.fits' % outprefix,clobber=True)
gcd[numpy.isnan(gcd)] = 0
doubleResids = cube-gcd
fitsfile[0].data = doubleResids
fitsfile.writeto('%s_doublegaussresids.fits' % outprefix,clobber=True)
#doubleB[4] = (doubleB[4]-v0) / dv + p3-1
#doubleB[3] = (doubleB[3]-v0) / dv + p3-1
doubleB[4] = (doubleB[4]-p3+1) * dv + v0
doubleB[3] = (doubleB[3]-p3+1) * dv + v0
fitsfile[0].data = asarray(doubleB)
fitsfile.writeto('%s_doublegausspars.fits' % outprefix,clobber=True)
if redo=='no':
singleB = asarray(collapse_gaussfit(cube,axis=0))
pickle.dump(singleB,open('%s_singleB.pysav' % outprefix,'w'))
else:
singleB = pickle.load(open('%s_singleB.pysav' % outprefix,'r'))
gc = gaussian(singleB[1],singleB[0],singleB[2])(indices(cube.shape)[0])
singleB[1] = (singleB[1]-p3+1) * dv + v0
fitsfile[0].data = gc
fitsfile.writeto('%s_singlegausscube.fits' % outprefix,clobber=True)
gc[numpy.isnan(gc)]=0
singleResids = cube-gc
fitsfile[0].data = singleResids
fitsfile.writeto('%s_singlegaussresids.fits' % outprefix,clobber=True)
fitsfile[0].data = asarray(singleB)
fitsfile.writeto('%s_singlegausspars.fits' % outprefix,clobber=True)
fitsfile[0].header.__delitem__('CD3_3')
fitsfile[0].header.__delitem__('CRVAL3')
fitsfile[0].header.__delitem__('CRPIX3')
fitsfile[0].header.__delitem__('CUNIT3')
fitsfile[0].header.__delitem__('CTYPE3')
doubleResids[numpy.isnan(doubleResids)] = 0
totalDResids = doubleResids.sum(axis=0)
fitsfile[0].data = totalDResids
fitsfile.writeto('%s_doublegauss_totalresids.fits' % outprefix,clobber=True)
singleResids[numpy.isnan(singleResids)] = 0
totalSResids = singleResids.sum(axis=0)
fitsfile[0].data = totalSResids
fitsfile.writeto('%s_singlegauss_totalresids.fits' % outprefix,clobber=True)
return singleB,doubleB
def wrap_collapse_adaptive(filename,outprefix,redo='no',nsig=5,nrsig=2,doplot=True):
"""
redo - if not equal to 'no', then...
if collapse_gaussfit succeeded (to the extent that the .pysav files were written),
but some part of the file writing or successive procedures failed, re-do those
procedures without redoing the whole collapse
"""
fitsfile = pyfits.open(filename)
dv,v0,p3 = fitsfile[0].header['CD3_3'],fitsfile[0].header['CRVAL3'],fitsfile[0].header['CRPIX3']
dr,r0,p1 = fitsfile[0].header['CD1_1'],fitsfile[0].header['CRVAL1'],fitsfile[0].header['CRPIX1']
dd,d0,p2 = fitsfile[0].header['CD2_2'],fitsfile[0].header['CRVAL2'],fitsfile[0].header['CRPIX2']
xtora = lambda x: (x-p1+1)*dr+r0 # convert pixel coordinates to RA/Dec/Velocity
ytodec = lambda y: (y-p2+1)*dd+d0
vconv = lambda v: (v-p3+1)*dv+v0
cube = fitsfile[0].data
cube = where(numpy.isnan(cube),0,cube)
if redo=='no':
adaptB = asarray(adaptive_collapse_gaussfit(cube,axis=0,prefix=outprefix+'_triple',
nsig=nsig,nrsig=nrsig,vconv=vconv,xtora=xtora,ytodec=ytodec,doplot=doplot))
adaptB[numpy.isnan(adaptB)] = 0
pickle.dump(adaptB,open('%s_adaptB.pysav' % outprefix,'w'))
else:
adaptB = pickle.load(open('%s_adaptB.pysav' % outprefix,'r'))
db = adaptB
gcd = double_gaussian(db[3],db[4],db[0],db[1],db[5],db[6])(indices(cube.shape)[0])
fitsfile[0].data = gcd
fitsfile.writeto('%s_adaptgausscube.fits' % outprefix,clobber=True)
gcd[numpy.isnan(gcd)] = 0
adaptResids = cube-gcd
fitsfile[0].data = adaptResids
fitsfile.writeto('%s_adaptgaussresids.fits' % outprefix,clobber=True)
#adaptB[4] = (adaptB[4]-v0) / dv + p3-1
#adaptB[3] = (adaptB[3]-v0) / dv + p3-1
adaptB[4] = (adaptB[4]-p3+1) * dv + v0
adaptB[3] = (adaptB[3]-p3+1) * dv + v0
fitsfile[0].data = asarray(adaptB)
fitsfile.writeto('%s_adaptgausspars.fits' % outprefix,clobber=True)
fitsfile[0].header.__delitem__('CD3_3')
fitsfile[0].header.__delitem__('CRVAL3')
fitsfile[0].header.__delitem__('CRPIX3')
fitsfile[0].header.__delitem__('CUNIT3')
fitsfile[0].header.__delitem__('CTYPE3')
adaptResids[numpy.isnan(adaptResids)] = 0
totalDResids = adaptResids.sum(axis=0)
fitsfile[0].data = totalDResids
fitsfile.writeto('%s_adaptgauss_totalresids.fits' % outprefix,clobber=True)
return adaptB
| mit |
mirams/sine-wave | Figures/figure_5/plot_figure_5_results.py | 1 | 10069 | import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.patches as patches
from matplotlib.ticker import FormatStrFormatter
import matplotlib as mpl
mpl.style.use('classic') # Use Matplotlib v1 defaults (plot was designed on this!)
mpl.rc('text', usetex=True)
from cycler import cycler
import numpy
from os.path import join, exists
import re
import sys
plt.switch_backend('pdf')
plt.tick_params(axis='both', which='minor', labelsize=16)
model_prediction_colour = [0,0.45,0.74]
fig = plt.figure(0, figsize=(8,12), dpi=900)
#fig.text(0.51, 0.9, r'{0}'.format('Title'), ha='center', va='center', fontsize=16)
gs = gridspec.GridSpec(5, 3, height_ratios=[3,3,3,3,3], width_ratios=[1,1,1] )
left_alignment_for_panel_label = -1.00
protocol_names = ['steady_activation','inactivation','deactivation']
for column in range(0,3):
protocol = protocol_names[column]
# Plot voltage protocols
if column==0:
ax1 = plt.subplot(gs[0,column])
ax1_col0 = ax1
ax1.set_title('Steady Activation (Pr3)')
else:
ax1 = plt.subplot(gs[0,column], sharey=ax1_col0)
if column==1:
ax1.set_title('Inactivation (Pr4)')
elif column==2:
ax1.set_title('Deactivation (Pr5)')
# Load voltage data
voltage_file = 'figure_5_' + protocol + '/' + 'figure_5_' + protocol + '_protocol.txt'
data = numpy.loadtxt(voltage_file, skiprows=0)
all_time = data[:, 0]
voltages = data[:,1:]
ax1.plot(all_time,voltages,'k-',lw=0.75)
ax1.set_xlabel('Time (s)', fontsize=12)
ax1.set_ylim([-130, 65])
ax1.set_xlim([0, numpy.amax(all_time)])
# Plot voltage protocols
ax2 = plt.subplot(gs[1,column])
# Experimental data
data_file = 'figure_5_' + protocol + '/' + 'figure_5_' + protocol + '_experiment.txt'
data = numpy.loadtxt(data_file, skiprows=0)
all_time = data[:, 0]
experimental_currents = data[:,1:]
ax2.plot(all_time,experimental_currents,'r-',lw=0.5)
ax2.set_xlabel('Time (s)', fontsize=12)
# Simulation
ax3 = plt.subplot(gs[2,column],sharex=ax2,sharey=ax2)
data_file = 'figure_5_' + protocol + '/' + 'figure_5_' + protocol + '_prediction.txt'
data = numpy.loadtxt(data_file, skiprows=0)
all_time = data[:, 0]
simulated_currents = data[:,1:]
ax3.plot(all_time,simulated_currents,'-',color=model_prediction_colour,lw=0.8)
ax3.set_xlabel('Time (s)', fontsize=12)
if column==0:
ax1.set_ylabel('Voltage\n(mV)', fontsize=14,rotation=0)
ax2.set_ylabel('Experimental\nCurrent (nA)', fontsize=14,rotation=0)
ax3.set_ylabel('Predicted\nCurrent (nA)', fontsize=14,rotation=0)
start_of_zoom_time = 0.6
length_of_zoom_time = 5.9
ax2.set_ylim([-1,2])
elif column==2:
start_of_zoom_time = 2.4
length_of_zoom_time = 5.6
ax2.set_ylim([-3.5,2])
elif column==1:
start_of_zoom_time = 1.2
length_of_zoom_time = 0.3
ax2.set_ylim([-5,10])
ax2.locator_params(axis='x', nbins=4)
ax2.set_xlim([start_of_zoom_time,start_of_zoom_time+length_of_zoom_time])
# Put a zoom section on
lower_voltage, tmp = ax1.get_ylim()
tmp, upper_v_time = ax1.get_xlim()
voltage_at_next_axes = -208
patch_vertices = numpy.array([[start_of_zoom_time,lower_voltage],
[0,voltage_at_next_axes],
[upper_v_time,voltage_at_next_axes],
[start_of_zoom_time+length_of_zoom_time,lower_voltage]])
ax1.add_artist(plt.Polygon(patch_vertices,
closed=True,
edgecolor="none",
facecolor="grey",
alpha=0.15,
clip_on=False
)
)
# Shift axis labels
axes_list = [ax1, ax2, ax3]
for ax in axes_list:
if column==0:
ax.get_yaxis().set_label_coords(-0.6,0.30)
ax.get_xaxis().set_label_coords(+0.5,-0.19)
if column == 0:
# Add subfigure text labels, relative to axes top left
ax1.text(left_alignment_for_panel_label, 1.05, 'A', verticalalignment='top', horizontalalignment='left', transform=ax1.transAxes, fontsize=20, fontweight='bold')
ax2.text(left_alignment_for_panel_label, 1.05, 'B', verticalalignment='top', horizontalalignment='left', transform=ax2.transAxes, fontsize=20, fontweight='bold')
ax3.text(left_alignment_for_panel_label, 1.05, 'C', verticalalignment='top', horizontalalignment='left', transform=ax3.transAxes, fontsize=20, fontweight='bold')
# Probably easier just to plot all the summary graphs without looping!
def get_model_name(argument):
switcher = {
0: "tentusscher",
1: "mazhari",
2: "diveroli",
3: "wang",
4: "zeng",
5: "experiment",
6: "prediction",
}
return switcher.get(argument, "nothing")
color_cycle = [[1,0,1], [0.47,0.67,0.19], 'c', [0.49,0.18,0.56],'DarkOrange','r',model_prediction_colour]
line_width_cycle = [0.5,0.5,0.5,0.5,0.5,2,2]
line_style_cycle = ['-','-','-','-','-','--','-']
###########################
# S.S. activation IV curve
###########################
ax4 = plt.subplot(gs[3,0])
###########################
# S.S. activation tau curve - not plotting this as it isn't something usually seen!
###########################
#ax5 = plt.subplot(gs[4,0])
###########################
# deactivation - deactivation tau curve
###########################
ax6 = plt.subplot(gs[3,2])
###########################
# deactivation - recovery from inactivtion tau curve
###########################
ax7 = plt.subplot(gs[4,2])
###########################
# inactivtion- instantaneous inactivation tau curve
###########################
ax8 = plt.subplot(gs[3,1])
legend_entry = []
for model_idx in range(0,7):
file_name = 'figure_5_steady_activation/figure_5_steady_activation_iv_curve/figure_5_steady_activation_iv_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
ax4.plot(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
# file_name = 'figure_5_steady_activation/figure_5_steady_activation_tau_v_curve/figure_5_steady_activation_tau_v_' + get_model_name(model_idx) +'.txt'
# data = numpy.loadtxt(file_name,skiprows=0)
# ax5.semilogy(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
file_name = 'figure_5_deactivation/figure_5_deactivation_tau_v/figure_5_deactivation_tau_v_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
[a] = ax6.semilogy(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
legend_entry.append(a)
# Don't plot inactivation or instantaneous inactivation tau curves for TT or Zeng, simulated curves not comparable.
if (get_model_name(model_idx) != "tentusscher") and (get_model_name(model_idx) != "zeng"):
file_name = 'figure_5_inactivation/figure_5_instantaneous_inactivation_tau/figure_5_instantaneous_inactivation_tau_v_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
ax8.plot(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
if (get_model_name(model_idx) != "tentusscher") and (get_model_name(model_idx) != "zeng"):
file_name = 'figure_5_deactivation/figure_5_inactivation_tau_v/figure_5_inactivation_tau_v_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
ax7.plot(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
ax4.set_xlabel('Voltage (mV)', fontsize=12)
ax4.set_ylabel('Current\n(normalized)', fontsize=12)
ax4.get_yaxis().set_label_coords(-0.26,0.5)
ax4.get_xaxis().set_label_coords(0.5,-0.19)
#ax5.set_xlabel('Voltage (mV)', fontsize=12)
#ax5.set_ylabel(r'Time constant $\tau$ (ms)', fontsize=12)
#ax5.set_ylim([8,10000])
#ax5.get_yaxis().set_label_coords(-0.3,0.4)
#ax5.get_xaxis().set_label_coords(0.5,-0.19)
ax6.set_xlabel('Voltage (mV)', fontsize=12)
ax6.set_ylabel(r'Deactivation $\tau$ (ms)', fontsize=12)
ax6.get_yaxis().set_label_coords(-0.16,0.5)
ax6.set_ylim([1,4000])
ax6.get_xaxis().set_label_coords(0.5,-0.19)
ax7.set_xlabel('Voltage (mV)', fontsize=12)
ax7.set_ylabel(r'Recovery inact. $\tau$ (ms)', fontsize=12)
ax7.get_yaxis().set_label_coords(-0.16,0.4)
ax7.get_xaxis().set_label_coords(0.5,-0.19)
ax8.set_xlabel('Voltage (mV)', fontsize=12)
ax8.set_ylabel(r'Inactivation $\tau$ (ms)', fontsize=12)
ax8.get_yaxis().set_label_coords(-0.16,0.5)
ax8.get_xaxis().set_label_coords(0.5,-0.19)
ax8.set_xlim([-50, 50])
ax6.locator_params(axis='x', nbins=4)
ax7.locator_params(axis='x', nbins=4)
ax7.locator_params(axis='y', nbins=6)
#ax5.locator_params(axis='y', nbins=6)
ax8.locator_params(axis='y', nbins=6)
ax4.text(-130, -0.4, 'Summary\nPlots', ha='center', fontsize=14)
ax7.legend(legend_entry, ["ten Tusscher `04","Mazhari `01","Di Veroli `13","Wang `97","Zeng `95","Experiment", "New model"], title="Legend", bbox_to_anchor=(-2.7, 0, 2.35, 1.5), loc='lower left', handletextpad=0.5,borderpad=0.5,labelspacing=0.35,columnspacing=4.5, ncol=2, borderaxespad=0.,fontsize=12)
ax4.text(left_alignment_for_panel_label, 1.05, 'D', verticalalignment='top', horizontalalignment='left',
transform=ax4.transAxes,fontsize=20, fontweight='bold')
#legend = ax4.legend(loc='center', shadow=False)
gs.update(wspace=0.35, hspace=0.4)
#fig.set_tight_layout(True)
#gs.tight_layout(fig, renderer=None, pad=0, h_pad=None, w_pad=None, rect=None)
#plt.tight_layout()
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.subplots_adjust(top=0.75, wspace=0.25)
plt.savefig('figure_5.pdf', bbox_inches='tight', dpi=900, pad_inches=0.05)
| bsd-3-clause |
mosra/m.css | plugins/m/plots.py | 1 | 13951 | #
# This file is part of m.css.
#
# Copyright © 2017, 2018, 2019, 2020 Vladimír Vondruš <[email protected]>
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
import re
from docutils import nodes, utils
from docutils.parsers import rst
from docutils.parsers.rst import directives
from docutils.parsers.rst.roles import set_classes
import matplotlib as mpl
mpl.use('Agg') # otherwise it will attempt to use X11
import matplotlib.pyplot as plt
import numpy as np
import io
mpl.rcParams['font.size'] = '11'
mpl.rcParams['axes.titlesize'] = '13'
# Plot background. Replaced with .m-plot .m-background later, equivalent to
# --default-filled-background-color
mpl.rcParams['axes.facecolor'] = '#cafe01'
# All of these should match --color, replaced with .m-plot .m-text
mpl.rcParams['text.color'] = '#cafe02'
mpl.rcParams['axes.labelcolor'] = '#cafe02'
mpl.rcParams['xtick.color'] = '#cafe02'
mpl.rcParams['ytick.color'] = '#cafe02'
# no need to have a border around the plot
mpl.rcParams['axes.spines.left'] = False
mpl.rcParams['axes.spines.right'] = False
mpl.rcParams['axes.spines.top'] = False
mpl.rcParams['axes.spines.bottom'] = False
mpl.rcParams['svg.fonttype'] = 'none' # otherwise it renders text to paths
mpl.rcParams['figure.autolayout'] = True # so it relayouts everything to fit
# Gets increased for every graph on a page to (hopefully) ensure unique SVG IDs
mpl.rcParams['svg.hashsalt'] = 0
# Color codes for bars. Keep in sync with latex2svgextra.
style_mapping = {
'default': '#cafe03',
'primary': '#cafe04',
'success': '#cafe05',
'warning': '#cafe06',
'danger': '#cafe07',
'info': '#cafe08',
'dim': '#cafe09'
}
# Patch to remove preamble and hardcoded sizes. Matplotlib 2.2 has a http URL
# while matplotlib 3 has a https URL, check for both. Matplotlib 3.3 has a new
# <metadata> field (which we're not interested in) and slightly different
# formatting of the global style after (which we unify to the compact version).
_patch_src = re.compile(r"""<\?xml version="1\.0" encoding="utf-8" standalone="no"\?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1\.1//EN"
"http://www\.w3\.org/Graphics/SVG/1\.1/DTD/svg11\.dtd">
<!-- Created with matplotlib \(https?://matplotlib.org/\) -->
<svg height="\d+(\.\d+)?pt" version="1.1" (?P<viewBox>viewBox="0 0 \d+ \d+(\.\d+)?") width="\d+(\.\d+)?pt" xmlns="http://www\.w3\.org/2000/svg" xmlns:xlink="http://www\.w3\.org/1999/xlink">(
<metadata>.+</metadata>)?
<defs>
<style type="text/css">
?\*{stroke-linecap:butt;stroke-linejoin:round;}(
)?</style>
</defs>
""", re.DOTALL)
_patch_dst = r"""<svg \g<viewBox>>
<defs>
<style type="text/css">*{stroke-linecap:butt;stroke-linejoin:round;}</style>
</defs>
"""
# Remove needless newlines and trailing space in path data
_path_patch_src = re.compile('(?P<prev>[\\dz]) ?\n(?P<next>[LMz])', re.MULTILINE)
_path_patch_dst = '\\g<prev> \\g<next>'
_path_patch2_src = re.compile(' ?\n"')
_path_patch2_dst = '"'
# Mapping from color codes to CSS classes
_class_mapping = [
# Graph background
('style="fill:#cafe01;"', 'class="m-background"'),
# Tick <path> definition in <defs>
('style="stroke:#cafe02;stroke-width:0.8;"', 'class="m-line"'),
# <use>, everything is defined in <defs>, no need to repeat
('<use style="fill:#cafe02;stroke:#cafe02;stroke-width:0.8;"', '<use'),
# Text styles have `font-stretch:normal;` added in matplotlib 3.3, so
# all of them are duplicated to handle this
# Label text on left
('style="fill:#cafe02;font-family:{font};font-size:11px;font-style:normal;font-weight:normal;"', 'class="m-label"'),
('style="fill:#cafe02;font-family:{font};font-size:11px;font-stretch:normal;font-style:normal;font-weight:normal;"', 'class="m-label"'),
# Label text on bottom (has extra style params)
('style="fill:#cafe02;font-family:{font};font-size:11px;font-style:normal;font-weight:normal;', 'class="m-label" style="'),
('style="fill:#cafe02;font-family:{font};font-size:11px;font-stretch:normal;font-style:normal;font-weight:normal;', 'class="m-label" style="'),
# Secondary label text
('style="fill:#cafe0b;font-family:{font};font-size:11px;font-style:normal;font-weight:normal;"', 'class="m-label m-dim"'),
('style="fill:#cafe0b;font-family:{font};font-size:11px;font-stretch:normal;font-style:normal;font-weight:normal;"', 'class="m-label m-dim"'),
# Title text
('style="fill:#cafe02;font-family:{font};font-size:13px;font-style:normal;font-weight:normal;', 'class="m-title" style="'),
('style="fill:#cafe02;font-family:{font};font-size:13px;font-stretch:normal;font-style:normal;font-weight:normal;', 'class="m-title" style="'),
# Bar colors. Keep in sync with latex2svgextra.
('style="fill:#cafe03;"', 'class="m-bar m-default"'),
('style="fill:#cafe04;"', 'class="m-bar m-primary"'),
('style="fill:#cafe05;"', 'class="m-bar m-success"'),
('style="fill:#cafe06;"', 'class="m-bar m-warning"'),
('style="fill:#cafe07;"', 'class="m-bar m-danger"'),
('style="fill:#cafe08;"', 'class="m-bar m-info"'),
('style="fill:#cafe09;"', 'class="m-bar m-dim"'),
# Error bar line
('style="fill:none;stroke:#cafe0a;stroke-width:1.5;"', 'class="m-error"'),
# Error bar <path> definition in <defs>
('style="stroke:#cafe0a;"', 'class="m-error"'),
# <use>, everything is defined in <defs>, no need to repeat
('<use style="fill:#cafe0a;stroke:#cafe0a;"', '<use'),
]
# Titles for bars
_bar_titles_src = '<g id="plot{}-value{}-{}">'
_bar_titles_dst = '<g id="plot{}-value{}-{}"><title>{} {}</title>'
_bar_titles_dst_error = '<g id="plot{}-value{}-{}"><title>{} ± {} {}</title>'
class Plot(rst.Directive):
required_arguments = 1
optional_arguments = 0
final_argument_whitespace = True
option_spec = {'class': directives.class_option,
'name': directives.unchanged,
'type': directives.unchanged_required,
'labels': directives.unchanged_required,
'labels-extra': directives.unchanged,
'units': directives.unchanged_required,
'values': directives.unchanged_required,
'errors': directives.unchanged,
'colors': directives.unchanged,
'plot-width': directives.unchanged,
'bar-height': directives.unchanged,
# Legacy options with ugly underscores instead of dashes
'labels_extra': directives.unchanged,
'bar_height': directives.unchanged}
has_content = False
def run(self):
set_classes(self.options)
# Type
assert self.options['type'] == 'barh'
# Graph title and axis labels. Value labels are one per line.
title = self.arguments[0]
units = self.options['units']
labels = self.options['labels'].split('\n')
# Legacy options, convert underscores to dashes
if 'labels_extra' in self.options:
self.options['labels-extra'] = self.options['labels_extra']
del self.options['labels_extra']
if 'bar_height' in self.options:
self.options['bar-height'] = self.options['bar_height']
del self.options['bar_height']
# Optional extra labels
if 'labels-extra' in self.options:
labels_extra = self.options['labels-extra'].split('\n')
assert len(labels_extra) == len(labels)
else:
labels_extra = None
# Values. Should be one for each label, if there are multiple lines
# then the values get stacked.
value_sets = []
for row in self.options['values'].split('\n'):
values = [float(v) for v in row.split()]
assert len(values) == len(labels)
value_sets += [values]
# Optional errors
if 'errors' in self.options:
error_sets = []
for row in self.options['errors'].split('\n'):
errors = [float(e) for e in row.split()]
assert len(errors) == len(values)
error_sets += [errors]
assert len(error_sets) == len(value_sets)
else:
error_sets = [None]*len(value_sets)
# Colors. Should be either one for all or one for every value
if 'colors' in self.options:
color_sets = []
for row in self.options['colors'].split('\n'):
colors = [style_mapping[c] for c in row.split()]
if len(colors) == 1: colors = colors[0]
else: assert len(colors) == len(labels)
color_sets += [colors]
assert len(color_sets) == len(value_sets)
else:
color_sets = [style_mapping['default']]*len(value_sets)
# Bar height
bar_height = float(self.options.get('bar-height', '0.4'))
# Increase hashsalt for every plot to ensure (hopefully) unique SVG IDs
mpl.rcParams['svg.hashsalt'] = int(mpl.rcParams['svg.hashsalt']) + 1
# Setup the graph
fig, ax = plt.subplots()
# TODO: let matplotlib calculate the height somehow
fig.set_size_inches(float(self.options.get('plot-width', 8)), 0.78 + len(labels)*bar_height)
yticks = np.arange(len(labels))
left = np.array([0.0]*len(labels))
for i in range(len(value_sets)):
plot = ax.barh(yticks, value_sets[i], xerr=error_sets[i],
align='center', color=color_sets[i], ecolor='#cafe0a', capsize=5*bar_height/0.4, left=left)
left += np.array(value_sets[i])
for j, v in enumerate(plot):
v.set_gid('plot{}-value{}-{}'.format(mpl.rcParams['svg.hashsalt'], i, j))
ax.set_yticks(yticks)
ax.invert_yaxis() # top-to-bottom
ax.set_xlabel(units)
ax.set_title(title)
# Value labels. If extra label is specified, create two multiline texts
# with first having the second line empty and second having the first
# line empty.
if labels_extra:
ax.set_yticklabels([y + ('' if labels_extra[i] == '..' else '\n') for i, y in enumerate(labels)])
for i, label in enumerate(ax.get_yticklabels()):
if labels_extra[i] == '..': continue
ax.text(0, i + 0.05, '\n' + labels_extra[i],
va='center', ha='right',
transform=label.get_transform(), color='#cafe0b')
else: ax.set_yticklabels(labels)
# Export to SVG
fig.patch.set_visible(False) # hide the white background
imgdata = io.StringIO()
fig.savefig(imgdata, format='svg')
plt.close() # otherwise it consumes a lot of memory in autoreload mode
# Patch the rendered output: remove preable and hardcoded size
imgdata = _patch_src.sub(_patch_dst, imgdata.getvalue())
# Remove needless newlines and trailing whitespace in path data
imgdata = _path_patch2_src.sub(_path_patch2_dst, _path_patch_src.sub(_path_patch_dst, imgdata))
# Replace color codes with CSS classes
for src, dst in _class_mapping: imgdata = imgdata.replace(src, dst)
# Add titles for bars
for i in range(len(value_sets)):
for j in range(len(labels)):
id = i*len(labels) + j
if error_sets[i]: imgdata = imgdata.replace(
_bar_titles_src.format(mpl.rcParams['svg.hashsalt'], i, j),
_bar_titles_dst_error.format(mpl.rcParams['svg.hashsalt'], i, j, value_sets[i][j], error_sets[i][j], units))
else: imgdata = imgdata.replace(
_bar_titles_src.format(mpl.rcParams['svg.hashsalt'], i, j),
_bar_titles_dst.format(mpl.rcParams['svg.hashsalt'], i, j, value_sets[i][j], units))
container = nodes.container(**self.options)
container['classes'] += ['m-plot']
node = nodes.raw('', imgdata, format='html')
container.append(node)
return [container]
def new_page(*args, **kwargs):
mpl.rcParams['svg.hashsalt'] = 0
def register_mcss(mcss_settings, hooks_pre_page, **kwargs):
font = mcss_settings.get('M_PLOTS_FONT', 'Source Sans Pro')
for i in range(len(_class_mapping)):
src, dst = _class_mapping[i]
_class_mapping[i] = (src.format(font=font), dst)
mpl.rcParams['font.family'] = font
hooks_pre_page += [new_page]
rst.directives.register_directive('plot', Plot)
# Below is only Pelican-specific functionality. If Pelican is not found, these
# do nothing.
def _pelican_configure(pelicanobj):
register_mcss(mcss_settings=pelicanobj.settings, hooks_pre_page=[])
def register(): # for Pelican
from pelican import signals
signals.initialized.connect(_pelican_configure)
signals.content_object_init.connect(new_page)
| mit |
antoniomezzacapo/qiskit-tutorial | community/games/game_engines/universal.py | 1 | 6658 | import numpy as np
import random
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Rectangle
import os
import copy
import networkx as nx
class layout:
"""Processing and display of data in ways that depend on the layout of a quantum device."""
def __init__(self,num,coupling_map,pos):
"""The device for which we make the plot is specified by
num = number of qubits
coupling_map = list of possible cnots, each specified by a list of the two qubits involved
pos = dictionary for which qubits are keys and positions are values
Rather than use the coupling map directly, we convert it into the `links` dictionary. This assigns a name to each coupling, to be used as keys. Labels for these links are also added to the `pos` dictionary.
"""
self.num = num
self.pos = pos
self.links = {}
char = 65
for pair in coupling_map:
self.links[chr(char)] = pair
char += 1
for pair in self.links:
self.pos[pair] = [(self.pos[self.links[pair][0]][j] + self.pos[self.links[pair][1]][j])/2 for j in range(2)]
def calculate_probs(self,raw_stats):
"""Given a counts dictionary as the input `raw_stats`, a dictionary of probabilities is returned. The keys for these are either integers (referring to qubits) or strings (referring to links of neighbouring qubits). For the qubit entries, the corresponding value is the probability that the qubit is in state `1`. For the pair entries, the values are the probabilities that the two qubits disagree (so either the outcome `01` or `10`."""
Z = 0
for string in raw_stats:
Z += raw_stats[string]
stats = {}
for string in raw_stats:
stats[string] = raw_stats[string]/Z
probs = {}
for n in self.pos:
probs[n] = 0
for string in stats:
for n in range(self.num):
if string[-n-1]=='1':
probs[n] += stats[string]
for pair in self.links:
if string[-self.links[pair][0]-1]!=string[-self.links[pair][1]-1]:
probs[pair] += stats[string]
return probs
def matching(self,weights={}):
if not weights:
for pair in self.links:
weights[pair] = random.random()
G=nx.Graph()
for pair in self.links:
G.add_edge(self.links[pair][0],self.links[pair][1],weight=weights[pair])
raw_pairs = nx.max_weight_matching(G, maxcardinality=True)
pairs = []
for pair in raw_pairs:
pairs.append(list(pair))
return pairs
def plot(self,probs={},labels={},colors={},sizes={}):
"""An image representing the device is created and displayed.
When no kwargs are supplied, qubits are labelled according to their numbers. The links of qubits for which a cnot is possible are shown by lines connecting the qubitsm, and are labelled with letters.
The kwargs should all be supplied in the form of dictionaries for which qubit numbers and pair labels are the keys (i.e., the same keys as for the `pos` attribute).
If `probs` is supplied (such as from the output of the `calculate_probs()` method, the labels, colors and sizes of qubits and links will be determined by these probabilities. Otherwise, the other kwargs set these properties directly."""
G=nx.Graph()
for pair in self.links:
G.add_edge(self.links[pair][0],self.links[pair][1])
G.add_edge(self.links[pair][0],pair)
G.add_edge(self.links[pair][1],pair)
if probs:
label_changes = copy.deepcopy(labels)
color_changes = copy.deepcopy(colors)
size_changes = copy.deepcopy(sizes)
labels = {}
colors = {}
sizes = {}
for node in G:
if probs[node]>1:
labels[node] = ""
colors[node] = 'grey'
sizes[node] = 3000
else:
labels[node] = "%.0f" % ( 100 * ( probs[node] ) )
colors[node] =( 1-probs[node],0,probs[node] )
if type(node)!=str:
if labels[node]=='0':
sizes[node] = 3000
else:
sizes[node] = 4000
else:
if labels[node]=='0':
sizes[node] = 800
else:
sizes[node] = 1150
for node in label_changes:
labels[node] = label_changes[node]
for node in color_changes:
colors[node] = color_changes[node]
for node in size_changes:
sizes[node] = size_changes[node]
else:
if not labels:
labels = {}
for node in G:
labels[node] = node
if not colors:
colors = {}
for node in G:
if type(node) is int:
colors[node] = (node/self.num,0,1-node/self.num)
else:
colors[node] = (0,0,0)
if not sizes:
sizes = {}
for node in G:
if type(node)!=str:
sizes[node] = 3000
else:
sizes[node] = 750
# convert to lists, which is required by nx
color_list = []
size_list = []
for node in G:
color_list.append(colors[node])
size_list.append(sizes[node])
area = [0,0]
for coord in self.pos.values():
for j in range(2):
area[j] = max(area[j],coord[j])
for j in range(2):
area[j] = (area[j] + 1 )*1.1
if area[0]>2*area[1]:
ratio = 0.65
else:
ratio = 1
plt.figure(2,figsize=(2*area[0],2*ratio*area[1]))
nx.draw(G, self.pos, node_color = color_list, node_size = size_list, labels = labels, with_labels = True,
font_color ='w', font_size = 18)
plt.show() | apache-2.0 |
neilhan/tensorflow | tensorflow/contrib/factorization/python/ops/gmm_test.py | 22 | 6248 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed 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.
# ==============================================================================
"""Tests for ops.gmm."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
FLAGS = tf.app.flags.FLAGS
class GMMTest(tf.test.TestCase):
def setUp(self):
np.random.seed(3)
tf.set_random_seed(2)
self.num_centers = 2
self.num_dims = 2
self.num_points = 4000
self.batch_size = 100
self.true_centers = self.make_random_centers(self.num_centers,
self.num_dims)
self.points, self.assignments, self.scores = self.make_random_points(
self.true_centers,
self.num_points)
self.true_score = np.add.reduce(self.scores)
# Use initial means from kmeans (just like scikit-learn does).
clusterer = tf.contrib.factorization.KMeansClustering(
num_clusters=self.num_centers)
clusterer.fit(self.points, steps=30)
self.initial_means = clusterer.clusters()
@staticmethod
def make_random_centers(num_centers, num_dims):
return np.round(np.random.rand(num_centers,
num_dims).astype(np.float32) * 500)
@staticmethod
def make_random_points(centers, num_points):
num_centers, num_dims = centers.shape
assignments = np.random.choice(num_centers, num_points)
offsets = np.round(np.random.randn(num_points,
num_dims).astype(np.float32) * 20)
points = centers[assignments] + offsets
means = [np.mean(points[assignments == center], axis=0)
for center in xrange(num_centers)]
covs = [np.cov(points[assignments == center].T)
for center in xrange(num_centers)]
scores = []
for r in xrange(num_points):
scores.append(np.sqrt(np.dot(
np.dot(points[r, :] - means[assignments[r]],
np.linalg.inv(covs[assignments[r]])),
points[r, :] - means[assignments[r]])))
return (points, assignments, scores)
def test_clusters(self):
"""Tests the shape of the clusters."""
gmm = tf.contrib.factorization.GMM(
self.num_centers,
initial_clusters=self.initial_means,
batch_size=self.batch_size,
steps=40,
continue_training=True,
random_seed=4,
config=tf.contrib.learn.RunConfig(tf_random_seed=2))
gmm.fit(x=self.points, steps=0)
clusters = gmm.clusters()
self.assertAllEqual(list(clusters.shape),
[self.num_centers, self.num_dims])
def test_fit(self):
gmm = tf.contrib.factorization.GMM(
self.num_centers,
initial_clusters='random',
batch_size=self.batch_size,
random_seed=4,
config=tf.contrib.learn.RunConfig(tf_random_seed=2))
gmm.fit(x=self.points, steps=1)
score1 = gmm.score(x=self.points)
gmm = tf.contrib.factorization.GMM(
self.num_centers,
initial_clusters='random',
batch_size=self.batch_size,
random_seed=4,
config=tf.contrib.learn.RunConfig(tf_random_seed=2))
gmm.fit(x=self.points, steps=10)
score2 = gmm.score(x=self.points)
self.assertGreater(score1, score2)
self.assertNear(self.true_score, score2, self.true_score * 0.15)
def test_infer(self):
gmm = tf.contrib.factorization.GMM(
self.num_centers,
initial_clusters=self.initial_means,
batch_size=self.batch_size,
steps=40,
continue_training=True,
random_seed=4,
config=tf.contrib.learn.RunConfig(tf_random_seed=2))
gmm.fit(x=self.points, steps=60)
clusters = gmm.clusters()
# Make a small test set
points, true_assignments, true_offsets = (
self.make_random_points(clusters, 40))
assignments = np.ravel(gmm.predict(points))
self.assertAllEqual(true_assignments, assignments)
# Test score
score = gmm.score(points)
self.assertNear(score, np.sum(true_offsets), 4.05)
def _compare_with_sklearn(self, cov_type):
# sklearn version.
iterations = 40
np.random.seed(5)
sklearn_assignments = np.asarray([0, 0, 1, 0, 0, 0, 1, 0, 0, 1])
sklearn_means = np.asarray([[144.83417719, 254.20130341],
[274.38754816, 353.16074346]])
sklearn_covs = np.asarray([[[395.0081194, -4.50389512],
[-4.50389512, 408.27543989]],
[[385.17484203, -31.27834935],
[-31.27834935, 391.74249925]]])
# skflow version.
gmm = tf.contrib.factorization.GMM(
self.num_centers,
initial_clusters=self.initial_means,
covariance_type=cov_type,
batch_size=self.num_points,
steps=iterations,
continue_training=True,
config=tf.contrib.learn.RunConfig(tf_random_seed=2))
gmm.fit(self.points)
skflow_assignments = gmm.predict(self.points[:10, :]).astype(int)
self.assertAllClose(sklearn_assignments,
np.ravel(skflow_assignments))
self.assertAllClose(sklearn_means, gmm.clusters())
if cov_type == 'full':
self.assertAllClose(sklearn_covs, gmm.covariances(), rtol=0.01)
else:
for d in [0, 1]:
self.assertAllClose(np.diag(sklearn_covs[d]),
gmm.covariances()[d, :], rtol=0.01)
def test_compare_full(self):
self._compare_with_sklearn('full')
def test_compare_diag(self):
self._compare_with_sklearn('diag')
if __name__ == '__main__':
tf.test.main()
| apache-2.0 |
zhaostephen/quant | quant-data/storage.py | 1 | 1097 | import tushare as ts
import storage as storage
import datetime as datetime
import sys
import os
from sqlalchemy import create_engine
import pandas as pd
storage = "D:/quant/data/"
def fileExists(filename):
return os.path.exists(getPath(filename))
def getPath(file):
return storage + file
def save(df, file):
if df is None:
return
path = storage + file
ensure_dir(path)
df.to_csv(path)
def ensure_dir(f):
d = os.path.dirname(f)
if not os.path.exists(d):
os.makedirs(d)
def getCodes():
stocks = None
codes = ['sh','sz','hs300','sz50','zxb','cyb']
stocks = ts.get_area_classified()
codes.extend(stocks['code'])
return codes
def save_sql(data, file, mode='replace', dtype=None,index=True):
if data is None:
return
splits = file.split('/', 1)
db = splits[0]
table = splits[1]
engine = create_engine('mysql://quant:[email protected]/' + db + '?charset=utf8')
print(mode + " sql to ", db, "/", table)
data.to_sql(table,engine,index=index,if_exists=mode, dtype=dtype)
| apache-2.0 |
effigies/mne-python | mne/coreg.py | 1 | 38779 | """Coregistration between different coordinate frames"""
# Authors: Christian Brodbeck <[email protected]>
#
# License: BSD (3-clause)
from .externals.six.moves import configparser
import fnmatch
from glob import glob, iglob
import os
import stat
import sys
import re
import shutil
from warnings import warn
import numpy as np
from numpy import dot
from scipy.optimize import leastsq
from scipy.spatial.distance import cdist
from .io.meas_info import read_fiducials, write_fiducials
from .label import read_label, Label
from .source_space import (add_source_space_distances, read_source_spaces,
write_source_spaces)
from .surface import (read_surface, write_surface, read_bem_surfaces,
write_bem_surface)
from .transforms import (rotation, rotation3d, scaling, translation,
get_ras_to_neuromag_trans)
from .utils import get_config, get_subjects_dir, logger, pformat
from functools import reduce
from .externals.six.moves import zip
# some path templates
trans_fname = os.path.join('{raw_dir}', '{subject}-trans.fif')
subject_dirname = os.path.join('{subjects_dir}', '{subject}')
bem_dirname = os.path.join(subject_dirname, 'bem')
surf_dirname = os.path.join(subject_dirname, 'surf')
bem_fname = os.path.join(bem_dirname, "{subject}-{name}.fif")
head_bem_fname = pformat(bem_fname, name='head')
fid_fname = pformat(bem_fname, name='fiducials')
fid_fname_general = os.path.join(bem_dirname, "{head}-fiducials.fif")
src_fname = os.path.join(bem_dirname, '{subject}-{spacing}-src.fif')
def _make_writable(fname):
os.chmod(fname, stat.S_IMODE(os.lstat(fname)[stat.ST_MODE]) | 128) # write
def _make_writable_recursive(path):
"""Recursively set writable"""
if sys.platform.startswith('win'):
return # can't safely set perms
for root, dirs, files in os.walk(path, topdown=False):
for f in dirs + files:
_make_writable(os.path.join(root, f))
def create_default_subject(mne_root=None, fs_home=None, update=False,
subjects_dir=None):
"""Create an average brain subject for subjects without structural MRI
Create a copy of fsaverage from the Freesurfer directory in subjects_dir
and add auxiliary files from the mne package.
Parameters
----------
mne_root : None | str
The mne root directory (only needed if MNE_ROOT is not specified as
environment variable).
fs_home : None | str
The freesurfer home directory (only needed if FREESURFER_HOME is not
specified as environment variable).
update : bool
In cases where a copy of the fsaverage brain already exists in the
subjects_dir, this option allows to only copy files that don't already
exist in the fsaverage directory.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable
(os.environ['SUBJECTS_DIR']) as destination for the new subject.
Notes
-----
When no structural MRI is available for a subject, an average brain can be
substituted. Freesurfer comes with such an average brain model, and MNE
comes with some auxiliary files which make coregistration easier.
:py:func:`create_default_subject` copies the relevant files from Freesurfer
into the current subjects_dir, and also adds the auxiliary files provided
by MNE.
The files provided by MNE are listed below and can be found under
``share/mne/mne_analyze/fsaverage`` in the MNE directory (see MNE manual
section 7.19 Working with the average brain):
fsaverage_head.fif:
The approximate head surface triangulation for fsaverage.
fsaverage_inner_skull-bem.fif:
The approximate inner skull surface for fsaverage.
fsaverage-fiducials.fif:
The locations of the fiducial points (LPA, RPA, and nasion).
fsaverage-trans.fif:
Contains a default MEG-MRI coordinate transformation suitable for
fsaverage.
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
if fs_home is None:
fs_home = get_config('FREESURFER_HOME', fs_home)
if fs_home is None:
raise ValueError(
"FREESURFER_HOME environment variable not found. Please "
"specify the fs_home parameter in your call to "
"create_default_subject().")
if mne_root is None:
mne_root = get_config('MNE_ROOT', mne_root)
if mne_root is None:
raise ValueError("MNE_ROOT environment variable not found. Please "
"specify the mne_root parameter in your call to "
"create_default_subject().")
# make sure freesurfer files exist
fs_src = os.path.join(fs_home, 'subjects', 'fsaverage')
if not os.path.exists(fs_src):
raise IOError('fsaverage not found at %r. Is fs_home specified '
'correctly?' % fs_src)
for name in ('label', 'mri', 'surf'):
dirname = os.path.join(fs_src, name)
if not os.path.isdir(dirname):
raise IOError("Freesurfer fsaverage seems to be incomplete: No "
"directory named %s found in %s" % (name, fs_src))
# make sure destination does not already exist
dest = os.path.join(subjects_dir, 'fsaverage')
if dest == fs_src:
raise IOError(
"Your subjects_dir points to the freesurfer subjects_dir (%r). "
"The default subject can not be created in the freesurfer "
"installation directory; please specify a different "
"subjects_dir." % subjects_dir)
elif (not update) and os.path.exists(dest):
raise IOError(
"Can not create fsaverage because %r already exists in "
"subjects_dir %r. Delete or rename the existing fsaverage "
"subject folder." % ('fsaverage', subjects_dir))
# make sure mne files exist
mne_fname = os.path.join(mne_root, 'share', 'mne', 'mne_analyze',
'fsaverage', 'fsaverage-%s.fif')
mne_files = ('fiducials', 'head', 'inner_skull-bem', 'trans')
for name in mne_files:
fname = mne_fname % name
if not os.path.isfile(fname):
raise IOError("MNE fsaverage incomplete: %s file not found at "
"%s" % (name, fname))
# copy fsaverage from freesurfer
logger.info("Copying fsaverage subject from freesurfer directory...")
if (not update) or not os.path.exists(dest):
shutil.copytree(fs_src, dest)
_make_writable_recursive(dest)
# add files from mne
dest_bem = os.path.join(dest, 'bem')
if not os.path.exists(dest_bem):
os.mkdir(dest_bem)
logger.info("Copying auxiliary fsaverage files from mne directory...")
dest_fname = os.path.join(dest_bem, 'fsaverage-%s.fif')
_make_writable_recursive(dest_bem)
for name in mne_files:
if not os.path.exists(dest_fname % name):
shutil.copy(mne_fname % name, dest_bem)
def _decimate_points(pts, res=10):
"""Decimate the number of points using a voxel grid
Create a voxel grid with a specified resolution and retain at most one
point per voxel. For each voxel, the point closest to its center is
retained.
Parameters
----------
pts : array, shape (n_points, 3)
The points making up the head shape.
res : scalar
The resolution of the voxel space (side length of each voxel).
Returns
-------
pts : array, shape = (n_points, 3)
The decimated points.
"""
pts = np.asarray(pts)
# find the bin edges for the voxel space
xmin, ymin, zmin = pts.min(0) - res / 2.
xmax, ymax, zmax = pts.max(0) + res
xax = np.arange(xmin, xmax, res)
yax = np.arange(ymin, ymax, res)
zax = np.arange(zmin, zmax, res)
# find voxels containing one or more point
H, _ = np.histogramdd(pts, bins=(xax, yax, zax), normed=False)
# for each voxel, select one point
X, Y, Z = pts.T
out = np.empty((np.sum(H > 0), 3))
for i, (xbin, ybin, zbin) in enumerate(zip(*np.nonzero(H))):
x = xax[xbin]
y = yax[ybin]
z = zax[zbin]
xi = np.logical_and(X >= x, X < x + res)
yi = np.logical_and(Y >= y, Y < y + res)
zi = np.logical_and(Z >= z, Z < z + res)
idx = np.logical_and(zi, np.logical_and(yi, xi))
ipts = pts[idx]
mid = np.array([x, y, z]) + res / 2.
dist = cdist(ipts, [mid])
i_min = np.argmin(dist)
ipt = ipts[i_min]
out[i] = ipt
return out
def _trans_from_params(param_info, params):
"""Convert transformation parameters into a transformation matrix
Parameters
----------
param_info : tuple, len = 3
Tuple describing the parameters in x (do_translate, do_rotate,
do_scale).
params : tuple
The transformation parameters.
Returns
-------
trans : array, shape = (4, 4)
Transformation matrix.
"""
do_rotate, do_translate, do_scale = param_info
i = 0
trans = []
if do_rotate:
x, y, z = params[:3]
trans.append(rotation(x, y, z))
i += 3
if do_translate:
x, y, z = params[i:i + 3]
trans.insert(0, translation(x, y, z))
i += 3
if do_scale == 1:
s = params[i]
trans.append(scaling(s, s, s))
elif do_scale == 3:
x, y, z = params[i:i + 3]
trans.append(scaling(x, y, z))
trans = reduce(dot, trans)
return trans
def fit_matched_points(src_pts, tgt_pts, rotate=True, translate=True,
scale=False, tol=None, x0=None, out='trans'):
"""Find a transform that minimizes the squared distance between two
matching sets of points.
Uses :func:`scipy.optimize.leastsq` to find a transformation involving
a combination of rotation, translation, and scaling (in that order).
Parameters
----------
src_pts : array, shape = (n, 3)
Points to which the transform should be applied.
tgt_pts : array, shape = (n, 3)
Points to which src_pts should be fitted. Each point in tgt_pts should
correspond to the point in src_pts with the same index.
rotate : bool
Allow rotation of the ``src_pts``.
translate : bool
Allow translation of the ``src_pts``.
scale : bool
Number of scaling parameters. With False, points are not scaled. With
True, points are scaled by the same factor along all axes.
tol : scalar | None
The error tolerance. If the distance between any of the matched points
exceeds this value in the solution, a RuntimeError is raised. With
None, no error check is performed.
x0 : None | tuple
Initial values for the fit parameters.
out : 'params' | 'trans'
In what format to return the estimate: 'params' returns a tuple with
the fit parameters; 'trans' returns a transformation matrix of shape
(4, 4).
Returns
-------
One of the following, depending on the ``out`` parameter:
trans : array, shape = (4, 4)
Transformation that, if applied to src_pts, minimizes the squared
distance to tgt_pts.
params : array, shape = (n_params, )
A single tuple containing the translation, rotation and scaling
parameters in that order.
"""
src_pts = np.atleast_2d(src_pts)
tgt_pts = np.atleast_2d(tgt_pts)
if src_pts.shape != tgt_pts.shape:
raise ValueError("src_pts and tgt_pts must have same shape (got "
"{0}, {1})".format(src_pts.shape, tgt_pts.shape))
rotate = bool(rotate)
translate = bool(translate)
scale = int(scale)
if translate:
src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1))))
param_info = (rotate, translate, scale)
if param_info == (True, False, 0):
def error(x):
rx, ry, rz = x
trans = rotation3d(rx, ry, rz)
est = dot(src_pts, trans.T)
return (tgt_pts - est).ravel()
if x0 is None:
x0 = (0, 0, 0)
elif param_info == (True, False, 1):
def error(x):
rx, ry, rz, s = x
trans = rotation3d(rx, ry, rz) * s
est = dot(src_pts, trans.T)
return (tgt_pts - est).ravel()
if x0 is None:
x0 = (0, 0, 0, 1)
elif param_info == (True, True, 0):
def error(x):
rx, ry, rz, tx, ty, tz = x
trans = dot(translation(tx, ty, tz), rotation(rx, ry, rz))
est = dot(src_pts, trans.T)
return (tgt_pts - est[:, :3]).ravel()
if x0 is None:
x0 = (0, 0, 0, 0, 0, 0)
elif param_info == (True, True, 1):
def error(x):
rx, ry, rz, tx, ty, tz, s = x
trans = reduce(dot, (translation(tx, ty, tz), rotation(rx, ry, rz),
scaling(s, s, s)))
est = dot(src_pts, trans.T)
return (tgt_pts - est[:, :3]).ravel()
if x0 is None:
x0 = (0, 0, 0, 0, 0, 0, 1)
else:
raise NotImplementedError(
"The specified parameter combination is not implemented: "
"rotate=%r, translate=%r, scale=%r" % param_info)
x, _, _, _, _ = leastsq(error, x0, full_output=True)
# re-create the final transformation matrix
if (tol is not None) or (out == 'trans'):
trans = _trans_from_params(param_info, x)
# assess the error of the solution
if tol is not None:
if not translate:
src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1))))
est_pts = dot(src_pts, trans.T)[:, :3]
err = np.sqrt(np.sum((est_pts - tgt_pts) ** 2, axis=1))
if np.any(err > tol):
raise RuntimeError("Error exceeds tolerance. Error = %r" % err)
if out == 'params':
return x
elif out == 'trans':
return trans
else:
raise ValueError("Invalid out parameter: %r. Needs to be 'params' or "
"'trans'." % out)
def _point_cloud_error(src_pts, tgt_pts):
"""Find the distance from each source point to its closest target point
Parameters
----------
src_pts : array, shape = (n, 3)
Source points.
tgt_pts : array, shape = (m, 3)
Target points.
Returns
-------
dist : array, shape = (n, )
For each point in ``src_pts``, the distance to the closest point in
``tgt_pts``.
"""
Y = cdist(src_pts, tgt_pts, 'euclidean')
dist = Y.min(axis=1)
return dist
def _point_cloud_error_balltree(src_pts, tgt_tree):
"""Find the distance from each source point to its closest target point
Uses sklearn.neighbors.BallTree for greater efficiency
Parameters
----------
src_pts : array, shape = (n, 3)
Source points.
tgt_tree : sklearn.neighbors.BallTree
BallTree of the target points.
Returns
-------
dist : array, shape = (n, )
For each point in ``src_pts``, the distance to the closest point in
``tgt_pts``.
"""
dist, _ = tgt_tree.query(src_pts)
return dist.ravel()
def fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=True,
scale=0, x0=None, leastsq_args={}, out='params'):
"""Find a transform that minimizes the squared distance from each source
point to its closest target point
Uses :func:`scipy.optimize.leastsq` to find a transformation involving
a combination of rotation, translation, and scaling (in that order).
Parameters
----------
src_pts : array, shape = (n, 3)
Points to which the transform should be applied.
tgt_pts : array, shape = (m, 3)
Points to which src_pts should be fitted. Each point in tgt_pts should
correspond to the point in src_pts with the same index.
rotate : bool
Allow rotation of the ``src_pts``.
translate : bool
Allow translation of the ``src_pts``.
scale : 0 | 1 | 3
Number of scaling parameters. With 0, points are not scaled. With 1,
points are scaled by the same factor along all axes. With 3, points are
scaled by a separate factor along each axis.
x0 : None | tuple
Initial values for the fit parameters.
leastsq_args : dict
Additional parameters to submit to :func:`scipy.optimize.leastsq`.
out : 'params' | 'trans'
In what format to return the estimate: 'params' returns a tuple with
the fit parameters; 'trans' returns a transformation matrix of shape
(4, 4).
Returns
-------
x : array, shape = (n_params, )
Estimated parameters for the transformation.
Notes
-----
Assumes that the target points form a dense enough point cloud so that
the distance of each src_pt to the closest tgt_pt can be used as an
estimate of the distance of src_pt to tgt_pts.
"""
kwargs = {'epsfcn': 0.01}
kwargs.update(leastsq_args)
# assert correct argument types
src_pts = np.atleast_2d(src_pts)
tgt_pts = np.atleast_2d(tgt_pts)
translate = bool(translate)
rotate = bool(rotate)
scale = int(scale)
if translate:
src_pts = np.hstack((src_pts, np.ones((len(src_pts), 1))))
try:
from sklearn.neighbors import BallTree
tgt_pts = BallTree(tgt_pts)
errfunc = _point_cloud_error_balltree
except ImportError:
warn("Sklearn could not be imported. Fitting points will be slower. "
"To improve performance, install the sklearn module.")
errfunc = _point_cloud_error
# for efficiency, define parameter specific error function
param_info = (rotate, translate, scale)
if param_info == (True, False, 0):
x0 = x0 or (0, 0, 0)
def error(x):
rx, ry, rz = x
trans = rotation3d(rx, ry, rz)
est = dot(src_pts, trans.T)
err = errfunc(est, tgt_pts)
return err
elif param_info == (True, False, 1):
x0 = x0 or (0, 0, 0, 1)
def error(x):
rx, ry, rz, s = x
trans = rotation3d(rx, ry, rz) * s
est = dot(src_pts, trans.T)
err = errfunc(est, tgt_pts)
return err
elif param_info == (True, False, 3):
x0 = x0 or (0, 0, 0, 1, 1, 1)
def error(x):
rx, ry, rz, sx, sy, sz = x
trans = rotation3d(rx, ry, rz) * [sx, sy, sz]
est = dot(src_pts, trans.T)
err = errfunc(est, tgt_pts)
return err
elif param_info == (True, True, 0):
x0 = x0 or (0, 0, 0, 0, 0, 0)
def error(x):
rx, ry, rz, tx, ty, tz = x
trans = dot(translation(tx, ty, tz), rotation(rx, ry, rz))
est = dot(src_pts, trans.T)
err = errfunc(est[:, :3], tgt_pts)
return err
else:
raise NotImplementedError(
"The specified parameter combination is not implemented: "
"rotate=%r, translate=%r, scale=%r" % param_info)
est, _, info, msg, _ = leastsq(error, x0, full_output=True, **kwargs)
logger.debug("fit_point_cloud leastsq (%i calls) info: %s", info['nfev'],
msg)
if out == 'params':
return est
elif out == 'trans':
return _trans_from_params(param_info, est)
else:
raise ValueError("Invalid out parameter: %r. Needs to be 'params' or "
"'trans'." % out)
def _find_label_paths(subject='fsaverage', pattern=None, subjects_dir=None):
"""Find paths to label files in a subject's label directory
Parameters
----------
subject : str
Name of the mri subject.
pattern : str | None
Pattern for finding the labels relative to the label directory in the
MRI subject directory (e.g., "aparc/*.label" will find all labels
in the "subject/label/aparc" directory). With None, find all labels.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable
(sys.environ['SUBJECTS_DIR'])
Returns
------
paths : list
List of paths relative to the subject's label directory
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
subject_dir = os.path.join(subjects_dir, subject)
lbl_dir = os.path.join(subject_dir, 'label')
if pattern is None:
paths = []
for dirpath, _, filenames in os.walk(lbl_dir):
rel_dir = os.path.relpath(dirpath, lbl_dir)
for filename in fnmatch.filter(filenames, '*.label'):
path = os.path.join(rel_dir, filename)
paths.append(path)
else:
paths = [os.path.relpath(path, lbl_dir) for path in iglob(pattern)]
return paths
def _find_mri_paths(subject='fsaverage', subjects_dir=None):
"""Find all files of an mri relevant for source transformation
Parameters
----------
subject : str
Name of the mri subject.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable
(sys.environ['SUBJECTS_DIR'])
Returns
-------
paths | dict
Dictionary whose keys are relevant file type names (str), and whose
values are lists of paths.
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
paths = {}
# directories to create
paths['dirs'] = [bem_dirname, surf_dirname]
# surf/ files
paths['surf'] = surf = []
surf_fname = os.path.join(surf_dirname, '{name}')
surf_names = ('inflated', 'sphere', 'sphere.reg', 'white')
if os.getenv('_MNE_FEW_SURFACES', '') != 'true': # for testing
surf_names = surf_names + (
'orig', 'orig_avg', 'inflated_avg', 'inflated_pre', 'pial',
'pial_avg', 'smoothwm', 'white_avg', 'sphere.reg.avg')
for name in surf_names:
for hemi in ('lh.', 'rh.'):
fname = pformat(surf_fname, name=hemi + name)
surf.append(fname)
# BEM files
paths['bem'] = bem = []
path = head_bem_fname.format(subjects_dir=subjects_dir, subject=subject)
if os.path.exists(path):
bem.append('head')
bem_pattern = pformat(bem_fname, subjects_dir=subjects_dir,
subject=subject, name='*-bem')
re_pattern = pformat(bem_fname, subjects_dir=subjects_dir, subject=subject,
name='(.+)')
for path in iglob(bem_pattern):
match = re.match(re_pattern, path)
name = match.group(1)
bem.append(name)
# fiducials
paths['fid'] = [fid_fname]
# duplicate curvature files
paths['duplicate'] = dup = []
path = os.path.join(surf_dirname, '{name}')
for name in ['lh.curv', 'rh.curv']:
fname = pformat(path, name=name)
dup.append(fname)
# check presence of required files
for ftype in ['surf', 'fid', 'duplicate']:
for fname in paths[ftype]:
path = fname.format(subjects_dir=subjects_dir, subject=subject)
path = os.path.realpath(path)
if not os.path.exists(path):
raise IOError("Required file not found: %r" % path)
# find source space files
paths['src'] = src = []
bem_dir = bem_dirname.format(subjects_dir=subjects_dir, subject=subject)
fnames = fnmatch.filter(os.listdir(bem_dir), '*-src.fif')
prefix = subject + '-'
for fname in fnames:
if fname.startswith(prefix):
fname = "{subject}-%s" % fname[len(prefix):]
path = os.path.join(bem_dirname, fname)
src.append(path)
return paths
def _is_mri_subject(subject, subjects_dir=None):
"""Check whether a directory in subjects_dir is an mri subject directory
Parameters
----------
subject : str
Name of the potential subject/directory.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
Returns
-------
is_mri_subject : bool
Whether ``subject`` is an mri subject.
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
fname = head_bem_fname.format(subjects_dir=subjects_dir, subject=subject)
if not os.path.exists(fname):
return False
return True
def _mri_subject_has_bem(subject, subjects_dir=None):
"""Check whether an mri subject has a file matching the bem pattern
Parameters
----------
subject : str
Name of the subject.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
Returns
-------
has_bem_file : bool
Whether ``subject`` has a bem file.
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
pattern = bem_fname.format(subjects_dir=subjects_dir, subject=subject,
name='*-bem')
fnames = glob(pattern)
return bool(len(fnames))
def read_mri_cfg(subject, subjects_dir=None):
"""Read information from the cfg file of a scaled MRI brain
Parameters
----------
subject : str
Name of the scaled MRI subject.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
Returns
-------
cfg : dict
Dictionary with entries from the MRI's cfg file.
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
fname = os.path.join(subjects_dir, subject, 'MRI scaling parameters.cfg')
if not os.path.exists(fname):
raise IOError("%r does not seem to be a scaled mri subject: %r does "
"not exist." % (subject, fname))
logger.info("Reading MRI cfg file %s" % fname)
config = configparser.RawConfigParser()
config.read(fname)
n_params = config.getint("MRI Scaling", 'n_params')
if n_params == 1:
scale = config.getfloat("MRI Scaling", 'scale')
elif n_params == 3:
scale_str = config.get("MRI Scaling", 'scale')
scale = np.array([float(s) for s in scale_str.split()])
else:
raise ValueError("Invalid n_params value in MRI cfg: %i" % n_params)
out = {'subject_from': config.get("MRI Scaling", 'subject_from'),
'n_params': n_params, 'scale': scale}
return out
def _write_mri_config(fname, subject_from, subject_to, scale):
"""Write the cfg file describing a scaled MRI subject
Parameters
----------
fname : str
Target file.
subject_from : str
Name of the source MRI subject.
subject_to : str
Name of the scaled MRI subject.
scale : float | array_like, shape = (3,)
The scaling parameter.
"""
scale = np.asarray(scale)
if np.isscalar(scale) or scale.shape == ():
n_params = 1
else:
n_params = 3
config = configparser.RawConfigParser()
config.add_section("MRI Scaling")
config.set("MRI Scaling", 'subject_from', subject_from)
config.set("MRI Scaling", 'subject_to', subject_to)
config.set("MRI Scaling", 'n_params', str(n_params))
if n_params == 1:
config.set("MRI Scaling", 'scale', str(scale))
else:
config.set("MRI Scaling", 'scale', ' '.join([str(s) for s in scale]))
config.set("MRI Scaling", 'version', '1')
with open(fname, 'w') as fid:
config.write(fid)
def _scale_params(subject_to, subject_from, scale, subjects_dir):
subjects_dir = get_subjects_dir(subjects_dir, True)
if (subject_from is None) != (scale is None):
raise TypeError("Need to provide either both subject_from and scale "
"parameters, or neither.")
if subject_from is None:
cfg = read_mri_cfg(subject_to, subjects_dir)
subject_from = cfg['subject_from']
n_params = cfg['n_params']
scale = cfg['scale']
else:
scale = np.asarray(scale)
if scale.ndim == 0:
n_params = 1
elif scale.shape == (3,):
n_params = 3
else:
raise ValueError("Invalid shape for scale parameer. Need scalar "
"or array of length 3. Got %s." % str(scale))
return subjects_dir, subject_from, n_params, scale
def scale_bem(subject_to, bem_name, subject_from=None, scale=None,
subjects_dir=None):
"""Scale a bem file
Parameters
----------
subject_to : str
Name of the scaled MRI subject (the destination mri subject).
bem_name : str
Name of the bem file. For example, to scale
``fsaverage-inner_skull-bem.fif``, the bem_name would be
"inner_skull-bem".
subject_from : None | str
The subject from which to read the source space. If None, subject_from
is read from subject_to's config file.
scale : None | float | array, shape = (3,)
Scaling factor. Has to be specified if subjects_from is specified,
otherwise it is read from subject_to's config file.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
"""
subjects_dir, subject_from, _, scale = _scale_params(subject_to,
subject_from, scale,
subjects_dir)
src = bem_fname.format(subjects_dir=subjects_dir, subject=subject_from,
name=bem_name)
dst = bem_fname.format(subjects_dir=subjects_dir, subject=subject_to,
name=bem_name)
if os.path.exists(dst):
raise IOError("File alredy exists: %s" % dst)
surfs = read_bem_surfaces(src)
if len(surfs) != 1:
raise NotImplementedError("BEM file with more than one surface: %r"
% src)
surf0 = surfs[0]
surf0['rr'] = surf0['rr'] * scale
write_bem_surface(dst, surf0)
def scale_labels(subject_to, pattern=None, overwrite=False, subject_from=None,
scale=None, subjects_dir=None):
"""Scale labels to match a brain that was previously created by scaling
Parameters
----------
subject_to : str
Name of the scaled MRI subject (the destination brain).
pattern : str | None
Pattern for finding the labels relative to the label directory in the
MRI subject directory (e.g., "lh.BA3a.label" will scale
"fsaverage/label/lh.BA3a.label"; "aparc/*.label" will find all labels
in the "fsaverage/label/aparc" directory). With None, scale all labels.
overwrite : bool
Overwrite any label file that already exists for subject_to (otherwise
existsing labels are skipped).
subject_from : None | str
Name of the original MRI subject (the brain that was scaled to create
subject_to). If None, the value is read from subject_to's cfg file.
scale : None | float | array_like, shape = (3,)
Scaling parameter. If None, the value is read from subject_to's cfg
file.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
"""
# read parameters from cfg
if scale is None or subject_from is None:
cfg = read_mri_cfg(subject_to, subjects_dir)
if subject_from is None:
subject_from = cfg['subject_from']
if scale is None:
scale = cfg['scale']
# find labels
paths = _find_label_paths(subject_from, pattern, subjects_dir)
if not paths:
return
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
src_root = os.path.join(subjects_dir, subject_from, 'label')
dst_root = os.path.join(subjects_dir, subject_to, 'label')
# scale labels
for fname in paths:
dst = os.path.join(dst_root, fname)
if not overwrite and os.path.exists(dst):
continue
dirname = os.path.dirname(dst)
if not os.path.exists(dirname):
os.makedirs(dirname)
src = os.path.join(src_root, fname)
l_old = read_label(src)
pos = l_old.pos * scale
l_new = Label(l_old.vertices, pos, l_old.values, l_old.hemi,
l_old.comment, subject=subject_to)
l_new.save(dst)
def scale_mri(subject_from, subject_to, scale, overwrite=False,
subjects_dir=None):
"""Create a scaled copy of an MRI subject
Parameters
----------
subject_from : str
Name of the subject providing the MRI.
subject_to : str
New subject name for which to save the scaled MRI.
scale : float | array_like, shape = (3,)
The scaling factor (one or 3 parameters).
overwrite : bool
If an MRI already exists for subject_to, overwrite it.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
See Also
--------
scale_labels : add labels to a scaled MRI
scale_source_space : add a source space to a scaled MRI
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
paths = _find_mri_paths(subject_from, subjects_dir=subjects_dir)
scale = np.asarray(scale)
# make sure we have an empty target directory
dest = subject_dirname.format(subject=subject_to,
subjects_dir=subjects_dir)
if os.path.exists(dest):
if overwrite:
shutil.rmtree(dest)
else:
raise IOError("Subject directory for %s already exists: %r"
% (subject_to, dest))
for dirname in paths['dirs']:
dir_ = dirname.format(subject=subject_to, subjects_dir=subjects_dir)
os.makedirs(dir_)
# save MRI scaling parameters
fname = os.path.join(dest, 'MRI scaling parameters.cfg')
_write_mri_config(fname, subject_from, subject_to, scale)
# surf files [in mm]
for fname in paths['surf']:
src = fname.format(subject=subject_from, subjects_dir=subjects_dir)
src = os.path.realpath(src)
dest = fname.format(subject=subject_to, subjects_dir=subjects_dir)
pts, tri = read_surface(src)
write_surface(dest, pts * scale, tri)
# BEM files [in m]
for bem_name in paths['bem']:
scale_bem(subject_to, bem_name, subject_from, scale, subjects_dir)
# fiducials [in m]
for fname in paths['fid']:
src = fname.format(subject=subject_from, subjects_dir=subjects_dir)
src = os.path.realpath(src)
pts, cframe = read_fiducials(src)
for pt in pts:
pt['r'] = pt['r'] * scale
dest = fname.format(subject=subject_to, subjects_dir=subjects_dir)
write_fiducials(dest, pts, cframe)
# duplicate files
for fname in paths['duplicate']:
src = fname.format(subject=subject_from, subjects_dir=subjects_dir)
dest = fname.format(subject=subject_to, subjects_dir=subjects_dir)
shutil.copyfile(src, dest)
# source spaces
for fname in paths['src']:
src_name = os.path.basename(fname)
scale_source_space(subject_to, src_name, subject_from, scale,
subjects_dir)
# labels [in m]
scale_labels(subject_to, subject_from=subject_from, scale=scale,
subjects_dir=subjects_dir)
def scale_source_space(subject_to, src_name, subject_from=None, scale=None,
subjects_dir=None, n_jobs=1):
"""Scale a source space for an mri created with scale_mri()
Parameters
----------
subject_to : str
Name of the scaled MRI subject (the destination mri subject).
src_name : str
Source space name. Can be a spacing parameter (e.g., ``'7'``,
``'ico4'``, ``'oct6'``) or a file name of a source space file relative
to the bem directory; if the file name contains the subject name, it
should be indicated as "{subject}" in ``src_name`` (e.g.,
``"{subject}-my_source_space-src.fif"``).
subject_from : None | str
The subject from which to read the source space. If None, subject_from
is read from subject_to's config file.
scale : None | float | array, shape = (3,)
Scaling factor. Has to be specified if subjects_from is specified,
otherwise it is read from subject_to's config file.
subjects_dir : None | str
Override the SUBJECTS_DIR environment variable.
n_jobs : int
Number of jobs to run in parallel if recomputing distances (only
applies if scale is an array of length 3, and will not use more cores
than there are source spaces).
"""
subjects_dir, subject_from, n_params, scale = _scale_params(subject_to,
subject_from,
scale,
subjects_dir)
# find the source space file names
if src_name.isdigit():
spacing = src_name # spacing in mm
src_pattern = src_fname
else:
match = re.match("(oct|ico)-?(\d+)$", src_name)
if match:
spacing = '-'.join(match.groups())
src_pattern = src_fname
else:
spacing = None
src_pattern = os.path.join(bem_dirname, src_name)
src = src_pattern.format(subjects_dir=subjects_dir, subject=subject_from,
spacing=spacing)
dst = src_pattern.format(subjects_dir=subjects_dir, subject=subject_to,
spacing=spacing)
# prepare scaling parameters
if n_params == 1:
norm_scale = None
elif n_params == 3:
norm_scale = 1. / scale
else:
raise RuntimeError("Invalid n_params entry in MRI cfg file: %s"
% str(n_params))
# read and scale the source space [in m]
sss = read_source_spaces(src)
logger.info("scaling source space %s: %s -> %s", spacing, subject_from,
subject_to)
logger.info("Scale factor: %s", scale)
add_dist = False
for ss in sss:
ss['subject_his_id'] = subject_to
ss['rr'] *= scale
# distances and patch info
if norm_scale is None:
if ss['dist'] is not None:
ss['dist'] *= scale
ss['nearest_dist'] *= scale
ss['dist_limit'] *= scale
else:
nn = ss['nn']
nn *= norm_scale
norm = np.sqrt(np.sum(nn ** 2, 1))
nn /= norm[:, np.newaxis]
if ss['dist'] is not None:
add_dist = True
if add_dist:
logger.info("Recomputing distances, this might take a while")
dist_limit = np.asscalar(sss[0]['dist_limit'])
add_source_space_distances(sss, dist_limit, n_jobs)
write_source_spaces(dst, sss)
| bsd-3-clause |
deot95/Tesis | Proyecto de Grado Ingeniería Electrónica/Workspace/Comparison/Small Linear/ddpg.py | 3 | 16268 | import linear_env
import sim_env
from actor import Actor
from critic import Critic
from replay_buffer import ReplayBuffer
import numpy as np
import tensorflow as tf
import keras.backend as kbck
import json
import time
import argparse
import matplotlib.pylab as plt
import os.path
def ou(x, mu, theta, sigma):
return theta * (mu - x) + sigma * np.random.randn(np.shape(x)[0])
def simulate(control, swmm ,flows):
best_reward = -1*np.inf
BUFFER_SIZE = 100000
BATCH_SIZE = 120
GAMMA = 0.99
TAU = 0.01 #Target Network HyperParameters
LRA = 0.0001 #Learning rate for Actor
LRC = 0.001 #Lerning rate for Critic
action_dim = 8
state_dim = 10
max_steps = 6000
np.random.seed(100)
EXPLORE = 100000.
episode_count = 1000
done = False
step = 0
epsilon = 1
if swmm:
if control:
#Tensorflow GPU optimization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
kbck.set_session(sess)
# Actor, critic and replay buffer creation
actor = Actor(sess, state_dim, action_dim, BATCH_SIZE, TAU, LRA,flows)
critic = Critic(sess, state_dim, action_dim, BATCH_SIZE, TAU, LRC)
buff = ReplayBuffer(BUFFER_SIZE)
# Get the linear environment
reward_hist = []
for i in range(episode_count):
print("Episode : " + str(i) + " Replay Buffer " + str(buff.count()))
inp_name = "swmm/modelo2.inp"
inp = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + inp_name
vref = np.zeros((state_dim,))
env = sim_env.sim_env(inp,vref)
rainfile()
s_t = np.divide(env.reset(),env.vmax)
total_reward = 0.
for j in range(max_steps):
## Noise addition for exploration
## Ornstein-Uhlenbeck process
loss = 0
epsilon -= 1.0 / EXPLORE
a_t = np.zeros([1,action_dim])
noise_t = np.zeros([1,action_dim])
a_t_original = actor.munet.predict(s_t.reshape(1, s_t.shape[0]))
noise_t[0,:] = max(epsilon, 0) * ou(a_t_original[0,:], 0.5 , 1 , 1.5)
#noise_t[0,4:] = max(epsilon, 0) * ou(a_t_original[0,4:], 0.5 , 1 , 1.5)
a_t[0] = np.minimum(np.maximum(a_t_original[0] + noise_t[0],np.zeros(np.shape(a_t_original))),np.ones(np.shape(a_t_original)))
#Act over the system and get info of the next states
s_t1 , r_t, done = env.step(list(a_t[0]))
s_t1 = np.divide(s_t1,env.vmax)
#Add replay buffer
buff.add(s_t, a_t[0], r_t, s_t1, done)
#Do the batch update
batch = buff.getBatch(BATCH_SIZE)
states = np.asarray([e[0] for e in batch])
actions = np.asarray([e[1] for e in batch])
rewards = np.asarray([e[2] for e in batch])
next_states = np.asarray([e[3] for e in batch])
dones = np.asarray([e[4] for e in batch])
# Get estimated q-values of the pair (next_state,mu(next_state))
actions_next = actor.target_munet.predict(next_states)
target_q_values = critic.target_qnet.predict([next_states, actions_next])
y_t = np.zeros(np.shape(actions))
for k in range(len(batch)):
if dones[k]:
y_t[k] = rewards[k]
else:
y_t[k] = rewards[k] + GAMMA*target_q_values[k]
loss += critic.qnet.train_on_batch([states,actions], y_t)
a_for_grad = actor.munet.predict(states)
grads = critic.gradients(states, a_for_grad)
actor.train(states, grads)
actor.target_train()
critic.target_train()
total_reward = total_reward + GAMMA*r_t
s_t = s_t1
if j%100==0:
print("Episode", i, "Step", j, "Reward", r_t, "Loss", loss)
if done:
break
reward_hist.append(total_reward)
np.save("reward_history_flows_"+str(flows).lower()+".npy",np.array(reward_hist))
if i%20 == 0:
print("Saving the networks...")
actor.munet.save_weights("./actors/anetwork_flows_"+str(flows).lower()+"_it_"+str(i)+".h5", overwrite=True)
critic.qnet.save_weights("./critics/cnetwork_flows_"+str(flows).lower()+"_it_"+str(i)+".h5", overwrite=True)
if total_reward > best_reward:
print("Saving Best Actor...")
np.save("best_reward"+"_flows_"+str(flows)+".npy",np.array(total_reward))
actor.munet.save_weights("./actors/best_anetwork_flows_"+str(flows).lower()+".h5", overwrite=True)
critic.qnet.save_weights("./critics/best_cnetwork_flows_"+str(flows).lower()+".h5", overwrite=True)
best_reward = total_reward
print("TOTAL REWARD @ " + str(i) +"-th Episode : Reward " + str(total_reward))
print("Total Step: " + str(step))
print("")
print("Finish.")
else:
inp_name = "swmm/modelo2.inp"
inp = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + inp_name
vref = np.zeros((state_dim,))
env = sim_env.sim_env(inp,vref)
resv = env.free_sim()
f , axarr = plt.subplots(nrows=2, ncols=1 )
print(np.shape(resv))
resv_norm = np.divide(resv,np.matlib.repmat(env.vmax,np.shape(resv)[0],1))
x = np.linspace(0,1800,np.shape(resv)[0])
## Plot Volume Results
lines = axarr[0].plot(x,resv_norm[:,:5])
axarr[0].legend(lines , list(map(lambda x: "v"+str(x+1),range(5))))
axarr[0].set_title("Volumes - Tanks 1 to 5")
axarr[0].set_xlabel("Times(s)")
axarr[0].set_ylabel("Volume(%vmax)")
lines = axarr[1].plot(x,resv_norm[:,5:10])
axarr[1].legend(lines , list(map(lambda x: "v"+str(x+1) if x+1!=10 else "vS",range(5,10))))
axarr[1].set_title("Volumes - Tanks 6 to 9 and Storm Tank")
axarr[1].set_xlabel("Times(s)")
axarr[1].set_ylabel("Volume(%vmax)")
#sns.despine()
plt.tight_layout()
plt.show()
else:
# Constants for the linear environment
Hs = 2400
A1 = 0.0020
mu1 = 250
sigma1 = 70
A2 = 0.0048
mu2 = 250
sigma2 = 70
dt = 1
x = np.arange(Hs)
d = np.zeros((2,Hs))
if control:
#Tensorflow GPU optimization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
kbck.set_session(sess)
# Actor, critic and replay buffer creation
actor = Actor(sess, state_dim, action_dim, BATCH_SIZE, TAU, LRA,flows)
critic = Critic(sess, state_dim, action_dim, BATCH_SIZE, TAU, LRC)
buff = ReplayBuffer(BUFFER_SIZE)
# Get the linear environment
reward_hist = []
for i in range(episode_count):
print("Episode : " + str(i) + " Replay Buffer " + str(buff.count()))
A1 += 0.0004*np.random.rand()
mu1 += 50*np.random.rand()
sigma1 += 14*np.random.rand()
A2 += 0.00096*np.random.rand()
mu2 += 50*np.random.rand()
sigma2 += 14*np.random.rand()
d[0,:] = A1*np.exp((-1*(x-mu1)**2)/(2*sigma1**2))
d[1,:] = A2*np.exp((-1*(x-mu2)**2)/(2*sigma2**2))
vref = np.zeros((state_dim,))
env = linear_env.env(dt,d,vref)
s_t = np.divide(env.reset(),env.vmax)
total_reward = 0.
for j in range(max_steps):
## Noise addition for exploration
## Ornstein-Uhlenbeck process
loss = 0
epsilon -= 1.0 / EXPLORE
a_t = np.zeros([1,action_dim])
noise_t = np.zeros([1,action_dim])
a_t_original = actor.munet.predict(s_t.reshape(1, s_t.shape[0]))
noise_t[0,:] = max(epsilon, 0) * ou(a_t_original[0,:], 0.5 , 1 , 1.5)
#noise_t[0,4:] = max(epsilon, 0) * ou(a_t_original[0,4:], 0.5 , 1 , 1.5)
a_t[0] = a_t_original[0] + noise_t[0]
#Act over the system and get info of the next states
s_t1 , r_t, done, _ = env.step(a_t[0],flows=flows)
s_t1 = np.divide(s_t1,env.vmax)
#Add replay buffer
buff.add(s_t, a_t[0], r_t, s_t1, done)
#Do the batch update
batch = buff.getBatch(BATCH_SIZE)
states = np.asarray([e[0] for e in batch])
actions = np.asarray([e[1] for e in batch])
rewards = np.asarray([e[2] for e in batch])
next_states = np.asarray([e[3] for e in batch])
dones = np.asarray([e[4] for e in batch])
# Get estimated q-values of the pair (next_state,mu(next_state))
actions_next = actor.target_munet.predict(next_states)
target_q_values = critic.target_qnet.predict([next_states, actions_next])
y_t = np.zeros(np.shape(actions))
for k in range(len(batch)):
if dones[k]:
y_t[k] = rewards[k]
else:
y_t[k] = rewards[k] + GAMMA*target_q_values[k]
loss += critic.qnet.train_on_batch([states,actions], y_t)
a_for_grad = actor.munet.predict(states)
grads = critic.gradients(states, a_for_grad)
actor.train(states, grads)
actor.target_train()
critic.target_train()
total_reward = total_reward + GAMMA*r_t
s_t = s_t1
if j%100==0:
print("Episode", i, "Step", j, "Reward", r_t, "Loss", loss)
if done:
break
reward_hist.append(total_reward)
np.save("reward_history_flows_"+str(flows).lower()+".npy",np.array(reward_hist))
if i%20 == 0:
print("Saving the networks...")
actor.munet.save_weights("./actors/anetwork_flows_"+str(flows).lower()+"_it_"+str(i)+".h5", overwrite=True)
critic.qnet.save_weights("./critics/cnetwork_flows_"+str(flows).lower()+"_it_"+str(i)+".h5", overwrite=True)
if total_reward > best_reward:
print("Saving Best Actor...")
np.save("best_reward"+"_flows_"+str(flows)+".npy",np.array(total_reward))
actor.munet.save_weights("./actors/best_anetwork_flows_"+str(flows).lower()+".h5", overwrite=True)
critic.qnet.save_weights("./critics/best_cnetwork_flows_"+str(flows).lower()+".h5", overwrite=True)
best_reward = total_reward
print("TOTAL REWARD @ " + str(i) +"-th Episode : Reward " + str(total_reward))
print("Total Step: " + str(step))
print("")
print("Finish.")
else:
d[0,:] = A1*np.exp((-1*(x-mu1)**2)/(2*sigma1**2))
d[1,:] = A2*np.exp((-1*(x-mu2)**2)/(2*sigma2**2))
vref = np.zeros((state_dim,))
env = linear_env.env(dt,d,vref)
resv, resf, resu = env.free_sim()
f , axarr = plt.subplots(nrows=2, ncols=2 )
resv_norm = np.divide(np.transpose(resv),np.matlib.repmat(env.vmax,Hs,1))
resu = np.transpose(np.asarray(resu))
## Plot Volume Results
lines = axarr[0,0].plot(x,resv_norm[:,:5])
axarr[0,0].legend(lines , list(map(lambda x: "v"+str(x+1),range(5))))
axarr[0,0].set_title("Volumes - Tanks 1 to 5")
axarr[0,0].set_xlabel("Times(s)")
axarr[0,0].set_ylabel("Volume(%vmax)")
lines = axarr[0,1].plot(x,resv_norm[:,5:10])
axarr[0,1].legend(lines , list(map(lambda x: "v"+str(x+1) if x+1!=10 else "vS",range(5,10))))
axarr[0,1].set_title("Volumes - Tanks 6 to 9 and Storm Tank")
axarr[0,1].set_xlabel("Times(s)")
axarr[0,1].set_ylabel("Volume(%vmax)")
lines = axarr[1,0].plot(x,resu[:,:4])
axarr[1,0].legend(lines , list(map(lambda x: "u"+str(x+1),range(4))))
axarr[1,0].set_title("Actions - Apertures")
axarr[1,0].set_xlabel("Times(s)")
axarr[1,0].set_ylabel("% Aperture")
lines = axarr[1,1].plot(x,resu[:,4:8])
axarr[1,1].legend(lines , list(map(lambda x: "u"+str(x+1),range(4,8))))
axarr[1,1].set_title("Actions - Apertures")
axarr[1,1].set_xlabel("Times(s)")
axarr[1,1].set_ylabel("% Aperture")
plt.suptitle("DDPG performance",y=1.05)
#sns.despine()
plt.tight_layout()
plt.show()
def rainfile():
from math import exp
import numpy as np
from matplotlib import pylab as plt
#Gaussian Extension
A1 = 0.008 + 0.0008*np.random.rand(); mu1 = 500+50*np.random.rand(); sigma1 = 250+25*np.random.rand()
A2 = 0.0063 + 0.00063*np.random.rand() ; mu2 = 500+50*np.random.rand(); sigma2 = 250+25*np.random.rand()
dt = 1
Hs = 1800
x = np.arange(0,Hs,dt)
d = [[],[]]
# dconst = 0.5*mpc_obj.k1*mpc_obj.vmax(1);
d[0] = A1*np.exp((-(x-mu1)**2)/(2*sigma1**2)) # Node 1 - left
d[1] = A2*np.exp((-(x-mu2)**2)/(2*sigma2**2)) # Node 2 - right
def secs_to_hour(secs_convert):
hour = secs_convert//3600
mins = (secs_convert%3600)//60
secs = secs_convert%60
return '{h:02d}:{m:02d}'.format(h=hour,m=mins)
secs_hour_vec = np.vectorize(secs_to_hour)
for k in (1,2):
with open('swmm/runoff%d.dat' % k, 'w') as f:
i = 0
for (t,val) in zip(secs_hour_vec(x), d[k-1]):
if i%60 == 0:
f.write(t+" "+str(val)+"\n")
i += 1
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-c","--control", type=int, choices = [0,1], help = "Choose between control(1) or free dynamics(0)")
parser.add_argument("-s","--swmm", type=int, choices = [0,1], help = "Choose between a simulation with swmm(1) or not(0)")
parser.add_argument("-f","--flow", type=int, choices = [0,1], help = "Choose between a simulation with flows(1) or not(0)")
args = parser.parse_args()
if args.flow == 1 and args.swmm == 1:
print("Conflicting option flow 1 and swmm 1")
else:
t0 = time.process_time()
simulate(control=args.control, swmm=args.swmm, flows = args.flow)
tf = time.process_time()
print("Elapsed time: ",tf-t0) | mit |
alliemacleay/MachineLearning_CS6140 | Homeworks/hw7.py | 1 | 13934 | import CS6140_A_MacLeay.utils as utils
import CS6140_A_MacLeay.Homeworks.HW6 as hw6u
import CS6140_A_MacLeay.Homeworks.HW3 as hw3u
import CS6140_A_MacLeay.Homeworks.HW4.data_load as dl
import CS6140_A_MacLeay.Homeworks.HW4 as hw4u
import CS6140_A_MacLeay.Homeworks.HW7 as hw7u
#import CS6140_A_MacLeay.Homeworks.HW7.speedy as hw7u
import CS6140_A_MacLeay.utils.Perceptron as perc
import CS6140_A_MacLeay.Homeworks.HW7.DualPerceptron as dperc
import numpy as np
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier, KernelDensity
from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier, OutputCodeClassifier
from sklearn.grid_search import GridSearchCV
from sklearn.metrics.pairwise import cosine_distances, cosine_similarity
import sklearn.linear_model as lm
from mlpy import *
__author__ = 'Allison MacLeay'
def q1a():
""" KNN on spambase
k = 1, j = 0
SciKit Accuracy: 0.921908893709 My Accuracy: 0.921908893709
k = 2, j = 0
SciKit Accuracy: 0.908893709328 My Accuracy: 0.908893709328
k = 3, j = 0
SciKit Accuracy: 0.919739696312 My Accuracy: 0.919739696312
k = 7, j = 0
SciKit Accuracy: 0.915401301518 My Accuracy: 0.915401301518
"""
i = 1 # controls k
j = 0 # controls the metric
runSpamKNN(i, j, features='all')
def runSpamKNN(i, j, features='all'):
n_neighbors = [1, 3, 7, 2]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = hw7u.Kernel(ktype=metric[j]).compute
skclassifier = KNeighborsClassifier(n_neighbors=n_neighbors[i], algorithm='brute', metric=ma, p=2)
myclassifier = hw7u.MyKNN(n_neighbors=n_neighbors[i], metric='euclidean')
SpamClassifier(features, skclassifier, myclassifier)
def runSpamDensity(_i, j, features='all'):
metric = ['gaussian', 'poly2', 'cosine_similarity', 'gaussian_density']
data = utils.pandas_to_data(utils.load_and_normalize_spam_data())
k = 10
all_folds = hw3u.partition_folds(data, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train)
y_test, X_test = hw4u.split_truth_from_data(kf_test)
print(len(X))
print(len(X_test))
myclassifier = hw7u.MyKNN(metric=metric[j], density=True)
print 'start MyKNN'
myclassifier.fit(X, y)
#print 'start scikit'
#knnsci = skclassifier.fit(X, y)
print 'start my pred'
y_pred = myclassifier.predict(X_test)
print(y_pred)
#print 'start sk pred'
#y_sci = knnsci.score(X_test)
#print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(fix_y(y_test), fix_y(y_sci)), accuracy_score(fix_y(y_test), fix_y(y_pred)))
print '2b: My Accuracy: {}'.format(accuracy_score(fix_y(y_test), fix_y(y_pred)))
def runSpamRadius(i, j, features='all'):
radius = [.5, .8, 2.5]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = hw7u.Kernel(ktype=metric[j]).compute
print 'spam radius is {} distance metric is {}'.format(radius[i], metric[j])
#skclassifier = RadiusNeighborsClassifier(radius=radius[i], algorithm='brute', metric='euclidean', p=2, outlier_label=-1)
skclassifier = RadiusNeighborsClassifier(radius=radius[i], algorithm='brute', metric='euclidean', outlier_label=-1)
myclassifier = hw7u.MyKNN(radius=radius[i], metric=metric[j], outlier_label=-1)
SpamClassifier(features, skclassifier, myclassifier)
def SpamClassifier(features, skclassifier, myclassifier):
data = utils.pandas_to_data(utils.load_and_normalize_spam_data())
k = 10
if features != 'all':
# Only use the features passed in the features array
new = []
t = utils.transpose_array(data)
for i in xrange(len(t)):
if i in features:
new.append(t[i])
data = utils.transpose_array(t)
all_folds = hw3u.partition_folds(data, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train)
y_test, X_test = hw4u.split_truth_from_data(kf_test)
print 'start MyKNN'
knn = hw7u.KNN(classifier=myclassifier)
print 'start scikit'
knnsci = hw7u.KNN(classifier=skclassifier)
print 'start my pred'
y_pred = knn.predict(X_test, X, y)
print 'My Accuracy: {}'.format(accuracy_score(fix_y(y_test), fix_y(y_pred)))
print 'start sk pred'
y_sci = knnsci.predict(X_test, X, y)
print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(fix_y(y_test), fix_y(y_sci)), accuracy_score(fix_y(y_test), fix_y(y_pred)))
def q1b():
""" KNN on digits
k = 1, j = 0, n = 2000
SciKit Accuracy: 0.85 My Accuracy: 0.85
k = 3, j = 0
k = 7, j = 0
k = 1, j = 1 (cosine)
n:2000 SciKit Accuracy: 0.895 My Accuracy: 0.895
k = 3, j = 1
k = 7, j = 1
k = 1, j = 2 (gaussian)
k = 3, j = 2 (gaussian)
k = 7, j = 2 (gaussian)
n:2000 .87
k = 1, j = 3 (poly2)
.58
k = 3, j = 3 (poly2)
k = 7, j = 3 (poly2)
"""
i = 1 # controls k [1,3,7,2]
j = 2 # controls the metric
n = 5000
runDigitsKNN(i, j, n)
def runDigitsKNN(i, j, n):
n_neighbors = [1, 3, 7]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = hw7u.Kernel(ktype=metric[j]+'_sci').compute
skclf = KNeighborsClassifier(n_neighbors=n_neighbors[i], algorithm='brute', metric=ma, p=2)
myclf = hw7u.MyKNN(n_neighbors=n_neighbors[i], metric=metric[j])
runDigits(n, skclf, myclf)
def runDigitsDensity(n,_i, j):
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = hw7u.Kernel(ktype=metric[j]+'_sci').compute
#skclf = KernelDensity(metric=ma)
myclf = hw7u.MyKNN(metric=metric[j], density=True)
mnsize = n
df = hw6u.load_mnist_features(mnsize)
data = utils.pandas_to_data(df)
k = 10
all_folds = hw3u.partition_folds(data, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train, replace_zeros=False)
y, X = np.asarray(y, dtype=np.float), np.asarray(X)
y_test, X_test = hw4u.split_truth_from_data(kf_test, replace_zeros=False)
y_test, X_test = np.asarray(y_test), np.asarray(X_test, dtype=np.float)
print 'my fit'
clf = OneVsRestClassifier(myclf).fit(X, y)
print 'scikit fit'
#skclf = skclf.fit(X, y)
print 'my predict'
y_pred = clf.predict(X_test)
myacc = accuracy_score(y_test, y_pred)
print '({})'.format(myacc)
#print 'scikit predict'
#sk_pred = skclf.predict(X_test)
#print sk_pred
print y_test
print y_pred
#print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(y_test, sk_pred), myacc)
print 'My Accuracy: {}'.format(myacc)
def runDigitsRadius(i, j, n):
radius = [.5, .1, 1.3, .83]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
print 'Digits radius is {} metric is {}'.format(radius[i], metric[j])
ma = hw7u.Kernel(ktype=metric[j]+'_sci').compute
#ma = cosine_distances
skclf = RadiusNeighborsClassifier(radius=radius[i], algorithm='brute', metric=ma, outlier_label=9)
myclf = hw7u.MyKNN(radius=radius[i], metric='cosine', outlier_label=-1)
runDigits(n, skclf, myclf)
def runDigits(n, skclf, myclf):
mnsize = n
df = hw6u.load_mnist_features(mnsize)
data = utils.pandas_to_data(df)
k = 10
all_folds = hw3u.partition_folds(data, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train, replace_zeros=False)
y, X = np.asarray(y, dtype=np.float), np.asarray(X)
y_test, X_test = hw4u.split_truth_from_data(kf_test, replace_zeros=False)
y_test, X_test = np.asarray(y_test), np.asarray(X_test, dtype=np.float)
print 'my fit'
clf = OneVsRestClassifier(myclf).fit(X, y)
print 'scikit fit'
skclf = skclf.fit(X, y)
print 'my predict'
y_pred = clf.predict(X_test)
myacc = accuracy_score(y_test, y_pred)
print '({})'.format(myacc)
print 'scikit predict'
sk_pred = skclf.predict(X_test)
print sk_pred
print y_test
print y_pred
print 'SciKit Accuracy: {} My Accuracy: {}'.format(accuracy_score(y_test, sk_pred), myacc)
#print 'My Accuracy: {}'.format(accuracy_score(y_test, y_pred))
def q2a():
"""A - KNN fixed window
Spam
max euclid = 5.0821092096899152
min euclid = 3.8265101632475996e-08
Digits
max_euclid = 7.862600580777185
min_euclid = 0.0041151139794844242
mean_euclid = 1.2903757736212245
A) Spam + Euclidian + R=2.5: test acc: 0.833
SciKit Accuracy: 0.605206073753 My Accuracy: 0.605206073753
Digits + Cosine + R=0.83: test acc: 0.886
n = 2000 SciKit Accuracy: 0.18 My Accuracy: 0.175 12/13 14:40
Digits + Cosine + R=0.1:
n = 2000 SciKit Accuracy: 0.84 My Accuracy: 0.815
Running Spam Radius
spam radius is 0.8
my predict euclidean
2 outliers
[42, 111]
SciKit Accuracy: 0.60737527115 My Accuracy: 0.60737527115
Running Digits Radius
Loading 2000 records from haar dataset
radius = [.5, .8, 2.5]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
"""
print 'Running Spam Radius'
# r = [1, 5, 10]
# (radius, metric)
#runSpamRadius(2, 0) # runSpamRadius(2, 0) e=.833
print 'Running Digits Radius'
# radius, metric, n_records
runDigitsRadius(3, 1, 5000) # cosine r=.83 e=.886
def q2b():
"""
B - KNN Kernel Density
B) Spam + Gaussian(sigma=1.0): test acc: 0.910
Digits + Guassian(sigma=1.0): test acc: 0.926
Digits + Poly(degree=2): test acc: 0.550
Cosine Similarity - My Accuracy: 0.8568329718
"""
#runSpamDensity(0, 2) # Cosine similarity # expect .91
#runSpamDensity(0, 3) # Gaussian # expect .91
runSpamDensity(0, 1) # Gaussian # expect .91
#runDigitsDensity(2000, 0, 2) # Gaussian # expect .96
#runDigitsDensity(2000, 0, 3) # Poly # expect .55
def q3a():
""" A - Dual version on Perceptron
B - Spirals
Try to run the dual perceptron (with dot product) and conclude that the perceptron does not work. Then run the dual perceptron with the Gaussian kernel and conclude the data is now separable.
Expected accuracy dot product kernel : 50% average oscillating between 66% and 33%
Expected accuracy with RBF kernel : 99.9% (depends on sigma)
"""
np.random.seed(2)
test, train = utils.load_perceptron_data()
c = train.columns[:-1]
y_train = list(train[4])
X_train = train[c].as_matrix()
y_test = list(test[4])
X_test = test[c].as_matrix()
dual_perc = dperc.DualPerceptron(T=100)
dual_perc.fit(X_train, y_train)
y_pred = dual_perc.predict(X_test)
print '3a: Dual Percepton AUC: {}'.format(roc_auc_score(y_test, dual_perc.decision_function(X_test)))
print '3a: Dual Percepton Accuracy (train): {}'.format(accuracy_score(y_train, dual_perc.predict(X_train)))
print '3a: Dual Percepton Accuracy (test): {}'.format(accuracy_score(y_test, y_pred))
def q3b():
X, y = dl.load_spirals()
X_test, y_test = [], []
X_train, y_train = [], []
idx = np.random.choice(range(len(X)), size=np.floor(len(X) * .2))
for i in range(len(X)):
if i in idx:
X_test.append(X[i])
y_test.append(y[i])
else:
X_train.append(X[i])
y_train.append(y[i])
X_test = np.array(X_test)
X_train = np.array(X_train)
y_test = np.array(y_test)
y_train = np.array(y_train)
dual_perc = dperc.DualPerceptron(T=100)
dual_perc.fit(X_train, y_train)
dot_p = dual_perc.predict(X_test)
dp_gauss = dperc.DualPerceptron(T=100, kname='gaussian')
dp_gauss.fit(X_train, y_train)
gauss_p = dp_gauss.predict(X_test)
print 'AUC Dot: {} Gaussian: {}'.format(accuracy_score(y_test, dot_p), accuracy_score(y_test, gauss_p))
def homework_2_perceptron():
""" Perceptron """
test, train = utils.load_perceptron_data()
print test[4]
print train.head(5)
model = perc.Perceptron(train, 4, .05, 100)
def q5():
""" RELIEF algorithm - Feature selection with KNN """
top_five = relief(5)
print top_five
i = 0 # controls k
j = 0 # controls the metric
# SciKit Accuracy: 0.921908893709 My Accuracy: 0.921908893709
runSpamKNN(i, j, features=top_five)
def relief(n):
max_iters = 1
j = 0
i = 1
n_neighbors = [1, 3, 7]
metric = ['minkowski', 'cosine', 'gaussian', 'poly2']
ma = hw7u.Kernel(ktype=metric[j]).compute
data = utils.pandas_to_data(utils.load_and_normalize_spam_data())
k = 10
all_folds = hw3u.partition_folds(data, k)
kf_train, kf_test = dl.get_train_and_test(all_folds, 0)
y, X = hw4u.split_truth_from_data(kf_train)
y_test, X_test = hw4u.split_truth_from_data(kf_test)
loops = 0
weights = np.zeros(len(X[0]))
loops += 1
n_features = len(X[0])
n_samples = len(X)
for j in range(n_features): #feature
for i in range(n_samples): # data
closest_same = None
closest_opp = None
for z_i in range(n_samples):
if z_i == i:
continue
diff = (X[z_i][j] - X[i][j]) ** 2
if y[z_i] == y[i]: # same
if closest_same is None or diff < closest_same:
closest_same = diff
else: # opp
if closest_opp is None or diff < closest_opp:
closest_opp = diff
weights[j] += (-closest_same + closest_opp)
if i % 1000 == 0:
print 'feature {} of {}, sample {} of {}'.format(j, n_features, i, n_samples)
print weights
return sorted(zip(weights, range(len(weights))), reverse=True)[:n][1]
def fix_y(y):
return [0 if y_i != -1 else y_i for y_i in y]
| mit |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/core/panel4d.py | 14 | 2648 | """ Panel4D: a 4-d dict like collection of panels """
import warnings
from pandas.core.panelnd import create_nd_panel_factory
from pandas.core.panel import Panel
Panel4D = create_nd_panel_factory(klass_name='Panel4D',
orders=['labels', 'items', 'major_axis',
'minor_axis'],
slices={'labels': 'labels',
'items': 'items',
'major_axis': 'major_axis',
'minor_axis': 'minor_axis'},
slicer=Panel,
aliases={'major': 'major_axis',
'minor': 'minor_axis'}, stat_axis=2,
ns=dict(__doc__="""
Panel4D is a 4-Dimensional named container very much like a Panel, but
having 4 named dimensions. It is intended as a test bed for more
N-Dimensional named containers.
DEPRECATED. Panel4D is deprecated and will be removed in a future version.
The recommended way to represent these types of n-dimensional data are with
the `xarray package <http://xarray.pydata.org/en/stable/>`__.
Pandas provides a `.to_xarray()` method to automate this conversion.
Parameters
----------
data : ndarray (labels x items x major x minor), or dict of Panels
labels : Index or array-like : axis=0
items : Index or array-like : axis=1
major_axis : Index or array-like: axis=2
minor_axis : Index or array-like: axis=3
dtype : dtype, default None
Data type to force, otherwise infer
copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input
"""))
def panel4d_init(self, data=None, labels=None, items=None, major_axis=None,
minor_axis=None, copy=False, dtype=None):
# deprecation GH13564
warnings.warn("\nPanel4D is deprecated and will be removed in a "
"future version.\nThe recommended way to represent "
"these types of n-dimensional data are with\n"
"the `xarray package "
"<http://xarray.pydata.org/en/stable/>`__.\n"
"Pandas provides a `.to_xarray()` method to help "
"automate this conversion.\n",
FutureWarning, stacklevel=2)
self._init_data(data=data, labels=labels, items=items,
major_axis=major_axis, minor_axis=minor_axis, copy=copy,
dtype=dtype)
Panel4D.__init__ = panel4d_init
| mit |
swc2124/skysurvey | skysurvey/gridplot.py | 1 | 19403 | """TODO"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from warnings import warn
import numpy as np
from numpy import arctan
from numpy import array
from numpy import float64
from numpy import linspace
from numpy import load
from numpy import log10
from numpy import logical_and
from numpy import pi
from numpy import save
from numpy import square
import matplotlib
matplotlib.use('AGG')
from matplotlib import pyplot as plt
'''
from matplotlib.pyplot import close
from matplotlib.pyplot import cm
from matplotlib.pyplot import colorbar
from matplotlib.pyplot import figure
from matplotlib.pyplot import grid
from matplotlib.pyplot import legend
from matplotlib.pyplot import pcolormesh
'''
from skysurvey.spinbin import spinone
import ConfigParser
import skysurvey
from skysurvey.new_config import SYS_CFG_FNAME
sys_config_fh = os.path.join(os.path.dirname(
os.path.realpath(skysurvey.__file__)), SYS_CFG_FNAME)
SysConfig = ConfigParser.ConfigParser()
SysConfig.read(sys_config_fh)
config_fh = SysConfig.get('skysurvey_global_settings', 'config_fh')
Config = ConfigParser.ConfigParser()
Config.read(config_fh)
def sqrdeg(d_Mpc=None):
'''
conversion mod to deg^2
'''
if d_Mpc == None:
d_Mpc = Config.getfloat('Distance', 'd_mpc')
# edge length of box _Grid - 6 Kpc
d = 6 * 1e3
# distance in Mpc - 0.75 ~ 11.0
D = d_Mpc * 1e6
# small angle formula
rad = 2 * (arctan((d / (2 * D))))
# convert radians to degrees
deg = rad * (180 / pi)
# square for area
return square(deg)
def sqr_arcmin(d_Mpc=None):
'''
conversion mod to arcmin^2
'''
if d_Mpc == None:
d_Mpc = Config.getfloat('Distance', 'd_mpc')
# edge length of box _Grid - 6 Kpc
d = 6.0 * 1e3
# distance in Mpc - 0.75 ~ 11.0
D = d_Mpc * 1e6
return square(3437.75 * (2.0 * (arctan((d / (2.0 * D))))))
def _Grid_list(_Grid_path=None, _d_Mpc=None, _f_type=None):
'''
Make a list of output files writing to be plotted. These files are
the output of spinbin.spinall ro spin.
'''
if _Grid_path == None:
_Grid_path = Config.get('PATH', 'grid_dir')
if _d_Mpc == None:
_d_Mpc = Config.getfloat('Distance', 'd_mpc')
if _f_type == None:
_f_type = Config.get('Filter', 'filter_type')
print('reading arrays from : ' + _Grid_path)
print('[' + str(_d_Mpc) + ']')
_Grids = [i for i in os.listdir(_Grid_path) if i.endswith(
'_' + str(_d_Mpc) + 'Mpc_' + _f_type + '_grid.npy')]
if len(_Grids) > 0:
_Grids.sort()
for _Grid in _Grids:
print(' -> ' + _Grid)
return _Grids
else:
warn((
'there are no arrays to be plotted!'
'Have you run < spinbin.spinall() > yet?'))
return []
def fix_rslice(grid, rslices=[14]):
center = grid.shape[0] / 2
ratio = (1.0 * Config.getint('grid_options', 'size')) / \
(2.0 * Config.getfloat('halo_data_settings', 'radial_cut'))
# if VERBOSE:
# print('fixing radial data slice')
# print('ratio: ', ratio)
# print('slices: ', rslices)
# print('center:', center)
for r in rslices:
for i in range(grid.shape[0]):
for q in range(grid.shape[1]):
value = np.sqrt(
(np.square(i - center) + np.square(q - center)))
value /= ratio
if value > 300.0:
value = 0.0
grid[i, q, r] = value
return grid
def nstars_per_sqdeg(d_Mpc=None, plot_path=None, f_type=None):
"""
Render and save the main plot pannal.
Extended description of function.
Parameters
----------
d_Mpc : float
The distance to the halo.
plot_path : str
/path/to/plotdir
Returns
-------
plot
Description of return value
"""
# figure dimensions
if plot_path == None:
plot_path = Config.get('PATH', 'plot_dir')
if d_Mpc == None:
d_Mpc = Config.getfloat('Distance', 'd_mpc')
if f_type == None:
f_type = Config.get('Filter', 'filter_type')
grid_dir = Config.get('PATH', 'grid_dir')
fig_x = 31
fig_y = 23
plotname = 'log(n_stars(m_app<26.87))/arcmin^2 vs. radial separation Kpc'
x0 = 0
x1 = 301
plot_path = os.path.join(plot_path, 'grid_plot')
xticks = [str(round(i)) for i in linspace(0, 300, 9)]
x_label = 'Radial Distance (Kpc)'
y_label = 'Log(N_stars)/arcmin^2'
mod = sqr_arcmin(d_Mpc=d_Mpc)
fig = plt.figure(figsize=(fig_x, fig_y))
fig.suptitle(plotname + '\n' + str(d_Mpc) +
' Mpc ' + f_type, fontsize=40)
_Grids = _Grid_list(_d_Mpc=d_Mpc, _f_type=f_type)
if len(_Grids) == 0:
warn('There are no arrays to be plotted')
return
min_strs = []
max_strs = []
print('finding min and max for y-axis')
for fh in _Grids:
filename = os.path.join(grid_dir, fh)
arr = load(filename)
strs = log10(arr[:, :, 0].flatten() / mod)
min_strs.append(strs[strs != -float('Inf')].min())
max_strs.append(strs.max())
y0 = min(min_strs)
y1 = max(max_strs)
print('y-axis min: ', round(y0, 2))
print('y-axis max: ', round(y1, 2))
print('plotting')
for i, _Grid in enumerate(_Grids):
print(' -> ' + _Grid)
filename = os.path.join(grid_dir, _Grid)
array = fix_rslice(load(filename))
ax = fig.add_subplot(3, 4, i + 1)
header = _Grid.split('_')
title = header[0]
ax.set_title(title, fontsize=30)
#mlim_min = log10(array[:, :, 3][array[:, :, 0] > 0.].flatten() / mod)
#mlim_med = log10(array[:, :, 4][array[:, :, 0] > 0.].flatten() / mod)
mlim_max = log10(array[:, :, 5][array[:, :, 0] > 0.].flatten() / mod)
rads = array[:, :, 6][array[:, :, 0] > 0.].flatten()
ax.scatter(rads, mlim_max, s=5, alpha=.75, color='k',
marker='o') # , label='(10^5) blue: m<' + str(mag_lim_max))
'''
ax.axhline(y=1.44, xmin=0, xmax=301, c="blue",
linewidth=7, alpha=.2, zorder=0)
ax.scatter(rads, mlim_med, s=9, alpha=.5, color='red',
marker='o', label='(10^4.6) red: m<' + str(mag_lim_mid))
ax.axhline(y=1.04, xmin=0, xmax=301, c="red",
linewidth=7, alpha=.2, zorder=0)
ax.scatter(rads, mlim_min, s=9, alpha=.5, color='black',
marker='o', label='(10^4) black: m<' + str(mag_lim_low))
ax.axhline(y=.44, xmin=0, xmax=301, c="black",
linewidth=7, alpha=.2, zorder=0)
#plt.legend(fontsize='large', numpoints=1)
'''
plt.grid()
ax.set_xlim([x0, x1])
# ax.set_xticklabels(xticks)
ax.set_xlabel(x_label, fontsize=25)
ax.set_ylim([y0, y1])
ax.set_ylabel(y_label, fontsize=25)
#ax12 = fig.add_subplot(3, 4, 12)
# ax12.axis('off')
#ax12.legend(['(10^5) blue: m<' + str(mag_lim_max), '(10^4.6) red: m<' + str(mag_lim_mid), '(10^4) black: m<' + str(mag_lim_low)],fontsize=30, shadow=True)
full_plot_filename = plot_path + 'sqdeg_' + \
str(d_Mpc).split('.')[0] + 'Mpc_' + f_type
print(full_plot_filename)
fig.savefig(full_plot_filename)
plt.close()
def nstars(d_Mpc=None, plot_path=None, f_type=None, lims=[100.0, 300.0]):
if plot_path == None:
plot_path = Config.get('PATH', 'plot_dir')
if d_Mpc == None:
d_Mpc = Config.getfloat('Distance', 'd_mpc')
if f_type == None:
f_type = Config.get('Filter', 'filter_type')
fig_x = 31
fig_y = 23
plotname = 'Log number of stars per square arcmin\n'
x0 = 0
x1 = 100
y0 = 0
y1 = 100
x_label = 'Kpc'
y_label = 'Kpc'
ticks = linspace(-300, 300, 6)
x_ticklabels = [str(round(i)) for i in ticks]
y_ticklabels = [str(round(i)) for i in ticks]
x_ticklabels[0] = ''
r0, r1 = lims
mod = sqr_arcmin(d_Mpc=d_Mpc)
fig = plt.figure(figsize=(fig_x, fig_y))
fig.suptitle(plotname + ' ' + str(d_Mpc) +
' Mpc ' + f_type + ' m_app < 26.78', fontsize=50)
_Grids = _Grid_list(_d_Mpc=d_Mpc, _f_type=f_type)
if len(_Grids) == 0:
warn('There are no arrays to be plotted')
return
print('plotting')
for i, _Grid in enumerate(_Grids):
filename = os.path.join(grid_dir, _Grid)
print(' -> ' + filename)
array = load(filename)
ax = fig.add_subplot(3, 4, i + 1)
header = _Grid.split('_')
title = header[0]
ax.set_title(title, fontsize=30)
hm = plt.pcolormesh(
log10(array[:, :, 5] / mod), cmap=plt.cm.plasma, vmin=-1, vmax=3.5)
cp = ax.contour(array[:, :, 6], [r0, r1], colors='white',
linewidths=3, alpha=.6, linestyles='dashed')
ax.clabel(cp, [r0, r1], inline=1, fmt='%s Kpc',
fontsize=15, color='white', linewidth=7, alpha=1)
# hm.set_linewidth(0.01)
if i == 10:
ax1 = fig.add_subplot(3, 4, i + 2)
cb = plt.colorbar(hm, ax1)
ax1.axes.grid(color='white', alpha=.8,
linewidth=5, linestyle='dashed')
cb.ax.tick_params(labelsize=28)
ax1.yaxis.set_label_position("right")
ax1.set_ylabel("Log Nstars/arcmin^2", fontsize=40)
ax.axes.grid(color='white', alpha=.4, linewidth=2, linestyle='dashed')
ax.axes.set_yticklabels(y_ticklabels, size=20, rotation=-30)
ax.axes.set_xticklabels(x_ticklabels, size=20, rotation=-30)
ax.axes.set_xlim([x0, x1])
ax.axes.set_ylim([y0, y1])
if i in [0, 4, 8]:
ax.axes.set_ylabel(y_label, fontsize=40)
if i in [8, 9, 10]:
ax.axes.set_xlabel(x_label, fontsize=40)
full_plot_filename = plot_path + 'nstars_' + \
str(d_Mpc).split('.')[0] + 'Mpc_' + f_type + '.png'
fig.savefig(full_plot_filename, dpi=fig.dpi)
plt.close()
def mixplot(plot_halos=['halo12', 'halo15', 'halo20'],
d_Mpc=None, plot_path=None,
f_type=None, radius=None):
if plot_path == None:
plot_path = Config.get('PATH', 'plot_dir')
if d_Mpc == None:
d_Mpc = Config.getfloat('Distance', 'd_mpc')
if f_type == None:
f_type = Config.get('Filter', 'filter_type')
grid_dir = Config.get('PATH', 'grid_dir')
plt_halos = []
print('loading filenames')
for i in _Grid_list(_d_Mpc=d_Mpc, _f_type=f_type):
breakdown = i.split('_')
if breakdown[0] in plot_halos:
plt_halos.append(i)
print(' -> [ selected ] ', i)
fig_x = 42
fig_y = 40
plotname = 'Log Number of stars per square arcmin'
fig = plt.figure(figsize=(fig_x, fig_y))
fig.suptitle(plotname + '\n' + str(d_Mpc) +
' Mpc ' + f_type + ' m_app < 26.78', fontsize=80)
mod = sqr_arcmin(d_Mpc=d_Mpc)
min_strs = []
max_strs = []
print('finding min and max for y-axis')
for fh in plt_halos:
filename = os.path.join(grid_dir, fh)
arr = load(filename)
strs = log10(arr[:, :, 0] / mod)
min_strs.append(strs[strs != -float('Inf')].min())
max_strs.append(strs.max())
_y0 = min(min_strs)
_y1 = max(max_strs)
print('y-axis min: ', round(_y0, 2))
print('y-axis max: ', round(_y1, 2))
for p_num, _file in enumerate(plt_halos):
if radius:
r0, r1 = radius[p_num]
else:
r0, r1 = 0, 1
header = _file.split('_')
title = header[0]
filename = os.path.join(grid_dir, _file)
print(' -> ' + filename)
array = fix_rslice(load(filename))
for i in range(3):
plot_number = (p_num * len(plt_halos)) + (i + 1)
print('plotting ', plot_number, title)
ax = fig.add_subplot(3, 3, plot_number)
if i in [0, 3, 6]:
plotname = 'Log(N_stars)/arcmin^2'
x0 = 0
x1 = 301
y0 = _y0
y1 = _y1
x_ticklabels = [str(int(i)) for i in linspace(0, 300, 7)]
#y_ticklabels = [str(i) for i in [-1,0,1,2,3]]
x_label = 'Radial Distance (Kpc)'
y_label = title + '\nlog(n_stars)_vs_arcmin^2'
'''
mlim_min = log10(
array[:, :, 3][array[:, :, 0] > 0.].flatten() / mod)
mlim_med = log10(
array[:, :, 4][array[:, :, 0] > 0.].flatten() / mod)
'''
mlim_max = log10(
array[:, :, 5][array[:, :, 0] > 0.].flatten() / mod)
rads = array[:, :, 14][array[:, :, 0] > 0.].flatten()
idx = logical_and(rads > r0, rads < r1)
ax.scatter(rads[idx], mlim_max[idx], s=80, alpha=.6, color='orange',
marker='o')
'''
ax.scatter(rads[idx], mlim_med[idx], s=80, alpha=.6, color='orange',
marker='o')
ax.scatter(rads[idx], mlim_min[idx], s=80, alpha=.6, color='orange',
marker='o')
'''
ax.scatter(rads, mlim_max, s=15, alpha=.5, color='k',
marker='o') # , label='(10^5) blue: m<' + str(mag_lim_max))
'''
ax.axhline(y=1.44, xmin=0, xmax=301, c="blue",
linewidth=7, alpha=.2, zorder=0)
ax.scatter(rads, mlim_med, s=9, alpha=.5, color='red',
marker='o', label='(10^4.6) red: m<' + str(mag_lim_mid))
ax.axhline(y=1.04, xmin=0, xmax=301, c="red",
linewidth=7, alpha=.2, zorder=0)
ax.scatter(rads, mlim_min, s=9, alpha=.5, color='black',
marker='o', label='(10^4) black: m<' + str(mag_lim_low))
ax.axhline(y=.44, xmin=0, xmax=301, c="black",
linewidth=7, alpha=.2, zorder=0)
'''
ax.axes.legend(fontsize='xx-large', numpoints=1, shadow=True)
ax.axes.grid(color='black', alpha=.5,
linewidth=3, linestyle='dashed')
y_ticklabels = [str(i) for i in range(-1, 4)]
ax.axes.set_yticks([-1.0, 0.0, 1.0, 2.0, 3.0])
# ax.axes.tick_params(axis=)
else:
hm = ax.pcolormesh(
log10(array[:, :, 0] / mod), cmap=plt.cm.plasma, vmin=-1, vmax=3.5)
cp = ax.contour(array[:, :, 14], [r0, r1], colors='white',
linewidths=7, alpha=.5, linestyles='dashed')
ax.clabel(cp, [r0, r1], inline=1, fmt='%s Kpc',
fontsize=25, color='white', linewidth=7, alpha=.9)
if i in [1, 4, 7]:
plotname = 'n_stars'
x0 = 0
x1 = 100
y0 = 0
y1 = 100
x_label = 'Kpc'
y_label = 'Kpc'
x_ticks = linspace(-300, 300, 6)
x_ticklabels = [str(int(i)) for i in x_ticks]
y_ticks = linspace(-300, 300, 6)
y_ticklabels = [str(int(i)) for i in x_ticks]
x_ticklabels[0] = ''
ax.axes.grid(color='white', alpha=.2,
linewidth=3, linestyle='dashed')
else:
plotname = 'n_stars_zoomed'
x0 = 20
x1 = 80
y0 = 20
y1 = 80
x_label = 'Kpc'
y_label = 'Kpc'
x_ticks = linspace(-180, 180, 7)
x_ticklabels = [str(int(i)) for i in x_ticks]
y_ticks = linspace(-180, 180, 7)
y_ticklabels = [str(int(i)) for i in y_ticks]
x_ticklabels[0] = ''
cax = plt.axes([0.92, 0.15, 0.05, 0.7])
cbar = plt.colorbar(hm, cax=cax)
cbar.ax.tick_params(labelsize=40)
cbar.ax.grid(color='white', alpha=.8,
linewidth=6, linestyle='dashed')
ax.axes.set_yticklabels(y_ticklabels, size=40, rotation=-30)
ax.set_title(title, fontsize=30)
ax.axes.set_xticklabels(x_ticklabels, size=30) # , rotation=-30)
ax.axes.set_title(title, fontsize=50)
ax.axes.set_xlim([x0, x1])
ax.axes.set_xlabel(x_label, fontsize=30)
ax.axes.set_ylim([y0, y1])
ax.axes.set_ylabel(y_label, fontsize=30)
if i in [0, 3, 6]:
ax.set_ylabel(y_label, fontsize=50)
print('done')
full_plot_filename = plot_path + 'mixplt_' + \
str(d_Mpc).split('.')[0] + 'Mpc_' + f_type + '.png'
fig.savefig(full_plot_filename)
plt.close()
def make_plots(distances=[2.0, 5.0], f_types=['h158'],
plot_path=None,
plot_halos=['halo08', 'halo15', 'halo20'],
radius=None):
if plot_path == None:
plot_path = Config.get('PATH', 'plot_dir')
for f_typ in f_types:
for dist in distances:
nstars_per_sqdeg(d_Mpc=dist, plot_path=plot_path, f_type=f_typ)
nstars(d_Mpc=dist, plot_path=plot_path, f_type=f_typ)
mixplot(plot_halos=plot_halos,
d_Mpc=dist, plot_path=plot_path,
f_type=f_typ, radius=radius)
def run_limits(target_n=85.0, radius=75.0, n_attemts=100, distances=[2.0, 5.0], f_typ=None):
if f_type == None:
f_type = Config.get('Filter', 'filter_type')
for distance in distances:
mod = sqr_arcmin(d_Mpc=distance)
for halo in _Grid_list(_d_Mpc=distance, _f_type=f_typ):
attemts = 0
best_arr = load(grid_dir + halo)
best_stars = 0.0
for ii in range(n_attemts):
stars = 0
boxes = 0
halo_name = halo.split('_')[0]
arr = load(grid_dir + halo)
for i, rad in enumerate(arr[:, :, 6].flatten()):
if rad >= radius:
if arr[:, :, 5].flatten()[i] / mod >= target_n:
stars += arr[:, :, 5].flatten()[i]
boxes += 1.0
curent_n = round((stars / mod) / (boxes), 2)
if curent_n > best_stars:
print('\nhalo: ', halo_name, '| stars: ', round(stars / mod, 2), '| boxes: ', boxes, '| best n: ',
best_stars, '| current n: ', curent_n, '| attempt [ ' + str(ii) + '/' + str(n_attemts) + ' ]', '\n')
best_stars = curent_n
best_arr = arr
save(grid_dir + halo, best_arr)
spinone(halo_name, m_lims=array(
[27.12, 27.87, 26.87], dtype=float64), d_mpc=distance, f_type=f_typ)
save(grid_dir + halo, best_arr)
nstars_per_sqdeg(d_Mpc=distance, f_type=f_typ)
| mit |
chibbargroup/CentralRepository | FLOURescence/Source/LinearRegression.py | 2 | 4989 | """
Linear_Regession.py
Written by D. Sanche and J. Hayes, Last Edit: 1/27/17
Purpose: Calculate the calibration curves via linear regression of the calibration data
Input:
1) cal_data_file: data file containing the calibration data
2) output_dir: where to save the output results
Note that the script will look for the cal_concentrations file in the output directory; if it finds
this file it will ask if the user would like to use these values for the calibration curve analysis
Output:
Saves the regression data to file calibration_curves.csv file located in the output directory
This file contains the following: slope, intercept, r-value, p-value, std error, # of calib. pts,
LOD, LOQ, std dev y, xi^2, and xi
Also saves the calibration values to the file cal_concentrations.csv in the specified output directory
"""
import pandas as pd
import numpy as np
from os import listdir
from os.path import isfile, join
from scipy import stats
#Gets the calibration concentrations as input from user; saves inputs as csv
def Get_Cal_Concs_From_User(data_file, output_dir):
df = pd.read_csv(data_file, index_col = 0)
concentrations = {}
for cal in df.columns:
cal_conc = Get_User_Input(cal)
concentrations[cal] = cal_conc
concentrations = pd.Series(concentrations).rename('Concentrations')
concentrations.to_csv(join(output_dir, 'cal_concentrations.csv'))
df = df.append(concentrations)
return(df)
#Gets concentration inputs from user; ensures user can only input numbers
def Get_User_Input(calibration_number):
is_number = False
while not is_number:
number = input("Please input the concentration of %s: " %calibration_number)
try:
float(number)
is_number = True
except ValueError:
is_number = False
print("I'm sorry Dave, I can't do that. It seems you didn't input a number...please concentrate")
return(float(number))
#Gets the calibration concentrations from the previously saved csv file
def Get_Cal_Concs_From_File(data_file, conc_file):
df = pd.read_csv(data_file, index_col = 0)
concentrations = pd.read_csv(conc_file, names = ['', 'Concentrations'], index_col = 0).transpose()
df = df.append(concentrations.ix['Concentrations'])
return(df)
#Adds calibration concentrations to dataframe containing calibration data
def Add_Cal_Concs(data_file, output_dir):
#Initialize some parameters/response lists
yea_response = ['Y', 'y', 'yes', 'Yes']
nay_response = ['N', 'n', 'No', 'no']
concentration_file = join(output_dir, 'cal_concentrations.csv')
#Run the logic flow
readable_input = False
if isfile(concentration_file):
while not readable_input:
use_previous = input("I see that there is already a concentration file, would you like me to use it? [Y/N]: ")
if use_previous in yea_response:
df = Get_Cal_Concs_From_File(data_file, concentration_file)
readable_input = True
elif use_previous in nay_response:
df = Get_Cal_Concs_From_User(data_file, output_dir)
readable_input = True
else:
print("I'm sorry Dave, I can't do that. Please try answering again.")
else:
df = Get_Cal_Concs_From_User(data_file, output_dir)
return(df)
#The hear to the program; runs the linear regression analysis for each element and returns
#the results as a dataframe
def Linear_Regression_Calculator(cal_data):
cal_curve_list = []
concentrations = np.array(cal_data.ix['Concentrations'])
for element in cal_data.index:
if element != "Concentrations":
data = np.array(cal_data.ix[element])
slope, intercept, r_value, p_value, std_error = stats.linregress(concentrations, data)
n, lod, loq, xi_2, xi = Regression_Stats(concentrations, std_error, intercept, slope)
ssr = SSR_Calculator(data, concentrations, slope, intercept)
reg_values = [slope, intercept, r_value, p_value, std_error, n, lod, loq, ssr, xi, xi_2]
reg_labels = ["slope", "intercept", "r_value", "p_value", "std_err", "n", "LOD", "LOQ", "ssr", "xi", "xi_2"]
cal_curve = pd.DataFrame(reg_values, columns=[element], index=reg_labels)
cal_curve_list += [cal_curve]
cal_curve_results = pd.concat(cal_curve_list, axis = 1)
return cal_curve_results
#Separate script to calculate various extra statistics about the regression;
#runs as part of the Linear_Regression_Calculator module
def Regression_Stats(concentrations, std_error, intercept, slope):
n = len(concentrations)
lod = 3.3*std_error/intercept
loq = 10*std_error/intercept
xi_2 = 0
xi = 0
for conc in concentrations:
xi += conc
xi_2 += conc**2
return n, lod, loq, xi_2, xi
#Calculate the standard deviation in the y values
def SSR_Calculator(cal_data, concentrations, slope, intercept):
ssr = 0
for i in range(0, len(cal_data)):
di = cal_data[i] - slope*concentrations[i] - intercept
ssr += di**2
return ssr
#Run the script
def Linear_Regression_Script(cal_data_file, output_dir):
data = Add_Cal_Concs(cal_data_file, output_dir)
cal_curves = Linear_Regression_Calculator(data)
cal_curves.to_csv(join(output_dir, "calibration_curves.csv"))
| mit |
LiaoPan/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more important.
The code below also illustrates how the construction and the computation
of the predictions can be parallelized within multiple jobs.
"""
print(__doc__)
from time import time
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_olivetti_faces
from sklearn.ensemble import ExtraTreesClassifier
# Number of cores to use to perform parallel fitting of the forest model
n_jobs = 1
# Load the faces dataset
data = fetch_olivetti_faces()
X = data.images.reshape((len(data.images), -1))
y = data.target
mask = y < 5 # Limit to 5 classes
X = X[mask]
y = y[mask]
# Build a forest and compute the pixel importances
print("Fitting ExtraTreesClassifier on faces data with %d cores..." % n_jobs)
t0 = time()
forest = ExtraTreesClassifier(n_estimators=1000,
max_features=128,
n_jobs=n_jobs,
random_state=0)
forest.fit(X, y)
print("done in %0.3fs" % (time() - t0))
importances = forest.feature_importances_
importances = importances.reshape(data.images[0].shape)
# Plot pixel importances
plt.matshow(importances, cmap=plt.cm.hot)
plt.title("Pixel importances with forests of trees")
plt.show()
| bsd-3-clause |
3manuek/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 286 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demonstrates Label Propagation learning a good boundary
even with a small amount of labeled data.
"""
print(__doc__)
# Authors: Clay Woolam <[email protected]>
# Licence: BSD
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import svm
from sklearn.semi_supervised import label_propagation
rng = np.random.RandomState(0)
iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target
# step size in the mesh
h = .02
y_30 = np.copy(y)
y_30[rng.rand(len(y)) < 0.3] = -1
y_50 = np.copy(y)
y_50[rng.rand(len(y)) < 0.5] = -1
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
ls30 = (label_propagation.LabelSpreading().fit(X, y_30),
y_30)
ls50 = (label_propagation.LabelSpreading().fit(X, y_50),
y_50)
ls100 = (label_propagation.LabelSpreading().fit(X, y), y)
rbf_svc = (svm.SVC(kernel='rbf').fit(X, y), y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['Label Spreading 30% data',
'Label Spreading 50% data',
'Label Spreading 100% data',
'SVC with rbf kernel']
color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}
for i, (clf, y_train) in enumerate((ls30, ls50, ls100, rbf_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
plt.subplot(2, 2, i + 1)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
plt.axis('off')
# Plot also the training points
colors = [color_map[y] for y in y_train]
plt.scatter(X[:, 0], X[:, 1], c=colors, cmap=plt.cm.Paired)
plt.title(titles[i])
plt.text(.90, 0, "Unlabeled points are colored white")
plt.show()
| bsd-3-clause |
almarklein/scikit-image | skimage/viewer/plugins/overlayplugin.py | 1 | 3588 | from warnings import warn
from skimage.util.dtype import dtype_range
from .base import Plugin
from ..utils import ClearColormap, update_axes_image
import six
__all__ = ['OverlayPlugin']
def recent_mpl_version():
import matplotlib
version = matplotlib.__version__.split('.')
return int(version[0]) == 1 and int(version[1]) >= 2
class OverlayPlugin(Plugin):
"""Plugin for ImageViewer that displays an overlay on top of main image.
The base Plugin class displays the filtered image directly on the viewer.
OverlayPlugin will instead overlay an image with a transparent colormap.
See base Plugin class for additional details.
Attributes
----------
overlay : array
Overlay displayed on top of image. This overlay defaults to a color map
with alpha values varying linearly from 0 to 1.
color : int
Color of overlay.
"""
colors = {'red': (1, 0, 0),
'yellow': (1, 1, 0),
'green': (0, 1, 0),
'cyan': (0, 1, 1)}
def __init__(self, **kwargs):
if not recent_mpl_version():
msg = "Matplotlib >= 1.2 required for OverlayPlugin."
warn(RuntimeWarning(msg))
super(OverlayPlugin, self).__init__(**kwargs)
self._overlay_plot = None
self._overlay = None
self.cmap = None
self.color_names = self.colors.keys()
def attach(self, image_viewer):
super(OverlayPlugin, self).attach(image_viewer)
#TODO: `color` doesn't update GUI widget when set manually.
self.color = 0
@property
def overlay(self):
return self._overlay
@overlay.setter
def overlay(self, image):
self._overlay = image
ax = self.image_viewer.ax
if image is None:
ax.images.remove(self._overlay_plot)
self._overlay_plot = None
elif self._overlay_plot is None:
vmin, vmax = dtype_range[image.dtype.type]
self._overlay_plot = ax.imshow(image, cmap=self.cmap,
vmin=vmin, vmax=vmax)
else:
update_axes_image(self._overlay_plot, image)
self.image_viewer.redraw()
@property
def color(self):
return self._color
@color.setter
def color(self, index):
# Update colormap whenever color is changed.
if isinstance(index, six.string_types) and \
index not in self.color_names:
raise ValueError("%s not defined in OverlayPlugin.colors" % index)
else:
name = self.color_names[index]
self._color = name
rgb = self.colors[name]
self.cmap = ClearColormap(rgb)
if self._overlay_plot is not None:
self._overlay_plot.set_cmap(self.cmap)
self.image_viewer.redraw()
@property
def filtered_image(self):
"""Return filtered image.
This "filtered image" is used when saving from the plugin.
"""
return self.overlay
def display_filtered_image(self, image):
"""Display filtered image as an overlay on top of image in viewer."""
self.overlay = image
def closeEvent(self, event):
# clear overlay from ImageViewer on close
self.overlay = None
super(OverlayPlugin, self).closeEvent(event)
def output(self):
"""Return the overlaid image.
Returns
-------
overlay : array, same shape as image
The overlay currently displayed.
data : None
"""
return (self.overlay, None)
| bsd-3-clause |
clemkoa/scikit-learn | sklearn/model_selection/_search.py | 8 | 56392 | """
The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the
parameters of an estimator.
"""
from __future__ import print_function
from __future__ import division
# Author: Alexandre Gramfort <[email protected]>,
# Gael Varoquaux <[email protected]>
# Andreas Mueller <[email protected]>
# Olivier Grisel <[email protected]>
# Raghav RV <[email protected]>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
from collections import Mapping, namedtuple, defaultdict, Sequence
from functools import partial, reduce
from itertools import product
import operator
import warnings
import numpy as np
from scipy.stats import rankdata
from ..base import BaseEstimator, is_classifier, clone
from ..base import MetaEstimatorMixin
from ._split import check_cv
from ._validation import _fit_and_score
from ._validation import _aggregate_score_dicts
from ..exceptions import NotFittedError
from ..externals.joblib import Parallel, delayed
from ..externals import six
from ..utils import check_random_state
from ..utils.fixes import sp_version
from ..utils.fixes import MaskedArray
from ..utils.random import sample_without_replacement
from ..utils.validation import indexable, check_is_fitted
from ..utils.metaestimators import if_delegate_has_method
from ..metrics.scorer import _check_multimetric_scoring
from ..metrics.scorer import check_scoring
__all__ = ['GridSearchCV', 'ParameterGrid', 'fit_grid_point',
'ParameterSampler', 'RandomizedSearchCV']
class ParameterGrid(object):
"""Grid of parameters with a discrete number of values for each.
Can be used to iterate over parameter value combinations with the
Python built-in function iter.
Read more in the :ref:`User Guide <search>`.
Parameters
----------
param_grid : dict of string to sequence, or sequence of such
The parameter grid to explore, as a dictionary mapping estimator
parameters to sequences of allowed values.
An empty dict signifies default parameters.
A sequence of dicts signifies a sequence of grids to search, and is
useful to avoid exploring parameter combinations that make no sense
or have no effect. See the examples below.
Examples
--------
>>> from sklearn.model_selection import ParameterGrid
>>> param_grid = {'a': [1, 2], 'b': [True, False]}
>>> list(ParameterGrid(param_grid)) == (
... [{'a': 1, 'b': True}, {'a': 1, 'b': False},
... {'a': 2, 'b': True}, {'a': 2, 'b': False}])
True
>>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
>>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
... {'kernel': 'rbf', 'gamma': 1},
... {'kernel': 'rbf', 'gamma': 10}]
True
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True
See also
--------
:class:`GridSearchCV`:
Uses :class:`ParameterGrid` to perform a full parallelized parameter
search.
"""
def __init__(self, param_grid):
if isinstance(param_grid, Mapping):
# wrap dictionary in a singleton list to support either dict
# or list of dicts
param_grid = [param_grid]
self.param_grid = param_grid
def __iter__(self):
"""Iterate over the points in the grid.
Returns
-------
params : iterator over dict of string to any
Yields dictionaries mapping each estimator parameter to one of its
allowed values.
"""
for p in self.param_grid:
# Always sort the keys of a dictionary, for reproducibility
items = sorted(p.items())
if not items:
yield {}
else:
keys, values = zip(*items)
for v in product(*values):
params = dict(zip(keys, v))
yield params
def __len__(self):
"""Number of points on the grid."""
# Product function that can handle iterables (np.product can't).
product = partial(reduce, operator.mul)
return sum(product(len(v) for v in p.values()) if p else 1
for p in self.param_grid)
def __getitem__(self, ind):
"""Get the parameters that would be ``ind``th in iteration
Parameters
----------
ind : int
The iteration index
Returns
-------
params : dict of string to any
Equal to list(self)[ind]
"""
# This is used to make discrete sampling without replacement memory
# efficient.
for sub_grid in self.param_grid:
# XXX: could memoize information used here
if not sub_grid:
if ind == 0:
return {}
else:
ind -= 1
continue
# Reverse so most frequent cycling parameter comes first
keys, values_lists = zip(*sorted(sub_grid.items())[::-1])
sizes = [len(v_list) for v_list in values_lists]
total = np.product(sizes)
if ind >= total:
# Try the next grid
ind -= total
else:
out = {}
for key, v_list, n in zip(keys, values_lists, sizes):
ind, offset = divmod(ind, n)
out[key] = v_list[offset]
return out
raise IndexError('ParameterGrid index out of range')
class ParameterSampler(object):
"""Generator on parameters sampled from given distributions.
Non-deterministic iterable over random candidate combinations for hyper-
parameter search. If all parameters are presented as a list,
sampling without replacement is performed. If at least one parameter
is given as a distribution, sampling with replacement is used.
It is highly recommended to use continuous distributions for continuous
parameters.
Note that before SciPy 0.16, the ``scipy.stats.distributions`` do not
accept a custom RNG instance and always use the singleton RNG from
``numpy.random``. Hence setting ``random_state`` will not guarantee a
deterministic iteration whenever ``scipy.stats`` distributions are used to
define the parameter search space. Deterministic behavior is however
guaranteed from SciPy 0.16 onwards.
Read more in the :ref:`User Guide <search>`.
Parameters
----------
param_distributions : dict
Dictionary where the keys are parameters and values
are distributions from which a parameter is to be sampled.
Distributions either have to provide a ``rvs`` function
to sample from them, or can be given as a list of values,
where a uniform distribution is assumed.
n_iter : integer
Number of parameter settings that are produced.
random_state : int, RandomState instance or None, optional (default=None)
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions.
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
params : dict of string to any
**Yields** dictionaries mapping each estimator parameter to
as sampled value.
Examples
--------
>>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> np.random.seed(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4))
>>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
... for d in param_list]
>>> rounded_list == [{'b': 0.89856, 'a': 1},
... {'b': 0.923223, 'a': 1},
... {'b': 1.878964, 'a': 2},
... {'b': 1.038159, 'a': 2}]
True
"""
def __init__(self, param_distributions, n_iter, random_state=None):
self.param_distributions = param_distributions
self.n_iter = n_iter
self.random_state = random_state
def __iter__(self):
# check if all distributions are given as lists
# in this case we want to sample without replacement
all_lists = np.all([not hasattr(v, "rvs")
for v in self.param_distributions.values()])
rnd = check_random_state(self.random_state)
if all_lists:
# look up sampled parameter settings in parameter grid
param_grid = ParameterGrid(self.param_distributions)
grid_size = len(param_grid)
if grid_size < self.n_iter:
raise ValueError(
"The total space of parameters %d is smaller "
"than n_iter=%d. For exhaustive searches, use "
"GridSearchCV." % (grid_size, self.n_iter))
for i in sample_without_replacement(grid_size, self.n_iter,
random_state=rnd):
yield param_grid[i]
else:
# Always sort the keys of a dictionary, for reproducibility
items = sorted(self.param_distributions.items())
for _ in six.moves.range(self.n_iter):
params = dict()
for k, v in items:
if hasattr(v, "rvs"):
if sp_version < (0, 16):
params[k] = v.rvs()
else:
params[k] = v.rvs(random_state=rnd)
else:
params[k] = v[rnd.randint(len(v))]
yield params
def __len__(self):
"""Number of points that will be sampled."""
return self.n_iter
def fit_grid_point(X, y, estimator, parameters, train, test, scorer,
verbose, error_score='raise', **fit_params):
"""Run fit on one set of parameters.
Parameters
----------
X : array-like, sparse matrix or list
Input data.
y : array-like or None
Targets for input data.
estimator : estimator object
A object of that type is instantiated for each grid point.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
parameters : dict
Parameters to be set on estimator for this grid point.
train : ndarray, dtype int or bool
Boolean mask or indices for training set.
test : ndarray, dtype int or bool
Boolean mask or indices for test set.
scorer : callable or None
The scorer callable object / function must have its signature as
``scorer(estimator, X, y)``.
If ``None`` the estimator's default scorer is used.
verbose : int
Verbosity level.
**fit_params : kwargs
Additional parameter passed to the fit function of the estimator.
error_score : 'raise' (default) or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.
Returns
-------
score : float
Score of this parameter setting on given training / test split.
parameters : dict
The parameters that have been evaluated.
n_samples_test : int
Number of test samples in this split.
"""
# NOTE we are not using the return value as the scorer by itself should be
# validated before. We use check_scoring only to reject multimetric scorer
check_scoring(estimator, scorer)
scores, n_samples_test = _fit_and_score(estimator, X, y,
scorer, train,
test, verbose, parameters,
fit_params=fit_params,
return_n_test_samples=True,
error_score=error_score)
return scores, parameters, n_samples_test
def _check_param_grid(param_grid):
if hasattr(param_grid, 'items'):
param_grid = [param_grid]
for p in param_grid:
for name, v in p.items():
if isinstance(v, np.ndarray) and v.ndim > 1:
raise ValueError("Parameter array should be one-dimensional.")
if (isinstance(v, six.string_types) or
not isinstance(v, (np.ndarray, Sequence))):
raise ValueError("Parameter values for parameter ({0}) need "
"to be a sequence(but not a string) or"
" np.ndarray.".format(name))
if len(v) == 0:
raise ValueError("Parameter values for parameter ({0}) need "
"to be a non-empty sequence.".format(name))
# XXX Remove in 0.20
class _CVScoreTuple (namedtuple('_CVScoreTuple',
('parameters',
'mean_validation_score',
'cv_validation_scores'))):
# A raw namedtuple is very memory efficient as it packs the attributes
# in a struct to get rid of the __dict__ of attributes in particular it
# does not copy the string for the keys on each instance.
# By deriving a namedtuple class just to introduce the __repr__ method we
# would also reintroduce the __dict__ on the instance. By telling the
# Python interpreter that this subclass uses static __slots__ instead of
# dynamic attributes. Furthermore we don't need any additional slot in the
# subclass so we set __slots__ to the empty tuple.
__slots__ = ()
def __repr__(self):
"""Simple custom repr to summarize the main info"""
return "mean: {0:.5f}, std: {1:.5f}, params: {2}".format(
self.mean_validation_score,
np.std(self.cv_validation_scores),
self.parameters)
class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
MetaEstimatorMixin)):
"""Base class for hyper parameter search with cross-validation."""
@abstractmethod
def __init__(self, estimator, scoring=None,
fit_params=None, n_jobs=1, iid=True,
refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs',
error_score='raise', return_train_score=True):
self.scoring = scoring
self.estimator = estimator
self.n_jobs = n_jobs
self.fit_params = fit_params
self.iid = iid
self.refit = refit
self.cv = cv
self.verbose = verbose
self.pre_dispatch = pre_dispatch
self.error_score = error_score
self.return_train_score = return_train_score
@property
def _estimator_type(self):
return self.estimator._estimator_type
def score(self, X, y=None):
"""Returns the score on the given data, if the estimator has been refit.
This uses the score defined by ``scoring`` where provided, and the
``best_estimator_.score`` method otherwise.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression;
None for unsupervised learning.
Returns
-------
score : float
"""
self._check_is_fitted('score')
if self.scorer_ is None:
raise ValueError("No score function explicitly defined, "
"and the estimator doesn't provide one %s"
% self.best_estimator_)
score = self.scorer_[self.refit] if self.multimetric_ else self.scorer_
return score(self.best_estimator_, X, y)
def _check_is_fitted(self, method_name):
if not self.refit:
raise NotFittedError('This %s instance was initialized '
'with refit=False. %s is '
'available only after refitting on the best '
'parameters. You can refit an estimator '
'manually using the ``best_parameters_`` '
'attribute'
% (type(self).__name__, method_name))
else:
check_is_fitted(self, 'best_estimator_')
@if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict(self, X):
"""Call predict on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``predict``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
self._check_is_fitted('predict')
return self.best_estimator_.predict(X)
@if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict_proba(self, X):
"""Call predict_proba on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``predict_proba``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
self._check_is_fitted('predict_proba')
return self.best_estimator_.predict_proba(X)
@if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def predict_log_proba(self, X):
"""Call predict_log_proba on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``predict_log_proba``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
self._check_is_fitted('predict_log_proba')
return self.best_estimator_.predict_log_proba(X)
@if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def decision_function(self, X):
"""Call decision_function on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``decision_function``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
self._check_is_fitted('decision_function')
return self.best_estimator_.decision_function(X)
@if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def transform(self, X):
"""Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports ``transform`` and
``refit=True``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
self._check_is_fitted('transform')
return self.best_estimator_.transform(X)
@if_delegate_has_method(delegate=('best_estimator_', 'estimator'))
def inverse_transform(self, Xt):
"""Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements
``inverse_transform`` and ``refit=True``.
Parameters
-----------
Xt : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
self._check_is_fitted('inverse_transform')
return self.best_estimator_.inverse_transform(Xt)
@property
def classes_(self):
self._check_is_fitted("classes_")
return self.best_estimator_.classes_
def fit(self, X, y=None, groups=None, **fit_params):
"""Run fit with all sets of parameters.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression;
None for unsupervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set.
**fit_params : dict of string -> object
Parameters passed to the ``fit`` method of the estimator
"""
if self.fit_params is not None:
warnings.warn('"fit_params" as a constructor argument was '
'deprecated in version 0.19 and will be removed '
'in version 0.21. Pass fit parameters to the '
'"fit" method instead.', DeprecationWarning)
if fit_params:
warnings.warn('Ignoring fit_params passed as a constructor '
'argument in favor of keyword arguments to '
'the "fit" method.', RuntimeWarning)
else:
fit_params = self.fit_params
estimator = self.estimator
cv = check_cv(self.cv, y, classifier=is_classifier(estimator))
scorers, self.multimetric_ = _check_multimetric_scoring(
self.estimator, scoring=self.scoring)
if self.multimetric_:
if self.refit is not False and (
not isinstance(self.refit, six.string_types) or
# This will work for both dict / list (tuple)
self.refit not in scorers):
raise ValueError("For multi-metric scoring, the parameter "
"refit must be set to a scorer key "
"to refit an estimator with the best "
"parameter setting on the whole data and "
"make the best_* attributes "
"available for that metric. If this is not "
"needed, refit should be set to False "
"explicitly. %r was passed." % self.refit)
else:
refit_metric = self.refit
else:
refit_metric = 'score'
X, y, groups = indexable(X, y, groups)
n_splits = cv.get_n_splits(X, y, groups)
# Regenerate parameter iterable for each fit
candidate_params = list(self._get_param_iterator())
n_candidates = len(candidate_params)
if self.verbose > 0:
print("Fitting {0} folds for each of {1} candidates, totalling"
" {2} fits".format(n_splits, n_candidates,
n_candidates * n_splits))
base_estimator = clone(self.estimator)
pre_dispatch = self.pre_dispatch
out = Parallel(
n_jobs=self.n_jobs, verbose=self.verbose,
pre_dispatch=pre_dispatch
)(delayed(_fit_and_score)(clone(base_estimator), X, y, scorers, train,
test, self.verbose, parameters,
fit_params=fit_params,
return_train_score=self.return_train_score,
return_n_test_samples=True,
return_times=True, return_parameters=False,
error_score=self.error_score)
for parameters, (train, test) in product(candidate_params,
cv.split(X, y, groups)))
# if one choose to see train score, "out" will contain train score info
if self.return_train_score:
(train_score_dicts, test_score_dicts, test_sample_counts, fit_time,
score_time) = zip(*out)
else:
(test_score_dicts, test_sample_counts, fit_time,
score_time) = zip(*out)
# test_score_dicts and train_score dicts are lists of dictionaries and
# we make them into dict of lists
test_scores = _aggregate_score_dicts(test_score_dicts)
if self.return_train_score:
train_scores = _aggregate_score_dicts(train_score_dicts)
results = dict()
def _store(key_name, array, weights=None, splits=False, rank=False):
"""A small helper to store the scores/times to the cv_results_"""
# When iterated first by splits, then by parameters
# We want `array` to have `n_candidates` rows and `n_splits` cols.
array = np.array(array, dtype=np.float64).reshape(n_candidates,
n_splits)
if splits:
for split_i in range(n_splits):
# Uses closure to alter the results
results["split%d_%s"
% (split_i, key_name)] = array[:, split_i]
array_means = np.average(array, axis=1, weights=weights)
results['mean_%s' % key_name] = array_means
# Weighted std is not directly available in numpy
array_stds = np.sqrt(np.average((array -
array_means[:, np.newaxis]) ** 2,
axis=1, weights=weights))
results['std_%s' % key_name] = array_stds
if rank:
results["rank_%s" % key_name] = np.asarray(
rankdata(-array_means, method='min'), dtype=np.int32)
_store('fit_time', fit_time)
_store('score_time', score_time)
# Use one MaskedArray and mask all the places where the param is not
# applicable for that candidate. Use defaultdict as each candidate may
# not contain all the params
param_results = defaultdict(partial(MaskedArray,
np.empty(n_candidates,),
mask=True,
dtype=object))
for cand_i, params in enumerate(candidate_params):
for name, value in params.items():
# An all masked empty array gets created for the key
# `"param_%s" % name` at the first occurence of `name`.
# Setting the value at an index also unmasks that index
param_results["param_%s" % name][cand_i] = value
results.update(param_results)
# Store a list of param dicts at the key 'params'
results['params'] = candidate_params
# NOTE test_sample counts (weights) remain the same for all candidates
test_sample_counts = np.array(test_sample_counts[:n_splits],
dtype=np.int)
for scorer_name in scorers.keys():
# Computed the (weighted) mean and std for test scores alone
_store('test_%s' % scorer_name, test_scores[scorer_name],
splits=True, rank=True,
weights=test_sample_counts if self.iid else None)
if self.return_train_score:
_store('train_%s' % scorer_name, train_scores[scorer_name],
splits=True)
# For multi-metric evaluation, store the best_index_, best_params_ and
# best_score_ iff refit is one of the scorer names
# In single metric evaluation, refit_metric is "score"
if self.refit or not self.multimetric_:
self.best_index_ = results["rank_test_%s" % refit_metric].argmin()
self.best_params_ = candidate_params[self.best_index_]
self.best_score_ = results["mean_test_%s" % refit_metric][
self.best_index_]
if self.refit:
self.best_estimator_ = clone(base_estimator).set_params(
**self.best_params_)
if y is not None:
self.best_estimator_.fit(X, y, **fit_params)
else:
self.best_estimator_.fit(X, **fit_params)
# Store the only scorer not as a dict for single metric evaluation
self.scorer_ = scorers if self.multimetric_ else scorers['score']
self.cv_results_ = results
self.n_splits_ = n_splits
return self
@property
def grid_scores_(self):
check_is_fitted(self, 'cv_results_')
if self.multimetric_:
raise AttributeError("grid_scores_ attribute is not available for"
" multi-metric evaluation.")
warnings.warn(
"The grid_scores_ attribute was deprecated in version 0.18"
" in favor of the more elaborate cv_results_ attribute."
" The grid_scores_ attribute will not be available from 0.20",
DeprecationWarning)
grid_scores = list()
for i, (params, mean, std) in enumerate(zip(
self.cv_results_['params'],
self.cv_results_['mean_test_score'],
self.cv_results_['std_test_score'])):
scores = np.array(list(self.cv_results_['split%d_test_score'
% s][i]
for s in range(self.n_splits_)),
dtype=np.float64)
grid_scores.append(_CVScoreTuple(params, mean, scores))
return grid_scores
class GridSearchCV(BaseSearchCV):
"""Exhaustive search over specified parameter values for an estimator.
Important members are fit, predict.
GridSearchCV implements a "fit" and a "score" method.
It also implements "predict", "predict_proba", "decision_function",
"transform" and "inverse_transform" if they are implemented in the
estimator used.
The parameters of the estimator used to apply these methods are optimized
by cross-validated grid-search over a parameter grid.
Read more in the :ref:`User Guide <grid_search>`.
Parameters
----------
estimator : estimator object.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of
parameter settings to try as values, or a list of such
dictionaries, in which case the grids spanned by each dictionary
in the list are explored. This enables searching over any sequence
of parameter settings.
scoring : string, callable, list/tuple, dict or None, default: None
A single string (see :ref:`scoring_parameter`) or a callable
(see :ref:`scoring`) to evaluate the predictions on the test set.
For evaluating multiple metrics, either give a list of (unique) strings
or a dict with names as keys and callables as values.
NOTE that when using custom scorers, each scorer should return a single
value. Metric functions returning a list/array of values can be wrapped
into multiple scorers that return one value each.
See :ref:`multimetric_grid_search` for an example.
If None, the estimator's default scorer (if available) is used.
fit_params : dict, optional
Parameters to pass to the fit method.
.. deprecated:: 0.19
``fit_params`` as a constructor argument was deprecated in version
0.19 and will be removed in version 0.21. Pass fit parameters to
the ``fit`` method instead.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
iid : boolean, default=True
If True, the data is assumed to be identically distributed across
the folds, and the loss minimized is the total loss per sample,
and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
refit : boolean, or string, default=True
Refit an estimator using the best found parameters on the whole
dataset.
For multiple metric evaluation, this needs to be a string denoting the
scorer is used to find the best parameters for refitting the estimator
at the end.
The refitted estimator is made available at the ``best_estimator_``
attribute and permits using ``predict`` directly on this
``GridSearchCV`` instance.
Also for multiple metric evaluation, the attributes ``best_index_``,
``best_score_`` and ``best_parameters_`` will only be available if
``refit`` is set and all of them will be determined w.r.t this specific
scorer.
See ``scoring`` parameter to know more about multiple metric
evaluation.
verbose : integer
Controls the verbosity: the higher, the more messages.
error_score : 'raise' (default) or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.
return_train_score : boolean, default=True
If ``'False'``, the ``cv_results_`` attribute will not include training
scores.
Examples
--------
>>> from sklearn import svm, datasets
>>> from sklearn.model_selection import GridSearchCV
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svc = svm.SVC()
>>> clf = GridSearchCV(svc, parameters)
>>> clf.fit(iris.data, iris.target)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
GridSearchCV(cv=None, error_score=...,
estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
decision_function_shape='ovr', degree=..., gamma=...,
kernel='rbf', max_iter=-1, probability=False,
random_state=None, shrinking=True, tol=...,
verbose=False),
fit_params=None, iid=..., n_jobs=1,
param_grid=..., pre_dispatch=..., refit=..., return_train_score=...,
scoring=..., verbose=...)
>>> sorted(clf.cv_results_.keys())
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
'mean_train_score', 'param_C', 'param_kernel', 'params',...
'rank_test_score', 'split0_test_score',...
'split0_train_score', 'split1_test_score', 'split1_train_score',...
'split2_test_score', 'split2_train_score',...
'std_fit_time', 'std_score_time', 'std_test_score', 'std_train_score'...]
Attributes
----------
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.
For instance the below given table
+------------+-----------+------------+-----------------+---+---------+
|param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|
+============+===========+============+=================+===+=========+
| 'poly' | -- | 2 | 0.8 |...| 2 |
+------------+-----------+------------+-----------------+---+---------+
| 'poly' | -- | 3 | 0.7 |...| 4 |
+------------+-----------+------------+-----------------+---+---------+
| 'rbf' | 0.1 | -- | 0.8 |...| 3 |
+------------+-----------+------------+-----------------+---+---------+
| 'rbf' | 0.2 | -- | 0.9 |...| 1 |
+------------+-----------+------------+-----------------+---+---------+
will be represented by a ``cv_results_`` dict of::
{
'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
mask = [False False False False]...)
'param_gamma': masked_array(data = [-- -- 0.1 0.2],
mask = [ True True False False]...),
'param_degree': masked_array(data = [2.0 3.0 -- --],
mask = [False False True True]...),
'split0_test_score' : [0.8, 0.7, 0.8, 0.9],
'split1_test_score' : [0.82, 0.5, 0.7, 0.78],
'mean_test_score' : [0.81, 0.60, 0.75, 0.82],
'std_test_score' : [0.02, 0.01, 0.03, 0.03],
'rank_test_score' : [2, 4, 3, 1],
'split0_train_score' : [0.8, 0.9, 0.7],
'split1_train_score' : [0.82, 0.5, 0.7],
'mean_train_score' : [0.81, 0.7, 0.7],
'std_train_score' : [0.03, 0.03, 0.04],
'mean_fit_time' : [0.73, 0.63, 0.43, 0.49],
'std_fit_time' : [0.01, 0.02, 0.01, 0.01],
'mean_score_time' : [0.007, 0.06, 0.04, 0.04],
'std_score_time' : [0.001, 0.002, 0.003, 0.005],
'params' : [{'kernel': 'poly', 'degree': 2}, ...],
}
NOTE
The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.
The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.
For multi-metric evaluation, the scores for all the scorers are
available in the ``cv_results_`` dict at the keys ending with that
scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
above. ('split0_test_precision', 'mean_train_precision' etc.)
best_estimator_ : estimator or dict
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if ``refit=False``.
See ``refit`` parameter for more information on allowed values.
best_score_ : float
Mean cross-validated score of the best_estimator
For multi-metric evaluation, this is present only if ``refit`` is
specified.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
For multi-metric evaluation, this is present only if ``refit`` is
specified.
best_index_ : int
The index (of the ``cv_results_`` arrays) which corresponds to the best
candidate parameter setting.
The dict at ``search.cv_results_['params'][search.best_index_]`` gives
the parameter setting for the best model, that gives the highest
mean score (``search.best_score_``).
For multi-metric evaluation, this is present only if ``refit`` is
specified.
scorer_ : function or a dict
Scorer function used on the held out data to choose the best
parameters for the model.
For multi-metric evaluation, this attribute holds the validated
``scoring`` dict which maps the scorer key to the scorer callable.
n_splits_ : int
The number of cross-validation splits (folds/iterations).
Notes
------
The parameters selected are those that maximize the score of the left out
data, unless an explicit score is passed in which case it is used instead.
If `n_jobs` was set to a value higher than one, the data is copied for each
point in the grid (and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
---------
:class:`ParameterGrid`:
generates all the combinations of a hyperparameter grid.
:func:`sklearn.model_selection.train_test_split`:
utility function to split the data into a development set usable
for fitting a GridSearchCV instance and an evaluation set for
its final evaluation.
:func:`sklearn.metrics.make_scorer`:
Make a scorer from a performance metric or loss function.
"""
def __init__(self, estimator, param_grid, scoring=None, fit_params=None,
n_jobs=1, iid=True, refit=True, cv=None, verbose=0,
pre_dispatch='2*n_jobs', error_score='raise',
return_train_score=True):
super(GridSearchCV, self).__init__(
estimator=estimator, scoring=scoring, fit_params=fit_params,
n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
pre_dispatch=pre_dispatch, error_score=error_score,
return_train_score=return_train_score)
self.param_grid = param_grid
_check_param_grid(param_grid)
def _get_param_iterator(self):
"""Return ParameterGrid instance for the given param_grid"""
return ParameterGrid(self.param_grid)
class RandomizedSearchCV(BaseSearchCV):
"""Randomized search on hyper parameters.
RandomizedSearchCV implements a "fit" and a "score" method.
It also implements "predict", "predict_proba", "decision_function",
"transform" and "inverse_transform" if they are implemented in the
estimator used.
The parameters of the estimator used to apply these methods are optimized
by cross-validated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but
rather a fixed number of parameter settings is sampled from the specified
distributions. The number of parameter settings that are tried is
given by n_iter.
If all parameters are presented as a list,
sampling without replacement is performed. If at least one parameter
is given as a distribution, sampling with replacement is used.
It is highly recommended to use continuous distributions for continuous
parameters.
Read more in the :ref:`User Guide <randomized_parameter_search>`.
Parameters
----------
estimator : estimator object.
A object of that type is instantiated for each grid point.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_distributions : dict
Dictionary with parameters names (string) as keys and distributions
or lists of parameters to try. Distributions must provide a ``rvs``
method for sampling (such as those from scipy.stats.distributions).
If a list is given, it is sampled uniformly.
n_iter : int, default=10
Number of parameter settings that are sampled. n_iter trades
off runtime vs quality of the solution.
scoring : string, callable, list/tuple, dict or None, default: None
A single string (see :ref:`scoring_parameter`) or a callable
(see :ref:`scoring`) to evaluate the predictions on the test set.
For evaluating multiple metrics, either give a list of (unique) strings
or a dict with names as keys and callables as values.
NOTE that when using custom scorers, each scorer should return a single
value. Metric functions returning a list/array of values can be wrapped
into multiple scorers that return one value each.
See :ref:`multimetric_grid_search` for an example.
If None, the estimator's default scorer (if available) is used.
fit_params : dict, optional
Parameters to pass to the fit method.
.. deprecated:: 0.19
``fit_params`` as a constructor argument was deprecated in version
0.19 and will be removed in version 0.21. Pass fit parameters to
the ``fit`` method instead.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
iid : boolean, default=True
If True, the data is assumed to be identically distributed across
the folds, and the loss minimized is the total loss per sample,
and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
refit : boolean, or string default=True
Refit an estimator using the best found parameters on the whole
dataset.
For multiple metric evaluation, this needs to be a string denoting the
scorer that would be used to find the best parameters for refitting
the estimator at the end.
The refitted estimator is made available at the ``best_estimator_``
attribute and permits using ``predict`` directly on this
``RandomizedSearchCV`` instance.
Also for multiple metric evaluation, the attributes ``best_index_``,
``best_score_`` and ``best_parameters_`` will only be available if
``refit`` is set and all of them will be determined w.r.t this specific
scorer.
See ``scoring`` parameter to know more about multiple metric
evaluation.
verbose : integer
Controls the verbosity: the higher, the more messages.
random_state : int, RandomState instance or None, optional, default=None
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions.
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
error_score : 'raise' (default) or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.
return_train_score : boolean, default=True
If ``'False'``, the ``cv_results_`` attribute will not include training
scores.
Attributes
----------
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.
For instance the below given table
+--------------+-------------+-------------------+---+---------------+
| param_kernel | param_gamma | split0_test_score |...|rank_test_score|
+==============+=============+===================+===+===============+
| 'rbf' | 0.1 | 0.8 |...| 2 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.2 | 0.9 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
| 'rbf' | 0.3 | 0.7 |...| 1 |
+--------------+-------------+-------------------+---+---------------+
will be represented by a ``cv_results_`` dict of::
{
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
mask = False),
'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score' : [0.8, 0.9, 0.7],
'split1_test_score' : [0.82, 0.5, 0.7],
'mean_test_score' : [0.81, 0.7, 0.7],
'std_test_score' : [0.02, 0.2, 0.],
'rank_test_score' : [3, 1, 1],
'split0_train_score' : [0.8, 0.9, 0.7],
'split1_train_score' : [0.82, 0.5, 0.7],
'mean_train_score' : [0.81, 0.7, 0.7],
'std_train_score' : [0.03, 0.03, 0.04],
'mean_fit_time' : [0.73, 0.63, 0.43, 0.49],
'std_fit_time' : [0.01, 0.02, 0.01, 0.01],
'mean_score_time' : [0.007, 0.06, 0.04, 0.04],
'std_score_time' : [0.001, 0.002, 0.003, 0.005],
'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}
NOTE
The key ``'params'`` is used to store a list of parameter
settings dicts for all the parameter candidates.
The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
``std_score_time`` are all in seconds.
For multi-metric evaluation, the scores for all the scorers are
available in the ``cv_results_`` dict at the keys ending with that
scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
above. ('split0_test_precision', 'mean_train_precision' etc.)
best_estimator_ : estimator or dict
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if ``refit=False``.
For multi-metric evaluation, this attribute is present only if
``refit`` is specified.
See ``refit`` parameter for more information on allowed values.
best_score_ : float
Mean cross-validated score of the best_estimator.
For multi-metric evaluation, this is not available if ``refit`` is
``False``. See ``refit`` parameter for more information.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
For multi-metric evaluation, this is not available if ``refit`` is
``False``. See ``refit`` parameter for more information.
best_index_ : int
The index (of the ``cv_results_`` arrays) which corresponds to the best
candidate parameter setting.
The dict at ``search.cv_results_['params'][search.best_index_]`` gives
the parameter setting for the best model, that gives the highest
mean score (``search.best_score_``).
For multi-metric evaluation, this is not available if ``refit`` is
``False``. See ``refit`` parameter for more information.
scorer_ : function or a dict
Scorer function used on the held out data to choose the best
parameters for the model.
For multi-metric evaluation, this attribute holds the validated
``scoring`` dict which maps the scorer key to the scorer callable.
n_splits_ : int
The number of cross-validation splits (folds/iterations).
Notes
-----
The parameters selected are those that maximize the score of the held-out
data, according to the scoring parameter.
If `n_jobs` was set to a value higher than one, the data is copied for each
parameter setting(and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
--------
:class:`GridSearchCV`:
Does exhaustive search over a grid of parameters.
:class:`ParameterSampler`:
A generator over parameter settins, constructed from
param_distributions.
"""
def __init__(self, estimator, param_distributions, n_iter=10, scoring=None,
fit_params=None, n_jobs=1, iid=True, refit=True, cv=None,
verbose=0, pre_dispatch='2*n_jobs', random_state=None,
error_score='raise', return_train_score=True):
self.param_distributions = param_distributions
self.n_iter = n_iter
self.random_state = random_state
super(RandomizedSearchCV, self).__init__(
estimator=estimator, scoring=scoring, fit_params=fit_params,
n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
pre_dispatch=pre_dispatch, error_score=error_score,
return_train_score=return_train_score)
def _get_param_iterator(self):
"""Return ParameterSampler instance for the given distributions"""
return ParameterSampler(
self.param_distributions, self.n_iter,
random_state=self.random_state)
| bsd-3-clause |
ahoyosid/scikit-learn | sklearn/utils/validation.py | 66 | 23629 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals import six
from inspect import getargspec
FLOAT_DTYPES = (np.float64, np.float32, np.float16)
class DataConversionWarning(UserWarning):
"""A warning on implicit data conversions happening in the code"""
pass
warnings.simplefilter("always", DataConversionWarning)
class NonBLASDotWarning(UserWarning):
"""A warning on implicit dispatch to numpy.dot"""
class NotFittedError(ValueError, AttributeError):
"""Exception class to raise if estimator is used before fitting
This class inherits from both ValueError and AttributeError to help with
exception handling and backward compatibility.
"""
# Silenced by default to reduce verbosity. Turn on at runtime for
# performance profiling.
warnings.simplefilter('ignore', NonBLASDotWarning)
def _assert_all_finite(X):
"""Like assert_all_finite, but only for ndarray."""
X = np.asanyarray(X)
# First try an O(n) time, O(1) space solution for the common case that
# everything is finite; fall back to O(n) space np.isfinite to prevent
# false positives from overflow in sum method.
if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
and not np.isfinite(X).all()):
raise ValueError("Input contains NaN, infinity"
" or a value too large for %r." % X.dtype)
def assert_all_finite(X):
"""Throw a ValueError if X contains NaN or infinity.
Input MUST be an np.ndarray instance or a scipy.sparse matrix."""
_assert_all_finite(X.data if sp.issparse(X) else X)
def as_float_array(X, copy=True, force_all_finite=True):
"""Converts an array-like to an array of floats
The new dtype will be np.float32 or np.float64, depending on the original
type. The function can create a copy or modify the argument depending
on the argument copy.
Parameters
----------
X : {array-like, sparse matrix}
copy : bool, optional
If True, a copy of X will be created. If False, a copy may still be
returned if X's dtype is not a floating point type.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
Returns
-------
XT : {array, sparse matrix}
An array of type np.float
"""
if isinstance(X, np.matrix) or (not isinstance(X, np.ndarray)
and not sp.issparse(X)):
return check_array(X, ['csr', 'csc', 'coo'], dtype=np.float64,
copy=copy, force_all_finite=force_all_finite,
ensure_2d=False)
elif sp.issparse(X) and X.dtype in [np.float32, np.float64]:
return X.copy() if copy else X
elif X.dtype in [np.float32, np.float64]: # is numpy array
return X.copy('F' if X.flags['F_CONTIGUOUS'] else 'C') if copy else X
else:
return X.astype(np.float32 if X.dtype == np.int32 else np.float64)
def _is_arraylike(x):
"""Returns whether the input is array-like"""
return (hasattr(x, '__len__') or
hasattr(x, 'shape') or
hasattr(x, '__array__'))
def _num_samples(x):
"""Return number of samples in array-like x."""
if hasattr(x, 'fit'):
# Don't get num_samples from an ensembles length!
raise TypeError('Expected sequence or array-like, got '
'estimator %s' % x)
if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
if hasattr(x, '__array__'):
x = np.asarray(x)
else:
raise TypeError("Expected sequence or array-like, got %s" %
type(x))
if hasattr(x, 'shape'):
if len(x.shape) == 0:
raise TypeError("Singleton array %r cannot be considered"
" a valid collection." % x)
return x.shape[0]
else:
return len(x)
def _shape_repr(shape):
"""Return a platform independent reprensentation of an array shape
Under Python 2, the `long` type introduces an 'L' suffix when using the
default %r format for tuples of integers (typically used to store the shape
of an array).
Under Windows 64 bit (and Python 2), the `long` type is used by default
in numpy shapes even when the integer dimensions are well below 32 bit.
The platform specific type causes string messages or doctests to change
from one platform to another which is not desirable.
Under Python 3, there is no more `long` type so the `L` suffix is never
introduced in string representation.
>>> _shape_repr((1, 2))
'(1, 2)'
>>> one = 2 ** 64 / 2 ** 64 # force an upcast to `long` under Python 2
>>> _shape_repr((one, 2 * one))
'(1, 2)'
>>> _shape_repr((1,))
'(1,)'
>>> _shape_repr(())
'()'
"""
if len(shape) == 0:
return "()"
joined = ", ".join("%d" % e for e in shape)
if len(shape) == 1:
# special notation for singleton tuples
joined += ','
return "(%s)" % joined
def check_consistent_length(*arrays):
"""Check that all arrays have consistent first dimensions.
Checks whether all objects in arrays have the same shape or length.
Parameters
----------
*arrays : list or tuple of input objects.
Objects that will be checked for consistent length.
"""
uniques = np.unique([_num_samples(X) for X in arrays if X is not None])
if len(uniques) > 1:
raise ValueError("Found arrays with inconsistent numbers of samples: "
"%s" % str(uniques))
def indexable(*iterables):
"""Make arrays indexable for cross-validation.
Checks consistent length, passes through None, and ensures that everything
can be indexed by converting sparse matrices to csr and converting
non-interable objects to arrays.
Parameters
----------
*iterables : lists, dataframes, arrays, sparse matrices
List of objects to ensure sliceability.
"""
result = []
for X in iterables:
if sp.issparse(X):
result.append(X.tocsr())
elif hasattr(X, "__getitem__") or hasattr(X, "iloc"):
result.append(X)
elif X is None:
result.append(X)
else:
result.append(np.array(X))
check_consistent_length(*result)
return result
def _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy,
force_all_finite):
"""Convert a sparse matrix to a given format.
Checks the sparse format of spmatrix and converts if necessary.
Parameters
----------
spmatrix : scipy sparse matrix
Input to validate and convert.
accept_sparse : string, list of string or None (default=None)
String[s] representing allowed sparse matrix formats ('csc',
'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). None means that sparse
matrix input will raise an error. If the input is sparse but not in
the allowed format, it will be converted to the first listed format.
dtype : string, type or None (default=none)
Data type of result. If None, the dtype of the input is preserved.
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
Returns
-------
spmatrix_converted : scipy sparse matrix.
Matrix that is ensured to have an allowed type.
"""
if accept_sparse in [None, False]:
raise TypeError('A sparse matrix was passed, but dense '
'data is required. Use X.toarray() to '
'convert to a dense numpy array.')
if dtype is None:
dtype = spmatrix.dtype
changed_format = False
if (isinstance(accept_sparse, (list, tuple))
and spmatrix.format not in accept_sparse):
# create new with correct sparse
spmatrix = spmatrix.asformat(accept_sparse[0])
changed_format = True
if dtype != spmatrix.dtype:
# convert dtype
spmatrix = spmatrix.astype(dtype)
elif copy and not changed_format:
# force copy
spmatrix = spmatrix.copy()
if force_all_finite:
if not hasattr(spmatrix, "data"):
warnings.warn("Can't check %s sparse matrix for nan or inf."
% spmatrix.format)
else:
_assert_all_finite(spmatrix.data)
return spmatrix
def check_array(array, accept_sparse=None, dtype="numeric", order=None,
copy=False, force_all_finite=True, ensure_2d=True,
allow_nd=False, ensure_min_samples=1, ensure_min_features=1,
warn_on_dtype=False, estimator=None):
"""Input validation on an array, list, sparse matrix or similar.
By default, the input is converted to an at least 2nd numpy array.
If the dtype of the array is object, attempt converting to float,
raising on failure.
Parameters
----------
array : object
Input object to check / convert.
accept_sparse : string, list of string or None (default=None)
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. None means that sparse matrix input will raise an error.
If the input is sparse but not in the allowed format, it will be
converted to the first listed format.
dtype : string, type, list of types or None (default="numeric")
Data type of result. If None, the dtype of the input is preserved.
If "numeric", dtype is preserved unless array.dtype is object.
If dtype is a list of types, conversion on the first type is only
performed if the dtype of the input is not in the list.
order : 'F', 'C' or None (default=None)
Whether an array will be forced to be fortran or c-style.
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
ensure_2d : boolean (default=True)
Whether to make X at least 2d.
allow_nd : boolean (default=False)
Whether to allow X.ndim > 2.
ensure_min_samples : int (default=1)
Make sure that the array has a minimum number of samples in its first
axis (rows for a 2D array). Setting to 0 disables this check.
ensure_min_features : int (default=1)
Make sure that the 2D array has some minimum number of features
(columns). The default value of 1 rejects empty datasets.
This check is only enforced when the input data has effectively 2
dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0
disables this check.
warn_on_dtype : boolean (default=False)
Raise DataConversionWarning if the dtype of the input data structure
does not match the requested dtype, causing a memory copy.
estimator : str or estimator instance (default=None)
If passed, include the name of the estimator in warning messages.
Returns
-------
X_converted : object
The converted and validated X.
"""
if isinstance(accept_sparse, str):
accept_sparse = [accept_sparse]
# store whether originally we wanted numeric dtype
dtype_numeric = dtype == "numeric"
dtype_orig = getattr(array, "dtype", None)
if not hasattr(dtype_orig, 'kind'):
# not a data type (e.g. a column named dtype in a pandas DataFrame)
dtype_orig = None
if dtype_numeric:
if dtype_orig is not None and dtype_orig.kind == "O":
# if input is object, convert to float.
dtype = np.float64
else:
dtype = None
if isinstance(dtype, (list, tuple)):
if dtype_orig is not None and dtype_orig in dtype:
# no dtype conversion required
dtype = None
else:
# dtype conversion required. Let's select the first element of the
# list of accepted types.
dtype = dtype[0]
if sp.issparse(array):
array = _ensure_sparse_format(array, accept_sparse, dtype, copy,
force_all_finite)
else:
if ensure_2d:
array = np.atleast_2d(array)
array = np.array(array, dtype=dtype, order=order, copy=copy)
# make sure we actually converted to numeric:
if dtype_numeric and array.dtype.kind == "O":
array = array.astype(np.float64)
if not allow_nd and array.ndim >= 3:
raise ValueError("Found array with dim %d. Expected <= 2" %
array.ndim)
if force_all_finite:
_assert_all_finite(array)
shape_repr = _shape_repr(array.shape)
if ensure_min_samples > 0:
n_samples = _num_samples(array)
if n_samples < ensure_min_samples:
raise ValueError("Found array with %d sample(s) (shape=%s) while a"
" minimum of %d is required."
% (n_samples, shape_repr, ensure_min_samples))
if ensure_min_features > 0 and array.ndim == 2:
n_features = array.shape[1]
if n_features < ensure_min_features:
raise ValueError("Found array with %d feature(s) (shape=%s) while"
" a minimum of %d is required."
% (n_features, shape_repr, ensure_min_features))
if warn_on_dtype and dtype_orig is not None and array.dtype != dtype_orig:
msg = ("Data with input dtype %s was converted to %s"
% (dtype_orig, array.dtype))
if estimator is not None:
if not isinstance(estimator, six.string_types):
estimator = estimator.__class__.__name__
msg += " by %s" % estimator
warnings.warn(msg, DataConversionWarning)
return array
def check_X_y(X, y, accept_sparse=None, dtype="numeric", order=None, copy=False,
force_all_finite=True, ensure_2d=True, allow_nd=False,
multi_output=False, ensure_min_samples=1,
ensure_min_features=1, y_numeric=False,
warn_on_dtype=False, estimator=None):
"""Input validation for standard estimators.
Checks X and y for consistent length, enforces X 2d and y 1d.
Standard input checks are only applied to y. For multi-label y,
set multi_output=True to allow 2d and sparse y.
If the dtype of X is object, attempt converting to float,
raising on failure.
Parameters
----------
X : nd-array, list or sparse matrix
Input data.
y : nd-array, list or sparse matrix
Labels.
accept_sparse : string, list of string or None (default=None)
String[s] representing allowed sparse matrix formats, such as 'csc',
'csr', etc. None means that sparse matrix input will raise an error.
If the input is sparse but not in the allowed format, it will be
converted to the first listed format.
dtype : string, type, list of types or None (default="numeric")
Data type of result. If None, the dtype of the input is preserved.
If "numeric", dtype is preserved unless array.dtype is object.
If dtype is a list of types, conversion on the first type is only
performed if the dtype of the input is not in the list.
order : 'F', 'C' or None (default=None)
Whether an array will be forced to be fortran or c-style.
copy : boolean (default=False)
Whether a forced copy will be triggered. If copy=False, a copy might
be triggered by a conversion.
force_all_finite : boolean (default=True)
Whether to raise an error on np.inf and np.nan in X.
ensure_2d : boolean (default=True)
Whether to make X at least 2d.
allow_nd : boolean (default=False)
Whether to allow X.ndim > 2.
multi_output : boolean (default=False)
Whether to allow 2-d y (array or sparse matrix). If false, y will be
validated as a vector.
ensure_min_samples : int (default=1)
Make sure that X has a minimum number of samples in its first
axis (rows for a 2D array).
ensure_min_features : int (default=1)
Make sure that the 2D array has some minimum number of features
(columns). The default value of 1 rejects empty datasets.
This check is only enforced when X has effectively 2 dimensions or
is originally 1D and ``ensure_2d`` is True. Setting to 0 disables
this check.
y_numeric : boolean (default=False)
Whether to ensure that y has a numeric type. If dtype of y is object,
it is converted to float64. Should only be used for regression
algorithms.
warn_on_dtype : boolean (default=False)
Raise DataConversionWarning if the dtype of the input data structure
does not match the requested dtype, causing a memory copy.
estimator : str or estimator instance (default=None)
If passed, include the name of the estimator in warning messages.
Returns
-------
X_converted : object
The converted and validated X.
"""
X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
ensure_2d, allow_nd, ensure_min_samples,
ensure_min_features, warn_on_dtype, estimator)
if multi_output:
y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
dtype=None)
else:
y = column_or_1d(y, warn=True)
_assert_all_finite(y)
if y_numeric and y.dtype.kind == 'O':
y = y.astype(np.float64)
check_consistent_length(X, y)
return X, y
def column_or_1d(y, warn=False):
""" Ravel column or 1d numpy array, else raises an error
Parameters
----------
y : array-like
warn : boolean, default False
To control display of warnings.
Returns
-------
y : array
"""
shape = np.shape(y)
if len(shape) == 1:
return np.ravel(y)
if len(shape) == 2 and shape[1] == 1:
if warn:
warnings.warn("A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples, ), for example using ravel().",
DataConversionWarning, stacklevel=2)
return np.ravel(y)
raise ValueError("bad input shape {0}".format(shape))
def check_random_state(seed):
"""Turn seed into a np.random.RandomState instance
If seed is None, return the RandomState singleton used by np.random.
If seed is an int, return a new RandomState instance seeded with seed.
If seed is already a RandomState instance, return it.
Otherwise raise ValueError.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.RandomState(seed)
if isinstance(seed, np.random.RandomState):
return seed
raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
' instance' % seed)
def has_fit_parameter(estimator, parameter):
"""Checks whether the estimator's fit method supports the given parameter.
Examples
--------
>>> from sklearn.svm import SVC
>>> has_fit_parameter(SVC(), "sample_weight")
True
"""
return parameter in getargspec(estimator.fit)[0]
def check_symmetric(array, tol=1E-10, raise_warning=True,
raise_exception=False):
"""Make sure that array is 2D, square and symmetric.
If the array is not symmetric, then a symmetrized version is returned.
Optionally, a warning or exception is raised if the matrix is not
symmetric.
Parameters
----------
array : nd-array or sparse matrix
Input object to check / convert. Must be two-dimensional and square,
otherwise a ValueError will be raised.
tol : float
Absolute tolerance for equivalence of arrays. Default = 1E-10.
raise_warning : boolean (default=True)
If True then raise a warning if conversion is required.
raise_exception : boolean (default=False)
If True then raise an exception if array is not symmetric.
Returns
-------
array_sym : ndarray or sparse matrix
Symmetrized version of the input array, i.e. the average of array
and array.transpose(). If sparse, then duplicate entries are first
summed and zeros are eliminated.
"""
if (array.ndim != 2) or (array.shape[0] != array.shape[1]):
raise ValueError("array must be 2-dimensional and square. "
"shape = {0}".format(array.shape))
if sp.issparse(array):
diff = array - array.T
# only csr, csc, and coo have `data` attribute
if diff.format not in ['csr', 'csc', 'coo']:
diff = diff.tocsr()
symmetric = np.all(abs(diff.data) < tol)
else:
symmetric = np.allclose(array, array.T, atol=tol)
if not symmetric:
if raise_exception:
raise ValueError("Array must be symmetric")
if raise_warning:
warnings.warn("Array is not symmetric, and will be converted "
"to symmetric by average with its transpose.")
if sp.issparse(array):
conversion = 'to' + array.format
array = getattr(0.5 * (array + array.T), conversion)()
else:
array = 0.5 * (array + array.T)
return array
def check_is_fitted(estimator, attributes, msg=None, all_or_any=all):
"""Perform is_fitted validation for estimator.
Checks if the estimator is fitted by verifying the presence of
"all_or_any" of the passed attributes and raises a NotFittedError with the
given message.
Parameters
----------
estimator : estimator instance.
estimator instance for which the check is performed.
attributes : attribute name(s) given as string or a list/tuple of strings
Eg. : ["coef_", "estimator_", ...], "coef_"
msg : string
The default error message is, "This %(name)s instance is not fitted
yet. Call 'fit' with appropriate arguments before using this method."
For custom messages if "%(name)s" is present in the message string,
it is substituted for the estimator name.
Eg. : "Estimator, %(name)s, must be fitted before sparsifying".
all_or_any : callable, {all, any}, default all
Specify whether all or any of the given attributes must exist.
"""
if msg is None:
msg = ("This %(name)s instance is not fitted yet. Call 'fit' with "
"appropriate arguments before using this method.")
if not hasattr(estimator, 'fit'):
raise TypeError("%s is not an estimator instance." % (estimator))
if not isinstance(attributes, (list, tuple)):
attributes = [attributes]
if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
raise NotFittedError(msg % {'name': type(estimator).__name__})
| bsd-3-clause |
luo66/scikit-learn | examples/classification/plot_classifier_comparison.py | 181 | 4699 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=====================
Classifier comparison
=====================
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.
This should be taken with a grain of salt, as the intuition conveyed by
these examples does not necessarily carry over to real datasets.
Particularly in high-dimensional spaces, data can more easily be separated
linearly and the simplicity of classifiers such as naive Bayes and linear SVMs
might lead to better generalization than is achieved by other classifiers.
The plots show training points in solid colors and testing points
semi-transparent. The lower right shows the classification accuracy on the test
set.
"""
print(__doc__)
# Code source: Gaël Varoquaux
# Andreas Müller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.lda import LDA
from sklearn.qda import QDA
h = .02 # step size in the mesh
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
"Random Forest", "AdaBoost", "Naive Bayes", "LDA", "QDA"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
AdaBoostClassifier(),
GaussianNB(),
LDA(),
QDA()]
X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable
]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds in datasets:
# preprocess dataset, split into training and test part
X, y = ds
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
if hasattr(clf, "decision_function"):
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
Z = Z.reshape(xx.shape)
ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)
# Plot also the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
alpha=0.6)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(name)
ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
size=15, horizontalalignment='right')
i += 1
figure.subplots_adjust(left=.02, right=.98)
plt.show()
| bsd-3-clause |
bnaul/scikit-learn | sklearn/decomposition/_nmf.py | 2 | 47427 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck
# Mathieu Blondel <[email protected]>
# Tom Dupre la Tour
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
import time
import warnings
from math import sqrt
from ._cdnmf_fast import _update_cdnmf_fast
from ..base import BaseEstimator, TransformerMixin
from ..exceptions import ConvergenceWarning
from ..utils import check_random_state, check_array
from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
from ..utils.validation import check_is_fitted, check_non_negative
from ..utils.validation import _deprecate_positional_args
EPSILON = np.finfo(np.float32).eps
def norm(x):
"""Dot product-based Euclidean norm implementation
See: http://fseoane.net/blog/2011/computing-the-vector-norm/
Parameters
----------
x : array-like
Vector for which to compute the norm
"""
return sqrt(squared_norm(x))
def trace_dot(X, Y):
"""Trace of np.dot(X, Y.T).
Parameters
----------
X : array-like
First matrix
Y : array-like
Second matrix
"""
return np.dot(X.ravel(), Y.ravel())
def _check_init(A, shape, whom):
A = check_array(A)
if np.shape(A) != shape:
raise ValueError('Array with wrong shape passed to %s. Expected %s, '
'but got %s ' % (whom, shape, np.shape(A)))
check_non_negative(A, whom)
if np.max(A) == 0:
raise ValueError('Array passed to %s is full of zeros.' % whom)
def _beta_divergence(X, W, H, beta, square_root=False):
"""Compute the beta-divergence of X and dot(W, H).
Parameters
----------
X : float or array-like, shape (n_samples, n_features)
W : float or dense array-like, shape (n_samples, n_components)
H : float or dense array-like, shape (n_components, n_features)
beta : float, string in {'frobenius', 'kullback-leibler', 'itakura-saito'}
Parameter of the beta-divergence.
If beta == 2, this is half the Frobenius *squared* norm.
If beta == 1, this is the generalized Kullback-Leibler divergence.
If beta == 0, this is the Itakura-Saito divergence.
Else, this is the general beta-divergence.
square_root : boolean, default False
If True, return np.sqrt(2 * res)
For beta == 2, it corresponds to the Frobenius norm.
Returns
-------
res : float
Beta divergence of X and np.dot(X, H)
"""
beta = _beta_loss_to_float(beta)
# The method can be called with scalars
if not sp.issparse(X):
X = np.atleast_2d(X)
W = np.atleast_2d(W)
H = np.atleast_2d(H)
# Frobenius norm
if beta == 2:
# Avoid the creation of the dense np.dot(W, H) if X is sparse.
if sp.issparse(X):
norm_X = np.dot(X.data, X.data)
norm_WH = trace_dot(np.linalg.multi_dot([W.T, W, H]), H)
cross_prod = trace_dot((X * H.T), W)
res = (norm_X + norm_WH - 2. * cross_prod) / 2.
else:
res = squared_norm(X - np.dot(W, H)) / 2.
if square_root:
return np.sqrt(res * 2)
else:
return res
if sp.issparse(X):
# compute np.dot(W, H) only where X is nonzero
WH_data = _special_sparse_dot(W, H, X).data
X_data = X.data
else:
WH = np.dot(W, H)
WH_data = WH.ravel()
X_data = X.ravel()
# do not affect the zeros: here 0 ** (-1) = 0 and not infinity
indices = X_data > EPSILON
WH_data = WH_data[indices]
X_data = X_data[indices]
# used to avoid division by zero
WH_data[WH_data == 0] = EPSILON
# generalized Kullback-Leibler divergence
if beta == 1:
# fast and memory efficient computation of np.sum(np.dot(W, H))
sum_WH = np.dot(np.sum(W, axis=0), np.sum(H, axis=1))
# computes np.sum(X * log(X / WH)) only where X is nonzero
div = X_data / WH_data
res = np.dot(X_data, np.log(div))
# add full np.sum(np.dot(W, H)) - np.sum(X)
res += sum_WH - X_data.sum()
# Itakura-Saito divergence
elif beta == 0:
div = X_data / WH_data
res = np.sum(div) - np.product(X.shape) - np.sum(np.log(div))
# beta-divergence, beta not in (0, 1, 2)
else:
if sp.issparse(X):
# slow loop, but memory efficient computation of :
# np.sum(np.dot(W, H) ** beta)
sum_WH_beta = 0
for i in range(X.shape[1]):
sum_WH_beta += np.sum(np.dot(W, H[:, i]) ** beta)
else:
sum_WH_beta = np.sum(WH ** beta)
sum_X_WH = np.dot(X_data, WH_data ** (beta - 1))
res = (X_data ** beta).sum() - beta * sum_X_WH
res += sum_WH_beta * (beta - 1)
res /= beta * (beta - 1)
if square_root:
return np.sqrt(2 * res)
else:
return res
def _special_sparse_dot(W, H, X):
"""Computes np.dot(W, H), only where X is non zero."""
if sp.issparse(X):
ii, jj = X.nonzero()
n_vals = ii.shape[0]
dot_vals = np.empty(n_vals)
n_components = W.shape[1]
batch_size = max(n_components, n_vals // n_components)
for start in range(0, n_vals, batch_size):
batch = slice(start, start + batch_size)
dot_vals[batch] = np.multiply(W[ii[batch], :],
H.T[jj[batch], :]).sum(axis=1)
WH = sp.coo_matrix((dot_vals, (ii, jj)), shape=X.shape)
return WH.tocsr()
else:
return np.dot(W, H)
def _compute_regularization(alpha, l1_ratio, regularization):
"""Compute L1 and L2 regularization coefficients for W and H"""
alpha_H = 0.
alpha_W = 0.
if regularization in ('both', 'components'):
alpha_H = float(alpha)
if regularization in ('both', 'transformation'):
alpha_W = float(alpha)
l1_reg_W = alpha_W * l1_ratio
l1_reg_H = alpha_H * l1_ratio
l2_reg_W = alpha_W * (1. - l1_ratio)
l2_reg_H = alpha_H * (1. - l1_ratio)
return l1_reg_W, l1_reg_H, l2_reg_W, l2_reg_H
def _check_string_param(solver, regularization, beta_loss, init):
allowed_solver = ('cd', 'mu')
if solver not in allowed_solver:
raise ValueError(
'Invalid solver parameter: got %r instead of one of %r' %
(solver, allowed_solver))
allowed_regularization = ('both', 'components', 'transformation', None)
if regularization not in allowed_regularization:
raise ValueError(
'Invalid regularization parameter: got %r instead of one of %r' %
(regularization, allowed_regularization))
# 'mu' is the only solver that handles other beta losses than 'frobenius'
if solver != 'mu' and beta_loss not in (2, 'frobenius'):
raise ValueError(
'Invalid beta_loss parameter: solver %r does not handle beta_loss'
' = %r' % (solver, beta_loss))
if solver == 'mu' and init == 'nndsvd':
warnings.warn("The multiplicative update ('mu') solver cannot update "
"zeros present in the initialization, and so leads to "
"poorer results when used jointly with init='nndsvd'. "
"You may try init='nndsvda' or init='nndsvdar' instead.",
UserWarning)
beta_loss = _beta_loss_to_float(beta_loss)
return beta_loss
def _beta_loss_to_float(beta_loss):
"""Convert string beta_loss to float"""
allowed_beta_loss = {'frobenius': 2,
'kullback-leibler': 1,
'itakura-saito': 0}
if isinstance(beta_loss, str) and beta_loss in allowed_beta_loss:
beta_loss = allowed_beta_loss[beta_loss]
if not isinstance(beta_loss, numbers.Number):
raise ValueError('Invalid beta_loss parameter: got %r instead '
'of one of %r, or a float.' %
(beta_loss, allowed_beta_loss.keys()))
return beta_loss
def _initialize_nmf(X, n_components, init=None, eps=1e-6,
random_state=None):
"""Algorithms for NMF initialization.
Computes an initial guess for the non-negative
rank k matrix approximation for X: X = WH
Parameters
----------
X : array-like, shape (n_samples, n_features)
The data matrix to be decomposed.
n_components : integer
The number of components desired in the approximation.
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar'
Method used to initialize the procedure.
Default: None.
Valid options:
- None: 'nndsvd' if n_components <= min(n_samples, n_features),
otherwise 'random'.
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H
eps : float
Truncate all values less then this in output to zero.
random_state : int, RandomState instance, default=None
Used when ``init`` == 'nndsvdar' or 'random'. Pass an int for
reproducible results across multiple function calls.
See :term:`Glossary <random_state>`.
Returns
-------
W : array-like, shape (n_samples, n_components)
Initial guesses for solving X ~= WH
H : array-like, shape (n_components, n_features)
Initial guesses for solving X ~= WH
References
----------
C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
nonnegative matrix factorization - Pattern Recognition, 2008
http://tinyurl.com/nndsvd
"""
check_non_negative(X, "NMF initialization")
n_samples, n_features = X.shape
if (init is not None and init != 'random'
and n_components > min(n_samples, n_features)):
raise ValueError("init = '{}' can only be used when "
"n_components <= min(n_samples, n_features)"
.format(init))
if init is None:
if n_components <= min(n_samples, n_features):
init = 'nndsvd'
else:
init = 'random'
# Random initialization
if init == 'random':
avg = np.sqrt(X.mean() / n_components)
rng = check_random_state(random_state)
H = avg * rng.randn(n_components, n_features).astype(X.dtype,
copy=False)
W = avg * rng.randn(n_samples, n_components).astype(X.dtype,
copy=False)
np.abs(H, out=H)
np.abs(W, out=W)
return W, H
# NNDSVD initialization
U, S, V = randomized_svd(X, n_components, random_state=random_state)
W = np.zeros_like(U)
H = np.zeros_like(V)
# The leading singular triplet is non-negative
# so it can be used as is for initialization.
W[:, 0] = np.sqrt(S[0]) * np.abs(U[:, 0])
H[0, :] = np.sqrt(S[0]) * np.abs(V[0, :])
for j in range(1, n_components):
x, y = U[:, j], V[j, :]
# extract positive and negative parts of column vectors
x_p, y_p = np.maximum(x, 0), np.maximum(y, 0)
x_n, y_n = np.abs(np.minimum(x, 0)), np.abs(np.minimum(y, 0))
# and their norms
x_p_nrm, y_p_nrm = norm(x_p), norm(y_p)
x_n_nrm, y_n_nrm = norm(x_n), norm(y_n)
m_p, m_n = x_p_nrm * y_p_nrm, x_n_nrm * y_n_nrm
# choose update
if m_p > m_n:
u = x_p / x_p_nrm
v = y_p / y_p_nrm
sigma = m_p
else:
u = x_n / x_n_nrm
v = y_n / y_n_nrm
sigma = m_n
lbd = np.sqrt(S[j] * sigma)
W[:, j] = lbd * u
H[j, :] = lbd * v
W[W < eps] = 0
H[H < eps] = 0
if init == "nndsvd":
pass
elif init == "nndsvda":
avg = X.mean()
W[W == 0] = avg
H[H == 0] = avg
elif init == "nndsvdar":
rng = check_random_state(random_state)
avg = X.mean()
W[W == 0] = abs(avg * rng.randn(len(W[W == 0])) / 100)
H[H == 0] = abs(avg * rng.randn(len(H[H == 0])) / 100)
else:
raise ValueError(
'Invalid init parameter: got %r instead of one of %r' %
(init, (None, 'random', 'nndsvd', 'nndsvda', 'nndsvdar')))
return W, H
def _update_coordinate_descent(X, W, Ht, l1_reg, l2_reg, shuffle,
random_state):
"""Helper function for _fit_coordinate_descent
Update W to minimize the objective function, iterating once over all
coordinates. By symmetry, to update H, one can call
_update_coordinate_descent(X.T, Ht, W, ...)
"""
n_components = Ht.shape[1]
HHt = np.dot(Ht.T, Ht)
XHt = safe_sparse_dot(X, Ht)
# L2 regularization corresponds to increase of the diagonal of HHt
if l2_reg != 0.:
# adds l2_reg only on the diagonal
HHt.flat[::n_components + 1] += l2_reg
# L1 regularization corresponds to decrease of each element of XHt
if l1_reg != 0.:
XHt -= l1_reg
if shuffle:
permutation = random_state.permutation(n_components)
else:
permutation = np.arange(n_components)
# The following seems to be required on 64-bit Windows w/ Python 3.5.
permutation = np.asarray(permutation, dtype=np.intp)
return _update_cdnmf_fast(W, HHt, XHt, permutation)
def _fit_coordinate_descent(X, W, H, tol=1e-4, max_iter=200, l1_reg_W=0,
l1_reg_H=0, l2_reg_W=0, l2_reg_H=0, update_H=True,
verbose=0, shuffle=False, random_state=None):
"""Compute Non-negative Matrix Factorization (NMF) with Coordinate Descent
The objective function is minimized with an alternating minimization of W
and H. Each minimization is done with a cyclic (up to a permutation of the
features) Coordinate Descent.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
Initial guess for the solution.
H : array-like, shape (n_components, n_features)
Initial guess for the solution.
tol : float, default: 1e-4
Tolerance of the stopping condition.
max_iter : integer, default: 200
Maximum number of iterations before timing out.
l1_reg_W : double, default: 0.
L1 regularization parameter for W.
l1_reg_H : double, default: 0.
L1 regularization parameter for H.
l2_reg_W : double, default: 0.
L2 regularization parameter for W.
l2_reg_H : double, default: 0.
L2 regularization parameter for H.
update_H : boolean, default: True
Set to True, both W and H will be estimated from initial guesses.
Set to False, only W will be estimated.
verbose : integer, default: 0
The verbosity level.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
random_state : int, RandomState instance, default=None
Used to randomize the coordinates in the CD solver, when
``shuffle`` is set to ``True``. Pass an int for reproducible
results across multiple function calls.
See :term:`Glossary <random_state>`.
Returns
-------
W : array-like, shape (n_samples, n_components)
Solution to the non-negative least squares problem.
H : array-like, shape (n_components, n_features)
Solution to the non-negative least squares problem.
n_iter : int
The number of iterations done by the algorithm.
References
----------
Cichocki, Andrzej, and Phan, Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
"""
# so W and Ht are both in C order in memory
Ht = check_array(H.T, order='C')
X = check_array(X, accept_sparse='csr')
rng = check_random_state(random_state)
for n_iter in range(1, max_iter + 1):
violation = 0.
# Update W
violation += _update_coordinate_descent(X, W, Ht, l1_reg_W,
l2_reg_W, shuffle, rng)
# Update H
if update_H:
violation += _update_coordinate_descent(X.T, Ht, W, l1_reg_H,
l2_reg_H, shuffle, rng)
if n_iter == 1:
violation_init = violation
if violation_init == 0:
break
if verbose:
print("violation:", violation / violation_init)
if violation / violation_init <= tol:
if verbose:
print("Converged at iteration", n_iter + 1)
break
return W, Ht.T, n_iter
def _multiplicative_update_w(X, W, H, beta_loss, l1_reg_W, l2_reg_W, gamma,
H_sum=None, HHt=None, XHt=None, update_H=True):
"""update W in Multiplicative Update NMF"""
if beta_loss == 2:
# Numerator
if XHt is None:
XHt = safe_sparse_dot(X, H.T)
if update_H:
# avoid a copy of XHt, which will be re-computed (update_H=True)
numerator = XHt
else:
# preserve the XHt, which is not re-computed (update_H=False)
numerator = XHt.copy()
# Denominator
if HHt is None:
HHt = np.dot(H, H.T)
denominator = np.dot(W, HHt)
else:
# Numerator
# if X is sparse, compute WH only where X is non zero
WH_safe_X = _special_sparse_dot(W, H, X)
if sp.issparse(X):
WH_safe_X_data = WH_safe_X.data
X_data = X.data
else:
WH_safe_X_data = WH_safe_X
X_data = X
# copy used in the Denominator
WH = WH_safe_X.copy()
if beta_loss - 1. < 0:
WH[WH == 0] = EPSILON
# to avoid taking a negative power of zero
if beta_loss - 2. < 0:
WH_safe_X_data[WH_safe_X_data == 0] = EPSILON
if beta_loss == 1:
np.divide(X_data, WH_safe_X_data, out=WH_safe_X_data)
elif beta_loss == 0:
# speeds up computation time
# refer to /numpy/numpy/issues/9363
WH_safe_X_data **= -1
WH_safe_X_data **= 2
# element-wise multiplication
WH_safe_X_data *= X_data
else:
WH_safe_X_data **= beta_loss - 2
# element-wise multiplication
WH_safe_X_data *= X_data
# here numerator = dot(X * (dot(W, H) ** (beta_loss - 2)), H.T)
numerator = safe_sparse_dot(WH_safe_X, H.T)
# Denominator
if beta_loss == 1:
if H_sum is None:
H_sum = np.sum(H, axis=1) # shape(n_components, )
denominator = H_sum[np.newaxis, :]
else:
# computation of WHHt = dot(dot(W, H) ** beta_loss - 1, H.T)
if sp.issparse(X):
# memory efficient computation
# (compute row by row, avoiding the dense matrix WH)
WHHt = np.empty(W.shape)
for i in range(X.shape[0]):
WHi = np.dot(W[i, :], H)
if beta_loss - 1 < 0:
WHi[WHi == 0] = EPSILON
WHi **= beta_loss - 1
WHHt[i, :] = np.dot(WHi, H.T)
else:
WH **= beta_loss - 1
WHHt = np.dot(WH, H.T)
denominator = WHHt
# Add L1 and L2 regularization
if l1_reg_W > 0:
denominator += l1_reg_W
if l2_reg_W > 0:
denominator = denominator + l2_reg_W * W
denominator[denominator == 0] = EPSILON
numerator /= denominator
delta_W = numerator
# gamma is in ]0, 1]
if gamma != 1:
delta_W **= gamma
return delta_W, H_sum, HHt, XHt
def _multiplicative_update_h(X, W, H, beta_loss, l1_reg_H, l2_reg_H, gamma):
"""update H in Multiplicative Update NMF"""
if beta_loss == 2:
numerator = safe_sparse_dot(W.T, X)
denominator = np.linalg.multi_dot([W.T, W, H])
else:
# Numerator
WH_safe_X = _special_sparse_dot(W, H, X)
if sp.issparse(X):
WH_safe_X_data = WH_safe_X.data
X_data = X.data
else:
WH_safe_X_data = WH_safe_X
X_data = X
# copy used in the Denominator
WH = WH_safe_X.copy()
if beta_loss - 1. < 0:
WH[WH == 0] = EPSILON
# to avoid division by zero
if beta_loss - 2. < 0:
WH_safe_X_data[WH_safe_X_data == 0] = EPSILON
if beta_loss == 1:
np.divide(X_data, WH_safe_X_data, out=WH_safe_X_data)
elif beta_loss == 0:
# speeds up computation time
# refer to /numpy/numpy/issues/9363
WH_safe_X_data **= -1
WH_safe_X_data **= 2
# element-wise multiplication
WH_safe_X_data *= X_data
else:
WH_safe_X_data **= beta_loss - 2
# element-wise multiplication
WH_safe_X_data *= X_data
# here numerator = dot(W.T, (dot(W, H) ** (beta_loss - 2)) * X)
numerator = safe_sparse_dot(W.T, WH_safe_X)
# Denominator
if beta_loss == 1:
W_sum = np.sum(W, axis=0) # shape(n_components, )
W_sum[W_sum == 0] = 1.
denominator = W_sum[:, np.newaxis]
# beta_loss not in (1, 2)
else:
# computation of WtWH = dot(W.T, dot(W, H) ** beta_loss - 1)
if sp.issparse(X):
# memory efficient computation
# (compute column by column, avoiding the dense matrix WH)
WtWH = np.empty(H.shape)
for i in range(X.shape[1]):
WHi = np.dot(W, H[:, i])
if beta_loss - 1 < 0:
WHi[WHi == 0] = EPSILON
WHi **= beta_loss - 1
WtWH[:, i] = np.dot(W.T, WHi)
else:
WH **= beta_loss - 1
WtWH = np.dot(W.T, WH)
denominator = WtWH
# Add L1 and L2 regularization
if l1_reg_H > 0:
denominator += l1_reg_H
if l2_reg_H > 0:
denominator = denominator + l2_reg_H * H
denominator[denominator == 0] = EPSILON
numerator /= denominator
delta_H = numerator
# gamma is in ]0, 1]
if gamma != 1:
delta_H **= gamma
return delta_H
def _fit_multiplicative_update(X, W, H, beta_loss='frobenius',
max_iter=200, tol=1e-4,
l1_reg_W=0, l1_reg_H=0, l2_reg_W=0, l2_reg_H=0,
update_H=True, verbose=0):
"""Compute Non-negative Matrix Factorization with Multiplicative Update
The objective function is _beta_divergence(X, WH) and is minimized with an
alternating minimization of W and H. Each minimization is done with a
Multiplicative Update.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant input matrix.
W : array-like, shape (n_samples, n_components)
Initial guess for the solution.
H : array-like, shape (n_components, n_features)
Initial guess for the solution.
beta_loss : float or string, default 'frobenius'
String must be in {'frobenius', 'kullback-leibler', 'itakura-saito'}.
Beta divergence to be minimized, measuring the distance between X
and the dot product WH. Note that values different from 'frobenius'
(or 2) and 'kullback-leibler' (or 1) lead to significantly slower
fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
matrix X cannot contain zeros.
max_iter : integer, default: 200
Number of iterations.
tol : float, default: 1e-4
Tolerance of the stopping condition.
l1_reg_W : double, default: 0.
L1 regularization parameter for W.
l1_reg_H : double, default: 0.
L1 regularization parameter for H.
l2_reg_W : double, default: 0.
L2 regularization parameter for W.
l2_reg_H : double, default: 0.
L2 regularization parameter for H.
update_H : boolean, default: True
Set to True, both W and H will be estimated from initial guesses.
Set to False, only W will be estimated.
verbose : integer, default: 0
The verbosity level.
Returns
-------
W : array, shape (n_samples, n_components)
Solution to the non-negative least squares problem.
H : array, shape (n_components, n_features)
Solution to the non-negative least squares problem.
n_iter : int
The number of iterations done by the algorithm.
References
----------
Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix
factorization with the beta-divergence. Neural Computation, 23(9).
"""
start_time = time.time()
beta_loss = _beta_loss_to_float(beta_loss)
# gamma for Maximization-Minimization (MM) algorithm [Fevotte 2011]
if beta_loss < 1:
gamma = 1. / (2. - beta_loss)
elif beta_loss > 2:
gamma = 1. / (beta_loss - 1.)
else:
gamma = 1.
# used for the convergence criterion
error_at_init = _beta_divergence(X, W, H, beta_loss, square_root=True)
previous_error = error_at_init
H_sum, HHt, XHt = None, None, None
for n_iter in range(1, max_iter + 1):
# update W
# H_sum, HHt and XHt are saved and reused if not update_H
delta_W, H_sum, HHt, XHt = _multiplicative_update_w(
X, W, H, beta_loss, l1_reg_W, l2_reg_W, gamma,
H_sum, HHt, XHt, update_H)
W *= delta_W
# necessary for stability with beta_loss < 1
if beta_loss < 1:
W[W < np.finfo(np.float64).eps] = 0.
# update H
if update_H:
delta_H = _multiplicative_update_h(X, W, H, beta_loss, l1_reg_H,
l2_reg_H, gamma)
H *= delta_H
# These values will be recomputed since H changed
H_sum, HHt, XHt = None, None, None
# necessary for stability with beta_loss < 1
if beta_loss <= 1:
H[H < np.finfo(np.float64).eps] = 0.
# test convergence criterion every 10 iterations
if tol > 0 and n_iter % 10 == 0:
error = _beta_divergence(X, W, H, beta_loss, square_root=True)
if verbose:
iter_time = time.time()
print("Epoch %02d reached after %.3f seconds, error: %f" %
(n_iter, iter_time - start_time, error))
if (previous_error - error) / error_at_init < tol:
break
previous_error = error
# do not print if we have already printed in the convergence test
if verbose and (tol == 0 or n_iter % 10 != 0):
end_time = time.time()
print("Epoch %02d reached after %.3f seconds." %
(n_iter, end_time - start_time))
return W, H, n_iter
@_deprecate_positional_args
def non_negative_factorization(X, W=None, H=None, n_components=None, *,
init=None, update_H=True, solver='cd',
beta_loss='frobenius', tol=1e-4,
max_iter=200, alpha=0., l1_ratio=0.,
regularization=None, random_state=None,
verbose=0, shuffle=False):
r"""Compute Non-negative Matrix Factorization (NMF)
Find two non-negative matrices (W, H) whose product approximates the non-
negative matrix X. This factorization can be used for example for
dimensionality reduction, source separation or topic extraction.
The objective function is::
0.5 * ||X - WH||_Fro^2
+ alpha * l1_ratio * ||vec(W)||_1
+ alpha * l1_ratio * ||vec(H)||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
+ 0.5 * alpha * (1 - l1_ratio) * ||H||_Fro^2
Where::
||A||_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm)
||vec(A)||_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
For multiplicative-update ('mu') solver, the Frobenius norm
(0.5 * ||X - WH||_Fro^2) can be changed into another beta-divergence loss,
by changing the beta_loss parameter.
The objective function is minimized with an alternating minimization of W
and H. If H is given and update_H=False, it solves for W only.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
If init='custom', it is used as initial guess for the solution.
H : array-like, shape (n_components, n_features)
If init='custom', it is used as initial guess for the solution.
If update_H=False, it is used as a constant, to solve for W only.
n_components : integer
Number of components, if n_components is not set all features
are kept.
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: None.
Valid options:
- None: 'nndsvd' if n_components < n_features, otherwise 'random'.
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H if `update_H=True`. If
`update_H=False`, then only custom matrix H is used.
.. versionchanged:: 0.23
The default value of `init` changed from 'random' to None in 0.23.
update_H : boolean, default: True
Set to True, both W and H will be estimated from initial guesses.
Set to False, only W will be estimated.
solver : 'cd' | 'mu'
Numerical solver to use:
- 'cd' is a Coordinate Descent solver that uses Fast Hierarchical
Alternating Least Squares (Fast HALS).
- 'mu' is a Multiplicative Update solver.
.. versionadded:: 0.17
Coordinate Descent solver.
.. versionadded:: 0.19
Multiplicative Update solver.
beta_loss : float or string, default 'frobenius'
String must be in {'frobenius', 'kullback-leibler', 'itakura-saito'}.
Beta divergence to be minimized, measuring the distance between X
and the dot product WH. Note that values different from 'frobenius'
(or 2) and 'kullback-leibler' (or 1) lead to significantly slower
fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
matrix X cannot contain zeros. Used only in 'mu' solver.
.. versionadded:: 0.19
tol : float, default: 1e-4
Tolerance of the stopping condition.
max_iter : integer, default: 200
Maximum number of iterations before timing out.
alpha : double, default: 0.
Constant that multiplies the regularization terms.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an elementwise L2 penalty
(aka Frobenius Norm).
For l1_ratio = 1 it is an elementwise L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
regularization : 'both' | 'components' | 'transformation' | None
Select whether the regularization affects the components (H), the
transformation (W), both or none of them.
random_state : int, RandomState instance, default=None
Used for NMF initialisation (when ``init`` == 'nndsvdar' or
'random'), and in Coordinate Descent. Pass an int for reproducible
results across multiple function calls.
See :term:`Glossary <random_state>`.
verbose : integer, default: 0
The verbosity level.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
Returns
-------
W : array-like, shape (n_samples, n_components)
Solution to the non-negative least squares problem.
H : array-like, shape (n_components, n_features)
Solution to the non-negative least squares problem.
n_iter : int
Actual number of iterations.
Examples
--------
>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> from sklearn.decomposition import non_negative_factorization
>>> W, H, n_iter = non_negative_factorization(X, n_components=2,
... init='random', random_state=0)
References
----------
Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix
factorization with the beta-divergence. Neural Computation, 23(9).
"""
X = check_array(X, accept_sparse=('csr', 'csc'),
dtype=[np.float64, np.float32])
check_non_negative(X, "NMF (input X)")
beta_loss = _check_string_param(solver, regularization, beta_loss, init)
if X.min() == 0 and beta_loss <= 0:
raise ValueError("When beta_loss <= 0 and X contains zeros, "
"the solver may diverge. Please add small values to "
"X, or use a positive beta_loss.")
n_samples, n_features = X.shape
if n_components is None:
n_components = n_features
if not isinstance(n_components, numbers.Integral) or n_components <= 0:
raise ValueError("Number of components must be a positive integer;"
" got (n_components=%r)" % n_components)
if not isinstance(max_iter, numbers.Integral) or max_iter < 0:
raise ValueError("Maximum number of iterations must be a positive "
"integer; got (max_iter=%r)" % max_iter)
if not isinstance(tol, numbers.Number) or tol < 0:
raise ValueError("Tolerance for stopping criteria must be "
"positive; got (tol=%r)" % tol)
# check W and H, or initialize them
if init == 'custom' and update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
_check_init(W, (n_samples, n_components), "NMF (input W)")
if H.dtype != X.dtype or W.dtype != X.dtype:
raise TypeError("H and W should have the same dtype as X. Got "
"H.dtype = {} and W.dtype = {}."
.format(H.dtype, W.dtype))
elif not update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
if H.dtype != X.dtype:
raise TypeError("H should have the same dtype as X. Got H.dtype = "
"{}.".format(H.dtype))
# 'mu' solver should not be initialized by zeros
if solver == 'mu':
avg = np.sqrt(X.mean() / n_components)
W = np.full((n_samples, n_components), avg, dtype=X.dtype)
else:
W = np.zeros((n_samples, n_components), dtype=X.dtype)
else:
W, H = _initialize_nmf(X, n_components, init=init,
random_state=random_state)
l1_reg_W, l1_reg_H, l2_reg_W, l2_reg_H = _compute_regularization(
alpha, l1_ratio, regularization)
if solver == 'cd':
W, H, n_iter = _fit_coordinate_descent(X, W, H, tol, max_iter,
l1_reg_W, l1_reg_H,
l2_reg_W, l2_reg_H,
update_H=update_H,
verbose=verbose,
shuffle=shuffle,
random_state=random_state)
elif solver == 'mu':
W, H, n_iter = _fit_multiplicative_update(X, W, H, beta_loss, max_iter,
tol, l1_reg_W, l1_reg_H,
l2_reg_W, l2_reg_H, update_H,
verbose)
else:
raise ValueError("Invalid solver parameter '%s'." % solver)
if n_iter == max_iter and tol > 0:
warnings.warn("Maximum number of iterations %d reached. Increase it to"
" improve convergence." % max_iter, ConvergenceWarning)
return W, H, n_iter
class NMF(TransformerMixin, BaseEstimator):
r"""Non-Negative Matrix Factorization (NMF)
Find two non-negative matrices (W, H) whose product approximates the non-
negative matrix X. This factorization can be used for example for
dimensionality reduction, source separation or topic extraction.
The objective function is::
0.5 * ||X - WH||_Fro^2
+ alpha * l1_ratio * ||vec(W)||_1
+ alpha * l1_ratio * ||vec(H)||_1
+ 0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
+ 0.5 * alpha * (1 - l1_ratio) * ||H||_Fro^2
Where::
||A||_Fro^2 = \sum_{i,j} A_{ij}^2 (Frobenius norm)
||vec(A)||_1 = \sum_{i,j} abs(A_{ij}) (Elementwise L1 norm)
For multiplicative-update ('mu') solver, the Frobenius norm
(0.5 * ||X - WH||_Fro^2) can be changed into another beta-divergence loss,
by changing the beta_loss parameter.
The objective function is minimized with an alternating minimization of W
and H.
Read more in the :ref:`User Guide <NMF>`.
Parameters
----------
n_components : int or None
Number of components, if n_components is not set all features
are kept.
init : None | 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'custom'
Method used to initialize the procedure.
Default: None.
Valid options:
- None: 'nndsvd' if n_components <= min(n_samples, n_features),
otherwise random.
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
- 'custom': use custom matrices W and H
solver : 'cd' | 'mu'
Numerical solver to use:
'cd' is a Coordinate Descent solver.
'mu' is a Multiplicative Update solver.
.. versionadded:: 0.17
Coordinate Descent solver.
.. versionadded:: 0.19
Multiplicative Update solver.
beta_loss : float or string, default 'frobenius'
String must be in {'frobenius', 'kullback-leibler', 'itakura-saito'}.
Beta divergence to be minimized, measuring the distance between X
and the dot product WH. Note that values different from 'frobenius'
(or 2) and 'kullback-leibler' (or 1) lead to significantly slower
fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input
matrix X cannot contain zeros. Used only in 'mu' solver.
.. versionadded:: 0.19
tol : float, default: 1e-4
Tolerance of the stopping condition.
max_iter : integer, default: 200
Maximum number of iterations before timing out.
random_state : int, RandomState instance, default=None
Used for initialisation (when ``init`` == 'nndsvdar' or
'random'), and in Coordinate Descent. Pass an int for reproducible
results across multiple function calls.
See :term:`Glossary <random_state>`.
alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to
have no regularization.
.. versionadded:: 0.17
*alpha* used in the Coordinate Descent solver.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an elementwise L2 penalty
(aka Frobenius Norm).
For l1_ratio = 1 it is an elementwise L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
.. versionadded:: 0.17
Regularization parameter *l1_ratio* used in the Coordinate Descent
solver.
verbose : bool, default=False
Whether to be verbose.
shuffle : boolean, default: False
If true, randomize the order of coordinates in the CD solver.
.. versionadded:: 0.17
*shuffle* parameter used in the Coordinate Descent solver.
Attributes
----------
components_ : array, [n_components, n_features]
Factorization matrix, sometimes called 'dictionary'.
n_components_ : integer
The number of components. It is same as the `n_components` parameter
if it was given. Otherwise, it will be same as the number of
features.
reconstruction_err_ : number
Frobenius norm of the matrix difference, or beta-divergence, between
the training data ``X`` and the reconstructed data ``WH`` from
the fitted model.
n_iter_ : int
Actual number of iterations.
Examples
--------
>>> import numpy as np
>>> X = np.array([[1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> from sklearn.decomposition import NMF
>>> model = NMF(n_components=2, init='random', random_state=0)
>>> W = model.fit_transform(X)
>>> H = model.components_
References
----------
Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix
factorization with the beta-divergence. Neural Computation, 23(9).
"""
@_deprecate_positional_args
def __init__(self, n_components=None, *, init=None, solver='cd',
beta_loss='frobenius', tol=1e-4, max_iter=200,
random_state=None, alpha=0., l1_ratio=0., verbose=0,
shuffle=False):
self.n_components = n_components
self.init = init
self.solver = solver
self.beta_loss = beta_loss
self.tol = tol
self.max_iter = max_iter
self.random_state = random_state
self.alpha = alpha
self.l1_ratio = l1_ratio
self.verbose = verbose
self.shuffle = shuffle
def _more_tags(self):
return {'requires_positive_X': True}
def fit_transform(self, X, y=None, W=None, H=None):
"""Learn a NMF model for the data X and returns the transformed data.
This is more efficient than calling fit followed by transform.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix to be decomposed
y : Ignored
W : array-like, shape (n_samples, n_components)
If init='custom', it is used as initial guess for the solution.
H : array-like, shape (n_components, n_features)
If init='custom', it is used as initial guess for the solution.
Returns
-------
W : array, shape (n_samples, n_components)
Transformed data.
"""
X = self._validate_data(X, accept_sparse=('csr', 'csc'),
dtype=[np.float64, np.float32])
W, H, n_iter_ = non_negative_factorization(
X=X, W=W, H=H, n_components=self.n_components, init=self.init,
update_H=True, solver=self.solver, beta_loss=self.beta_loss,
tol=self.tol, max_iter=self.max_iter, alpha=self.alpha,
l1_ratio=self.l1_ratio, regularization='both',
random_state=self.random_state, verbose=self.verbose,
shuffle=self.shuffle)
self.reconstruction_err_ = _beta_divergence(X, W, H, self.beta_loss,
square_root=True)
self.n_components_ = H.shape[0]
self.components_ = H
self.n_iter_ = n_iter_
return W
def fit(self, X, y=None, **params):
"""Learn a NMF model for the data X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix to be decomposed
y : Ignored
Returns
-------
self
"""
self.fit_transform(X, **params)
return self
def transform(self, X):
"""Transform the data X according to the fitted NMF model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix to be transformed by the model
Returns
-------
W : array, shape (n_samples, n_components)
Transformed data
"""
check_is_fitted(self)
W, _, n_iter_ = non_negative_factorization(
X=X, W=None, H=self.components_, n_components=self.n_components_,
init=self.init, update_H=False, solver=self.solver,
beta_loss=self.beta_loss, tol=self.tol, max_iter=self.max_iter,
alpha=self.alpha, l1_ratio=self.l1_ratio, regularization='both',
random_state=self.random_state, verbose=self.verbose,
shuffle=self.shuffle)
return W
def inverse_transform(self, W):
"""Transform data back to its original space.
Parameters
----------
W : {array-like, sparse matrix}, shape (n_samples, n_components)
Transformed data matrix
Returns
-------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Data matrix of original shape
.. versionadded:: 0.18
"""
check_is_fitted(self)
return np.dot(W, self.components_)
| bsd-3-clause |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/matplotlib/hatch.py | 8 | 7187 | """
Contains a classes for generating hatch patterns.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
from matplotlib.externals.six.moves import xrange
import numpy as np
from matplotlib.path import Path
class HatchPatternBase(object):
"""
The base class for a hatch pattern.
"""
pass
class HorizontalHatch(HatchPatternBase):
def __init__(self, hatch, density):
self.num_lines = int((hatch.count('-') + hatch.count('+')) * density)
self.num_vertices = self.num_lines * 2
def set_vertices_and_codes(self, vertices, codes):
steps, stepsize = np.linspace(0.0, 1.0, self.num_lines, False,
retstep=True)
steps += stepsize / 2.
vertices[0::2, 0] = 0.0
vertices[0::2, 1] = steps
vertices[1::2, 0] = 1.0
vertices[1::2, 1] = steps
codes[0::2] = Path.MOVETO
codes[1::2] = Path.LINETO
class VerticalHatch(HatchPatternBase):
def __init__(self, hatch, density):
self.num_lines = int((hatch.count('|') + hatch.count('+')) * density)
self.num_vertices = self.num_lines * 2
def set_vertices_and_codes(self, vertices, codes):
steps, stepsize = np.linspace(0.0, 1.0, self.num_lines, False,
retstep=True)
steps += stepsize / 2.
vertices[0::2, 0] = steps
vertices[0::2, 1] = 0.0
vertices[1::2, 0] = steps
vertices[1::2, 1] = 1.0
codes[0::2] = Path.MOVETO
codes[1::2] = Path.LINETO
class NorthEastHatch(HatchPatternBase):
def __init__(self, hatch, density):
self.num_lines = int((hatch.count('/') + hatch.count('x') +
hatch.count('X')) * density)
if self.num_lines:
self.num_vertices = (self.num_lines + 1) * 2
else:
self.num_vertices = 0
def set_vertices_and_codes(self, vertices, codes):
steps = np.linspace(-0.5, 0.5, self.num_lines + 1, True)
vertices[0::2, 0] = 0.0 + steps
vertices[0::2, 1] = 0.0 - steps
vertices[1::2, 0] = 1.0 + steps
vertices[1::2, 1] = 1.0 - steps
codes[0::2] = Path.MOVETO
codes[1::2] = Path.LINETO
class SouthEastHatch(HatchPatternBase):
def __init__(self, hatch, density):
self.num_lines = int((hatch.count('\\') + hatch.count('x') +
hatch.count('X')) * density)
self.num_vertices = (self.num_lines + 1) * 2
if self.num_lines:
self.num_vertices = (self.num_lines + 1) * 2
else:
self.num_vertices = 0
def set_vertices_and_codes(self, vertices, codes):
steps = np.linspace(-0.5, 0.5, self.num_lines + 1, True)
vertices[0::2, 0] = 0.0 + steps
vertices[0::2, 1] = 1.0 + steps
vertices[1::2, 0] = 1.0 + steps
vertices[1::2, 1] = 0.0 + steps
codes[0::2] = Path.MOVETO
codes[1::2] = Path.LINETO
class Shapes(HatchPatternBase):
filled = False
def __init__(self, hatch, density):
if self.num_rows == 0:
self.num_shapes = 0
self.num_vertices = 0
else:
self.num_shapes = ((self.num_rows // 2 + 1) * (self.num_rows + 1) +
(self.num_rows // 2) * (self.num_rows))
self.num_vertices = (self.num_shapes *
len(self.shape_vertices) *
(self.filled and 1 or 2))
def set_vertices_and_codes(self, vertices, codes):
offset = 1.0 / self.num_rows
shape_vertices = self.shape_vertices * offset * self.size
if not self.filled:
inner_vertices = shape_vertices[::-1] * 0.9
shape_codes = self.shape_codes
shape_size = len(shape_vertices)
cursor = 0
for row in xrange(self.num_rows + 1):
if row % 2 == 0:
cols = np.linspace(0.0, 1.0, self.num_rows + 1, True)
else:
cols = np.linspace(offset / 2.0, 1.0 - offset / 2.0,
self.num_rows, True)
row_pos = row * offset
for col_pos in cols:
vertices[cursor:cursor + shape_size] = (shape_vertices +
(col_pos, row_pos))
codes[cursor:cursor + shape_size] = shape_codes
cursor += shape_size
if not self.filled:
vertices[cursor:cursor + shape_size] = (inner_vertices +
(col_pos, row_pos))
codes[cursor:cursor + shape_size] = shape_codes
cursor += shape_size
class Circles(Shapes):
def __init__(self, hatch, density):
path = Path.unit_circle()
self.shape_vertices = path.vertices
self.shape_codes = path.codes
Shapes.__init__(self, hatch, density)
class SmallCircles(Circles):
size = 0.2
def __init__(self, hatch, density):
self.num_rows = (hatch.count('o')) * density
Circles.__init__(self, hatch, density)
class LargeCircles(Circles):
size = 0.35
def __init__(self, hatch, density):
self.num_rows = (hatch.count('O')) * density
Circles.__init__(self, hatch, density)
class SmallFilledCircles(SmallCircles):
size = 0.1
filled = True
def __init__(self, hatch, density):
self.num_rows = (hatch.count('.')) * density
Circles.__init__(self, hatch, density)
class Stars(Shapes):
size = 1.0 / 3.0
filled = True
def __init__(self, hatch, density):
self.num_rows = (hatch.count('*')) * density
path = Path.unit_regular_star(5)
self.shape_vertices = path.vertices
self.shape_codes = np.ones(len(self.shape_vertices)) * Path.LINETO
self.shape_codes[0] = Path.MOVETO
Shapes.__init__(self, hatch, density)
_hatch_types = [
HorizontalHatch,
VerticalHatch,
NorthEastHatch,
SouthEastHatch,
SmallCircles,
LargeCircles,
SmallFilledCircles,
Stars
]
def get_path(hatchpattern, density=6):
"""
Given a hatch specifier, *hatchpattern*, generates Path to render
the hatch in a unit square. *density* is the number of lines per
unit square.
"""
density = int(density)
patterns = [hatch_type(hatchpattern, density)
for hatch_type in _hatch_types]
num_vertices = sum([pattern.num_vertices for pattern in patterns])
if num_vertices == 0:
return Path(np.empty((0, 2)))
vertices = np.empty((num_vertices, 2))
codes = np.empty((num_vertices,), np.uint8)
cursor = 0
for pattern in patterns:
if pattern.num_vertices != 0:
vertices_chunk = vertices[cursor:cursor + pattern.num_vertices]
codes_chunk = codes[cursor:cursor + pattern.num_vertices]
pattern.set_vertices_and_codes(vertices_chunk, codes_chunk)
cursor += pattern.num_vertices
return Path(vertices, codes)
| mit |
roxyboy/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 272 | 6972 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, where the three corners correspond to the three classes.
Arrows point from the probability vectors predicted by an uncalibrated
classifier to the probability vectors predicted by the same classifier after
sigmoid calibration on a hold-out validation set. Colors indicate the true
class of an instance (red: class 1, green: class 2, blue: class 3).
The base classifier is a random forest classifier with 25 base estimators
(trees). If this classifier is trained on all 800 training datapoints, it is
overly confident in its predictions and thus incurs a large log-loss.
Calibrating an identical classifier, which was trained on 600 datapoints, with
method='sigmoid' on the remaining 200 datapoints reduces the confidence of the
predictions, i.e., moves the probability vectors from the edges of the simplex
towards the center. This calibration results in a lower log-loss. Note that an
alternative would have been to increase the number of base estimators which
would have resulted in a similar decrease in log-loss.
"""
print(__doc__)
# Author: Jan Hendrik Metzen <[email protected]>
# License: BSD Style.
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import log_loss
np.random.seed(0)
# Generate data
X, y = make_blobs(n_samples=1000, n_features=2, random_state=42,
cluster_std=5.0)
X_train, y_train = X[:600], y[:600]
X_valid, y_valid = X[600:800], y[600:800]
X_train_valid, y_train_valid = X[:800], y[:800]
X_test, y_test = X[800:], y[800:]
# Train uncalibrated random forest classifier on whole train and validation
# data and evaluate on test data
clf = RandomForestClassifier(n_estimators=25)
clf.fit(X_train_valid, y_train_valid)
clf_probs = clf.predict_proba(X_test)
score = log_loss(y_test, clf_probs)
# Train random forest classifier, calibrate on validation data and evaluate
# on test data
clf = RandomForestClassifier(n_estimators=25)
clf.fit(X_train, y_train)
clf_probs = clf.predict_proba(X_test)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid", cv="prefit")
sig_clf.fit(X_valid, y_valid)
sig_clf_probs = sig_clf.predict_proba(X_test)
sig_score = log_loss(y_test, sig_clf_probs)
# Plot changes in predicted probabilities via arrows
plt.figure(0)
colors = ["r", "g", "b"]
for i in range(clf_probs.shape[0]):
plt.arrow(clf_probs[i, 0], clf_probs[i, 1],
sig_clf_probs[i, 0] - clf_probs[i, 0],
sig_clf_probs[i, 1] - clf_probs[i, 1],
color=colors[y_test[i]], head_width=1e-2)
# Plot perfect predictions
plt.plot([1.0], [0.0], 'ro', ms=20, label="Class 1")
plt.plot([0.0], [1.0], 'go', ms=20, label="Class 2")
plt.plot([0.0], [0.0], 'bo', ms=20, label="Class 3")
# Plot boundaries of unit simplex
plt.plot([0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], 'k', label="Simplex")
# Annotate points on the simplex
plt.annotate(r'($\frac{1}{3}$, $\frac{1}{3}$, $\frac{1}{3}$)',
xy=(1.0/3, 1.0/3), xytext=(1.0/3, .23), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.plot([1.0/3], [1.0/3], 'ko', ms=5)
plt.annotate(r'($\frac{1}{2}$, $0$, $\frac{1}{2}$)',
xy=(.5, .0), xytext=(.5, .1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($0$, $\frac{1}{2}$, $\frac{1}{2}$)',
xy=(.0, .5), xytext=(.1, .5), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($\frac{1}{2}$, $\frac{1}{2}$, $0$)',
xy=(.5, .5), xytext=(.6, .6), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($0$, $0$, $1$)',
xy=(0, 0), xytext=(.1, .1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($1$, $0$, $0$)',
xy=(1, 0), xytext=(1, .1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
plt.annotate(r'($0$, $1$, $0$)',
xy=(0, 1), xytext=(.1, 1), xycoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
horizontalalignment='center', verticalalignment='center')
# Add grid
plt.grid("off")
for x in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
plt.plot([0, x], [x, 0], 'k', alpha=0.2)
plt.plot([0, 0 + (1-x)/2], [x, x + (1-x)/2], 'k', alpha=0.2)
plt.plot([x, x + (1-x)/2], [0, 0 + (1-x)/2], 'k', alpha=0.2)
plt.title("Change of predicted probabilities after sigmoid calibration")
plt.xlabel("Probability class 1")
plt.ylabel("Probability class 2")
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.legend(loc="best")
print("Log-loss of")
print(" * uncalibrated classifier trained on 800 datapoints: %.3f "
% score)
print(" * classifier trained on 600 datapoints and calibrated on "
"200 datapoint: %.3f" % sig_score)
# Illustrate calibrator
plt.figure(1)
# generate grid over 2-simplex
p1d = np.linspace(0, 1, 20)
p0, p1 = np.meshgrid(p1d, p1d)
p2 = 1 - p0 - p1
p = np.c_[p0.ravel(), p1.ravel(), p2.ravel()]
p = p[p[:, 2] >= 0]
calibrated_classifier = sig_clf.calibrated_classifiers_[0]
prediction = np.vstack([calibrator.predict(this_p)
for calibrator, this_p in
zip(calibrated_classifier.calibrators_, p.T)]).T
prediction /= prediction.sum(axis=1)[:, None]
# Ploit modifications of calibrator
for i in range(prediction.shape[0]):
plt.arrow(p[i, 0], p[i, 1],
prediction[i, 0] - p[i, 0], prediction[i, 1] - p[i, 1],
head_width=1e-2, color=colors[np.argmax(p[i])])
# Plot boundaries of unit simplex
plt.plot([0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], 'k', label="Simplex")
plt.grid("off")
for x in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
plt.plot([0, x], [x, 0], 'k', alpha=0.2)
plt.plot([0, 0 + (1-x)/2], [x, x + (1-x)/2], 'k', alpha=0.2)
plt.plot([x, x + (1-x)/2], [0, 0 + (1-x)/2], 'k', alpha=0.2)
plt.title("Illustration of sigmoid calibrator")
plt.xlabel("Probability class 1")
plt.ylabel("Probability class 2")
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05, 1.05)
plt.show()
| bsd-3-clause |
raghavrv/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 7 | 7314 | import numpy as np
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import skip_if_32bit
from sklearn import datasets
from sklearn.linear_model import LogisticRegression, SGDClassifier, Lasso
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import PassiveAggressiveClassifier
iris = datasets.load_iris()
data, y = iris.data, iris.target
rng = np.random.RandomState(0)
def test_invalid_input():
clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True,
random_state=None, tol=None)
for threshold in ["gobbledigook", ".5 * gobbledigook"]:
model = SelectFromModel(clf, threshold=threshold)
model.fit(data, y)
assert_raises(ValueError, model.transform, data)
def test_input_estimator_unchanged():
# Test that SelectFromModel fits on a clone of the estimator.
est = RandomForestClassifier()
transformer = SelectFromModel(estimator=est)
transformer.fit(data, y)
assert_true(transformer.estimator is est)
@skip_if_32bit
def test_feature_importances():
X, y = datasets.make_classification(
n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
n_repeated=0, shuffle=False, random_state=0)
est = RandomForestClassifier(n_estimators=50, random_state=0)
for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
transformer = SelectFromModel(estimator=est, threshold=threshold)
transformer.fit(X, y)
assert_true(hasattr(transformer.estimator_, 'feature_importances_'))
X_new = transformer.transform(X)
assert_less(X_new.shape[1], X.shape[1])
importances = transformer.estimator_.feature_importances_
feature_mask = np.abs(importances) > func(importances)
assert_array_almost_equal(X_new, X[:, feature_mask])
# Check with sample weights
sample_weight = np.ones(y.shape)
sample_weight[y == 1] *= 100
est = RandomForestClassifier(n_estimators=50, random_state=0)
transformer = SelectFromModel(estimator=est)
transformer.fit(X, y, sample_weight=sample_weight)
importances = transformer.estimator_.feature_importances_
transformer.fit(X, y, sample_weight=3 * sample_weight)
importances_bis = transformer.estimator_.feature_importances_
assert_almost_equal(importances, importances_bis)
# For the Lasso and related models, the threshold defaults to 1e-5
transformer = SelectFromModel(estimator=Lasso(alpha=0.1))
transformer.fit(X, y)
X_new = transformer.transform(X)
mask = np.abs(transformer.estimator_.coef_) > 1e-5
assert_array_equal(X_new, X[:, mask])
@skip_if_32bit
def test_feature_importances_2d_coef():
X, y = datasets.make_classification(
n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
n_repeated=0, shuffle=False, random_state=0, n_classes=4)
est = LogisticRegression()
for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
for order in [1, 2, np.inf]:
# Fit SelectFromModel a multi-class problem
transformer = SelectFromModel(estimator=LogisticRegression(),
threshold=threshold,
norm_order=order)
transformer.fit(X, y)
assert_true(hasattr(transformer.estimator_, 'coef_'))
X_new = transformer.transform(X)
assert_less(X_new.shape[1], X.shape[1])
# Manually check that the norm is correctly performed
est.fit(X, y)
importances = np.linalg.norm(est.coef_, axis=0, ord=order)
feature_mask = importances > func(importances)
assert_array_equal(X_new, X[:, feature_mask])
def test_partial_fit():
est = PassiveAggressiveClassifier(random_state=0, shuffle=False,
max_iter=5, tol=None)
transformer = SelectFromModel(estimator=est)
transformer.partial_fit(data, y,
classes=np.unique(y))
old_model = transformer.estimator_
transformer.partial_fit(data, y,
classes=np.unique(y))
new_model = transformer.estimator_
assert_true(old_model is new_model)
X_transform = transformer.transform(data)
transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
assert_array_equal(X_transform, transformer.transform(data))
# check that if est doesn't have partial_fit, neither does SelectFromModel
transformer = SelectFromModel(estimator=RandomForestClassifier())
assert_false(hasattr(transformer, "partial_fit"))
def test_calling_fit_reinitializes():
est = LinearSVC(random_state=0)
transformer = SelectFromModel(estimator=est)
transformer.fit(data, y)
transformer.set_params(estimator__C=100)
transformer.fit(data, y)
assert_equal(transformer.estimator_.C, 100)
def test_prefit():
# Test all possible combinations of the prefit parameter.
# Passing a prefit parameter with the selected model
# and fitting a unfit model with prefit=False should give same results.
clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True,
random_state=0, tol=None)
model = SelectFromModel(clf)
model.fit(data, y)
X_transform = model.transform(data)
clf.fit(data, y)
model = SelectFromModel(clf, prefit=True)
assert_array_equal(model.transform(data), X_transform)
# Check that the model is rewritten if prefit=False and a fitted model is
# passed
model = SelectFromModel(clf, prefit=False)
model.fit(data, y)
assert_array_equal(model.transform(data), X_transform)
# Check that prefit=True and calling fit raises a ValueError
model = SelectFromModel(clf, prefit=True)
assert_raises(ValueError, model.fit, data, y)
def test_threshold_string():
est = RandomForestClassifier(n_estimators=50, random_state=0)
model = SelectFromModel(est, threshold="0.5*mean")
model.fit(data, y)
X_transform = model.transform(data)
# Calculate the threshold from the estimator directly.
est.fit(data, y)
threshold = 0.5 * np.mean(est.feature_importances_)
mask = est.feature_importances_ > threshold
assert_array_equal(X_transform, data[:, mask])
def test_threshold_without_refitting():
# Test that the threshold can be set without refitting the model.
clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True,
random_state=0, tol=None)
model = SelectFromModel(clf, threshold="0.1 * mean")
model.fit(data, y)
X_transform = model.transform(data)
# Set a higher threshold to filter out more features.
model.threshold = "1.0 * mean"
assert_greater(X_transform.shape[1], model.transform(data).shape[1])
| bsd-3-clause |
leriomaggio/deep-learning-keras-tensorflow | 2. Deep Learning Frameworks/kaggle_data.py | 3 | 1800 | """
Utility functions to load Kaggle Otto Group Challenge Data.
Since these data/functions are used in many notebooks, it is better
to centralise functions to load and manipulate data so
to not replicate code.
"""
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
def load_data(path, train=True):
"""Load data from a CSV File
Parameters
----------
path: str
The path to the CSV file
train: bool (default True)
Decide whether or not data are *training data*.
If True, some random shuffling is applied.
Return
------
X: numpy.ndarray
The data as a multi dimensional array of floats
ids: numpy.ndarray
A vector of ids for each sample
"""
df = pd.read_csv(path)
X = df.values.copy()
if train:
np.random.shuffle(X) # https://youtu.be/uyUXoap67N8
X, labels = X[:, 1:-1].astype(np.float32), X[:, -1]
return X, labels
else:
X, ids = X[:, 1:].astype(np.float32), X[:, 0].astype(str)
return X, ids
def preprocess_data(X, scaler=None):
"""Preprocess input data by standardise features
by removing the mean and scaling to unit variance"""
if not scaler:
scaler = StandardScaler()
scaler.fit(X)
X = scaler.transform(X)
return X, scaler
def preprocess_labels(labels, encoder=None, categorical=True):
"""Encode labels with values among 0 and `n-classes-1`"""
if not encoder:
encoder = LabelEncoder()
encoder.fit(labels)
y = encoder.transform(labels).astype(np.int32)
if categorical:
y = np_utils.to_categorical(y)
return y, encoder | mit |
fabianp/scikit-learn | examples/feature_selection/plot_permutation_test_for_classification.py | 250 | 2233 | """
=================================================================
Test with permutations the significance of a classification score
=================================================================
In order to test if a classification score is significative a technique
in repeating the classification procedure after randomizing, permuting,
the labels. The p-value is then given by the percentage of runs for
which the score obtained is greater than the classification score
obtained in the first place.
"""
# Author: Alexandre Gramfort <[email protected]>
# License: BSD 3 clause
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.cross_validation import StratifiedKFold, permutation_test_score
from sklearn import datasets
##############################################################################
# Loading a dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
n_classes = np.unique(y).size
# Some noisy data not correlated
random = np.random.RandomState(seed=0)
E = random.normal(size=(len(X), 2200))
# Add noisy data to the informative features for make the task harder
X = np.c_[X, E]
svm = SVC(kernel='linear')
cv = StratifiedKFold(y, 2)
score, permutation_scores, pvalue = permutation_test_score(
svm, X, y, scoring="accuracy", cv=cv, n_permutations=100, n_jobs=1)
print("Classification score %s (pvalue : %s)" % (score, pvalue))
###############################################################################
# View histogram of permutation scores
plt.hist(permutation_scores, 20, label='Permutation scores')
ylim = plt.ylim()
# BUG: vlines(..., linestyle='--') fails on older versions of matplotlib
#plt.vlines(score, ylim[0], ylim[1], linestyle='--',
# color='g', linewidth=3, label='Classification Score'
# ' (pvalue %s)' % pvalue)
#plt.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
# color='k', linewidth=3, label='Luck')
plt.plot(2 * [score], ylim, '--g', linewidth=3,
label='Classification Score'
' (pvalue %s)' % pvalue)
plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
plt.ylim(ylim)
plt.legend()
plt.xlabel('Score')
plt.show()
| bsd-3-clause |
QudevETH/PycQED_py3 | pycqed/analysis/process_tomography.py | 1 | 12150 | import numpy as np
import qutip as qtp
from pycqed.analysis import analysis_toolbox as a_tools
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pycqed.analysis.tools.plotting import *
import time
import os
rotation_matrixes = [qtp.qeye(2).full(),
qtp.sigmax().full(),
qtp.rotation(qtp.sigmay(), np.pi / 2).full(),
qtp.rotation(qtp.sigmay(), -np.pi / 2).full(),
qtp.rotation(qtp.sigmax(), np.pi / 2).full(),
qtp.rotation(qtp.sigmax(), -np.pi / 2).full()]
pauli_matrixes = [qtp.qeye(2).full(),
qtp.sigmax().full(),
qtp.sigmay().full(),
qtp.sigmaz().full()]
pauli_ro = [qtp.qeye(2).full(),
qtp.sigmaz().full()]
def get_rotation(idx):
# j inner loop
# i outer loop
j = idx % 6
i = ((idx - j)//6) % 6
return np.kron(rotation_matrixes[i], rotation_matrixes[j])
# return qtp.tensor(rotation_matrixes[i],rotation_matrixes[j])
# return i,j
def get_pauli(idx):
# j inner loop
# i outer loop
j = idx % 4
i = ((idx - j)//4) % 4
return np.kron(pauli_matrixes[i], pauli_matrixes[j])
# return qtp.tensor(pauli_matrixes[i],pauli_matrixes[j])
def get_measurement_pauli(idx):
# j inner loop
# i outer loop
j = idx % 2
i = ((idx - j)//2) % 2
return np.kron(pauli_ro[i], pauli_ro[j])
# return qtp.tensor(pauli_ro[i],pauli_ro[j])
def unroll_mn(idx):
# j inner loop
# i outer loop
j = idx % 16
i = ((idx - j)//16) % 16
return i, j
def unroll_lk(idx):
# j inner loop
# i outer loop
j = idx % 36
i = ((idx - j)//36) % 36
return i, j
def get_pauli_txt(idx):
# j inner loop
# i outer loop
j = idx % 4
i = ((idx - j)//4) % 4
return pauli_matrixes_txt[i]+pauli_matrixes_txt[j]
pauli_matrixes_txt = ['I', 'X', 'Y', 'Z']
def qpt_matrix_term(l, n, k, j, m):
# l preparation index
# k tomo index
# j beta index
# m,n process index
# rho00 = qtp.Qobj(np.array([[1,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]),dims=[[2,2],[2,2]])
ul = get_rotation(l)
pm = get_pauli(m)
uk = get_rotation(k)
pj = get_measurement_pauli(j)
pn = get_pauli(n)
# trace = (ul.dag()*pn.dag()*uk.dag()*pj*uk*pm*ul*rho00).tr()
trace = np.dot(dagger(ul), np.dot(
dagger(pn), np.dot(dagger(uk), np.dot(pj, np.dot(uk, np.dot(pm, ul))))))[0, 0]
# print(trace)
return trace
def dagger(op):
return np.conjugate(np.transpose(op))
def qtp_matrix_element(mn, lk, beta):
# beta is wrong!
m, n = unroll_mn(mn)
l, k = unroll_lk(lk)
element = 0.
for j in range(4):
element += beta[j]*qpt_matrix_term(l, n, k, j, m)
# print(mn,element)
return element
def calc_fidelity1(dens_mat1, dens_mat2):
sqrt_2 = qtp.Qobj(dens_mat2).sqrtm()
fid = ((sqrt_2 * qtp.Qobj(dens_mat1) * sqrt_2).sqrtm()).tr()
return np.real(fid)
def analyze_qpt(t_start, t_stop, label): # identity tomo
opt_dict = {'scan_label': label}
pdict = {'I': 'I',
'Q': 'Q',
'times': 'sweep_points'}
nparams = ['I', 'Q', 'times']
tomo_scans = ca.quick_analysis(t_start=t_start, t_stop=t_stop,
options_dict=opt_dict,
params_dict_TD=pdict,
numeric_params=nparams)
assert(len(tomo_scans.TD_timestamps[:]) == 36)
nr_segments = 64
measurement_number = 36
shots_q0 = np.zeros(
(measurement_number, nr_segments, int(len(tomo_scans.TD_dict['I'][0])/nr_segments)))
shots_q1 = np.zeros(
(measurement_number, nr_segments, int(len(tomo_scans.TD_dict['Q'][0])/nr_segments)))
for j in range(measurement_number):
for i in range(nr_segments):
shots_q0[j, i, :] = tomo_scans.TD_dict['I'][j][i::nr_segments]
shots_q1[j, i, :] = tomo_scans.TD_dict['Q'][j][i::nr_segments]
shots_q0q1 = np.multiply(shots_q1, shots_q0)
avg_h1 = np.mean(shots_q0, axis=2)
h1_00 = np.mean(avg_h1[:, 36:36+7], axis=1)
h1_01 = np.mean(avg_h1[:, 43:43+7], axis=1)
h1_10 = np.mean(avg_h1[:, 50:50+7], axis=1)
h1_11 = np.mean(avg_h1[:, 57:], axis=1)
avg_h2 = np.mean(shots_q1, axis=2)
h2_00 = np.mean(avg_h2[:, 36:36+7], axis=1)
h2_01 = np.mean(avg_h2[:, 43:43+7], axis=1)
h2_10 = np.mean(avg_h2[:, 50:50+7], axis=1)
h2_11 = np.mean(avg_h2[:, 57:], axis=1)
avg_h12 = np.mean(shots_q0q1, axis=2)
h12_00 = np.mean(avg_h12[:, 36:36+7], axis=1)
h12_01 = np.mean(avg_h12[:, 43:43+7], axis=1)
h12_10 = np.mean(avg_h12[:, 50:50+7], axis=1)
h12_11 = np.mean(avg_h12[:, 57:], axis=1)
avg_h12 = np.mean(shots_q0q1, axis=2)
measurements_tomo = np.zeros((measurement_number*measurement_number*3))
for i in range(measurement_number):
# measurements_tomo[i*measurement_number*3:(i+1)*measurement_number*3] = (
# np.array([avg_h1[i,0:36], avg_h2[i,0:36],
# avg_h12[i,0:36]])).flatten()
measurements_tomo[i*36:(i+1)*36] = avg_h1[i, 0:36]
measurements_tomo[i*36+measurement_number*measurement_number:
(i+1)*36+measurement_number*measurement_number] = avg_h2[i, 0:36]
measurements_tomo[i*36+2*measurement_number*measurement_number:
(i+1)*36+2*measurement_number*measurement_number] = avg_h12[i, 0:36]
measurements_cal = np.array([[h1_00, h1_01, h1_10, h1_11],
[h2_00, h2_01, h2_10, h2_11],
[h12_00, h12_01, h12_10, h12_11]])
t0 = time.time()
# get the betas
betas = np.zeros((3, 4, measurement_number))
matrix = np.array(
[[1, 1, 1, 1], [1, -1, 1, -1], [1, 1, -1, -1], [1, -1, -1, 1]])
for i in range(measurement_number):
betas[0, :, i] = np.dot(
np.linalg.inv(matrix), measurements_cal[0, :, i])
betas[1, :, i] = np.dot(
np.linalg.inv(matrix), measurements_cal[1, :, i])
betas[2, :, i] = np.dot(
np.linalg.inv(matrix), measurements_cal[2, :, i])
# define the matrix
qtp_matrix = np.zeros(
(measurement_number*measurement_number*3, 16*16), dtype=np.complex128)
# fill the matrix
for i in range(measurement_number*measurement_number):
if ((i % 50) == 0):
print(i/(measurement_number*measurement_number))
l, k = unroll_lk(i)
for s in range(16*16):
qtp_matrix[i, s] = qtp_matrix_element(s, i, betas[0, :, l])
qtp_matrix[i+measurement_number*measurement_number,
s] = qtp_matrix_element(s, i, betas[1, :, l])
qtp_matrix[i+2*measurement_number*measurement_number,
s] = qtp_matrix_element(s, i, betas[2, :, l])
t1 = time.time()
# print((t1-t0)/(i+1))
inv_matrix = np.linalg.pinv(qtp_matrix)
chi_mat = np.dot(inv_matrix, measurements_tomo)
t2 = time.time()
chi_mat = chi_mat.reshape((16, 16))
def fid(chi_mat, phi1, phi2, phi_2Q=np.pi, option=0):
# fidelity calculation
chi_mat_theory = np.zeros(chi_mat.shape, dtype=np.complex128)
chi_mat_theory[0, 0] = 0.25
chi_mat_theory[0, 3] = 0.25*np.exp(-1j*phi1)
chi_mat_theory[0, 12] = 0.25*np.exp(-1j*phi2)
chi_mat_theory[0, 15] = 0.25*np.exp(-1j*(phi1+phi2+phi_2Q))
chi_mat_theory[3, 0] = 0.25*np.exp(1j*phi1)
chi_mat_theory[3, 3] = 0.25
chi_mat_theory[3, 12] = 0.25*np.exp(-1j*(phi1-phi2))
chi_mat_theory[3, 15] = 0.25*np.exp(1j*(phi2+phi_2Q))
chi_mat_theory[12, 0] = 0.25*np.exp(-1j*phi2)
chi_mat_theory[12, 3] = 0.25*np.exp(-1j*(-phi1+phi2))
chi_mat_theory[12, 12] = 0.25
chi_mat_theory[12, 15] = 0.25*np.exp(1j*(phi1+phi_2Q))
chi_mat_theory[15, 0] = 0.25*np.exp(1j*(phi1+phi2+phi_2Q))
chi_mat_theory[15, 3] = 0.25*np.exp(-1j*(phi2+phi_2Q))
chi_mat_theory[15, 12] = 0.25*np.exp(1j*(phi1+phi_2Q))
chi_mat_theory[15, 15] = 0.25
d = 4
f_pro = calc_fidelity1(chi_mat, chi_mat_theory)
f_avg = (((d*f_pro)+1)/(d+1))
f_pro, f_avg = np.real_if_close(f_pro), np.real_if_close(f_avg)
if option == 0:
return np.real(f_avg)
else:
return np.real(f_pro)
phi1_vec = np.linspace(-20, 20, 200)*np.pi/180.
phi2_vec = np.linspace(-20, 20, 200)*np.pi/180.
fid_mat = np.zeros((200, 200))
for i, phi1 in enumerate(phi1_vec):
for j, phi2 in enumerate(phi2_vec):
fid_mat[i, j] = fid(chi_mat, phi1, phi2, np.pi)
f_ave_opt = fid_mat.max()
f_pro_opt = (f_ave_opt*5-1)/4
# figures
plot_times = np.arange(16)
plot_step = plot_times[1]-plot_times[0]
plot_x = np.arange(16)
x_step = plot_x[1]-plot_x[0]
# fig = plt.figure(figsize=(8,6))
# ax = fig.add_subplot(111)
fig, axs = plt.subplots(1, 2, figsize=(15, 5))
ax = axs[0]
cmin, cmax = -0.3, 0.3 # chi_mat.min(),chi_mat.max()
fig_clim = [cmin, cmax]
out = flex_colormesh_plot_vs_xy(ax=ax, clim=fig_clim, cmap='RdBu',
xvals=plot_times,
yvals=plot_x,
zvals=np.real(chi_mat))
ax.set_xlabel(r'Operators')
ax.set_ylabel(r'Operators')
# ax.set_xlim(xmin, xmax)
ax.set_ylim(plot_x.min()-x_step/2., plot_x.max()+x_step/2.)
ax.set_xlim(plot_times.min()-plot_step/2., plot_times.max()+plot_step/2.)
ax.set_xticks(plot_times)
ax.set_xticklabels([get_pauli_txt(i) for i in range(16)])
ax.set_yticks(plot_x)
ax.set_yticklabels([get_pauli_txt(i) for i in range(16)])
# ax.set_xlim(0,50)
ax_divider = make_axes_locatable(ax)
cax = ax_divider.append_axes('right', size='10%', pad='5%')
cbar = plt.colorbar(out['cmap'], cax=cax)
cbar.set_ticks(
np.arange(fig_clim[0], 1.01*fig_clim[1], (fig_clim[1]-fig_clim[0])/5.))
cbar.set_ticklabels([str(fig_clim[0]), '', '', '', '', str(fig_clim[1])])
cbar.set_label('Process Tomography')
ax = axs[1]
out = flex_colormesh_plot_vs_xy(ax=ax, clim=fig_clim, cmap='RdBu',
xvals=plot_times,
yvals=plot_x,
zvals=np.imag(chi_mat))
ax.set_xlabel(r'Operators')
ax.set_ylabel(r'Operators')
# ax.set_xlim(xmin, xmax)
ax.set_ylim(plot_x.min()-x_step/2., plot_x.max()+x_step/2.)
ax.set_xlim(plot_times.min()-plot_step/2., plot_times.max()+plot_step/2.)
ax.set_xticks(plot_times)
ax.set_xticklabels([get_pauli_txt(i) for i in range(16)])
ax.set_yticks(plot_x)
ax.set_yticklabels([get_pauli_txt(i) for i in range(16)])
# ax.set_xlim(0,50)
ax_divider = make_axes_locatable(ax)
cax = ax_divider.append_axes('right', size='10%', pad='5%')
cbar = plt.colorbar(out['cmap'], cax=cax)
cbar.set_ticks(
np.arange(fig_clim[0], 1.01*fig_clim[1], (fig_clim[1]-fig_clim[0])/5.))
cbar.set_ticklabels([str(fig_clim[0]), '', '', '', '', str(fig_clim[1])])
cbar.set_label('Process Tomography')
fig.tight_layout()
fig.suptitle('%s - %s: Quantum Process Tomography. F_avg = %.4f; F_opt = %.4f' % (tomo_scans.TD_timestamps[0],
tomo_scans.TD_timestamps[
-1],
f_ave_opt, f_pro_opt))
figname = '%s_QTP_manhattan.PNG' % tomo_scans.TD_timestamps[0]
# savename = os.path.abspath(os.path.join(
# savefolder, figname))
# # value of 450dpi is arbitrary but higher than default
# fig.savefig(savename, format='png', dpi=450)
return chi_mat, f_ave_opt
def chi2PTM():
return
def PTM2chi():
return
def chi_PTM_matrices():
return
| mit |
Patrick-Cole/pygmi | pygmi/clust/segmentation.py | 1 | 18164 | # -----------------------------------------------------------------------------
# Name: segmentation.py (part of PyGMI)
#
# Author: Patrick Cole
# E-Mail: [email protected]
#
# Copyright: (c) 2019 Council for Geoscience
# Licence: GPL-3.0
#
# This file is part of PyGMI
#
# PyGMI is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# PyGMI is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# -----------------------------------------------------------------------------
"""Image segmentation routines."""
import copy
import numpy as np
from sklearn.cluster import DBSCAN
import skimage
import sklearn.preprocessing as skp
from numba import jit
from PyQt5 import QtWidgets, QtCore, QtGui
import pygmi.menu_default as menu_default
class ImageSeg(QtWidgets.QDialog):
"""
Image Segmentation.
Attributes
----------
name : str
item name
pbar : progressbar
reference to a progress bar.
parent : parent
reference to the parent routine
outdata : dictionary
dictionary of output datasets
ifile : str
input file name. Used in main.py
"""
def __init__(self, parent=None):
super().__init__(parent)
if parent is None:
self.showprocesslog = print
self.pbar = None
else:
self.showprocesslog = parent.showprocesslog
self.pbar = parent.pbar
self.parent = parent
self.indata = {}
self.outdata = {}
self.ifile = ''
self.scale = QtWidgets.QLineEdit('1000')
self.wcompact = QtWidgets.QLineEdit('0.5')
self.wcolor = QtWidgets.QLineEdit('0.9')
self.eps = QtWidgets.QLineEdit('0.1')
self.dbscan = QtWidgets.QCheckBox('Use DBSCAN to group segments')
self.setupui()
def setupui(self):
"""
Set up UI.
Returns
-------
None.
"""
gridlayout_main = QtWidgets.QGridLayout(self)
buttonbox = QtWidgets.QDialogButtonBox()
helpdocs = menu_default.HelpButton('pygmi.clust.segmentation')
lbl_wcompact = QtWidgets.QLabel('Compactness weight')
lbl_wcolor = QtWidgets.QLabel('Color weight')
lbl_scale = QtWidgets.QLabel('Maximum allowable cost function')
lbl_eps = QtWidgets.QLabel('DBSCAN eps')
val = QtGui.QDoubleValidator(0.0, 1.0, 2)
val.setNotation(QtGui.QDoubleValidator.StandardNotation)
val.setLocale(QtCore.QLocale(QtCore.QLocale.C))
self.wcompact.setValidator(val)
self.wcolor.setValidator(val)
self.dbscan.setChecked(False)
val = QtGui.QDoubleValidator()
val.setBottom = 0
self.scale.setValidator(QtGui.QIntValidator(self))
buttonbox.setOrientation(QtCore.Qt.Horizontal)
buttonbox.setCenterButtons(True)
buttonbox.setStandardButtons(buttonbox.Cancel | buttonbox.Ok)
self.setWindowTitle(r'Image Segmentation')
gridlayout_main.addWidget(lbl_wcompact, 0, 0, 1, 1)
gridlayout_main.addWidget(self.wcompact, 0, 1, 1, 1)
gridlayout_main.addWidget(lbl_wcolor, 1, 0, 1, 1)
gridlayout_main.addWidget(self.wcolor, 1, 1, 1, 1)
gridlayout_main.addWidget(lbl_scale, 2, 0, 1, 1)
gridlayout_main.addWidget(self.scale, 2, 1, 1, 1)
gridlayout_main.addWidget(self.dbscan, 3, 0, 1, 2)
gridlayout_main.addWidget(lbl_eps, 4, 0, 1, 1)
gridlayout_main.addWidget(self.eps, 4, 1, 1, 1)
gridlayout_main.addWidget(helpdocs, 5, 0, 1, 1)
gridlayout_main.addWidget(buttonbox, 5, 1, 1, 3)
buttonbox.accepted.connect(self.accept)
buttonbox.rejected.connect(self.reject)
def settings(self, nodialog=False):
"""
Entry point into item.
Returns
-------
bool
True if successful, False otherwise.
"""
if 'Raster' not in self.indata:
return False
data1 = []
for i in self.indata['Raster']:
data1.append(i.data.data)
data1 = np.array(data1)
data1 = np.moveaxis(data1, 0, -1)
if not nodialog:
tmp = self.exec_()
if tmp != 1:
return False
scale = float(self.scale.text())
wcolor = float(self.wcolor.text())
wcompact = float(self.wcompact.text())
eps = float(self.eps.text())
doshape = True
omap = self.segment1(data1, scale=scale, wcolor=wcolor,
wcompact=wcompact, doshape=doshape)
odat = copy.deepcopy(self.indata['Raster'][0])
odat.data = np.ma.array(omap, mask=self.indata['Raster'][0].data.mask)
odat.dataid = 'Segments'
self.outdata['Raster'] = [odat]
if not self.dbscan.isChecked():
return True
means = []
for i in range(odat.data.max()+1):
tmp = data1[odat.data == i]
if tmp.size == 0:
continue
means.append(tmp.mean(0))
means = np.array(means)
means = skp.StandardScaler().fit_transform(means)
dbout = DBSCAN(eps=eps).fit_predict(means)
data2 = odat.data.copy()
newmax = dbout.max()+1
for i, val in enumerate(dbout):
filt = (odat.data == i)
if val == -1:
data2[filt] = newmax
newmax += 1
continue
data2[filt] = val
odat.data = data2
self.outdata['Raster'] = [odat]
return True
def loadproj(self, projdata):
"""
Load project data into class.
Parameters
----------
projdata : dictionary
Project data loaded from JSON project file.
Returns
-------
chk : bool
A check to see if settings was successfully run.
"""
self.scale.setText(projdata['scale'])
self.scale.setText(projdata['wcolor'])
self.scale.setText(projdata['wcompact'])
return False
def saveproj(self):
"""
Save project data from class.
Returns
-------
projdata : dictionary
Project data to be saved to JSON project file.
"""
projdata = {}
projdata['scale'] = self.scale.text()
projdata['wcolor'] = self.wcolor.text()
projdata['wcompact'] = self.wcompact.text()
return projdata
def segment1(self, data, scale=500, wcolor=0.5, wcompact=0.5,
doshape=True):
"""
Segment Part 1.
Parameters
----------
data : numpy array
Input data.
scale : TYPE, optional
Scale. The default is 500.
wcolor : float, optional
Color weight. The default is 0.5.
wcompact : float, optional
Compactness weight. The default is 0.5.
doshape : bool, optional
Perform shape segmentation. The default is True.
Returns
-------
omap : numpy array
Output data.
"""
rows, cols, bands = data.shape
self.showprocesslog('Initialising...')
olist = {}
slist = {}
mlist = {}
nlist = {}
omap = np.zeros((rows, cols))
for i in range(rows):
for j in range(cols):
tmp = []
for ii in range(max(i-1, 0), min(i+2, rows)):
for jj in range(max(j-1, 0), min(j+2, cols)):
if ii == i and jj == j:
continue
tmp.append(ii*cols+jj)
olist[i*cols+j] = set(tmp)
for k in range(bands):
slist[(k, i*cols+j)] = 0
mlist[(k, i*cols+j)] = data[i, j, k]
nlist[(k, i*cols+j)] = 1
omap[i, j] = i*cols+j
self.showprocesslog('merging...')
omap = self.segment2(omap, olist, slist, mlist, nlist, bands,
doshape, wcompact, wcolor, scale)
self.showprocesslog('renumbering...')
tmp = np.unique(omap)
for i, val in enumerate(tmp):
omap[omap == val] = i
return omap.astype(int)
def segment2(self, omap, olist, slist, mlist, nlist, bands, doshape,
wcompact, wcolor, scale):
"""
Segment Part 2.
Parameters
----------
omap : numpy array
DESCRIPTION.
olist : dictionary
DESCRIPTION.
slist : dictionary
DESCRIPTION.
mlist : dictionary
DESCRIPTION.
nlist : dictionary
DESCRIPTION.
bands : int
Number of bands in data.
doshape : bool, optional
Perform shape segmentation. The default is True.
wcompact : float, optional
Compactness weight. The default is 0.5.
wcolor : float, optional
Color weight. The default is 0.5.
scale : TYPE, optional
Scale. The default is 500.
Returns
-------
omap : TYPE
DESCRIPTION.
"""
wband = np.ones(bands)/bands
cnt = 0
oldlen = len(olist.keys())+1
_, cols = omap.shape
rminmax = {}
cminmax = {}
for i in olist:
rminmax[i] = [i//cols]*2
cminmax[i] = [i-rminmax[i][0]*cols]*2
while len(olist.keys()) != oldlen:
oldlen = len(olist.keys())
cnt += 1
elist = set(olist.keys())
clen = len(elist)
if self.pbar is not None:
self.pbar.setMaximum(clen)
self.pbar.setMinimum(0)
self.pbar.setValue(0)
self.showprocesslog('Iteration number: '+str(cnt))
oldperc = 0
olist3 = olist.copy()
while elist:
i = elist.pop()
if not olist3[i]:
continue
hcolor = 0.
sm2 = []
nm2 = []
mean2 = []
ollist = list(olist3[i])
for k in range(bands):
s2 = np.array([slist[(k, j)] for j in ollist])
x2 = np.array([mlist[(k, j)] for j in ollist])
n2 = np.array([nlist[(k, j)] for j in ollist])
n1 = nlist[(k, i)]
x1 = mlist[(k, i)]
s1 = slist[(k, i)]
nm = (n1+n2)
mean = (n1*x1+n2*x2)/nm
sm = np.sqrt((n1*(s1**2+(x1-mean)**2) +
n2*(s2**2+(x2-mean)**2))/nm)
hcolor += np.abs(wband[k]*(nm*sm-(n1*s1+n2*s2)))
sm2.append(sm)
mean2.append(mean)
nm2.append(nm)
if cnt > 1 and doshape is True:
rmin, rmax = rminmax[i]
cmin, cmax = cminmax[i]
somap = omap[max(0, rmin-1):rmax+2, max(0, cmin-1):cmax+2]
l1 = get_l(somap == i)
b1 = (rmax-rmin+cmax-cmin+2)*2
l1, b1 = 2, 4
l2 = []
b2 = []
lm = []
bm = []
for ol in ollist:
rmin1, rmax1 = rminmax[ol]
cmin1, cmax1 = cminmax[ol]
somap = omap[max(0, rmin1-1):rmax1+2,
max(0, cmin1-1):cmax1+2]
ltmp = get_l(somap == ol)
btmp = (rmax1-rmin1+cmax1-cmin1+2)*2
ltmp, btmp = 4, 2
l2.append(ltmp)
b2.append(btmp)
rmin2 = min(rmin1, rmin)
rmax2 = max(rmax1, rmax)
cmin2 = min(cmin1, cmin)
cmax2 = max(cmax1, cmax)
somap = omap[max(0, rmin2-1):rmax2+2,
max(0, cmin2-1):cmax2+2]
filt = (somap == ol) + (somap == i)
ltmp2 = get_l(filt)
btmp2 = (rmax2-rmin2+cmax2-cmin2+2)*2
ltmp2, btmp2 = ltmp+l1, btmp+b1
lm.append(ltmp2)
bm.append(btmp2)
l2 = np.array(l2)
b2 = np.array(b2)
lm = np.array(lm)
bm = np.array(bm)
hsmooth = nm*lm/bm-(n1*l1/b1+n2*l2/b2)
hcompact = np.sqrt(nm)*lm-(np.sqrt(n1)*l1+np.sqrt(n2)*l2)
hshape = wcompact*hcompact + (1-wcompact)*hsmooth
hdiff = wcolor*hcolor+(1-wcolor)*hshape
else:
hdiff = hcolor
if hdiff.min() > scale:
continue
mindiff = hdiff.argmin()
hind = ollist[mindiff]
olist[i] = olist[i] | olist[hind]
olist[i].remove(i)
olist[i].remove(hind)
olist3[i] = olist[i].copy()
rmm1 = min(rminmax[i][0], rminmax[hind][0])
rmm2 = max(rminmax[i][1], rminmax[hind][1])
rminmax[i] = [rmm1, rmm2]
cmm1 = min(cminmax[i][0], cminmax[hind][0])
cmm2 = max(cminmax[i][1], cminmax[hind][1])
cminmax[i] = [cmm1, cmm2]
for k in range(bands):
slist[(k, i)] = sm2[k][mindiff]
mlist[(k, i)] = mean2[k][mindiff]
nlist[(k, i)] = nm2[k][mindiff]
del slist[(k, hind)]
del mlist[(k, hind)]
del nlist[(k, hind)]
for j in olist[hind]:
if j == i:
continue
olist[j].discard(hind)
olist[j].add(i)
olist3[j] = olist[j].copy()
olist3[j].discard(i)
del olist[hind]
elist.discard(hind)
cnow = clen-len(elist)
if cnow*1000//clen-oldperc > 0:
if self.pbar is not None:
self.pbar.setValue(cnow)
oldperc = cnow*1000//clen
rmin, rmax = rminmax[i]
cmin, cmax = cminmax[i]
omap[rmin:rmax+1, cmin:cmax+1][omap[rmin:rmax+1, cmin:cmax+1] == hind] = i
return omap
@jit(nopython=True, fastmath=True)
def get_l(data):
"""
Get bounding box length.
Parameters
----------
data : numpy array
Input data.
Returns
-------
ltmp : int
Bounding box length.
"""
rows, cols = data.shape
ltmp = 0
for i in range(rows):
for j in range(cols-1):
ltmp += abs(data[i, j+1]-data[i, j])
for i in range(rows-1):
for j in range(cols):
ltmp += abs(data[i+1, j]-data[i, j])
return ltmp
def _testfn():
"""Test routine."""
import sys
import matplotlib.pyplot as plt
from pygmi.raster.datatypes import Data
from pygmi.misc import PTime
from pygmi.raster import iodefs
from matplotlib import rcParams
rcParams['figure.dpi'] = 300
APP = QtWidgets.QApplication(sys.argv) # Necessary to test Qt Classes
data1 = skimage.data.coffee() # 400x600 48.5 secs
# data1 = data1[:, :300] # 26
plt.imshow(data1)
plt.axis('off')
plt.show()
wcolor = 0.5
wcompact = 0.5
doshape = True
scale = 1000
b1 = Data()
b1.data = np.ma.array(data1[:, :, 0])
b2 = Data()
b2.data = np.ma.array(data1[:, :, 1])
b3 = Data()
b3.data = np.ma.array(data1[:, :, 2])
data = [b1, b2, b3]
# ifile = r'E:\Workdata\testdata.hdr'
# data = iodefs.get_raster(ifile)
ttt = PTime()
IS = ImageSeg()
IS.indata = {'Raster': data}
# IS.settings(True)
IS.settings()
ttt.since_last_call()
odata = IS.outdata['Raster'][0]
plt.imshow(odata.data)
plt.axis('off')
plt.show()
# means = []
# for i in range(odata.data.max()+1):
# tmp = data1[odata.data==i]
# means.append(tmp.mean(0))
# means = np.array(means)
# means = skp.StandardScaler().fit_transform(means)
# dbout = DBSCAN(eps=0.15).fit_predict(means)
# data2 = odata.data.copy()
# newmax = dbout.max()+1
# for i, val in enumerate(dbout):
# filt = (odata.data==i)
# if val == -1:
# data2[filt] = newmax
# newmax += 1
# continue
# data2[filt] = val
# plt.imshow(data2, cmap='inferno')
# plt.colorbar()
# plt.show()
# omap = segment1(data1, scale=scale, wcolor=wcolor,
# wcompact=wcompact, doshape=doshape)
# plt.figure(figsize=(8, 8))
# plt.imshow(omap)
# plt.show()
# xall = []
# yall = []
# for shp, _ in rasterio.features.shapes(omap):
# for crds in shp['coordinates']:
# x = np.array(crds)[:, 0]
# y = np.array(crds)[:, 1]
# xall.append(x)
# yall.append(y)
#
# plt.figure(figsize=(8, 8))
# for i, _ in enumerate(xall):
# plt.plot(xall[i], yall[i], 'k')
# plt.imshow(data1)
# plt.show()
if __name__ == "__main__":
_testfn()
print('Finished!')
| gpl-3.0 |
shyamalschandra/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 169 | 8809 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.linear_model import PassiveAggressiveRegressor
iris = load_iris()
random_state = check_random_state(12)
indices = np.arange(iris.data.shape[0])
random_state.shuffle(indices)
X = iris.data[indices]
y = iris.target[indices]
X_csr = sp.csr_matrix(X)
class MyPassiveAggressive(ClassifierMixin):
def __init__(self, C=1.0, epsilon=0.01, loss="hinge",
fit_intercept=True, n_iter=1, random_state=None):
self.C = C
self.epsilon = epsilon
self.loss = loss
self.fit_intercept = fit_intercept
self.n_iter = n_iter
def fit(self, X, y):
n_samples, n_features = X.shape
self.w = np.zeros(n_features, dtype=np.float64)
self.b = 0.0
for t in range(self.n_iter):
for i in range(n_samples):
p = self.project(X[i])
if self.loss in ("hinge", "squared_hinge"):
loss = max(1 - y[i] * p, 0)
else:
loss = max(np.abs(p - y[i]) - self.epsilon, 0)
sqnorm = np.dot(X[i], X[i])
if self.loss in ("hinge", "epsilon_insensitive"):
step = min(self.C, loss / sqnorm)
elif self.loss in ("squared_hinge",
"squared_epsilon_insensitive"):
step = loss / (sqnorm + 1.0 / (2 * self.C))
if self.loss in ("hinge", "squared_hinge"):
step *= y[i]
else:
step *= np.sign(y[i] - p)
self.w += step * X[i]
if self.fit_intercept:
self.b += step
def project(self, X):
return np.dot(X, self.w) + self.b
def test_classifier_accuracy():
for data in (X, X_csr):
for fit_intercept in (True, False):
clf = PassiveAggressiveClassifier(C=1.0, n_iter=30,
fit_intercept=fit_intercept,
random_state=0)
clf.fit(data, y)
score = clf.score(data, y)
assert_greater(score, 0.79)
def test_classifier_partial_fit():
classes = np.unique(y)
for data in (X, X_csr):
clf = PassiveAggressiveClassifier(C=1.0,
fit_intercept=True,
random_state=0)
for t in range(30):
clf.partial_fit(data, y, classes)
score = clf.score(data, y)
assert_greater(score, 0.79)
def test_classifier_refit():
# Classifier can be retrained on different labels and features.
clf = PassiveAggressiveClassifier().fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
clf.fit(X[:, :-1], iris.target_names[y])
assert_array_equal(clf.classes_, iris.target_names)
def test_classifier_correctness():
y_bin = y.copy()
y_bin[y != 1] = -1
for loss in ("hinge", "squared_hinge"):
clf1 = MyPassiveAggressive(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2)
clf1.fit(X, y_bin)
for data in (X, X_csr):
clf2 = PassiveAggressiveClassifier(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2, shuffle=False)
clf2.fit(data, y_bin)
assert_array_almost_equal(clf1.w, clf2.coef_.ravel(), decimal=2)
def test_classifier_undefined_methods():
clf = PassiveAggressiveClassifier()
for meth in ("predict_proba", "predict_log_proba", "transform"):
assert_raises(AttributeError, lambda x: getattr(clf, x), meth)
def test_class_weights():
# Test class weights.
X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y2 = [1, 1, 1, -1, -1]
clf = PassiveAggressiveClassifier(C=0.1, n_iter=100, class_weight=None,
random_state=100)
clf.fit(X2, y2)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf = PassiveAggressiveClassifier(C=0.1, n_iter=100,
class_weight={1: 0.001},
random_state=100)
clf.fit(X2, y2)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
def test_partial_fit_weight_class_balanced():
# partial_fit with class_weight='balanced' not supported
clf = PassiveAggressiveClassifier(class_weight="balanced")
assert_raises(ValueError, clf.partial_fit, X, y, classes=np.unique(y))
def test_equal_class_weight():
X2 = [[1, 0], [1, 0], [0, 1], [0, 1]]
y2 = [0, 0, 1, 1]
clf = PassiveAggressiveClassifier(C=0.1, n_iter=1000, class_weight=None)
clf.fit(X2, y2)
# Already balanced, so "balanced" weights should have no effect
clf_balanced = PassiveAggressiveClassifier(C=0.1, n_iter=1000,
class_weight="balanced")
clf_balanced.fit(X2, y2)
clf_weighted = PassiveAggressiveClassifier(C=0.1, n_iter=1000,
class_weight={0: 0.5, 1: 0.5})
clf_weighted.fit(X2, y2)
# should be similar up to some epsilon due to learning rate schedule
assert_almost_equal(clf.coef_, clf_weighted.coef_, decimal=2)
assert_almost_equal(clf.coef_, clf_balanced.coef_, decimal=2)
def test_wrong_class_weight_label():
# ValueError due to wrong class_weight label.
X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y2 = [1, 1, 1, -1, -1]
clf = PassiveAggressiveClassifier(class_weight={0: 0.5})
assert_raises(ValueError, clf.fit, X2, y2)
def test_wrong_class_weight_format():
# ValueError due to wrong class_weight argument type.
X2 = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y2 = [1, 1, 1, -1, -1]
clf = PassiveAggressiveClassifier(class_weight=[0.5])
assert_raises(ValueError, clf.fit, X2, y2)
clf = PassiveAggressiveClassifier(class_weight="the larch")
assert_raises(ValueError, clf.fit, X2, y2)
def test_regressor_mse():
y_bin = y.copy()
y_bin[y != 1] = -1
for data in (X, X_csr):
for fit_intercept in (True, False):
reg = PassiveAggressiveRegressor(C=1.0, n_iter=50,
fit_intercept=fit_intercept,
random_state=0)
reg.fit(data, y_bin)
pred = reg.predict(data)
assert_less(np.mean((pred - y_bin) ** 2), 1.7)
def test_regressor_partial_fit():
y_bin = y.copy()
y_bin[y != 1] = -1
for data in (X, X_csr):
reg = PassiveAggressiveRegressor(C=1.0,
fit_intercept=True,
random_state=0)
for t in range(50):
reg.partial_fit(data, y_bin)
pred = reg.predict(data)
assert_less(np.mean((pred - y_bin) ** 2), 1.7)
def test_regressor_correctness():
y_bin = y.copy()
y_bin[y != 1] = -1
for loss in ("epsilon_insensitive", "squared_epsilon_insensitive"):
reg1 = MyPassiveAggressive(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2)
reg1.fit(X, y_bin)
for data in (X, X_csr):
reg2 = PassiveAggressiveRegressor(C=1.0,
loss=loss,
fit_intercept=True,
n_iter=2, shuffle=False)
reg2.fit(data, y_bin)
assert_array_almost_equal(reg1.w, reg2.coef_.ravel(), decimal=2)
def test_regressor_undefined_methods():
reg = PassiveAggressiveRegressor()
for meth in ("transform",):
assert_raises(AttributeError, lambda x: getattr(reg, x), meth)
| bsd-3-clause |
jonyroda97/redbot-amigosprovaveis | lib/matplotlib/testing/determinism.py | 2 | 4923 | """
Provides utilities to test output reproducibility.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import io
import os
import re
import sys
from subprocess import check_output
import pytest
import matplotlib
from matplotlib import pyplot as plt
def _determinism_save(objects='mhi', format="pdf", usetex=False):
# save current value of SOURCE_DATE_EPOCH and set it
# to a constant value, so that time difference is not
# taken into account
sde = os.environ.pop('SOURCE_DATE_EPOCH', None)
os.environ['SOURCE_DATE_EPOCH'] = "946684800"
matplotlib.rcParams['text.usetex'] = usetex
fig = plt.figure()
if 'm' in objects:
# use different markers...
ax1 = fig.add_subplot(1, 6, 1)
x = range(10)
ax1.plot(x, [1] * 10, marker=u'D')
ax1.plot(x, [2] * 10, marker=u'x')
ax1.plot(x, [3] * 10, marker=u'^')
ax1.plot(x, [4] * 10, marker=u'H')
ax1.plot(x, [5] * 10, marker=u'v')
if 'h' in objects:
# also use different hatch patterns
ax2 = fig.add_subplot(1, 6, 2)
bars = (ax2.bar(range(1, 5), range(1, 5)) +
ax2.bar(range(1, 5), [6] * 4, bottom=range(1, 5)))
ax2.set_xticks([1.5, 2.5, 3.5, 4.5])
patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.')
for bar, pattern in zip(bars, patterns):
bar.set_hatch(pattern)
if 'i' in objects:
# also use different images
A = [[1, 2, 3], [2, 3, 1], [3, 1, 2]]
fig.add_subplot(1, 6, 3).imshow(A, interpolation='nearest')
A = [[1, 3, 2], [1, 2, 3], [3, 1, 2]]
fig.add_subplot(1, 6, 4).imshow(A, interpolation='bilinear')
A = [[2, 3, 1], [1, 2, 3], [2, 1, 3]]
fig.add_subplot(1, 6, 5).imshow(A, interpolation='bicubic')
x = range(5)
fig.add_subplot(1, 6, 6).plot(x, x)
if six.PY2 and format == 'ps':
stdout = io.StringIO()
else:
stdout = getattr(sys.stdout, 'buffer', sys.stdout)
fig.savefig(stdout, format=format)
if six.PY2 and format == 'ps':
sys.stdout.write(stdout.getvalue())
# Restores SOURCE_DATE_EPOCH
if sde is None:
os.environ.pop('SOURCE_DATE_EPOCH', None)
else:
os.environ['SOURCE_DATE_EPOCH'] = sde
def _determinism_check(objects='mhi', format="pdf", usetex=False):
"""
Output three times the same graphs and checks that the outputs are exactly
the same.
Parameters
----------
objects : str
contains characters corresponding to objects to be included in the test
document: 'm' for markers, 'h' for hatch patterns, 'i' for images. The
default value is "mhi", so that the test includes all these objects.
format : str
format string. The default value is "pdf".
"""
plots = []
for i in range(3):
result = check_output([sys.executable, '-R', '-c',
'import matplotlib; '
'matplotlib._called_from_pytest = True; '
'matplotlib.use(%r); '
'from matplotlib.testing.determinism '
'import _determinism_save;'
'_determinism_save(%r,%r,%r)'
% (format, objects, format, usetex)])
plots.append(result)
for p in plots[1:]:
if usetex:
if p != plots[0]:
pytest.skip("failed, maybe due to ghostscript timestamps")
else:
assert p == plots[0]
def _determinism_source_date_epoch(format, string, keyword=b"CreationDate"):
"""
Test SOURCE_DATE_EPOCH support. Output a document with the envionment
variable SOURCE_DATE_EPOCH set to 2000-01-01 00:00 UTC and check that the
document contains the timestamp that corresponds to this date (given as an
argument).
Parameters
----------
format : str
format string, such as "pdf".
string : str
timestamp string for 2000-01-01 00:00 UTC.
keyword : bytes
a string to look at when searching for the timestamp in the document
(used in case the test fails).
"""
buff = check_output([sys.executable, '-R', '-c',
'import matplotlib; '
'matplotlib._called_from_pytest = True; '
'matplotlib.use(%r); '
'from matplotlib.testing.determinism '
'import _determinism_save;'
'_determinism_save(%r,%r)'
% (format, "", format)])
find_keyword = re.compile(b".*" + keyword + b".*")
key = find_keyword.search(buff)
if key:
print(key.group())
else:
print("Timestamp keyword (%s) not found!" % keyword)
assert string in buff
| gpl-3.0 |
cqychen/quants | quants/loaddata/skyeye_ods_invest_refer_achi_forcast.py | 1 | 2997 | #coding=utf8
import tushare as ts;
import pymysql;
import time as dt
from datashape.coretypes import string
from pandas.io.sql import SQLDatabase
import sqlalchemy
import datetime
from sqlalchemy import create_engine
from pandas.io import sql
import threading
import pandas as pd;
import numpy as np
import math
import sys
sys.path.append('../') #添加配置文件
from common_function import *
def create_table(table_name):
cmd='''
create table if not exists %s
(
`code` VARCHAR (10) comment '股票代码'
,`name` VARCHAR (63) comment '股票名称'
,`type` VARCHAR (63) comment '业绩类型'
,report_date VARCHAR (63) comment '发布日期'
,pre_eps DOUBLE comment '上年同期每股收益'
,`range` DOUBLE comment '业绩变动范围'
,`year` VARCHAR (63) comment '业绩年份'
,`quarter` VARCHAR (63) comment '业绩季度'
,index(code)
)DEFAULT CHARSET=utf8
'''%table_name
print (cmd)
run_mysql_cmd(cmd,conn)
def get_data_date(year,quarter):
cmd='''delete from %s where `year`='%s' and quarter='%s' '''%(table_name,year,quarter)
print cmd
run_mysql_cmd(cmd=cmd,conn=conn) #先删除指定年和指定季度的数据
rs=ts.forecast_data(year=int(year),quarter=int(quarter))
rs['year']=year
rs['quarter']=quarter
rs=rs.drop_duplicates() #去除重复的数据，没想到还有重复的，心塞塞，这个api不咋地啊，挖地兔
pd.DataFrame.to_sql(rs, table_name, con=conn , flavor='mysql', if_exists='append',index=False)
return rs
def load_data():
#下载公司基本信息，包括股票代码、pe、市盈率等数据
max_year=int(get_max_year_achi_forcast()) #还要获取前一年的情况
(year,quarter,mon,day,hour,min,sec,wday,yday,isdst)=get_date_struct()
while True:
print(max_year,year)
if(max_year<=year):
print("geting year is %s"%max_year)
get_data_date(year=max_year,quarter=1)
get_data_date(year=max_year,quarter=2)
get_data_date(year=max_year,quarter=3)
get_data_date(year=max_year,quarter=4)
print("\n\n")
max_year=max_year+1
else:
break
if __name__ == '__main__':
#--------------------设置基本信息---------------------------------
print("--------------加载公司基本信息开始-----------------------------")
startTime=dt.time()
iphost,user,passwd=get_mysql_conn()
db='ods_data'
charset='utf8'
table_name='ods_invest_refer_achi_forcast'
conn = pymysql.connect(user=user, passwd=passwd,host=iphost, db=db,charset=charset)
#--------------------脚本运行开始--------------------------------
create_table(table_name=table_name) #建立表格
load_data()
endTime=dt.time()
print("---------------脚本运行完毕,共计耗费时间%sS------------------"%(endTime-startTime))
| epl-1.0 |
daodaoliang/neural-network-animation | matplotlib/finance.py | 10 | 51311 | """
A collection of functions for collecting, analyzing and plotting
financial data. User contributions welcome!
This module is deprecated in 1.4 and will be moved to `mpl_toolkits`
or it's own project in the future.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange, zip
import contextlib
import os
import sys
import warnings
if six.PY3:
from urllib.request import urlopen
else:
from urllib2 import urlopen
if six.PY3:
import hashlib
md5 = lambda x: hashlib.md5(x.encode())
else:
from hashlib import md5
import datetime
import numpy as np
from matplotlib import verbose, get_cachedir
from matplotlib.dates import date2num
from matplotlib.cbook import iterable, mkdirs
from matplotlib.collections import LineCollection, PolyCollection
from matplotlib.colors import colorConverter
from matplotlib.lines import Line2D, TICKLEFT, TICKRIGHT
from matplotlib.patches import Rectangle
from matplotlib.transforms import Affine2D
from matplotlib.cbook import mplDeprecation
cachedir = get_cachedir()
# cachedir will be None if there is no writable directory.
if cachedir is not None:
cachedir = os.path.join(cachedir, 'finance.cache')
else:
# Should only happen in a restricted environment (such as Google App
# Engine). Deal with this gracefully by not caching finance data.
cachedir = None
stock_dt_ohlc = np.dtype([
(str('date'), object),
(str('year'), np.int16),
(str('month'), np.int8),
(str('day'), np.int8),
(str('d'), np.float), # mpl datenum
(str('open'), np.float),
(str('high'), np.float),
(str('low'), np.float),
(str('close'), np.float),
(str('volume'), np.float),
(str('aclose'), np.float)])
stock_dt_ochl = np.dtype(
[(str('date'), object),
(str('year'), np.int16),
(str('month'), np.int8),
(str('day'), np.int8),
(str('d'), np.float), # mpl datenum
(str('open'), np.float),
(str('close'), np.float),
(str('high'), np.float),
(str('low'), np.float),
(str('volume'), np.float),
(str('aclose'), np.float)])
_warn_str = ("This function has been deprecated in 1.4 in favor "
"of `{fun}_ochl`, "
"which maintains the original argument order, "
"or `{fun}_ohlc`, "
"which uses the open-high-low-close order. "
"This function will be removed in 1.5")
def parse_yahoo_historical_ochl(fh, adjusted=True, asobject=False):
"""Parse the historical data in file handle fh from yahoo finance.
Parameters
----------
adjusted : bool
If True (default) replace open, close, high, low prices with
their adjusted values. The adjustment is by a scale factor, S =
adjusted_close/close. Adjusted prices are actual prices
multiplied by S.
Volume is not adjusted as it is already backward split adjusted
by Yahoo. If you want to compute dollars traded, multiply volume
by the adjusted close, regardless of whether you choose adjusted
= True|False.
asobject : bool or None
If False (default for compatibility with earlier versions)
return a list of tuples containing
d, open, close, high, low, volume
If None (preferred alternative to False), return
a 2-D ndarray corresponding to the list of tuples.
Otherwise return a numpy recarray with
date, year, month, day, d, open, close, high, low,
volume, adjusted_close
where d is a floating poing representation of date,
as returned by date2num, and date is a python standard
library datetime.date instance.
The name of this kwarg is a historical artifact. Formerly,
True returned a cbook Bunch
holding 1-D ndarrays. The behavior of a numpy recarray is
very similar to the Bunch.
"""
return _parse_yahoo_historical(fh, adjusted=adjusted, asobject=asobject,
ochl=True)
def parse_yahoo_historical_ohlc(fh, adjusted=True, asobject=False):
"""Parse the historical data in file handle fh from yahoo finance.
Parameters
----------
adjusted : bool
If True (default) replace open, high, low, close prices with
their adjusted values. The adjustment is by a scale factor, S =
adjusted_close/close. Adjusted prices are actual prices
multiplied by S.
Volume is not adjusted as it is already backward split adjusted
by Yahoo. If you want to compute dollars traded, multiply volume
by the adjusted close, regardless of whether you choose adjusted
= True|False.
asobject : bool or None
If False (default for compatibility with earlier versions)
return a list of tuples containing
d, open, high, low, close, volume
If None (preferred alternative to False), return
a 2-D ndarray corresponding to the list of tuples.
Otherwise return a numpy recarray with
date, year, month, day, d, open, high, low, close,
volume, adjusted_close
where d is a floating poing representation of date,
as returned by date2num, and date is a python standard
library datetime.date instance.
The name of this kwarg is a historical artifact. Formerly,
True returned a cbook Bunch
holding 1-D ndarrays. The behavior of a numpy recarray is
very similar to the Bunch.
"""
return _parse_yahoo_historical(fh, adjusted=adjusted, asobject=asobject,
ochl=False)
def parse_yahoo_historical(fh, adjusted=True, asobject=False):
"""Parse the historical data in file handle fh from yahoo finance.
This function has been deprecated in 1.4 in favor of
`parse_yahoo_historical_ochl`, which maintains the original argument
order, or `parse_yahoo_historical_ohlc`, which uses the
open-high-low-close order. This function will be removed in 1.5
Parameters
----------
adjusted : bool
If True (default) replace open, close, high, low prices with
their adjusted values. The adjustment is by a scale factor, S =
adjusted_close/close. Adjusted prices are actual prices
multiplied by S.
Volume is not adjusted as it is already backward split adjusted
by Yahoo. If you want to compute dollars traded, multiply volume
by the adjusted close, regardless of whether you choose adjusted
= True|False.
asobject : bool or None
If False (default for compatibility with earlier versions)
return a list of tuples containing
d, open, close, high, low, volume
If None (preferred alternative to False), return
a 2-D ndarray corresponding to the list of tuples.
Otherwise return a numpy recarray with
date, year, month, day, d, open, close, high, low,
volume, adjusted_close
where d is a floating poing representation of date,
as returned by date2num, and date is a python standard
library datetime.date instance.
The name of this kwarg is a historical artifact. Formerly,
True returned a cbook Bunch
holding 1-D ndarrays. The behavior of a numpy recarray is
very similar to the Bunch.
ochl : bool
Temporary argument to select between ochl and ohlc ordering.
Defaults to True to preserve original functionality.
"""
warnings.warn(_warn_str.format(fun='parse_yahoo_historical'),
mplDeprecation)
return _parse_yahoo_historical(fh, adjusted=adjusted, asobject=asobject,
ochl=True)
def _parse_yahoo_historical(fh, adjusted=True, asobject=False,
ochl=True):
"""Parse the historical data in file handle fh from yahoo finance.
Parameters
----------
adjusted : bool
If True (default) replace open, high, low, close prices with
their adjusted values. The adjustment is by a scale factor, S =
adjusted_close/close. Adjusted prices are actual prices
multiplied by S.
Volume is not adjusted as it is already backward split adjusted
by Yahoo. If you want to compute dollars traded, multiply volume
by the adjusted close, regardless of whether you choose adjusted
= True|False.
asobject : bool or None
If False (default for compatibility with earlier versions)
return a list of tuples containing
d, open, high, low, close, volume
or
d, open, close, high, low, volume
depending on `ochl`
If None (preferred alternative to False), return
a 2-D ndarray corresponding to the list of tuples.
Otherwise return a numpy recarray with
date, year, month, day, d, open, high, low, close,
volume, adjusted_close
where d is a floating poing representation of date,
as returned by date2num, and date is a python standard
library datetime.date instance.
The name of this kwarg is a historical artifact. Formerly,
True returned a cbook Bunch
holding 1-D ndarrays. The behavior of a numpy recarray is
very similar to the Bunch.
ochl : bool
Selects between ochl and ohlc ordering.
Defaults to True to preserve original functionality.
"""
if ochl:
stock_dt = stock_dt_ochl
else:
stock_dt = stock_dt_ohlc
results = []
# datefmt = '%Y-%m-%d'
fh.readline() # discard heading
for line in fh:
vals = line.split(',')
if len(vals) != 7:
continue # add warning?
datestr = vals[0]
#dt = datetime.date(*time.strptime(datestr, datefmt)[:3])
# Using strptime doubles the runtime. With the present
# format, we don't need it.
dt = datetime.date(*[int(val) for val in datestr.split('-')])
dnum = date2num(dt)
open, high, low, close = [float(val) for val in vals[1:5]]
volume = float(vals[5])
aclose = float(vals[6])
if ochl:
results.append((dt, dt.year, dt.month, dt.day,
dnum, open, close, high, low, volume, aclose))
else:
results.append((dt, dt.year, dt.month, dt.day,
dnum, open, high, low, close, volume, aclose))
results.reverse()
d = np.array(results, dtype=stock_dt)
if adjusted:
scale = d['aclose'] / d['close']
scale[np.isinf(scale)] = np.nan
d['open'] *= scale
d['high'] *= scale
d['low'] *= scale
d['close'] *= scale
if not asobject:
# 2-D sequence; formerly list of tuples, now ndarray
ret = np.zeros((len(d), 6), dtype=np.float)
ret[:, 0] = d['d']
if ochl:
ret[:, 1] = d['open']
ret[:, 2] = d['close']
ret[:, 3] = d['high']
ret[:, 4] = d['low']
else:
ret[:, 1] = d['open']
ret[:, 2] = d['high']
ret[:, 3] = d['low']
ret[:, 4] = d['close']
ret[:, 5] = d['volume']
if asobject is None:
return ret
return [tuple(row) for row in ret]
return d.view(np.recarray) # Close enough to former Bunch return
def fetch_historical_yahoo(ticker, date1, date2, cachename=None,
dividends=False):
"""
Fetch historical data for ticker between date1 and date2. date1 and
date2 are date or datetime instances, or (year, month, day) sequences.
Parameters
----------
ticker : str
ticker
date1 : sequence of form (year, month, day), `datetime`, or `date`
start date
date2 : sequence of form (year, month, day), `datetime`, or `date`
end date
cachename : str
cachename is the name of the local file cache. If None, will
default to the md5 hash or the url (which incorporates the ticker
and date range)
dividends : bool
set dividends=True to return dividends instead of price data. With
this option set, parse functions will not work
Returns
-------
file_handle : file handle
a file handle is returned
Examples
--------
>>> fh = fetch_historical_yahoo('^GSPC', (2000, 1, 1), (2001, 12, 31))
"""
ticker = ticker.upper()
if iterable(date1):
d1 = (date1[1] - 1, date1[2], date1[0])
else:
d1 = (date1.month - 1, date1.day, date1.year)
if iterable(date2):
d2 = (date2[1] - 1, date2[2], date2[0])
else:
d2 = (date2.month - 1, date2.day, date2.year)
if dividends:
g = 'v'
verbose.report('Retrieving dividends instead of prices')
else:
g = 'd'
urlFmt = ('http://ichart.yahoo.com/table.csv?a=%d&b=%d&' +
'c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=%s&ignore=.csv')
url = urlFmt % (d1[0], d1[1], d1[2],
d2[0], d2[1], d2[2], ticker, g)
# Cache the finance data if cachename is supplied, or there is a writable
# cache directory.
if cachename is None and cachedir is not None:
cachename = os.path.join(cachedir, md5(url).hexdigest())
if cachename is not None:
if os.path.exists(cachename):
fh = open(cachename)
verbose.report('Using cachefile %s for '
'%s' % (cachename, ticker))
else:
mkdirs(os.path.abspath(os.path.dirname(cachename)))
with contextlib.closing(urlopen(url)) as urlfh:
with open(cachename, 'wb') as fh:
fh.write(urlfh.read())
verbose.report('Saved %s data to cache file '
'%s' % (ticker, cachename))
fh = open(cachename, 'r')
return fh
else:
return urlopen(url)
def quotes_historical_yahoo(ticker, date1, date2, asobject=False,
adjusted=True, cachename=None):
""" Get historical data for ticker between date1 and date2.
This function has been deprecated in 1.4 in favor of
`quotes_yahoo_historical_ochl`, which maintains the original argument
order, or `quotes_yahoo_historical_ohlc`, which uses the
open-high-low-close order. This function will be removed in 1.5
See :func:`parse_yahoo_historical` for explanation of output formats
and the *asobject* and *adjusted* kwargs.
Parameters
----------
ticker : str
stock ticker
date1 : sequence of form (year, month, day), `datetime`, or `date`
start date
date2 : sequence of form (year, month, day), `datetime`, or `date`
end date
cachename : str or `None`
is the name of the local file cache. If None, will
default to the md5 hash or the url (which incorporates the ticker
and date range)
Examples
--------
>>> sp = f.quotes_historical_yahoo('^GSPC', d1, d2,
asobject=True, adjusted=True)
>>> returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:]
>>> [n,bins,patches] = hist(returns, 100)
>>> mu = mean(returns)
>>> sigma = std(returns)
>>> x = normpdf(bins, mu, sigma)
>>> plot(bins, x, color='red', lw=2)
"""
warnings.warn(_warn_str.format(fun='quotes_historical_yahoo'),
mplDeprecation)
return _quotes_historical_yahoo(ticker, date1, date2, asobject=asobject,
adjusted=adjusted, cachename=cachename,
ochl=True)
def quotes_historical_yahoo_ochl(ticker, date1, date2, asobject=False,
adjusted=True, cachename=None):
""" Get historical data for ticker between date1 and date2.
See :func:`parse_yahoo_historical` for explanation of output formats
and the *asobject* and *adjusted* kwargs.
Parameters
----------
ticker : str
stock ticker
date1 : sequence of form (year, month, day), `datetime`, or `date`
start date
date2 : sequence of form (year, month, day), `datetime`, or `date`
end date
cachename : str or `None`
is the name of the local file cache. If None, will
default to the md5 hash or the url (which incorporates the ticker
and date range)
Examples
--------
>>> sp = f.quotes_historical_yahoo_ochl('^GSPC', d1, d2,
asobject=True, adjusted=True)
>>> returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:]
>>> [n,bins,patches] = hist(returns, 100)
>>> mu = mean(returns)
>>> sigma = std(returns)
>>> x = normpdf(bins, mu, sigma)
>>> plot(bins, x, color='red', lw=2)
"""
return _quotes_historical_yahoo(ticker, date1, date2, asobject=asobject,
adjusted=adjusted, cachename=cachename,
ochl=True)
def quotes_historical_yahoo_ohlc(ticker, date1, date2, asobject=False,
adjusted=True, cachename=None):
""" Get historical data for ticker between date1 and date2.
See :func:`parse_yahoo_historical` for explanation of output formats
and the *asobject* and *adjusted* kwargs.
Parameters
----------
ticker : str
stock ticker
date1 : sequence of form (year, month, day), `datetime`, or `date`
start date
date2 : sequence of form (year, month, day), `datetime`, or `date`
end date
cachename : str or `None`
is the name of the local file cache. If None, will
default to the md5 hash or the url (which incorporates the ticker
and date range)
Examples
--------
>>> sp = f.quotes_historical_yahoo_ohlc('^GSPC', d1, d2,
asobject=True, adjusted=True)
>>> returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:]
>>> [n,bins,patches] = hist(returns, 100)
>>> mu = mean(returns)
>>> sigma = std(returns)
>>> x = normpdf(bins, mu, sigma)
>>> plot(bins, x, color='red', lw=2)
"""
return _quotes_historical_yahoo(ticker, date1, date2, asobject=asobject,
adjusted=adjusted, cachename=cachename,
ochl=False)
def _quotes_historical_yahoo(ticker, date1, date2, asobject=False,
adjusted=True, cachename=None,
ochl=True):
""" Get historical data for ticker between date1 and date2.
See :func:`parse_yahoo_historical` for explanation of output formats
and the *asobject* and *adjusted* kwargs.
Parameters
----------
ticker : str
stock ticker
date1 : sequence of form (year, month, day), `datetime`, or `date`
start date
date2 : sequence of form (year, month, day), `datetime`, or `date`
end date
cachename : str or `None`
is the name of the local file cache. If None, will
default to the md5 hash or the url (which incorporates the ticker
and date range)
ochl: bool
temporary argument to select between ochl and ohlc ordering
Examples
--------
>>> sp = f.quotes_historical_yahoo('^GSPC', d1, d2,
asobject=True, adjusted=True)
>>> returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:]
>>> [n,bins,patches] = hist(returns, 100)
>>> mu = mean(returns)
>>> sigma = std(returns)
>>> x = normpdf(bins, mu, sigma)
>>> plot(bins, x, color='red', lw=2)
"""
# Maybe enable a warning later as part of a slow transition
# to using None instead of False.
#if asobject is False:
# warnings.warn("Recommend changing to asobject=None")
fh = fetch_historical_yahoo(ticker, date1, date2, cachename)
try:
ret = _parse_yahoo_historical(fh, asobject=asobject,
adjusted=adjusted, ochl=ochl)
if len(ret) == 0:
return None
except IOError as exc:
warnings.warn('fh failure\n%s' % (exc.strerror[1]))
return None
return ret
def plot_day_summary(ax, quotes, ticksize=3,
colorup='k', colordown='r',
):
"""Plots day summary
Represent the time, open, close, high, low as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
This function has been deprecated in 1.4 in favor of
`plot_day_summary_ochl`, which maintains the original argument
order, or `plot_day_summary_ohlc`, which uses the
open-high-low-close order. This function will be removed in 1.5
Parameters
----------
ax : `Axes`
an `Axes` instance to plot to
quotes : sequence of (time, open, close, high, low, ...) sequences
data to plot. time must be in float date format - see date2num
ticksize : int
open/close tick marker in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
Returns
-------
lines : list
list of tuples of the lines added (one tuple per quote)
"""
warnings.warn(_warn_str.format(fun='plot_day_summary'),
mplDeprecation)
return _plot_day_summary(ax, quotes, ticksize=ticksize,
colorup=colorup, colordown=colordown,
ochl=True)
def plot_day_summary_oclh(ax, quotes, ticksize=3,
colorup='k', colordown='r',
):
"""Plots day summary
Represent the time, open, close, high, low as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
Parameters
----------
ax : `Axes`
an `Axes` instance to plot to
quotes : sequence of (time, open, close, high, low, ...) sequences
data to plot. time must be in float date format - see date2num
ticksize : int
open/close tick marker in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
Returns
-------
lines : list
list of tuples of the lines added (one tuple per quote)
"""
return _plot_day_summary(ax, quotes, ticksize=ticksize,
colorup=colorup, colordown=colordown,
ochl=True)
def plot_day_summary_ohlc(ax, quotes, ticksize=3,
colorup='k', colordown='r',
):
"""Plots day summary
Represent the time, open, high, low, close as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
Parameters
----------
ax : `Axes`
an `Axes` instance to plot to
quotes : sequence of (time, open, high, low, close, ...) sequences
data to plot. time must be in float date format - see date2num
ticksize : int
open/close tick marker in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
Returns
-------
lines : list
list of tuples of the lines added (one tuple per quote)
"""
return _plot_day_summary(ax, quotes, ticksize=ticksize,
colorup=colorup, colordown=colordown,
ochl=False)
def _plot_day_summary(ax, quotes, ticksize=3,
colorup='k', colordown='r',
ochl=True
):
"""Plots day summary
Represent the time, open, high, low, close as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
Parameters
----------
ax : `Axes`
an `Axes` instance to plot to
quotes : sequence of quote sequences
data to plot. time must be in float date format - see date2num
(time, open, high, low, close, ...) vs
(time, open, close, high, low, ...)
set by `ochl`
ticksize : int
open/close tick marker in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
ochl: bool
argument to select between ochl and ohlc ordering of quotes
Returns
-------
lines : list
list of tuples of the lines added (one tuple per quote)
"""
# unfortunately this has a different return type than plot_day_summary2_*
lines = []
for q in quotes:
if ochl:
t, open, close, high, low = q[:5]
else:
t, open, high, low, close = q[:5]
if close >= open:
color = colorup
else:
color = colordown
vline = Line2D(xdata=(t, t), ydata=(low, high),
color=color,
antialiased=False, # no need to antialias vert lines
)
oline = Line2D(xdata=(t, t), ydata=(open, open),
color=color,
antialiased=False,
marker=TICKLEFT,
markersize=ticksize,
)
cline = Line2D(xdata=(t, t), ydata=(close, close),
color=color,
antialiased=False,
markersize=ticksize,
marker=TICKRIGHT)
lines.extend((vline, oline, cline))
ax.add_line(vline)
ax.add_line(oline)
ax.add_line(cline)
ax.autoscale_view()
return lines
def candlestick(ax, quotes, width=0.2, colorup='k', colordown='r',
alpha=1.0):
"""
Plot the time, open, close, high, low as a vertical line ranging
from low to high. Use a rectangular bar to represent the
open-close span. If close >= open, use colorup to color the bar,
otherwise use colordown
This function has been deprecated in 1.4 in favor of
`candlestick_ochl`, which maintains the original argument
order, or `candlestick_ohlc`, which uses the
open-high-low-close order. This function will be removed in 1.5
Parameters
----------
ax : `Axes`
an Axes instance to plot to
quotes : sequence of (time, open, close, high, low, ...) sequences
As long as the first 5 elements are these values,
the record can be as long as you want (e.g., it may store volume).
time must be in float days format - see date2num
width : float
fraction of a day for the rectangle width
colorup : color
the color of the rectangle where close >= open
colordown : color
the color of the rectangle where close < open
alpha : float
the rectangle alpha level
Returns
-------
ret : tuple
returns (lines, patches) where lines is a list of lines
added and patches is a list of the rectangle patches added
"""
warnings.warn(_warn_str.format(fun='candlestick'),
mplDeprecation)
return _candlestick(ax, quotes, width=width, colorup=colorup,
colordown=colordown,
alpha=alpha, ochl=True)
def candlestick_ochl(ax, quotes, width=0.2, colorup='k', colordown='r',
alpha=1.0):
"""
Plot the time, open, close, high, low as a vertical line ranging
from low to high. Use a rectangular bar to represent the
open-close span. If close >= open, use colorup to color the bar,
otherwise use colordown
Parameters
----------
ax : `Axes`
an Axes instance to plot to
quotes : sequence of (time, open, close, high, low, ...) sequences
As long as the first 5 elements are these values,
the record can be as long as you want (e.g., it may store volume).
time must be in float days format - see date2num
width : float
fraction of a day for the rectangle width
colorup : color
the color of the rectangle where close >= open
colordown : color
the color of the rectangle where close < open
alpha : float
the rectangle alpha level
Returns
-------
ret : tuple
returns (lines, patches) where lines is a list of lines
added and patches is a list of the rectangle patches added
"""
return _candlestick(ax, quotes, width=width, colorup=colorup,
colordown=colordown,
alpha=alpha, ochl=True)
def candlestick_ohlc(ax, quotes, width=0.2, colorup='k', colordown='r',
alpha=1.0):
"""
Plot the time, open, high, low, close as a vertical line ranging
from low to high. Use a rectangular bar to represent the
open-close span. If close >= open, use colorup to color the bar,
otherwise use colordown
Parameters
----------
ax : `Axes`
an Axes instance to plot to
quotes : sequence of (time, open, high, low, close, ...) sequences
As long as the first 5 elements are these values,
the record can be as long as you want (e.g., it may store volume).
time must be in float days format - see date2num
width : float
fraction of a day for the rectangle width
colorup : color
the color of the rectangle where close >= open
colordown : color
the color of the rectangle where close < open
alpha : float
the rectangle alpha level
Returns
-------
ret : tuple
returns (lines, patches) where lines is a list of lines
added and patches is a list of the rectangle patches added
"""
return _candlestick(ax, quotes, width=width, colorup=colorup,
colordown=colordown,
alpha=alpha, ochl=False)
def _candlestick(ax, quotes, width=0.2, colorup='k', colordown='r',
alpha=1.0, ochl=True):
"""
Plot the time, open, high, low, close as a vertical line ranging
from low to high. Use a rectangular bar to represent the
open-close span. If close >= open, use colorup to color the bar,
otherwise use colordown
Parameters
----------
ax : `Axes`
an Axes instance to plot to
quotes : sequence of quote sequences
data to plot. time must be in float date format - see date2num
(time, open, high, low, close, ...) vs
(time, open, close, high, low, ...)
set by `ochl`
width : float
fraction of a day for the rectangle width
colorup : color
the color of the rectangle where close >= open
colordown : color
the color of the rectangle where close < open
alpha : float
the rectangle alpha level
ochl: bool
argument to select between ochl and ohlc ordering of quotes
Returns
-------
ret : tuple
returns (lines, patches) where lines is a list of lines
added and patches is a list of the rectangle patches added
"""
OFFSET = width / 2.0
lines = []
patches = []
for q in quotes:
if ochl:
t, open, close, high, low = q[:5]
else:
t, open, high, low, close = q[:5]
if close >= open:
color = colorup
lower = open
height = close - open
else:
color = colordown
lower = close
height = open - close
vline = Line2D(
xdata=(t, t), ydata=(low, high),
color=color,
linewidth=0.5,
antialiased=True,
)
rect = Rectangle(
xy=(t - OFFSET, lower),
width = width,
height = height,
facecolor = color,
edgecolor = color,
)
rect.set_alpha(alpha)
lines.append(vline)
patches.append(rect)
ax.add_line(vline)
ax.add_patch(rect)
ax.autoscale_view()
return lines, patches
def plot_day_summary2(ax, opens, closes, highs, lows, ticksize=4,
colorup='k', colordown='r',
):
"""Represent the time, open, close, high, low, as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
This function has been deprecated in 1.4 in favor of
`plot_day_summary2_ochl`, which maintains the original argument
order, or `plot_day_summary2_ohlc`, which uses the
open-high-low-close order. This function will be removed in 1.5
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
closes : sequence
sequence of closing values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
ticksize : int
size of open and close ticks in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
Returns
-------
ret : list
a list of lines added to the axes
"""
warnings.warn(_warn_str.format(fun='plot_day_summary2'), mplDeprecation)
return plot_day_summary2_ohlc(ax, opens, highs, lows, closes, ticksize,
colorup, colordown)
def plot_day_summary2_ochl(ax, opens, closes, highs, lows, ticksize=4,
colorup='k', colordown='r',
):
"""Represent the time, open, close, high, low, as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
closes : sequence
sequence of closing values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
ticksize : int
size of open and close ticks in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
Returns
-------
ret : list
a list of lines added to the axes
"""
return plot_day_summary2_ohlc(ax, opens, highs, lows, closes, ticksize,
colorup, colordown)
def plot_day_summary2_ohlc(ax, opens, highs, lows, closes, ticksize=4,
colorup='k', colordown='r',
):
"""Represent the time, open, high, low, close as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
closes : sequence
sequence of closing values
ticksize : int
size of open and close ticks in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
Returns
-------
ret : list
a list of lines added to the axes
"""
# note this code assumes if any value open, high, low, close is
# missing they all are missing
rangeSegments = [((i, low), (i, high)) for i, low, high in
zip(xrange(len(lows)), lows, highs) if low != -1]
# the ticks will be from ticksize to 0 in points at the origin and
# we'll translate these to the i, close location
openSegments = [((-ticksize, 0), (0, 0))]
# the ticks will be from 0 to ticksize in points at the origin and
# we'll translate these to the i, close location
closeSegments = [((0, 0), (ticksize, 0))]
offsetsOpen = [(i, open) for i, open in
zip(xrange(len(opens)), opens) if open != -1]
offsetsClose = [(i, close) for i, close in
zip(xrange(len(closes)), closes) if close != -1]
scale = ax.figure.dpi * (1.0 / 72.0)
tickTransform = Affine2D().scale(scale, 0.0)
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, 1
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, 1
colord = {True: colorup,
False: colordown,
}
colors = [colord[open < close] for open, close in
zip(opens, closes) if open != -1 and close != -1]
assert(len(rangeSegments) == len(offsetsOpen))
assert(len(offsetsOpen) == len(offsetsClose))
assert(len(offsetsClose) == len(colors))
useAA = 0, # use tuple here
lw = 1, # and here
rangeCollection = LineCollection(rangeSegments,
colors=colors,
linewidths=lw,
antialiaseds=useAA,
)
openCollection = LineCollection(openSegments,
colors=colors,
antialiaseds=useAA,
linewidths=lw,
offsets=offsetsOpen,
transOffset=ax.transData,
)
openCollection.set_transform(tickTransform)
closeCollection = LineCollection(closeSegments,
colors=colors,
antialiaseds=useAA,
linewidths=lw,
offsets=offsetsClose,
transOffset=ax.transData,
)
closeCollection.set_transform(tickTransform)
minpy, maxx = (0, len(rangeSegments))
miny = min([low for low in lows if low != -1])
maxy = max([high for high in highs if high != -1])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
ax.add_collection(rangeCollection)
ax.add_collection(openCollection)
ax.add_collection(closeCollection)
return rangeCollection, openCollection, closeCollection
def candlestick2_ochl(ax, opens, closes, highs, lows, width=4,
colorup='k', colordown='r',
alpha=0.75,
):
"""Represent the open, close as a bar line and high low range as a
vertical line.
Preserves the original argument order.
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
closes : sequence
sequence of closing values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
ticksize : int
size of open and close ticks in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
alpha : float
bar transparency
Returns
-------
ret : tuple
(lineCollection, barCollection)
"""
candlestick2_ohlc(ax, opens, highs, closes, lows, width=width,
colorup=colorup, colordown=colordown,
alpha=alpha)
def candlestick2(ax, opens, closes, highs, lows, width=4,
colorup='k', colordown='r',
alpha=0.75,
):
"""Represent the open, close as a bar line and high low range as a
vertical line.
This function has been deprecated in 1.4 in favor of
`candlestick2_ochl`, which maintains the original argument order,
or `candlestick2_ohlc`, which uses the open-high-low-close order.
This function will be removed in 1.5
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
closes : sequence
sequence of closing values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
ticksize : int
size of open and close ticks in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
alpha : float
bar transparency
Returns
-------
ret : tuple
(lineCollection, barCollection)
"""
warnings.warn(_warn_str.format(fun='candlestick2'),
mplDeprecation)
candlestick2_ohlc(ax, opens, highs, lows, closes, width=width,
colorup=colorup, colordown=colordown,
alpha=alpha)
def candlestick2_ohlc(ax, opens, highs, lows, closes, width=4,
colorup='k', colordown='r',
alpha=0.75,
):
"""Represent the open, close as a bar line and high low range as a
vertical line.
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
closes : sequence
sequence of closing values
ticksize : int
size of open and close ticks in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
alpha : float
bar transparency
Returns
-------
ret : tuple
(lineCollection, barCollection)
"""
# note this code assumes if any value open, low, high, close is
# missing they all are missing
delta = width / 2.
barVerts = [((i - delta, open),
(i - delta, close),
(i + delta, close),
(i + delta, open))
for i, open, close in zip(xrange(len(opens)), opens, closes)
if open != -1 and close != -1]
rangeSegments = [((i, low), (i, high))
for i, low, high in zip(xrange(len(lows)), lows, highs)
if low != -1]
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, alpha
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, alpha
colord = {True: colorup,
False: colordown,
}
colors = [colord[open < close]
for open, close in zip(opens, closes)
if open != -1 and close != -1]
assert(len(barVerts) == len(rangeSegments))
useAA = 0, # use tuple here
lw = 0.5, # and here
rangeCollection = LineCollection(rangeSegments,
colors=((0, 0, 0, 1), ),
linewidths=lw,
antialiaseds = useAA,
)
barCollection = PolyCollection(barVerts,
facecolors=colors,
edgecolors=((0, 0, 0, 1), ),
antialiaseds=useAA,
linewidths=lw,
)
minx, maxx = 0, len(rangeSegments)
miny = min([low for low in lows if low != -1])
maxy = max([high for high in highs if high != -1])
corners = (minx, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
ax.add_collection(barCollection)
ax.add_collection(rangeCollection)
return rangeCollection, barCollection
def volume_overlay(ax, opens, closes, volumes,
colorup='k', colordown='r',
width=4, alpha=1.0):
"""Add a volume overlay to the current axes. The opens and closes
are used to determine the color of the bar. -1 is missing. If a
value is missing on one it must be missing on all
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
a sequence of opens
closes : sequence
a sequence of closes
volumes : sequence
a sequence of volumes
width : int
the bar width in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
alpha : float
bar transparency
Returns
-------
ret : `barCollection`
The `barrCollection` added to the axes
"""
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, alpha
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, alpha
colord = {True: colorup,
False: colordown,
}
colors = [colord[open < close]
for open, close in zip(opens, closes)
if open != -1 and close != -1]
delta = width / 2.
bars = [((i - delta, 0), (i - delta, v), (i + delta, v), (i + delta, 0))
for i, v in enumerate(volumes)
if v != -1]
barCollection = PolyCollection(bars,
facecolors=colors,
edgecolors=((0, 0, 0, 1), ),
antialiaseds=(0,),
linewidths=(0.5,),
)
ax.add_collection(barCollection)
corners = (0, 0), (len(bars), max(volumes))
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
return barCollection
def volume_overlay2(ax, closes, volumes,
colorup='k', colordown='r',
width=4, alpha=1.0):
"""
Add a volume overlay to the current axes. The closes are used to
determine the color of the bar. -1 is missing. If a value is
missing on one it must be missing on all
nb: first point is not displayed - it is used only for choosing the
right color
Parameters
----------
ax : `Axes`
an Axes instance to plot to
closes : sequence
a sequence of closes
volumes : sequence
a sequence of volumes
width : int
the bar width in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
alpha : float
bar transparency
Returns
-------
ret : `barCollection`
The `barrCollection` added to the axes
"""
return volume_overlay(ax, closes[:-1], closes[1:], volumes[1:],
colorup, colordown, width, alpha)
def volume_overlay3(ax, quotes,
colorup='k', colordown='r',
width=4, alpha=1.0):
"""Add a volume overlay to the current axes. quotes is a list of (d,
open, high, low, close, volume) and close-open is used to
determine the color of the bar
Parameters
----------
ax : `Axes`
an Axes instance to plot to
quotes : sequence of (time, open, high, low, close, ...) sequences
data to plot. time must be in float date format - see date2num
width : int
the bar width in points
colorup : color
the color of the lines where close1 >= close0
colordown : color
the color of the lines where close1 < close0
alpha : float
bar transparency
Returns
-------
ret : `barCollection`
The `barrCollection` added to the axes
"""
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, alpha
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, alpha
colord = {True: colorup,
False: colordown,
}
dates, opens, highs, lows, closes, volumes = list(zip(*quotes))
colors = [colord[close1 >= close0]
for close0, close1 in zip(closes[:-1], closes[1:])
if close0 != -1 and close1 != -1]
colors.insert(0, colord[closes[0] >= opens[0]])
right = width / 2.0
left = -width / 2.0
bars = [((left, 0), (left, volume), (right, volume), (right, 0))
for d, open, high, low, close, volume in quotes]
sx = ax.figure.dpi * (1.0 / 72.0) # scale for points
sy = ax.bbox.height / ax.viewLim.height
barTransform = Affine2D().scale(sx, sy)
dates = [d for d, open, high, low, close, volume in quotes]
offsetsBars = [(d, 0) for d in dates]
useAA = 0, # use tuple here
lw = 0.5, # and here
barCollection = PolyCollection(bars,
facecolors=colors,
edgecolors=((0, 0, 0, 1),),
antialiaseds=useAA,
linewidths=lw,
offsets=offsetsBars,
transOffset=ax.transData,
)
barCollection.set_transform(barTransform)
minpy, maxx = (min(dates), max(dates))
miny = 0
maxy = max([volume for d, open, high, low, close, volume in quotes])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
#print 'datalim', ax.dataLim.bounds
#print 'viewlim', ax.viewLim.bounds
ax.add_collection(barCollection)
ax.autoscale_view()
return barCollection
def index_bar(ax, vals,
facecolor='b', edgecolor='l',
width=4, alpha=1.0, ):
"""Add a bar collection graph with height vals (-1 is missing).
Parameters
----------
ax : `Axes`
an Axes instance to plot to
vals : sequence
a sequence of values
facecolor : color
the color of the bar face
edgecolor : color
the color of the bar edges
width : int
the bar width in points
alpha : float
bar transparency
Returns
-------
ret : `barCollection`
The `barrCollection` added to the axes
"""
facecolors = (colorConverter.to_rgba(facecolor, alpha),)
edgecolors = (colorConverter.to_rgba(edgecolor, alpha),)
right = width / 2.0
left = -width / 2.0
bars = [((left, 0), (left, v), (right, v), (right, 0))
for v in vals if v != -1]
sx = ax.figure.dpi * (1.0 / 72.0) # scale for points
sy = ax.bbox.height / ax.viewLim.height
barTransform = Affine2D().scale(sx, sy)
offsetsBars = [(i, 0) for i, v in enumerate(vals) if v != -1]
barCollection = PolyCollection(bars,
facecolors=facecolors,
edgecolors=edgecolors,
antialiaseds=(0,),
linewidths=(0.5,),
offsets=offsetsBars,
transOffset=ax.transData,
)
barCollection.set_transform(barTransform)
minpy, maxx = (0, len(offsetsBars))
miny = 0
maxy = max([v for v in vals if v != -1])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
ax.add_collection(barCollection)
return barCollection
| mit |
Tong-Chen/scikit-learn | examples/svm/plot_svm_regression.py | 4 | 1429 | """
===================================================================
Support Vector Regression (SVR) using linear and non-linear kernels
===================================================================
Toy example of 1D regression using linear, polynomial and RBF kernels.
"""
print(__doc__)
###############################################################################
# Generate sample data
import numpy as np
X = np.sort(5 * np.random.rand(40, 1), axis=0)
y = np.sin(X).ravel()
###############################################################################
# Add noise to targets
y[::5] += 3 * (0.5 - np.random.rand(8))
###############################################################################
# Fit regression model
from sklearn.svm import SVR
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_lin = SVR(kernel='linear', C=1e3)
svr_poly = SVR(kernel='poly', C=1e3, degree=2)
y_rbf = svr_rbf.fit(X, y).predict(X)
y_lin = svr_lin.fit(X, y).predict(X)
y_poly = svr_poly.fit(X, y).predict(X)
###############################################################################
# look at the results
import pylab as pl
pl.scatter(X, y, c='k', label='data')
pl.hold('on')
pl.plot(X, y_rbf, c='g', label='RBF model')
pl.plot(X, y_lin, c='r', label='Linear model')
pl.plot(X, y_poly, c='b', label='Polynomial model')
pl.xlabel('data')
pl.ylabel('target')
pl.title('Support Vector Regression')
pl.legend()
pl.show()
| bsd-3-clause |
nvoron23/scikit-learn | benchmarks/bench_covertype.py | 120 | 7381 | """
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It is
low dimensional with 54 features and a sparsity of approx. 23%. Here, we
consider the task of predicting class 1 (spruce/fir). The classification
performance of SGD is competitive with Liblinear while being two orders of
magnitude faster to train::
[..]
Classification performance:
===========================
Classifier train-time test-time error-rate
--------------------------------------------
liblinear 15.9744s 0.0705s 0.2305
GaussianNB 3.0666s 0.3884s 0.4841
SGD 1.0558s 0.1152s 0.2300
CART 79.4296s 0.0523s 0.0469
RandomForest 1190.1620s 0.5881s 0.0243
ExtraTrees 640.3194s 0.6495s 0.0198
The same task has been used in a number of papers including:
* `"SVM Optimization: Inverse Dependence on Training Set Size"
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.2112>`_
S. Shalev-Shwartz, N. Srebro - In Proceedings of ICML '08.
* `"Pegasos: Primal estimated sub-gradient solver for svm"
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_
S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
* `"Training Linear SVMs in Linear Time"
<www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_
T. Joachims - In SIGKDD '06
[1] http://archive.ics.uci.edu/ml/datasets/Covertype
"""
from __future__ import division, print_function
# Author: Peter Prettenhofer <[email protected]>
# Arnaud Joly <[email protected]>
# License: BSD 3 clause
import os
from time import time
import argparse
import numpy as np
from sklearn.datasets import fetch_covtype, get_data_home
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier, LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import zero_one_loss
from sklearn.externals.joblib import Memory
from sklearn.utils import check_array
# Memoize the data extraction and memory map the resulting
# train / test splits in readonly mode
memory = Memory(os.path.join(get_data_home(), 'covertype_benchmark_data'),
mmap_mode='r')
@memory.cache
def load_data(dtype=np.float32, order='C', random_state=13):
"""Load the data, then cache and memmap the train/test split"""
######################################################################
## Load dataset
print("Loading dataset...")
data = fetch_covtype(download_if_missing=True, shuffle=True,
random_state=random_state)
X = check_array(data['data'], dtype=dtype, order=order)
y = (data['target'] != 1).astype(np.int)
## Create train-test split (as [Joachims, 2006])
print("Creating train-test split...")
n_train = 522911
X_train = X[:n_train]
y_train = y[:n_train]
X_test = X[n_train:]
y_test = y[n_train:]
## Standardize first 10 features (the numerical ones)
mean = X_train.mean(axis=0)
std = X_train.std(axis=0)
mean[10:] = 0.0
std[10:] = 1.0
X_train = (X_train - mean) / std
X_test = (X_test - mean) / std
return X_train, X_test, y_train, y_test
ESTIMATORS = {
'GBRT': GradientBoostingClassifier(n_estimators=250),
'ExtraTrees': ExtraTreesClassifier(n_estimators=20),
'RandomForest': RandomForestClassifier(n_estimators=20),
'CART': DecisionTreeClassifier(min_samples_split=5),
'SGD': SGDClassifier(alpha=0.001, n_iter=2),
'GaussianNB': GaussianNB(),
'liblinear': LinearSVC(loss="l2", penalty="l2", C=1000, dual=False,
tol=1e-3),
'SAG': LogisticRegression(solver='sag', max_iter=2, C=1000)
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--classifiers', nargs="+",
choices=ESTIMATORS, type=str,
default=['liblinear', 'GaussianNB', 'SGD', 'CART'],
help="list of classifiers to benchmark.")
parser.add_argument('--n-jobs', nargs="?", default=1, type=int,
help="Number of concurrently running workers for "
"models that support parallelism.")
parser.add_argument('--order', nargs="?", default="C", type=str,
choices=["F", "C"],
help="Allow to choose between fortran and C ordered "
"data")
parser.add_argument('--random-seed', nargs="?", default=13, type=int,
help="Common seed used by random number generator.")
args = vars(parser.parse_args())
print(__doc__)
X_train, X_test, y_train, y_test = load_data(
order=args["order"], random_state=args["random_seed"])
print("")
print("Dataset statistics:")
print("===================")
print("%s %d" % ("number of features:".ljust(25), X_train.shape[1]))
print("%s %d" % ("number of classes:".ljust(25), np.unique(y_train).size))
print("%s %s" % ("data type:".ljust(25), X_train.dtype))
print("%s %d (pos=%d, neg=%d, size=%dMB)"
% ("number of train samples:".ljust(25),
X_train.shape[0], np.sum(y_train == 1),
np.sum(y_train == 0), int(X_train.nbytes / 1e6)))
print("%s %d (pos=%d, neg=%d, size=%dMB)"
% ("number of test samples:".ljust(25),
X_test.shape[0], np.sum(y_test == 1),
np.sum(y_test == 0), int(X_test.nbytes / 1e6)))
print()
print("Training Classifiers")
print("====================")
error, train_time, test_time = {}, {}, {}
for name in sorted(args["classifiers"]):
print("Training %s ... " % name, end="")
estimator = ESTIMATORS[name]
estimator_params = estimator.get_params()
estimator.set_params(**{p: args["random_seed"]
for p in estimator_params
if p.endswith("random_state")})
if "n_jobs" in estimator_params:
estimator.set_params(n_jobs=args["n_jobs"])
time_start = time()
estimator.fit(X_train, y_train)
train_time[name] = time() - time_start
time_start = time()
y_pred = estimator.predict(X_test)
test_time[name] = time() - time_start
error[name] = zero_one_loss(y_test, y_pred)
print("done")
print()
print("Classification performance:")
print("===========================")
print("%s %s %s %s"
% ("Classifier ", "train-time", "test-time", "error-rate"))
print("-" * 44)
for name in sorted(args["classifiers"], key=error.get):
print("%s %s %s %s" % (name.ljust(12),
("%.4fs" % train_time[name]).center(10),
("%.4fs" % test_time[name]).center(10),
("%.4f" % error[name]).center(10)))
print()
| bsd-3-clause |
anntzer/scikit-learn | sklearn/utils/tests/test_extmath.py | 1 | 30713 | # Authors: Olivier Grisel <[email protected]>
# Mathieu Blondel <[email protected]>
# Denis Engemann <[email protected]>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from scipy.special import expit
import pytest
from sklearn.utils import gen_batches
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_allclose_dense_sparse
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import skip_if_32bit
from sklearn.utils.extmath import density, _safe_accumulator_op
from sklearn.utils.extmath import randomized_svd
from sklearn.utils.extmath import row_norms
from sklearn.utils.extmath import weighted_mode
from sklearn.utils.extmath import cartesian
from sklearn.utils.extmath import log_logistic
from sklearn.utils.extmath import svd_flip
from sklearn.utils.extmath import _incremental_mean_and_var
from sklearn.utils.extmath import _deterministic_vector_sign_flip
from sklearn.utils.extmath import softmax
from sklearn.utils.extmath import stable_cumsum
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.datasets import make_low_rank_matrix
def test_density():
rng = np.random.RandomState(0)
X = rng.randint(10, size=(10, 5))
X[1, 2] = 0
X[5, 3] = 0
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
X_coo = sparse.coo_matrix(X)
X_lil = sparse.lil_matrix(X)
for X_ in (X_csr, X_csc, X_coo, X_lil):
assert density(X_) == density(X)
def test_uniform_weights():
# with uniform weights, results should be identical to stats.mode
rng = np.random.RandomState(0)
x = rng.randint(10, size=(10, 5))
weights = np.ones(x.shape)
for axis in (None, 0, 1):
mode, score = stats.mode(x, axis)
mode2, score2 = weighted_mode(x, weights, axis=axis)
assert_array_equal(mode, mode2)
assert_array_equal(score, score2)
def test_random_weights():
# set this up so that each row should have a weighted mode of 6,
# with a score that is easily reproduced
mode_result = 6
rng = np.random.RandomState(0)
x = rng.randint(mode_result, size=(100, 10))
w = rng.random_sample(x.shape)
x[:, :5] = mode_result
w[:, :5] += 1
mode, score = weighted_mode(x, w, axis=1)
assert_array_equal(mode, mode_result)
assert_array_almost_equal(score.ravel(), w[:, :5].sum(1))
def check_randomized_svd_low_rank(dtype):
# Check that extmath.randomized_svd is consistent with linalg.svd
n_samples = 100
n_features = 500
rank = 5
k = 10
decimal = 5 if dtype == np.float32 else 7
dtype = np.dtype(dtype)
# generate a matrix X of approximate effective rank `rank` and no noise
# component (very structured signal):
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=0.0,
random_state=0).astype(dtype, copy=False)
assert X.shape == (n_samples, n_features)
# compute the singular values of X using the slow exact method
U, s, Vt = linalg.svd(X, full_matrices=False)
# Convert the singular values to the specific dtype
U = U.astype(dtype, copy=False)
s = s.astype(dtype, copy=False)
Vt = Vt.astype(dtype, copy=False)
for normalizer in ['auto', 'LU', 'QR']: # 'none' would not be stable
# compute the singular values of X using the fast approximate method
Ua, sa, Va = randomized_svd(
X, k, power_iteration_normalizer=normalizer, random_state=0)
# If the input dtype is float, then the output dtype is float of the
# same bit size (f32 is not upcast to f64)
# But if the input dtype is int, the output dtype is float64
if dtype.kind == 'f':
assert Ua.dtype == dtype
assert sa.dtype == dtype
assert Va.dtype == dtype
else:
assert Ua.dtype == np.float64
assert sa.dtype == np.float64
assert Va.dtype == np.float64
assert Ua.shape == (n_samples, k)
assert sa.shape == (k,)
assert Va.shape == (k, n_features)
# ensure that the singular values of both methods are equal up to the
# real rank of the matrix
assert_almost_equal(s[:k], sa, decimal=decimal)
# check the singular vectors too (while not checking the sign)
assert_almost_equal(np.dot(U[:, :k], Vt[:k, :]), np.dot(Ua, Va),
decimal=decimal)
# check the sparse matrix representation
X = sparse.csr_matrix(X)
# compute the singular values of X using the fast approximate method
Ua, sa, Va = \
randomized_svd(X, k, power_iteration_normalizer=normalizer,
random_state=0)
if dtype.kind == 'f':
assert Ua.dtype == dtype
assert sa.dtype == dtype
assert Va.dtype == dtype
else:
assert Ua.dtype.kind == 'f'
assert sa.dtype.kind == 'f'
assert Va.dtype.kind == 'f'
assert_almost_equal(s[:rank], sa[:rank], decimal=decimal)
@pytest.mark.parametrize('dtype',
(np.int32, np.int64, np.float32, np.float64))
def test_randomized_svd_low_rank_all_dtypes(dtype):
check_randomized_svd_low_rank(dtype)
@pytest.mark.parametrize('dtype',
(np.float32, np.float64))
def test_row_norms(dtype):
X = np.random.RandomState(42).randn(100, 100)
if dtype is np.float32:
precision = 4
else:
precision = 5
X = X.astype(dtype, copy=False)
sq_norm = (X ** 2).sum(axis=1)
assert_array_almost_equal(sq_norm, row_norms(X, squared=True),
precision)
assert_array_almost_equal(np.sqrt(sq_norm), row_norms(X), precision)
for csr_index_dtype in [np.int32, np.int64]:
Xcsr = sparse.csr_matrix(X, dtype=dtype)
# csr_matrix will use int32 indices by default,
# up-casting those to int64 when necessary
if csr_index_dtype is np.int64:
Xcsr.indptr = Xcsr.indptr.astype(csr_index_dtype, copy=False)
Xcsr.indices = Xcsr.indices.astype(csr_index_dtype, copy=False)
assert Xcsr.indices.dtype == csr_index_dtype
assert Xcsr.indptr.dtype == csr_index_dtype
assert_array_almost_equal(sq_norm, row_norms(Xcsr, squared=True),
precision)
assert_array_almost_equal(np.sqrt(sq_norm), row_norms(Xcsr),
precision)
def test_randomized_svd_low_rank_with_noise():
# Check that extmath.randomized_svd can handle noisy matrices
n_samples = 100
n_features = 500
rank = 5
k = 10
# generate a matrix X wity structure approximate rank `rank` and an
# important noisy component
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=0.1,
random_state=0)
assert X.shape == (n_samples, n_features)
# compute the singular values of X using the slow exact method
_, s, _ = linalg.svd(X, full_matrices=False)
for normalizer in ['auto', 'none', 'LU', 'QR']:
# compute the singular values of X using the fast approximate
# method without the iterated power method
_, sa, _ = randomized_svd(X, k, n_iter=0,
power_iteration_normalizer=normalizer,
random_state=0)
# the approximation does not tolerate the noise:
assert np.abs(s[:k] - sa).max() > 0.01
# compute the singular values of X using the fast approximate
# method with iterated power method
_, sap, _ = randomized_svd(X, k,
power_iteration_normalizer=normalizer,
random_state=0)
# the iterated power method is helping getting rid of the noise:
assert_almost_equal(s[:k], sap, decimal=3)
def test_randomized_svd_infinite_rank():
# Check that extmath.randomized_svd can handle noisy matrices
n_samples = 100
n_features = 500
rank = 5
k = 10
# let us try again without 'low_rank component': just regularly but slowly
# decreasing singular values: the rank of the data matrix is infinite
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=1.0,
random_state=0)
assert X.shape == (n_samples, n_features)
# compute the singular values of X using the slow exact method
_, s, _ = linalg.svd(X, full_matrices=False)
for normalizer in ['auto', 'none', 'LU', 'QR']:
# compute the singular values of X using the fast approximate method
# without the iterated power method
_, sa, _ = randomized_svd(X, k, n_iter=0,
power_iteration_normalizer=normalizer)
# the approximation does not tolerate the noise:
assert np.abs(s[:k] - sa).max() > 0.1
# compute the singular values of X using the fast approximate method
# with iterated power method
_, sap, _ = randomized_svd(X, k, n_iter=5,
power_iteration_normalizer=normalizer)
# the iterated power method is still managing to get most of the
# structure at the requested rank
assert_almost_equal(s[:k], sap, decimal=3)
def test_randomized_svd_transpose_consistency():
# Check that transposing the design matrix has limited impact
n_samples = 100
n_features = 500
rank = 4
k = 10
X = make_low_rank_matrix(n_samples=n_samples, n_features=n_features,
effective_rank=rank, tail_strength=0.5,
random_state=0)
assert X.shape == (n_samples, n_features)
U1, s1, V1 = randomized_svd(X, k, n_iter=3, transpose=False,
random_state=0)
U2, s2, V2 = randomized_svd(X, k, n_iter=3, transpose=True,
random_state=0)
U3, s3, V3 = randomized_svd(X, k, n_iter=3, transpose='auto',
random_state=0)
U4, s4, V4 = linalg.svd(X, full_matrices=False)
assert_almost_equal(s1, s4[:k], decimal=3)
assert_almost_equal(s2, s4[:k], decimal=3)
assert_almost_equal(s3, s4[:k], decimal=3)
assert_almost_equal(np.dot(U1, V1), np.dot(U4[:, :k], V4[:k, :]),
decimal=2)
assert_almost_equal(np.dot(U2, V2), np.dot(U4[:, :k], V4[:k, :]),
decimal=2)
# in this case 'auto' is equivalent to transpose
assert_almost_equal(s2, s3)
def test_randomized_svd_power_iteration_normalizer():
# randomized_svd with power_iteration_normalized='none' diverges for
# large number of power iterations on this dataset
rng = np.random.RandomState(42)
X = make_low_rank_matrix(100, 500, effective_rank=50, random_state=rng)
X += 3 * rng.randint(0, 2, size=X.shape)
n_components = 50
# Check that it diverges with many (non-normalized) power iterations
U, s, Vt = randomized_svd(X, n_components, n_iter=2,
power_iteration_normalizer='none')
A = X - U.dot(np.diag(s).dot(Vt))
error_2 = linalg.norm(A, ord='fro')
U, s, Vt = randomized_svd(X, n_components, n_iter=20,
power_iteration_normalizer='none')
A = X - U.dot(np.diag(s).dot(Vt))
error_20 = linalg.norm(A, ord='fro')
assert np.abs(error_2 - error_20) > 100
for normalizer in ['LU', 'QR', 'auto']:
U, s, Vt = randomized_svd(X, n_components, n_iter=2,
power_iteration_normalizer=normalizer,
random_state=0)
A = X - U.dot(np.diag(s).dot(Vt))
error_2 = linalg.norm(A, ord='fro')
for i in [5, 10, 50]:
U, s, Vt = randomized_svd(X, n_components, n_iter=i,
power_iteration_normalizer=normalizer,
random_state=0)
A = X - U.dot(np.diag(s).dot(Vt))
error = linalg.norm(A, ord='fro')
assert 15 > np.abs(error_2 - error)
def test_randomized_svd_sparse_warnings():
# randomized_svd throws a warning for lil and dok matrix
rng = np.random.RandomState(42)
X = make_low_rank_matrix(50, 20, effective_rank=10, random_state=rng)
n_components = 5
for cls in (sparse.lil_matrix, sparse.dok_matrix):
X = cls(X)
assert_warns_message(
sparse.SparseEfficiencyWarning,
"Calculating SVD of a {} is expensive. "
"csr_matrix is more efficient.".format(cls.__name__),
randomized_svd, X, n_components, n_iter=1,
power_iteration_normalizer='none')
def test_svd_flip():
# Check that svd_flip works in both situations, and reconstructs input.
rs = np.random.RandomState(1999)
n_samples = 20
n_features = 10
X = rs.randn(n_samples, n_features)
# Check matrix reconstruction
U, S, Vt = linalg.svd(X, full_matrices=False)
U1, V1 = svd_flip(U, Vt, u_based_decision=False)
assert_almost_equal(np.dot(U1 * S, V1), X, decimal=6)
# Check transposed matrix reconstruction
XT = X.T
U, S, Vt = linalg.svd(XT, full_matrices=False)
U2, V2 = svd_flip(U, Vt, u_based_decision=True)
assert_almost_equal(np.dot(U2 * S, V2), XT, decimal=6)
# Check that different flip methods are equivalent under reconstruction
U_flip1, V_flip1 = svd_flip(U, Vt, u_based_decision=True)
assert_almost_equal(np.dot(U_flip1 * S, V_flip1), XT, decimal=6)
U_flip2, V_flip2 = svd_flip(U, Vt, u_based_decision=False)
assert_almost_equal(np.dot(U_flip2 * S, V_flip2), XT, decimal=6)
def test_randomized_svd_sign_flip():
a = np.array([[2.0, 0.0], [0.0, 1.0]])
u1, s1, v1 = randomized_svd(a, 2, flip_sign=True, random_state=41)
for seed in range(10):
u2, s2, v2 = randomized_svd(a, 2, flip_sign=True, random_state=seed)
assert_almost_equal(u1, u2)
assert_almost_equal(v1, v2)
assert_almost_equal(np.dot(u2 * s2, v2), a)
assert_almost_equal(np.dot(u2.T, u2), np.eye(2))
assert_almost_equal(np.dot(v2.T, v2), np.eye(2))
def test_randomized_svd_sign_flip_with_transpose():
# Check if the randomized_svd sign flipping is always done based on u
# irrespective of transpose.
# See https://github.com/scikit-learn/scikit-learn/issues/5608
# for more details.
def max_loading_is_positive(u, v):
"""
returns bool tuple indicating if the values maximising np.abs
are positive across all rows for u and across all columns for v.
"""
u_based = (np.abs(u).max(axis=0) == u.max(axis=0)).all()
v_based = (np.abs(v).max(axis=1) == v.max(axis=1)).all()
return u_based, v_based
mat = np.arange(10 * 8).reshape(10, -1)
# Without transpose
u_flipped, _, v_flipped = randomized_svd(mat, 3, flip_sign=True)
u_based, v_based = max_loading_is_positive(u_flipped, v_flipped)
assert u_based
assert not v_based
# With transpose
u_flipped_with_transpose, _, v_flipped_with_transpose = randomized_svd(
mat, 3, flip_sign=True, transpose=True)
u_based, v_based = max_loading_is_positive(
u_flipped_with_transpose, v_flipped_with_transpose)
assert u_based
assert not v_based
def test_cartesian():
# Check if cartesian product delivers the right results
axes = (np.array([1, 2, 3]), np.array([4, 5]), np.array([6, 7]))
true_out = np.array([[1, 4, 6],
[1, 4, 7],
[1, 5, 6],
[1, 5, 7],
[2, 4, 6],
[2, 4, 7],
[2, 5, 6],
[2, 5, 7],
[3, 4, 6],
[3, 4, 7],
[3, 5, 6],
[3, 5, 7]])
out = cartesian(axes)
assert_array_equal(true_out, out)
# check single axis
x = np.arange(3)
assert_array_equal(x[:, np.newaxis], cartesian((x,)))
def test_logistic_sigmoid():
# Check correctness and robustness of logistic sigmoid implementation
def naive_log_logistic(x):
return np.log(expit(x))
x = np.linspace(-2, 2, 50)
assert_array_almost_equal(log_logistic(x), naive_log_logistic(x))
extreme_x = np.array([-100., 100.])
assert_array_almost_equal(log_logistic(extreme_x), [-100, 0])
@pytest.fixture()
def rng():
return np.random.RandomState(42)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_incremental_weighted_mean_and_variance_simple(rng, dtype):
mult = 10
X = rng.rand(1000, 20).astype(dtype)*mult
sample_weight = rng.rand(X.shape[0]) * mult
mean, var, _ = _incremental_mean_and_var(X, 0, 0, 0,
sample_weight=sample_weight)
expected_mean = np.average(X, weights=sample_weight, axis=0)
expected_var = np.average(X**2, weights=sample_weight, axis=0) - \
expected_mean**2
assert_almost_equal(mean, expected_mean)
assert_almost_equal(var, expected_var)
@pytest.mark.parametrize("mean", [0, 1e7, -1e7])
@pytest.mark.parametrize("var", [1, 1e-8, 1e5])
@pytest.mark.parametrize("weight_loc, weight_scale", [
(0, 1), (0, 1e-8), (1, 1e-8), (10, 1), (1e7, 1)])
def test_incremental_weighted_mean_and_variance(mean, var, weight_loc,
weight_scale, rng):
# Testing of correctness and numerical stability
def _assert(X, sample_weight, expected_mean, expected_var):
n = X.shape[0]
for chunk_size in [1, n//10 + 1, n//4 + 1, n//2 + 1, n]:
last_mean, last_weight_sum, last_var = 0, 0, 0
for batch in gen_batches(n, chunk_size):
last_mean, last_var, last_weight_sum = \
_incremental_mean_and_var(
X[batch], last_mean, last_var, last_weight_sum,
sample_weight=sample_weight[batch])
assert_allclose(last_mean, expected_mean)
assert_allclose(last_var, expected_var, atol=1e-6)
size = (100, 20)
weight = rng.normal(loc=weight_loc, scale=weight_scale, size=size[0])
# Compare to weighted average: np.average
X = rng.normal(loc=mean, scale=var, size=size)
expected_mean = _safe_accumulator_op(np.average, X, weights=weight, axis=0)
expected_var = _safe_accumulator_op(
np.average, (X - expected_mean) ** 2, weights=weight, axis=0)
_assert(X, weight, expected_mean, expected_var)
# Compare to unweighted mean: np.mean
X = rng.normal(loc=mean, scale=var, size=size)
ones_weight = np.ones(size[0])
expected_mean = _safe_accumulator_op(np.mean, X, axis=0)
expected_var = _safe_accumulator_op(np.var, X, axis=0)
_assert(X, ones_weight, expected_mean, expected_var)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_incremental_weighted_mean_and_variance_ignore_nan(dtype):
old_means = np.array([535., 535., 535., 535.])
old_variances = np.array([4225., 4225., 4225., 4225.])
old_weight_sum = np.array([2, 2, 2, 2], dtype=np.int32)
sample_weights_X = np.ones(3)
sample_weights_X_nan = np.ones(4)
X = np.array([[170, 170, 170, 170],
[430, 430, 430, 430],
[300, 300, 300, 300]]).astype(dtype)
X_nan = np.array([[170, np.nan, 170, 170],
[np.nan, 170, 430, 430],
[430, 430, np.nan, 300],
[300, 300, 300, np.nan]]).astype(dtype)
X_means, X_variances, X_count = \
_incremental_mean_and_var(X,
old_means,
old_variances,
old_weight_sum,
sample_weight=sample_weights_X)
X_nan_means, X_nan_variances, X_nan_count = \
_incremental_mean_and_var(X_nan,
old_means,
old_variances,
old_weight_sum,
sample_weight=sample_weights_X_nan)
assert_allclose(X_nan_means, X_means)
assert_allclose(X_nan_variances, X_variances)
assert_allclose(X_nan_count, X_count)
def test_incremental_variance_update_formulas():
# Test Youngs and Cramer incremental variance formulas.
# Doggie data from https://www.mathsisfun.com/data/standard-deviation.html
A = np.array([[600, 470, 170, 430, 300],
[600, 470, 170, 430, 300],
[600, 470, 170, 430, 300],
[600, 470, 170, 430, 300]]).T
idx = 2
X1 = A[:idx, :]
X2 = A[idx:, :]
old_means = X1.mean(axis=0)
old_variances = X1.var(axis=0)
old_sample_count = np.full(X1.shape[1], X1.shape[0], dtype=np.int32)
final_means, final_variances, final_count = \
_incremental_mean_and_var(X2, old_means, old_variances,
old_sample_count)
assert_almost_equal(final_means, A.mean(axis=0), 6)
assert_almost_equal(final_variances, A.var(axis=0), 6)
assert_almost_equal(final_count, A.shape[0])
def test_incremental_mean_and_variance_ignore_nan():
old_means = np.array([535., 535., 535., 535.])
old_variances = np.array([4225., 4225., 4225., 4225.])
old_sample_count = np.array([2, 2, 2, 2], dtype=np.int32)
X = np.array([[170, 170, 170, 170],
[430, 430, 430, 430],
[300, 300, 300, 300]])
X_nan = np.array([[170, np.nan, 170, 170],
[np.nan, 170, 430, 430],
[430, 430, np.nan, 300],
[300, 300, 300, np.nan]])
X_means, X_variances, X_count = _incremental_mean_and_var(
X, old_means, old_variances, old_sample_count)
X_nan_means, X_nan_variances, X_nan_count = _incremental_mean_and_var(
X_nan, old_means, old_variances, old_sample_count)
assert_allclose(X_nan_means, X_means)
assert_allclose(X_nan_variances, X_variances)
assert_allclose(X_nan_count, X_count)
@skip_if_32bit
def test_incremental_variance_numerical_stability():
# Test Youngs and Cramer incremental variance formulas.
def np_var(A):
return A.var(axis=0)
# Naive one pass variance computation - not numerically stable
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
def one_pass_var(X):
n = X.shape[0]
exp_x2 = (X ** 2).sum(axis=0) / n
expx_2 = (X.sum(axis=0) / n) ** 2
return exp_x2 - expx_2
# Two-pass algorithm, stable.
# We use it as a benchmark. It is not an online algorithm
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Two-pass_algorithm
def two_pass_var(X):
mean = X.mean(axis=0)
Y = X.copy()
return np.mean((Y - mean)**2, axis=0)
# Naive online implementation
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm
# This works only for chunks for size 1
def naive_mean_variance_update(x, last_mean, last_variance,
last_sample_count):
updated_sample_count = (last_sample_count + 1)
samples_ratio = last_sample_count / float(updated_sample_count)
updated_mean = x / updated_sample_count + last_mean * samples_ratio
updated_variance = last_variance * samples_ratio + \
(x - last_mean) * (x - updated_mean) / updated_sample_count
return updated_mean, updated_variance, updated_sample_count
# We want to show a case when one_pass_var has error > 1e-3 while
# _batch_mean_variance_update has less.
tol = 200
n_features = 2
n_samples = 10000
x1 = np.array(1e8, dtype=np.float64)
x2 = np.log(1e-5, dtype=np.float64)
A0 = np.full((n_samples // 2, n_features), x1, dtype=np.float64)
A1 = np.full((n_samples // 2, n_features), x2, dtype=np.float64)
A = np.vstack((A0, A1))
# Naive one pass var: >tol (=1063)
assert np.abs(np_var(A) - one_pass_var(A)).max() > tol
# Starting point for online algorithms: after A0
# Naive implementation: >tol (436)
mean, var, n = A0[0, :], np.zeros(n_features), n_samples // 2
for i in range(A1.shape[0]):
mean, var, n = \
naive_mean_variance_update(A1[i, :], mean, var, n)
assert n == A.shape[0]
# the mean is also slightly unstable
assert np.abs(A.mean(axis=0) - mean).max() > 1e-6
assert np.abs(np_var(A) - var).max() > tol
# Robust implementation: <tol (177)
mean, var = A0[0, :], np.zeros(n_features)
n = np.full(n_features, n_samples // 2, dtype=np.int32)
for i in range(A1.shape[0]):
mean, var, n = \
_incremental_mean_and_var(A1[i, :].reshape((1, A1.shape[1])),
mean, var, n)
assert_array_equal(n, A.shape[0])
assert_array_almost_equal(A.mean(axis=0), mean)
assert tol > np.abs(np_var(A) - var).max()
def test_incremental_variance_ddof():
# Test that degrees of freedom parameter for calculations are correct.
rng = np.random.RandomState(1999)
X = rng.randn(50, 10)
n_samples, n_features = X.shape
for batch_size in [11, 20, 37]:
steps = np.arange(0, X.shape[0], batch_size)
if steps[-1] != X.shape[0]:
steps = np.hstack([steps, n_samples])
for i, j in zip(steps[:-1], steps[1:]):
batch = X[i:j, :]
if i == 0:
incremental_means = batch.mean(axis=0)
incremental_variances = batch.var(axis=0)
# Assign this twice so that the test logic is consistent
incremental_count = batch.shape[0]
sample_count = np.full(batch.shape[1], batch.shape[0],
dtype=np.int32)
else:
result = _incremental_mean_and_var(
batch, incremental_means, incremental_variances,
sample_count)
(incremental_means, incremental_variances,
incremental_count) = result
sample_count += batch.shape[0]
calculated_means = np.mean(X[:j], axis=0)
calculated_variances = np.var(X[:j], axis=0)
assert_almost_equal(incremental_means, calculated_means, 6)
assert_almost_equal(incremental_variances,
calculated_variances, 6)
assert_array_equal(incremental_count, sample_count)
def test_vector_sign_flip():
# Testing that sign flip is working & largest value has positive sign
data = np.random.RandomState(36).randn(5, 5)
max_abs_rows = np.argmax(np.abs(data), axis=1)
data_flipped = _deterministic_vector_sign_flip(data)
max_rows = np.argmax(data_flipped, axis=1)
assert_array_equal(max_abs_rows, max_rows)
signs = np.sign(data[range(data.shape[0]), max_abs_rows])
assert_array_equal(data, data_flipped * signs[:, np.newaxis])
def test_softmax():
rng = np.random.RandomState(0)
X = rng.randn(3, 5)
exp_X = np.exp(X)
sum_exp_X = np.sum(exp_X, axis=1).reshape((-1, 1))
assert_array_almost_equal(softmax(X), exp_X / sum_exp_X)
def test_stable_cumsum():
assert_array_equal(stable_cumsum([1, 2, 3]), np.cumsum([1, 2, 3]))
r = np.random.RandomState(0).rand(100000)
assert_warns(RuntimeWarning, stable_cumsum, r, rtol=0, atol=0)
# test axis parameter
A = np.random.RandomState(36).randint(1000, size=(5, 5, 5))
assert_array_equal(stable_cumsum(A, axis=0), np.cumsum(A, axis=0))
assert_array_equal(stable_cumsum(A, axis=1), np.cumsum(A, axis=1))
assert_array_equal(stable_cumsum(A, axis=2), np.cumsum(A, axis=2))
@pytest.mark.parametrize("A_array_constr", [np.array, sparse.csr_matrix],
ids=["dense", "sparse"])
@pytest.mark.parametrize("B_array_constr", [np.array, sparse.csr_matrix],
ids=["dense", "sparse"])
def test_safe_sparse_dot_2d(A_array_constr, B_array_constr):
rng = np.random.RandomState(0)
A = rng.random_sample((30, 10))
B = rng.random_sample((10, 20))
expected = np.dot(A, B)
A = A_array_constr(A)
B = B_array_constr(B)
actual = safe_sparse_dot(A, B, dense_output=True)
assert_allclose(actual, expected)
def test_safe_sparse_dot_nd():
rng = np.random.RandomState(0)
# dense ND / sparse
A = rng.random_sample((2, 3, 4, 5, 6))
B = rng.random_sample((6, 7))
expected = np.dot(A, B)
B = sparse.csr_matrix(B)
actual = safe_sparse_dot(A, B)
assert_allclose(actual, expected)
# sparse / dense ND
A = rng.random_sample((2, 3))
B = rng.random_sample((4, 5, 3, 6))
expected = np.dot(A, B)
A = sparse.csr_matrix(A)
actual = safe_sparse_dot(A, B)
assert_allclose(actual, expected)
@pytest.mark.parametrize("A_array_constr", [np.array, sparse.csr_matrix],
ids=["dense", "sparse"])
def test_safe_sparse_dot_2d_1d(A_array_constr):
rng = np.random.RandomState(0)
B = rng.random_sample((10))
# 2D @ 1D
A = rng.random_sample((30, 10))
expected = np.dot(A, B)
A = A_array_constr(A)
actual = safe_sparse_dot(A, B)
assert_allclose(actual, expected)
# 1D @ 2D
A = rng.random_sample((10, 30))
expected = np.dot(B, A)
A = A_array_constr(A)
actual = safe_sparse_dot(B, A)
assert_allclose(actual, expected)
@pytest.mark.parametrize("dense_output", [True, False])
def test_safe_sparse_dot_dense_output(dense_output):
rng = np.random.RandomState(0)
A = sparse.random(30, 10, density=0.1, random_state=rng)
B = sparse.random(10, 20, density=0.1, random_state=rng)
expected = A.dot(B)
actual = safe_sparse_dot(A, B, dense_output=dense_output)
assert sparse.issparse(actual) == (not dense_output)
if dense_output:
expected = expected.toarray()
assert_allclose_dense_sparse(actual, expected)
| bsd-3-clause |
herilalaina/scikit-learn | examples/manifold/plot_mds.py | 88 | 2731 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <[email protected]>
# License: BSD
print(__doc__)
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
from sklearn import manifold
from sklearn.metrics import euclidean_distances
from sklearn.decomposition import PCA
n_samples = 20
seed = np.random.RandomState(seed=3)
X_true = seed.randint(0, 20, 2 * n_samples).astype(np.float)
X_true = X_true.reshape((n_samples, 2))
# Center the data
X_true -= X_true.mean()
similarities = euclidean_distances(X_true)
# Add noise to the similarities
noise = np.random.rand(n_samples, n_samples)
noise = noise + noise.T
noise[np.arange(noise.shape[0]), np.arange(noise.shape[0])] = 0
similarities += noise
mds = manifold.MDS(n_components=2, max_iter=3000, eps=1e-9, random_state=seed,
dissimilarity="precomputed", n_jobs=1)
pos = mds.fit(similarities).embedding_
nmds = manifold.MDS(n_components=2, metric=False, max_iter=3000, eps=1e-12,
dissimilarity="precomputed", random_state=seed, n_jobs=1,
n_init=1)
npos = nmds.fit_transform(similarities, init=pos)
# Rescale the data
pos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((pos ** 2).sum())
npos *= np.sqrt((X_true ** 2).sum()) / np.sqrt((npos ** 2).sum())
# Rotate the data
clf = PCA(n_components=2)
X_true = clf.fit_transform(X_true)
pos = clf.fit_transform(pos)
npos = clf.fit_transform(npos)
fig = plt.figure(1)
ax = plt.axes([0., 0., 1., 1.])
s = 100
plt.scatter(X_true[:, 0], X_true[:, 1], color='navy', s=s, lw=0,
label='True Position')
plt.scatter(pos[:, 0], pos[:, 1], color='turquoise', s=s, lw=0, label='MDS')
plt.scatter(npos[:, 0], npos[:, 1], color='darkorange', s=s, lw=0, label='NMDS')
plt.legend(scatterpoints=1, loc='best', shadow=False)
similarities = similarities.max() / similarities * 100
similarities[np.isinf(similarities)] = 0
# Plot the edges
start_idx, end_idx = np.where(pos)
# a sequence of (*line0*, *line1*, *line2*), where::
# linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [[X_true[i, :], X_true[j, :]]
for i in range(len(pos)) for j in range(len(pos))]
values = np.abs(similarities)
lc = LineCollection(segments,
zorder=0, cmap=plt.cm.Blues,
norm=plt.Normalize(0, values.max()))
lc.set_array(similarities.flatten())
lc.set_linewidths(0.5 * np.ones(len(segments)))
ax.add_collection(lc)
plt.show()
| bsd-3-clause |
smartscheduling/scikit-learn-categorical-tree | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingClassifier
from sklearn.linear_model import Perceptron
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
def test_base_zero_n_estimators():
# Check that instantiating a BaseEnsemble with n_estimators<=0 raises
# a ValueError.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=0)
iris = load_iris()
assert_raise_message(ValueError,
"n_estimators must be greater than zero, got 0.",
ensemble.fit, iris.data, iris.target)
| bsd-3-clause |
allinpaybusiness/ACS | allinpay projects/creditscore_TLSW_fyz/creditscore_logistic.py | 1 | 17667 | # -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import sys;
import os;
sys.path.append("allinpay projects")
from imp import reload
import creditscore_TLSW_fyz.creditscore_tlsw
reload(creditscore_TLSW_fyz.creditscore_tlsw)
from creditscore_TLSW_fyz.creditscore_tlsw import TLSWscoring
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import KFold
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import RFECV
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import Imputer
from sklearn.ensemble import BaggingClassifier
from sklearn.externals import joblib
class TLSWscoring_logistic(TLSWscoring):
def logistic_trainandtest(self, unionscores, cutscore, testsize, cv, feature_sel, varthreshold, nclusters, cmethod, resmethod, preprocess):
#分割数据集为训练集和测试集
if unionscores == True:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'rsk_score'], axis = 1)
else:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'cst_score',
'cnp_score', 'cnt_score', 'chv_score', 'dsi_score','rsk_score'], axis = 1)
data_target = (self.data['rsk_score'] < cutscore).astype('int')
X_train, X_test, y_train, y_test = train_test_split(data_feature, data_target, test_size=testsize, random_state=0)
if testsize == 0:
X_test, y_test = X_train.head(5), y_train.head(5)
#对训练集做变量粗分类和woe转化，并据此对测试集做粗分类和woe转化
X_train, X_test = self.binandwoe_traintest_pkl(X_train, y_train, X_test, nclusters, cmethod, self.label)
#在train中做变量筛选, sklearn.feature_selection中的方法
if feature_sel == "VarianceThreshold":
selector = VarianceThreshold(threshold = varthreshold)
X_train1 = pd.DataFrame(selector.fit_transform(X_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "RFECV":
estimator = LogisticRegression()
selector = RFECV(estimator, step=1, cv=cv)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectFromModel":
estimator = LogisticRegression()
selector = SelectFromModel(estimator)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectKBest":
selector = SelectKBest()
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
else:
X_train1, X_test1 = X_train, X_test
testcolumns = X_test1.columns
#缺失值处理
#均值
#imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
#中位数
#imp = Imputer(missing_values='NaN', strategy='median', axis=0)
#最频繁出现的
#imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
#X_train1 = imp.fit_transform(X_train1)
#X_test1 = imp.transform(X_test1)
#数据预处理
X_train1, X_test1 = self.preprocessData (X_train1, X_test1, preprocess)
#重采样resampling 解决样本不平衡问题
X_train1, y_train = self.imbalanceddata (X_train1, y_train, resmethod)
#训练并预测模型
classifier = LogisticRegression() # 使用类，参数全是默认的
classifier.fit(X_train1, y_train)
probability = classifier.predict_proba(X_test1)
predresult = pd.DataFrame({'target' : y_test, 'probability' : probability[:,1]})
predresult = pd.concat([predresult, X_test], axis = 1)
if self.label != None:#label==None 用于建模训练，label！=None用于保存生产模型
joblib.dump(classifier, "allinpay projects\\creditscore_TLSW_fyz\\pkl\\classifier_" + self.label + '.pkl')
joblib.dump(testcolumns, "allinpay projects\\creditscore_TLSW_fyz\\pkl\\testcolumns_" + self.label + '.pkl')
return predresult
def logistic_trainandtest_kfold(self, unionscores, nsplit, cutscore, cv, feature_sel, varthreshold, nclusters, cmethod, resmethod, preprocess):
if unionscores == True:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'rsk_score'], axis = 1)
else:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'cst_score',
'cnp_score', 'cnt_score', 'chv_score', 'dsi_score','rsk_score'], axis = 1)
data_target = (self.data['rsk_score'] < cutscore).astype('int')
#将数据集分割成k个分段分别进行训练和测试，对每个分段，该分段为测试集，其余数据为训练集
kf = KFold(n_splits=nsplit, shuffle=True)
predresult = pd.DataFrame()
for train_index, test_index in kf.split(data_feature):
X_train, X_test = data_feature.iloc[train_index, ], data_feature.iloc[test_index, ]
y_train, y_test = data_target.iloc[train_index, ], data_target.iloc[test_index, ]
#如果随机抽样造成train或者test中只有一个分类，跳过此次预测
if (len(y_train.unique()) == 1) or (len(y_test.unique()) == 1):
continue
#对训练集做变量粗分类和woe转化，并据此对测试集做粗分类和woe转化
X_train, X_test = self.binandwoe_traintest(X_train, y_train, X_test, nclusters, cmethod)
#在train中做变量筛选, sklearn.feature_selection中的方法
if feature_sel == "VarianceThreshold":
selector = VarianceThreshold(threshold = varthreshold)
X_train1 = pd.DataFrame(selector.fit_transform(X_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "RFECV":
estimator = LogisticRegression()
selector = RFECV(estimator, step=1, cv=cv)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectFromModel":
estimator = LogisticRegression()
selector = SelectFromModel(estimator)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectKBest":
selector = SelectKBest()
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
else:
X_train1, X_test1 = X_train, X_test
#缺失值处理
#均值
#imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
#中位数
#imp = Imputer(missing_values='NaN', strategy='median', axis=0)
#最频繁出现的
#imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
#X_train1 = imp.fit_transform(X_train1)
#X_test1 = imp.transform(X_test1)
#数据预处理
X_train1, X_test1 = self.preprocessData (X_train1, X_test1, preprocess)
#重采样resampling 解决样本不平衡问题
X_train1, y_train = self.imbalanceddata (X_train1, y_train, resmethod)
#训练并预测模型
classifier = LogisticRegression() # 使用类，参数全是默认的
classifier.fit(X_train1, y_train)
probability = classifier.predict_proba(X_test1)
temp = pd.DataFrame({'target' : y_test, 'probability' : probability[:,1]})
temp = pd.concat([temp, data_feature.iloc[test_index, ]], axis = 1)
predresult = pd.concat([predresult, temp], ignore_index = True)
return predresult
def logistic_bagging_trainandtest(self, unionscores, cutscore, testsize, cv, feature_sel, varthreshold, nclusters, cmethod, resmethod, preprocess):
#分割数据集为训练集和测试集
if unionscores == True:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'rsk_score'], axis = 1)
else:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'cst_score',
'cnp_score', 'cnt_score', 'chv_score', 'dsi_score','rsk_score'], axis = 1)
data_target = (self.data['rsk_score'] < cutscore).astype('int')
X_train, X_test, y_train, y_test = train_test_split(data_feature, data_target, test_size=testsize, random_state=0)
if testsize == 0:
X_test, y_test = X_train.head(5), y_train.head(5)
#对训练集做变量粗分类和woe转化，并据此对测试集做粗分类和woe转化
X_train, X_test = self.binandwoe_traintest_pkl(X_train, y_train, X_test, nclusters, cmethod, self.label)
#在train中做变量筛选, sklearn.feature_selection中的方法
if feature_sel == "VarianceThreshold":
selector = VarianceThreshold(threshold = varthreshold)
X_train1 = pd.DataFrame(selector.fit_transform(X_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "RFECV":
estimator = LogisticRegression()
selector = RFECV(estimator, step=1, cv=cv)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectFromModel":
estimator = LogisticRegression()
selector = SelectFromModel(estimator)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectKBest":
selector = SelectKBest()
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
else:
X_train1, X_test1 = X_train, X_test
testcolumns = X_test1.columns
#缺失值处理
#均值
#imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
#中位数
#imp = Imputer(missing_values='NaN', strategy='median', axis=0)
#最频繁出现的
#imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
#X_train1 = imp.fit_transform(X_train1)
#X_test1 = imp.transform(X_test1)
#数据预处理
X_train1, X_test1 = self.preprocessData (X_train1, X_test1, preprocess)
#重采样resampling 解决样本不平衡问题
X_train1, y_train = self.imbalanceddata (X_train1, y_train, resmethod)
#训练并预测模型
# 使用类，参数全是默认的
classifier = BaggingClassifier(LogisticRegression(), max_samples=0.5)
classifier.fit(X_train1, y_train)
probability = classifier.predict_proba(X_test1)
predresult = pd.DataFrame({'target' : y_test, 'probability' : probability[:,1]})
predresult = pd.concat([predresult, X_test], axis = 1)
if self.label != None:#label==None 用于建模训练，label！=None用于保存生产模型
joblib.dump(classifier, "allinpay projects\\creditscore_TLSW_fyz\\pkl\\classifier_" + self.label + '.pkl')
joblib.dump(testcolumns, "allinpay projects\\creditscore_TLSW_fyz\\pkl\\testcolumns_" + self.label + '.pkl')
return predresult
def logistic_bagging_trainandtest_kfold(self, unionscores, nsplit, cutscore, cv, feature_sel, varthreshold, nclusters, cmethod, resmethod, preprocess):
if unionscores == True:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'rsk_score'], axis = 1)
else:
data_feature = self.data.drop(['name', 'idCard', 'mobileNum', 'cardNum', 'cst_score',
'cnp_score', 'cnt_score', 'chv_score', 'dsi_score','rsk_score'], axis = 1)
data_target = (self.data['rsk_score'] < cutscore).astype('int')
#将数据集分割成k个分段分别进行训练和测试，对每个分段，该分段为测试集，其余数据为训练集
kf = KFold(n_splits=nsplit, shuffle=True)
predresult = pd.DataFrame()
for train_index, test_index in kf.split(data_feature):
X_train, X_test = data_feature.iloc[train_index, ], data_feature.iloc[test_index, ]
y_train, y_test = data_target.iloc[train_index, ], data_target.iloc[test_index, ]
#如果随机抽样造成train或者test中只有一个分类，跳过此次预测
if (len(y_train.unique()) == 1) or (len(y_test.unique()) == 1):
continue
#对训练集做变量粗分类和woe转化，并据此对测试集做粗分类和woe转化
X_train, X_test = self.binandwoe_traintest(X_train, y_train, X_test, nclusters, cmethod)
#在train中做变量筛选, sklearn.feature_selection中的方法
if feature_sel == "VarianceThreshold":
selector = VarianceThreshold(threshold = varthreshold)
X_train1 = pd.DataFrame(selector.fit_transform(X_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "RFECV":
estimator = LogisticRegression()
selector = RFECV(estimator, step=1, cv=cv)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectFromModel":
estimator = LogisticRegression()
selector = SelectFromModel(estimator)
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
elif feature_sel == "SelectKBest":
selector = SelectKBest()
X_train1 = pd.DataFrame(selector.fit_transform(X_train, y_train))
X_train1.columns = X_train.columns[selector.get_support(True)]
X_test1 = X_test[X_train1.columns]
else:
X_train1, X_test1 = X_train, X_test
#缺失值处理
#均值
#imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
#中位数
#imp = Imputer(missing_values='NaN', strategy='median', axis=0)
#最频繁出现的
#imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)
#X_train1 = imp.fit_transform(X_train1)
#X_test1 = imp.transform(X_test1)
#数据预处理
X_train1, X_test1 = self.preprocessData (X_train1, X_test1, preprocess)
#重采样resampling 解决样本不平衡问题
X_train1, y_train = self.imbalanceddata (X_train1, y_train, resmethod)
#训练并预测模型
# 使用类，参数全是默认的
bagging = BaggingClassifier(LogisticRegression(), max_samples=0.5)
bagging.fit(X_train1, y_train)
probability = bagging.predict_proba(X_test1)
temp = pd.DataFrame({'target' : y_test, 'probability' : probability[:,1]})
temp = pd.concat([temp, data_feature.iloc[test_index, ]], axis = 1)
predresult = pd.concat([predresult, temp], ignore_index = True)
return predresult
| apache-2.0 |
quantenschaum/ctplot | ctplot/wsgi.py | 1 | 6688 | #!/usr/bin/env python
import os, json, random, string
from os.path import join, abspath, basename
from mimetypes import guess_type
from time import time
from cgi import FieldStorage
from threading import Lock
from pkg_resources import resource_string, resource_exists, resource_isdir, resource_listdir
import matplotlib
matplotlib.use('Agg') # headless backend
import ctplot.plot
from ctplot.utils import hashargs
_config = None
def get_config():
global _config
if _config:
return _config
env = os.environ
prefix = 'ctplot_'
basekey = (prefix + 'basedir').upper()
basedir = abspath(env[basekey] if basekey in env else 'data')
_config = {'cachedir':join(basedir, 'cache'),
'datadir':join(basedir, 'data'),
'plotdir':join(basedir, 'plots'),
'sessiondir':join(basedir, 'sessions')}
for k in _config.keys():
ek = prefix + k.upper()
if ek in env:
_config[k] = env[ek]
return _config
def getpath(environ):
return environ['PATH_INFO'] if 'PATH_INFO' in environ else ''
# This is our application object. It could have any name,
# except when using mod_wsgi where it must be "application"
# see http://webpython.codepoint.net/wsgi_application_interface
def application(environ, start_response):
path = getpath(environ)
if path == '/webplot.py' or path.startswith('/plot'):
return dynamic_content(environ, start_response)
else:
return static_content(environ, start_response)
# http://www.mobify.com/blog/beginners-guide-to-http-cache-headers/
# http://www.mnot.net/cache_docs/
# http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html
cc_nocache = 'Cache-Control', 'no-cache, max-age=0'
cc_cache = 'Cache-Control', 'public, max-age=86400'
def content_type(path = ''):
mime_type = None
if path:
mime_type = guess_type(path)[0]
if not mime_type:
mime_type = 'text/plain'
return 'Content-Type', mime_type
def static_content(environ, start_response):
path = getpath(environ)
if not path: # redirect
start_response('301 Redirect', [content_type(), ('Location', environ['REQUEST_URI'] + '/')])
return []
if path == '/':
path = 'web/index.html' # map / to index.html
else:
path = ('web/' + path).replace('//', '/')
if path == 'web/js': # combined java scripts
scripts = {}
for s in resource_listdir('ctplot', 'web/js'):
scripts[s] = '\n// {}\n\n'.format(s) + resource_string('ctplot', 'web/js/' + s)
start_response('200 OK', [content_type('combined.js'), cc_cache])
return [scripts[k] for k in sorted(scripts.keys())]
if not resource_exists('ctplot', path): # 404
start_response('404 Not Found', [content_type()])
return ['404\n', '{} not found!'.format(path)]
elif resource_isdir('ctplot', path): # 403
start_response('403 Forbidden', [content_type()])
return ['403 Forbidden']
else:
start_response('200 OK', [content_type(path), cc_cache])
return resource_string('ctplot', path)
def dynamic_content(environ, start_response):
path = getpath(environ)
config = get_config()
if path.startswith('/plots'):
return serve_plot(path, start_response, config)
else:
return handle_action(environ, start_response, config)
def serve_plot(path, start_response, config):
with open(join(config['plotdir'], basename(path))) as f:
start_response('200 OK', [content_type(path), cc_cache])
return [f.read()]
def serve_json(data, start_response):
start_response('200 OK', [content_type(), cc_nocache])
return [json.dumps(data)]
def serve_plain(data, start_response):
start_response('200 OK', [content_type(), cc_nocache])
return [data]
plot_lock = Lock()
def make_plot(settings, config):
basename = 'plot{}'.format(hashargs(settings))
name = os.path.join(config['plotdir'], basename).replace('\\', '/')
# try to get plot from cache
if config['cachedir'] and os.path.isfile(name + '.png'):
return dict([(e, name + '.' + e) for e in ['png', 'svg', 'pdf']])
else:
# lock long running plot creation
with plot_lock:
p = ctplot.plot.Plot(config, **settings)
return p.save(name)
def randomChars(n):
return ''.join(random.choice(string.ascii_lowercase + string.ascii_uppercase + string.digits) for _ in range(n))
available_tables = None
def handle_action(environ, start_response, config):
global available_tables
fields = FieldStorage(fp = environ['wsgi.input'], environ = environ)
action = fields.getfirst('a')
datadir = config['datadir']
sessiondir = config['sessiondir']
if action in ['plot', 'png', 'svg', 'pdf']:
settings = {}
for k in fields.keys():
if k[0] in 'xyzcmsorntwhfgl':
settings[k] = fields.getfirst(k).strip().decode('utf8', errors = 'ignore')
images = make_plot(settings, config)
for k, v in images.items():
images[k] = 'plots/' + basename(v)
if action == 'plot':
return serve_json(images, start_response)
elif action in ['png', 'svg', 'pdf']:
return serve_plot(images[action], start_response, config)
elif action == 'list':
if not available_tables or time() - available_tables[0] > 86400:
available_tables = time(), ctplot.plot.available_tables(datadir)
return serve_json(available_tables[1], start_response)
elif action == 'save':
id = fields.getfirst('id').strip()
if len(id) < 8: raise RuntimeError('session id must have at least 8 digits')
data = fields.getfirst('data').strip()
with open(os.path.join(sessiondir, '{}.session'.format(id)), 'w') as f:
f.write(data.replace('},{', '},\n{'))
return serve_json('saved {}'.format(id), start_response)
elif action == 'load':
id = fields.getfirst('id').strip()
if len(id) < 8: raise RuntimeError('session id must have at least 8 digits')
try:
with open(os.path.join(sessiondir, '{}.session'.format(id))) as f:
return serve_plain(f.read(), start_response)
except:
return serve_json('no data for {}'.format(id), start_response)
elif action == 'newid':
id = randomChars(16)
while os.path.isfile(os.path.join(sessiondir, '{}.session'.format(id))):
id = randomChars(16)
return serve_plain(id, start_response)
else:
raise ValueError('unknown action {}'.format(action))
| gpl-3.0 |
chrsrds/scikit-learn | sklearn/linear_model/ransac.py | 2 | 19008 | # coding: utf-8
# Author: Johannes Schönberger
#
# License: BSD 3 clause
import numpy as np
import warnings
from ..base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone
from ..base import MultiOutputMixin
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.random import sample_without_replacement
from ..utils.validation import check_is_fitted
from .base import LinearRegression
from ..utils.validation import has_fit_parameter
from ..exceptions import ConvergenceWarning
_EPSILON = np.spacing(1)
def _dynamic_max_trials(n_inliers, n_samples, min_samples, probability):
"""Determine number trials such that at least one outlier-free subset is
sampled for the given inlier/outlier ratio.
Parameters
----------
n_inliers : int
Number of inliers in the data.
n_samples : int
Total number of samples in the data.
min_samples : int
Minimum number of samples chosen randomly from original data.
probability : float
Probability (confidence) that one outlier-free sample is generated.
Returns
-------
trials : int
Number of trials.
"""
inlier_ratio = n_inliers / float(n_samples)
nom = max(_EPSILON, 1 - probability)
denom = max(_EPSILON, 1 - inlier_ratio ** min_samples)
if nom == 1:
return 0
if denom == 1:
return float('inf')
return abs(float(np.ceil(np.log(nom) / np.log(denom))))
class RANSACRegressor(BaseEstimator, MetaEstimatorMixin, RegressorMixin,
MultiOutputMixin):
"""RANSAC (RANdom SAmple Consensus) algorithm.
RANSAC is an iterative algorithm for the robust estimation of parameters
from a subset of inliers from the complete data set. More information can
be found in the general documentation of linear models.
A detailed description of the algorithm can be found in the documentation
of the ``linear_model`` sub-package.
Read more in the :ref:`User Guide <ransac_regression>`.
Parameters
----------
base_estimator : object, optional
Base estimator object which implements the following methods:
* `fit(X, y)`: Fit model to given training data and target values.
* `score(X, y)`: Returns the mean accuracy on the given test data,
which is used for the stop criterion defined by `stop_score`.
Additionally, the score is used to decide which of two equally
large consensus sets is chosen as the better one.
* `predict(X)`: Returns predicted values using the linear model,
which is used to compute residual error using loss function.
If `base_estimator` is None, then
``base_estimator=sklearn.linear_model.LinearRegression()`` is used for
target values of dtype float.
Note that the current implementation only supports regression
estimators.
min_samples : int (>= 1) or float ([0, 1]), optional
Minimum number of samples chosen randomly from original data. Treated
as an absolute number of samples for `min_samples >= 1`, treated as a
relative number `ceil(min_samples * X.shape[0]`) for
`min_samples < 1`. This is typically chosen as the minimal number of
samples necessary to estimate the given `base_estimator`. By default a
``sklearn.linear_model.LinearRegression()`` estimator is assumed and
`min_samples` is chosen as ``X.shape[1] + 1``.
residual_threshold : float, optional
Maximum residual for a data sample to be classified as an inlier.
By default the threshold is chosen as the MAD (median absolute
deviation) of the target values `y`.
is_data_valid : callable, optional
This function is called with the randomly selected data before the
model is fitted to it: `is_data_valid(X, y)`. If its return value is
False the current randomly chosen sub-sample is skipped.
is_model_valid : callable, optional
This function is called with the estimated model and the randomly
selected data: `is_model_valid(model, X, y)`. If its return value is
False the current randomly chosen sub-sample is skipped.
Rejecting samples with this function is computationally costlier than
with `is_data_valid`. `is_model_valid` should therefore only be used if
the estimated model is needed for making the rejection decision.
max_trials : int, optional
Maximum number of iterations for random sample selection.
max_skips : int, optional
Maximum number of iterations that can be skipped due to finding zero
inliers or invalid data defined by ``is_data_valid`` or invalid models
defined by ``is_model_valid``.
.. versionadded:: 0.19
stop_n_inliers : int, optional
Stop iteration if at least this number of inliers are found.
stop_score : float, optional
Stop iteration if score is greater equal than this threshold.
stop_probability : float in range [0, 1], optional
RANSAC iteration stops if at least one outlier-free set of the training
data is sampled in RANSAC. This requires to generate at least N
samples (iterations)::
N >= log(1 - probability) / log(1 - e**m)
where the probability (confidence) is typically set to high value such
as 0.99 (the default) and e is the current fraction of inliers w.r.t.
the total number of samples.
loss : string, callable, optional, default "absolute_loss"
String inputs, "absolute_loss" and "squared_loss" are supported which
find the absolute loss and squared loss per sample
respectively.
If ``loss`` is a callable, then it should be a function that takes
two arrays as inputs, the true and predicted value and returns a 1-D
array with the i-th value of the array corresponding to the loss
on ``X[i]``.
If the loss on a sample is greater than the ``residual_threshold``,
then this sample is classified as an outlier.
random_state : int, RandomState instance or None, optional, default None
The generator used to initialize the centers. If int, random_state is
the seed used by the random number generator; If RandomState instance,
random_state is the random number generator; If None, the random number
generator is the RandomState instance used by `np.random`.
Attributes
----------
estimator_ : object
Best fitted model (copy of the `base_estimator` object).
n_trials_ : int
Number of random selection trials until one of the stop criteria is
met. It is always ``<= max_trials``.
inlier_mask_ : bool array of shape [n_samples]
Boolean mask of inliers classified as ``True``.
n_skips_no_inliers_ : int
Number of iterations skipped due to finding zero inliers.
.. versionadded:: 0.19
n_skips_invalid_data_ : int
Number of iterations skipped due to invalid data defined by
``is_data_valid``.
.. versionadded:: 0.19
n_skips_invalid_model_ : int
Number of iterations skipped due to an invalid model defined by
``is_model_valid``.
.. versionadded:: 0.19
Examples
--------
>>> from sklearn.linear_model import RANSACRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(
... n_samples=200, n_features=2, noise=4.0, random_state=0)
>>> reg = RANSACRegressor(random_state=0).fit(X, y)
>>> reg.score(X, y)
0.9885...
>>> reg.predict(X[:1,])
array([-31.9417...])
References
----------
.. [1] https://en.wikipedia.org/wiki/RANSAC
.. [2] https://www.sri.com/sites/default/files/publications/ransac-publication.pdf
.. [3] http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf
"""
def __init__(self, base_estimator=None, min_samples=None,
residual_threshold=None, is_data_valid=None,
is_model_valid=None, max_trials=100, max_skips=np.inf,
stop_n_inliers=np.inf, stop_score=np.inf,
stop_probability=0.99, loss='absolute_loss',
random_state=None):
self.base_estimator = base_estimator
self.min_samples = min_samples
self.residual_threshold = residual_threshold
self.is_data_valid = is_data_valid
self.is_model_valid = is_model_valid
self.max_trials = max_trials
self.max_skips = max_skips
self.stop_n_inliers = stop_n_inliers
self.stop_score = stop_score
self.stop_probability = stop_probability
self.random_state = random_state
self.loss = loss
def fit(self, X, y, sample_weight=None):
"""Fit estimator using RANSAC algorithm.
Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_features]
Training data.
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values.
sample_weight : array-like, shape = [n_samples]
Individual weights for each sample
raises error if sample_weight is passed and base_estimator
fit method does not support it.
Raises
------
ValueError
If no valid consensus set could be found. This occurs if
`is_data_valid` and `is_model_valid` return False for all
`max_trials` randomly chosen sub-samples.
"""
X = check_array(X, accept_sparse='csr')
y = check_array(y, ensure_2d=False)
check_consistent_length(X, y)
if self.base_estimator is not None:
base_estimator = clone(self.base_estimator)
else:
base_estimator = LinearRegression()
if self.min_samples is None:
# assume linear model by default
min_samples = X.shape[1] + 1
elif 0 < self.min_samples < 1:
min_samples = np.ceil(self.min_samples * X.shape[0])
elif self.min_samples >= 1:
if self.min_samples % 1 != 0:
raise ValueError("Absolute number of samples must be an "
"integer value.")
min_samples = self.min_samples
else:
raise ValueError("Value for `min_samples` must be scalar and "
"positive.")
if min_samples > X.shape[0]:
raise ValueError("`min_samples` may not be larger than number "
"of samples: n_samples = %d." % (X.shape[0]))
if self.stop_probability < 0 or self.stop_probability > 1:
raise ValueError("`stop_probability` must be in range [0, 1].")
if self.residual_threshold is None:
# MAD (median absolute deviation)
residual_threshold = np.median(np.abs(y - np.median(y)))
else:
residual_threshold = self.residual_threshold
if self.loss == "absolute_loss":
if y.ndim == 1:
loss_function = lambda y_true, y_pred: np.abs(y_true - y_pred)
else:
loss_function = lambda \
y_true, y_pred: np.sum(np.abs(y_true - y_pred), axis=1)
elif self.loss == "squared_loss":
if y.ndim == 1:
loss_function = lambda y_true, y_pred: (y_true - y_pred) ** 2
else:
loss_function = lambda \
y_true, y_pred: np.sum((y_true - y_pred) ** 2, axis=1)
elif callable(self.loss):
loss_function = self.loss
else:
raise ValueError(
"loss should be 'absolute_loss', 'squared_loss' or a callable."
"Got %s. " % self.loss)
random_state = check_random_state(self.random_state)
try: # Not all estimator accept a random_state
base_estimator.set_params(random_state=random_state)
except ValueError:
pass
estimator_fit_has_sample_weight = has_fit_parameter(base_estimator,
"sample_weight")
estimator_name = type(base_estimator).__name__
if (sample_weight is not None and not
estimator_fit_has_sample_weight):
raise ValueError("%s does not support sample_weight. Samples"
" weights are only used for the calibration"
" itself." % estimator_name)
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
n_inliers_best = 1
score_best = -np.inf
inlier_mask_best = None
X_inlier_best = None
y_inlier_best = None
self.n_skips_no_inliers_ = 0
self.n_skips_invalid_data_ = 0
self.n_skips_invalid_model_ = 0
# number of data samples
n_samples = X.shape[0]
sample_idxs = np.arange(n_samples)
n_samples, _ = X.shape
self.n_trials_ = 0
max_trials = self.max_trials
while self.n_trials_ < max_trials:
self.n_trials_ += 1
if (self.n_skips_no_inliers_ + self.n_skips_invalid_data_ +
self.n_skips_invalid_model_) > self.max_skips:
break
# choose random sample set
subset_idxs = sample_without_replacement(n_samples, min_samples,
random_state=random_state)
X_subset = X[subset_idxs]
y_subset = y[subset_idxs]
# check if random sample set is valid
if (self.is_data_valid is not None
and not self.is_data_valid(X_subset, y_subset)):
self.n_skips_invalid_data_ += 1
continue
# fit model for current random sample set
if sample_weight is None:
base_estimator.fit(X_subset, y_subset)
else:
base_estimator.fit(X_subset, y_subset,
sample_weight=sample_weight[subset_idxs])
# check if estimated model is valid
if (self.is_model_valid is not None and not
self.is_model_valid(base_estimator, X_subset, y_subset)):
self.n_skips_invalid_model_ += 1
continue
# residuals of all data for current random sample model
y_pred = base_estimator.predict(X)
residuals_subset = loss_function(y, y_pred)
# classify data into inliers and outliers
inlier_mask_subset = residuals_subset < residual_threshold
n_inliers_subset = np.sum(inlier_mask_subset)
# less inliers -> skip current random sample
if n_inliers_subset < n_inliers_best:
self.n_skips_no_inliers_ += 1
continue
# extract inlier data set
inlier_idxs_subset = sample_idxs[inlier_mask_subset]
X_inlier_subset = X[inlier_idxs_subset]
y_inlier_subset = y[inlier_idxs_subset]
# score of inlier data set
score_subset = base_estimator.score(X_inlier_subset,
y_inlier_subset)
# same number of inliers but worse score -> skip current random
# sample
if (n_inliers_subset == n_inliers_best
and score_subset < score_best):
continue
# save current random sample as best sample
n_inliers_best = n_inliers_subset
score_best = score_subset
inlier_mask_best = inlier_mask_subset
X_inlier_best = X_inlier_subset
y_inlier_best = y_inlier_subset
max_trials = min(
max_trials,
_dynamic_max_trials(n_inliers_best, n_samples,
min_samples, self.stop_probability))
# break if sufficient number of inliers or score is reached
if n_inliers_best >= self.stop_n_inliers or \
score_best >= self.stop_score:
break
# if none of the iterations met the required criteria
if inlier_mask_best is None:
if ((self.n_skips_no_inliers_ + self.n_skips_invalid_data_ +
self.n_skips_invalid_model_) > self.max_skips):
raise ValueError(
"RANSAC skipped more iterations than `max_skips` without"
" finding a valid consensus set. Iterations were skipped"
" because each randomly chosen sub-sample failed the"
" passing criteria. See estimator attributes for"
" diagnostics (n_skips*).")
else:
raise ValueError(
"RANSAC could not find a valid consensus set. All"
" `max_trials` iterations were skipped because each"
" randomly chosen sub-sample failed the passing criteria."
" See estimator attributes for diagnostics (n_skips*).")
else:
if (self.n_skips_no_inliers_ + self.n_skips_invalid_data_ +
self.n_skips_invalid_model_) > self.max_skips:
warnings.warn("RANSAC found a valid consensus set but exited"
" early due to skipping more iterations than"
" `max_skips`. See estimator attributes for"
" diagnostics (n_skips*).",
ConvergenceWarning)
# estimate final model using all inliers
base_estimator.fit(X_inlier_best, y_inlier_best)
self.estimator_ = base_estimator
self.inlier_mask_ = inlier_mask_best
return self
def predict(self, X):
"""Predict using the estimated model.
This is a wrapper for `estimator_.predict(X)`.
Parameters
----------
X : numpy array of shape [n_samples, n_features]
Returns
-------
y : array, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.
"""
check_is_fitted(self, 'estimator_')
return self.estimator_.predict(X)
def score(self, X, y):
"""Returns the score of the prediction.
This is a wrapper for `estimator_.score(X, y)`.
Parameters
----------
X : numpy array or sparse matrix of shape [n_samples, n_features]
Training data.
y : array, shape = [n_samples] or [n_samples, n_targets]
Target values.
Returns
-------
z : float
Score of the prediction.
"""
check_is_fitted(self, 'estimator_')
return self.estimator_.score(X, y)
| bsd-3-clause |
yanlend/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 9 | 3845 | # Author: Alexandre Gramfort <[email protected]>
# Gael Varoquaux <[email protected]>
# Virgile Fritsch <[email protected]>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises, assert_warns
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.validation import NotFittedError
from sklearn import datasets
from sklearn.covariance import empirical_covariance, MinCovDet, \
EllipticEnvelope
from sklearn.covariance import fast_mcd
X = datasets.load_iris().data
X_1d = X[:, 0]
n_samples, n_features = X.shape
def test_mcd():
# Tests the FastMCD algorithm implementation
# Small data set
# test without outliers (random independent normal data)
launch_mcd_on_dataset(100, 5, 0, 0.01, 0.1, 80)
# test with a contaminated data set (medium contamination)
launch_mcd_on_dataset(100, 5, 20, 0.01, 0.01, 70)
# test with a contaminated data set (strong contamination)
launch_mcd_on_dataset(100, 5, 40, 0.1, 0.1, 50)
# Medium data set
launch_mcd_on_dataset(1000, 5, 450, 0.1, 0.1, 540)
# Large data set
launch_mcd_on_dataset(1700, 5, 800, 0.1, 0.1, 870)
# 1D data set
launch_mcd_on_dataset(500, 1, 100, 0.001, 0.001, 350)
def test_fast_mcd_on_invalid_input():
X = np.arange(100)
assert_raise_message(ValueError, 'fast_mcd expects at least 2 samples',
fast_mcd, X)
def test_mcd_class_on_invalid_input():
X = np.arange(100)
mcd = MinCovDet()
assert_raise_message(ValueError, 'MinCovDet expects at least 2 samples',
mcd.fit, X)
def launch_mcd_on_dataset(n_samples, n_features, n_outliers, tol_loc, tol_cov,
tol_support):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MinCovDet(random_state=rand_gen).fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
H = mcd_fit.support_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
assert(error_location < tol_loc)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
assert(error_cov < tol_cov)
assert(np.sum(H) >= tol_support)
assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
def test_mcd_issue1127():
# Check that the code does not break with X.shape = (3, 1)
# (i.e. n_support = n_samples)
rnd = np.random.RandomState(0)
X = rnd.normal(size=(3, 1))
mcd = MinCovDet()
mcd.fit(X)
def test_outlier_detection():
rnd = np.random.RandomState(0)
X = rnd.randn(100, 10)
clf = EllipticEnvelope(contamination=0.1)
assert_raises(NotFittedError, clf.predict, X)
assert_raises(NotFittedError, clf.decision_function, X)
clf.fit(X)
y_pred = clf.predict(X)
decision = clf.decision_function(X, raw_values=True)
decision_transformed = clf.decision_function(X, raw_values=False)
assert_array_almost_equal(
decision, clf.mahalanobis(X))
assert_array_almost_equal(clf.mahalanobis(X), clf.dist_)
assert_almost_equal(clf.score(X, np.ones(100)),
(100 - y_pred[y_pred == -1].size) / 100.)
assert(sum(y_pred == -1) == sum(decision_transformed < 0))
| bsd-3-clause |
tedmeeds/tcga_encoder | tcga_encoder/analyses/dna_from_z_space_mut_landscape.py | 1 | 25985 | from tcga_encoder.utils.helpers import *
from tcga_encoder.data.data import *
from tcga_encoder.definitions.tcga import *
#from tcga_encoder.definitions.nn import *
from tcga_encoder.definitions.locations import *
#from tcga_encoder.algorithms import *
import seaborn as sns
from sklearn.manifold import TSNE, locally_linear_embedding
from scipy import stats, special
landscape_file = "tcga_encoder/data/mutational_landscape_genes.yaml"
def view_results( results_location, store, gene, n_permutations, source, method, disease_string, title_str = "", max_nbr = 100, zoom = True ):
mean_aucs = store["/%s/%s/%s/%s/labels_0/xval_aucs"%(disease_string,gene,source, method)]
mean_auc = store["/%s/%s/%s/%s/labels_0/xval_aucs"%(disease_string,gene,source, method)].mean()
var_auc = store["/%s/%s/%s/%s/labels_0/xval_aucs"%(disease_string,gene,source, method)].var()
barcodes = store["/%s/%s/%s/%s/labels_0/xval_predictions"%(disease_string,gene,source, method)].index.values
diseases = np.array( [s.split("_")[0] for s in barcodes])
u_diseases = np.unique( diseases )
disease_aucs = store[ "/%s/%s/%s/%s/labels_0/xval_disease_aucs"%(disease_string,gene,source, method)]
mean_disease_aucs = disease_aucs.mean(1)
var_disease_aucs = disease_aucs.var(1)
std_auc = np.sqrt( var_auc )
ordered_mean_aucs = store["/%s/%s/%s/%s/labels_0/xval_aucs_elementwise"%(disease_string,gene,source, method)].mean(1).sort_values(ascending=False)
ordered_source_genes = ordered_mean_aucs.index.values
ordered_var_aucs = store["/%s/%s/%s/%s/labels_0/xval_aucs_elementwise"%(disease_string,gene,source, method)].loc[ordered_source_genes].var(1)
order_std_aucs = np.sqrt(ordered_var_aucs)
D = len(ordered_mean_aucs.values)
element_aucs = store["/%s/%s/%s/%s/labels_0/xval_aucs_elementwise"%(disease_string,gene,source, method)]
element_aucs=element_aucs.T
element_aucs["ALL"] = mean_aucs
element_aucs=element_aucs.T
orientation = "horizontal"
if zoom is True:
marker = 'o'
else:
marker = '.'
nD = np.minimum( D, max_nbr )
if orientation == "vertical":
f1=pp.figure( figsize=(6,16))
else:
f1=pp.figure( figsize=(16,6))
ax11 = f1.add_subplot(111)
#
# disease_aucs = []
# for disease in u_diseases:
# disease_aucs.append( store[ "/%s/%s/%s/%s/labels_0/diseases/%s/xval_aucs_elementwise"%(disease_string,gene,source, method, disease)].mean(1) )
#
# disease_aucs = pd.concat(disease_aucs,axis=1)
#pdb.set_trace()
if orientation == "vertical":
# for d_idx in range(len(u_diseases)):
# disease = u_diseases[d_idx]
# results_store[ "/%s/%s/%s/labels_%d/diseases/%s/xval_aucs_elementwise"%(dna_gene,source, method,label_permutation_idx,disease)] = pd.DataFrame( elementwise_aucs_by_disease[d_idx,:,:], index = source_data.columns, columns=xval_columns )
# results_store[ "/%s/%s/%s/labels_%d/diseases/%s/xval_aucs"%(dna_gene,source, method,label_permutation_idx,disease)] = pd.DataFrame( np.array(aucs_by_disease).T, index = u_diseases, columns=xval_columns )
#
ax11.vlines( mean_disease_aucs.values, 0, nD-1, color='g' )
for disease in u_diseases:
aucs =store[ "/%s/%s/%s/%s/labels_0/diseases/%s/xval_aucs_elementwise"%(disease_string,gene,source, method, disease)].mean(1)
ax11.plot( aucs.loc[ordered_source_genes].values[:nD], nD-np.arange(nD)-1, '.-', mec = 'k', label = "%s"%(disease) )
#pdb.set_trace()
ax11.plot( ordered_mean_aucs.values[:nD], nD-np.arange(nD)-1, 'b'+marker+"-", mec = 'k', label = "True" )
ax11.fill_betweenx( nD-np.arange(nD), \
ordered_mean_aucs.values[:nD] + 2*order_std_aucs.values[:nD], \
ordered_mean_aucs.values[:nD] - 2*order_std_aucs.values[:nD], facecolor='blue', edgecolor = 'k', alpha=0.5 )
ax11.plot( ordered_mean_aucs.values[:nD], nD-np.arange(nD)-1, 'b'+marker+"-", mec = 'k', label = "True" )
ax11.fill_betweenx( nD-np.arange(nD), \
mean_auc*np.ones(nD) -2*std_auc, \
mean_auc*np.ones(nD) +2*std_auc, facecolor='blue',edgecolor='k', alpha=0.5 )
ax11.vlines( mean_auc, 0, nD-1, color='b' )
if zoom is True:
ax11.set_yticks( nD-1-np.arange(nD) )
ax11.set_yticklabels( ordered_source_genes[:nD], rotation='horizontal', fontsize=8 )
else:
#ax11.fill_between( 2+np.arange(nD), \
# ordered_mean_aucs.values[:nD] + 2*order_std_aucs.values[:nD], \
# ordered_mean_aucs.values[:nD] - 2*order_std_aucs.values[:nD], facecolor='blue', edgecolor = 'k', alpha=0.5 )
#ax11.plot( np.arange(nD)+2, ordered_mean_aucs.values[:nD], 'b'+marker+"-", mec = 'k', label = "True" )
ax11.plot( np.arange(nD)+2, ordered_mean_aucs.values[:nD], 'b-', mec = 'k', label = "True" )
# ax11.fill_between( 1+np.arange(nD), \
# mean_auc*np.ones(nD) -2*std_auc, \
# mean_auc*np.ones(nD) +2*std_auc, facecolor='blue',edgecolor='k', alpha=0.5 )
#
# ax11.hlines( mean_auc, 1, nD, color='b' )
if zoom is True:
ax11.set_xticks( 2+np.arange(nD) )
ax11.set_xticklabels( ordered_source_genes[:nD], rotation='vertical', fontsize=8 )
#
#pdb.set_trace()
#ax11.plot( np.ones( len(mean_aucs.values)), mean_aucs.values, 'o', ms=10, color='orange', mec='k', alpha=0.75)
#ax11.plot( [1], [mean_auc], 'd', color='orchid',mec='orchid' ,ms=30, mew=2, lw=2, alpha=0.75 )
permutations = []
combined_permutations = []
for permutation_idx in range(n_permutations):
mean_auc_p = store["/%s/%s/%s/%s/labels_%d/xval_aucs"%(disease_string,gene,source, method,permutation_idx+1)].mean()
combined_permutations.append( mean_auc_p)
combined_permutations = pd.Series( np.array(combined_permutations), index = np.arange(n_permutations) )
#permutations.append(combined_permutations )
for permutation_idx in range(n_permutations):
mean_auc_p = store["/%s/%s/%s/%s/labels_%d/xval_aucs"%(disease_string,gene,source, method,permutation_idx+1)].mean()
var_auc_p = store["/%s/%s/%s/%s/labels_%d/xval_aucs"%(disease_string,gene,source, method, permutation_idx+1)].var()
std_auc_p = np.sqrt( var_auc_p )
mean_aucs = store["/%s/%s/%s/%s/labels_%d/xval_aucs_elementwise"%(disease_string,gene,source, method,permutation_idx+1)].loc[ordered_source_genes].mean(1)
#permutations.append( store["/%s/%s/%s/%s/labels_%d/xval_aucs_elementwise"%(disease_string,gene,source, method,permutation_idx+1)].loc[ordered_source_genes] )
permutations.append( mean_aucs )
# if orientation == "vertical":
# ax11.vlines( mean_auc_p, 0, nD-1, color='r' )
# #ax11.plot( mean_aucs[:nD], nD-1-np.arange(nD), 'o', color='orange', mec='k', alpha=0.5)
# else:
# ax11.hlines( mean_auc_p, 0, nD-1, color='r' )
# #ax11.plot( nD-1-np.arange(nD), mean_aucs[:nD], 'o', color='orange', mec='k', alpha=0.5)
#
permutations = pd.concat( permutations,axis=1 )
permutations = permutations.T
permutations["ALL"] = combined_permutations
new_order = ["ALL"]
new_order.extend(ordered_source_genes[:nD] )
permutations = permutations.T.loc[new_order]
element_aucs=element_aucs.loc[new_order]
print permutations
#pdb.set_trace()
correct_labels = store["/%s/%s/%s/%s/labels_%d/xval_aucs_elementwise"%(disease_string,gene,source, method,0)].loc[ordered_source_genes]
if orientation == "vertical":
color = dict(boxes='DarkRed', whiskers='DarkOrange', medians='Red', caps='Black')
color2 = dict(boxes='DarkBlue', whiskers='DarkBlue', medians='DarkBlue', caps='Cyan')
permutations.T.boxplot(ax=ax11,color=color)
element_aucs.T.boxplot(ax=ax11,color=color2)
else:
color = dict(boxes='LightCoral', whiskers='DarkRed', medians='DarkRed', caps='LightCoral')
color2 = dict(boxes='SkyBlue', whiskers='DarkBlue', medians='DarkBlue', caps='SkyBlue')
permutations.T.plot.box(ax=ax11,color=color,patch_artist=True)
element_aucs.T.plot.box(ax=ax11,color=color2,patch_artist=True, widths=0.25)
if zoom is True:
ax11.set_xticks( 1+np.arange(len(new_order)) )
ax11.set_xticklabels( new_order, rotation='vertical', fontsize=8 )
#pdb.set_trace()
t_tests = []
for this_gene in ordered_source_genes[:nD]:
k = n_permutations
p_value = ( np.sum( correct_labels.loc[this_gene].values.mean() < permutations.loc[this_gene].values ) + 1.0 )/ (k+1.0)
#t_tests.append( [gene,stats.ttest_ind( permutations.loc[gene].values, correct_labels.loc[gene], equal_var=False )] )
t_tests.append(p_value)
#pdb.set_trace()
pp.grid('on')
pp.title( "%s using %s of %s with %s mean AUC = %0.3f"%(gene,disease_string, source, method, mean_auc))
pp.subplots_adjust(bottom=0.2)
figname1 = os.path.join( HOME_DIR, os.path.dirname(results_location) ) + "/aucs_%s_%s_%s_%s_%s.png"%(gene,source, method,disease_string,title_str)
f1.savefig( figname1, dpi=300 )
def auc_standard_error( theta, nA, nN ):
# from: Hanley and McNeil (1982), The Meaning and Use of the Area under the ROC Curve
# theta: estimated AUC, can be 0.5 for a random test
# nA size of population A
# nN size of population N
Q1=theta/(2.0-theta); Q2=2*theta*theta/(1+theta)
SE = np.sqrt( (theta*(1-theta)+(nA-1)*(Q1-theta*theta) + (nN-1)*(Q2-theta*theta) )/(nA*nN) )
return SE
def main( data_location, project_location, results_location, alpha=0.02 ):
data_path = os.path.join( HOME_DIR ,data_location ) #, "data.h5" )
results_path = os.path.join( HOME_DIR, results_location )
landscape_path = os.path.join( HOME_DIR, project_location, landscape_file )
data_filename = os.path.join( data_path, "data.h5")
fill_filename = os.path.join( results_path, "full_vae_fill.h5" )
dna_dir = os.path.join( results_path, "landscape_dna_prediction" )
check_and_mkdir(dna_dir)
aucs_dir = os.path.join( dna_dir, "aucs_viz" )
check_and_mkdir(aucs_dir)
print "HOME_DIR: ", HOME_DIR
print "data_filename: ", data_filename
print "fill_filename: ", fill_filename
#dna_genes = ["TP53","APC","PIK3CA"]
landscape_yaml = load_yaml( landscape_path )
process2genes = OrderedDict()
n_genes = 0
n_processes = len(landscape_yaml["cellular_processes"])
for process in landscape_yaml["cellular_processes"]:
name = process["name"]
genes = process["genes"]
process2genes[name] = genes
n_genes += len(genes)
#pdb.set_trace()
#dna_genes = ["APC","TP53","KRAS","PIK3CA","FBXW7","SMAD4","NRAS","ARID1A","ATM","CTNNB1"]
print "LOADING stores"
data_store = pd.HDFStore( data_filename, "r" )
fill_store = pd.HDFStore( fill_filename, "r" )
Z_train = fill_store["/Z/TRAIN/Z/mu"]
Z_val = fill_store["/Z/VAL/Z/mu"]
Z = np.vstack( (Z_train.values, Z_val.values) )
n_z = Z.shape[1]
#pdb.set_trace()
z_names = ["z_%d"%z_idx for z_idx in range(Z.shape[1])]
Z = pd.DataFrame( Z, index = np.hstack( (Z_train.index.values, Z_val.index.values)), columns = z_names )
barcodes = np.union1d( Z_train.index.values, Z_val.index.values )
dna_observed_bcs = data_store["/CLINICAL/observed"][ data_store["/CLINICAL/observed"]["DNA"]==1 ].index.values
dna_store = data_store["/DNA/channel/0"]
has_genes = OrderedDict()
for gene in dna_store.columns:
has_genes[gene] = 1
barcodes = np.intersect1d( dna_observed_bcs, barcodes )
Z=Z.loc[barcodes]
Z_values = Z.values
#pdb.set_trace()
tissues = data_store["/CLINICAL/TISSUE"].loc[barcodes]
tissue_names = tissues.columns
tissue_idx = np.argmax( tissues.values, 1 )
n = len(Z)
n_tissues = len(tissue_names)
n_trials = 10
trial_names = ["r_%d"%(trial_idx) for trial_idx in range(n_trials)]
aucs_true = OrderedDict() #np.ones( (n_tissues,n_z), dtype=float)
aucs_true_not = OrderedDict()
aucs_random = OrderedDict() #np.ones( (n_tissues,n_trials), dtype=float)
se_auc_true = OrderedDict()
se_auc_random = OrderedDict()
se_auc_true_not = OrderedDict()
true_y = np.ones(n, dtype=int)
auc_pvalues = OrderedDict()
dna_genes = []
gene_idx = 0
process_idx = 0
gene2process_idx = OrderedDict()
gene2process = OrderedDict()
for cellular_process, process_gene_list in process2genes.iteritems():
for dna_gene in process_gene_list:
if has_genes.has_key(dna_gene) is True:
aucs_true[dna_gene] = 0.5*np.ones((n_tissues,n_z))
aucs_true_not[dna_gene] = 0.5*np.ones((n_tissues,n_z))
aucs_random[dna_gene] = 0.5*np.ones((n_tissues,n_z,n_trials))
se_auc_true[dna_gene] = np.ones((n_tissues,n_z))
se_auc_true_not[dna_gene] = np.ones((n_tissues,n_z))
se_auc_random[dna_gene] = np.ones(n_tissues)
auc_pvalues[dna_gene] = np.ones((n_tissues,n_z))
dna_genes.append(dna_gene)
gene_idx+=1
gene2process[dna_gene] = cellular_process
gene2process_idx[dna_gene] = process_idx
process_idx+=1
dna_store = data_store["/DNA/channel/0"].loc[barcodes]
dna = data_store["/DNA/channel/0"].loc[barcodes][dna_genes]
p_values_by_gene = pd.DataFrame( np.ones( ( len(dna_genes), n_z ) ), index = dna_genes, columns = z_names )
auc_by_gene = pd.DataFrame( np.ones( ( len(dna_genes), n_z ) ), index = dna_genes, columns = z_names )
se_by_gene = pd.DataFrame( np.ones( ( len(dna_genes), n_z ) ), index = dna_genes, columns = z_names )
se_by_gene_random = pd.DataFrame( np.ones( ( len(dna_genes), n_z ) ), index = dna_genes, columns = z_names )
gene_idx = 0
for cellular_process, process_gene_list in process2genes.iteritems():
print "process = ", cellular_process
for dna_gene in process_gene_list:
if has_genes.has_key(dna_gene) is False:
print " skip gene ", dna_gene
continue
dna_by_gene = dna[ dna_gene ]
print " gene = ", dna_gene
this_dna_barcodes = []
# get barcodes of cohorts that have at least one mutation of this gene
for t_idx in range(n_tissues):
tissue_name = tissue_names[t_idx]
t_ids_cohort = tissue_idx == t_idx
bcs_cohort = barcodes[pp.find(t_ids_cohort)]
z_cohort = Z.loc[bcs_cohort]
dna_cohort = dna_by_gene.loc[bcs_cohort]
if dna_cohort.sum() > 0:
print " adding %s"%(tissue_name)
this_dna_barcodes.extend(bcs_cohort)
else:
print " skipping %s"%(tissue_name)
dna_values = dna_by_gene.loc[ this_dna_barcodes ]
z_gene = Z.loc[ this_dna_barcodes ]
n_1 = dna_values.sum()
n_0 = len(dna_values)-n_1
true_y = dna_values.values
p_values = np.ones( n_z, dtype=float )
#pdb.set_trace()
for z_idx in range(n_z):
z_values = z_gene.values[:,z_idx]
if true_y.sum() == 0 or true_y.sum() == len(true_y):
pdb.set_trace()
auc_pos = roc_auc_score( true_y, z_values )
if auc_pos > 0.5:
auc = auc_pos
se_auc = auc_standard_error( auc , n_1, n_0 )
se_auc_random = auc_standard_error( 0.5, n_1, n_0 )
else:
auc = roc_auc_score( true_y, -z_values )
se_auc = auc_standard_error( auc , n_0, n_1 )
se_auc_random = auc_standard_error( 0.5, n_0, n_1 )
se_combined = np.sqrt( se_auc**2 + se_auc_random**2 )
difference = auc - 0.5
z_values = difference / se_combined
p_value = 1.0 - stats.norm.cdf( np.abs(z_values) )
se_by_gene_random.loc[dna_gene]["z_%d"%z_idx] = se_auc_random
se_by_gene.loc[dna_gene]["z_%d"%z_idx] = se_auc
auc_by_gene.loc[dna_gene]["z_%d"%z_idx] = auc
p_values_by_gene.loc[dna_gene]["z_%d"%z_idx] = p_value
print p_values_by_gene.loc[dna_gene].sort_values()
p_values_by_gene.to_csv( dna_dir + "/p_values_by_gene.csv" )
auc_by_gene.to_csv( dna_dir + "/auc_by_gene.csv" )
se_by_gene.to_csv( dna_dir + "/se_by_gene.csv")
se_by_gene_random.to_csv( dna_dir + "/se_by_gene_random.csv" )
f=pp.figure()
ax1 = f.add_subplot(121)
ax2 = f.add_subplot(122)
ax1.hist( p_values_by_gene.values.flatten(), bins = np.linspace(0,0.5,41), lw=2, histtype="step", normed=True, range=(0,0.5) )
ax2.hist( auc_by_gene.values.flatten(), bins = np.linspace(0.5,1,41), lw=2, histtype="step", normed=True, range=(0.5,1) )
pp.savefig( dna_dir + "/global_p_values_and_auc.png", fmt = "png", dpi=300)
f=pp.figure()
ax1 = f.add_subplot(121)
ax2 = f.add_subplot(122)
ax1.hist( p_values_by_gene.values.flatten(), bins = np.linspace(0,0.5,61), lw=2, histtype="step", normed=True, range=(0,0.5) )
ax2.hist( auc_by_gene.values.flatten(), bins = np.linspace(0.5,1,61), lw=2, histtype="step", normed=True, range=(0.5,1) )
pp.savefig( dna_dir + "/global_p_values_and_auc2.png", fmt = "png", dpi=300)
f=pp.figure()
ax1 = f.add_subplot(121)
ax2 = f.add_subplot(122)
ax1.hist( p_values_by_gene.values.flatten(), bins = np.linspace(0,0.5,101), lw=2, histtype="step", normed=True, range=(0,0.5) )
ax2.hist( auc_by_gene.values.flatten(), bins = np.linspace(0.5,1,101), lw=2, histtype="step", normed=True, range=(0.5,1) )
pp.savefig( dna_dir + "/global_p_values_and_auc3.png", fmt = "png", dpi=300)
from sklearn.cluster import MiniBatchKMeans
K_z = 10
kmeans_z = MiniBatchKMeans(n_clusters=K_z, random_state=0).fit(Z_values.T)
kmeans_z_labels = kmeans_z.labels_
order_z = np.argsort(kmeans_z_labels)
z_pallette = sns.husl_palette(K_z)
k_pallette = sns.hls_palette(len(process2genes))
k_colors = np.array([k_pallette[i] for i in gene2process_idx.values()] )
z_colors = np.array([z_pallette[i] for i in kmeans_z_labels] )
f = sns.clustermap( np.log(1e-12+p_values_by_gene).T.corr(), figsize=(16,16), row_colors = k_colors, col_colors = k_colors )
#f = sns.clustermap( np.log(p_values_by_gene), square=False, figsize=(8,10), row_cluster=False, row_colors= gene2process_idx.values())
#
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=10)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=10)
#
pp.savefig( dna_dir + "/clustermap_log_p_genes.png", format="png", dpi=300)
f = sns.clustermap( pow(1.0-p_values_by_gene,3).T.corr(), figsize=(16,16), row_colors = k_colors, col_colors = k_colors )
#f = sns.clustermap( np.log(p_values_by_gene), square=False, figsize=(8,10), row_cluster=False, row_colors= gene2process_idx.values())
#
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=10)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=10)
#
pp.savefig( dna_dir + "/clustermap_p_genes.png", format="png", dpi=300)
ordered = pd.DataFrame( np.log( p_values_by_gene+1e-3).values[:,order_z], index = auc_by_gene.index )
f = sns.clustermap( ordered-0.5, figsize=(16,16), square=False, row_colors = k_colors, row_cluster=False, col_colors=z_colors[order_z],col_cluster=False )
#f = sns.clustermap( np.log( p_values_by_gene+1e-3), figsize=(16,16), square=False, row_colors = k_colors, row_cluster=False )
#f = sns.clustermap( np.log(p_values_by_gene), square=False, figsize=(8,10), row_cluster=False, row_colors= gene2process_idx.values())
#
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=10)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=10)
#
pp.savefig( dna_dir + "/clustermap_gene_by_z.png", format="png", dpi=300)
#ordered_auc_by_gene = pd.DataFrame( auc_by_gene.values[:,order_z], index = auc_by_gene.index )
#f = sns.clustermap( ordered_auc_by_gene-0.5, figsize=(16,16), square=False, row_colors = k_colors, row_cluster=False, col_colors=z_colors[order_z],col_cluster=False )
#pdb.set_trace()
arg_ = pd.DataFrame( pow(1.0-np.argsort( p_values_by_gene.values,1)/float(n_z),4), p_values_by_gene.index, columns=z_names)
f = sns.clustermap( arg_, square=False, figsize=(16,16), row_cluster=False, row_colors= k_colors)
#
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=10)
pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=10)
#
pp.savefig( dna_dir + "/clustermap_auc_gene_by_z.png", format="png", dpi=300)
#pdb.set_trace()
# print "summarizing..."
# sorted_pan = []
# sorted_pan_sig=[]
# auc_sigs = {}
# for dna_gene in dna_genes:
# aucs = aucs_true[dna_gene]
# ses = se_auc_true[dna_gene]
# ses_not = se_auc_true_not[dna_gene]
# #aucs_r = aucs_random[dna_gene]
# se_r = se_auc_random[dna_gene]
#
#
# for t_idx in range(n_tissues):
# f = pp.figure()
# tissue_name = tissue_names[t_idx]
# print "working %s"%(tissue_name)
#
# aucs_tissue = aucs[t_idx]
# ses_tissue = ses[t_idx]
# ses_not_tissue = ses_not[t_idx]
# #aucs_r_tissue = aucs_r[t_idx]
# se_r_tissue = se_r[t_idx]
#
# se_combined = np.sqrt( ses_tissue**2 + se_r_tissue**2 )
# se_combined_not = np.sqrt( ses_not_tissue**2 + se_r_tissue**2 )
#
# difference = aucs_tissue - 0.5
# z_values = difference / se_combined
# z_values_not = difference / se_combined_not
# sign_difference = np.sign(difference)
#
#
# p_values = np.maximum( 1.0 - stats.norm.cdf( np.abs(z_values) ), 1.0 - stats.norm.cdf( np.abs(z_values_not) ) )
#
# ranked_zs = np.argsort(p_values)
#
# ax = f.add_subplot(111)
# best_aucs = np.maximum( aucs_tissue[ranked_zs], 1-aucs_tissue[ranked_zs])
# best_ses = ses_tissue[ ranked_zs ]
#
# #ax.plot( aucs_r_tissue.mean(1)[ranked_zs], 'r-', label="Random")
#
# ax.fill_between( np.arange(n_z), 0.5*np.ones(n_z)-2*se_r_tissue, 0.5*np.ones(n_z)+2*se_r_tissue, color="red", alpha=0.5)
#
# ax.fill_between( np.arange(n_z), best_aucs-2*best_ses, best_aucs+2*best_ses, color="blue", alpha=0.5)
# ax.plot( best_aucs, 'bo-', label="True" )
#
# x_tick_names = []
# for z_idx in ranked_zs:
# if sign_difference[z_idx]<0:
# x_tick_names.append( "-z_%d"%z_idx)
# else:
# x_tick_names.append( "z_%d"%z_idx)
#
# ax.set_xticks(np.arange(n_z))
# ax.set_xticklabels(x_tick_names, rotation=90, fontsize=6)
#
# auc_pvalues[dna_gene][t_idx,:] = p_values
# #ax.set_p
# #ax.plot( aucs_r_tissue[ranked_zs,:], 'r.', label="Random")
# pp.ylim(0.5,1)
# pp.title( "Predicting %s on cohort %s"%(dna_gene,tissue_name ) )#,n_1,n_0,n_1+n_0) )
# pp.xlabel( "Ranked z")
# pp.ylabel( "AUC")
# if np.any(p_values<alpha):
# pp.savefig( aucs_dir + "/auc_tests_%s_%s"%(dna_gene,tissue_name))
# pp.close('all')
# #pp.show()
# auc_pvalues[dna_gene] = pd.DataFrame( auc_pvalues[dna_gene], index=tissue_names, columns = z_names )
# auc_sigs[dna_gene] = pd.DataFrame( (auc_pvalues[dna_gene].values<alpha).astype(int), index=tissue_names, columns = z_names )
# auc_sigs[dna_gene].to_csv( dna_dir + "/pan_sig_z_for_dna_%s.csv"%(dna_gene) )
# reduced_ = auc_sigs[dna_gene]
# rows = reduced_.sum(1)[ reduced_.sum(1)>0 ].index.values
# cols = reduced_.sum(0)[ reduced_.sum(0)>0 ].index.values
# reduced_ = reduced_.loc[rows]
# reduced_ = reduced_[cols]
# size_per_unit = 0.25
# size1 = max( int( len(rows)*size_per_unit ), 12 )
# size2 = max( int( len(cols)*size_per_unit ), 12 )
# f = sns.clustermap( reduced_, square=False, figsize=(size1,size2) )
#
# pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
# pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
# pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=12)
# pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=12)
#
# pp.savefig( dna_dir + "/z_for_dna_clustermap_sig_%s.png"%(dna_gene), format="png", dpi=300)
#
#
# sorted_pan_sig.append(auc_sigs[dna_gene].sum(0))
# sorted_pan.append( np.log( auc_pvalues[dna_gene] + 1e-12 ).sum(0) ) #.sort_values()
# sorted_pan = pd.concat(sorted_pan,axis=1)
# sorted_pan.columns = dna_genes
# sorted_pan.to_csv( dna_dir + "/pan_logpvals_z_for_dna.csv" )
#
# sorted_pan_sig = pd.concat(sorted_pan_sig,axis=1)
# sorted_pan_sig.columns = dna_genes
# sorted_pan_sig.to_csv( dna_dir + "/pan_sig_z_for_dna.csv" )
# size1 = max( int( n_z*size_per_unit ), 12 )
# size2 = max( int( len(dna_genes)*size_per_unit ), 12 )
# f = sns.clustermap( sorted_pan.T, figsize=(size1,size2) )
#
# pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
# pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
# pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=12)
# pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=12)
# pp.savefig( dna_dir + "/z_for_dna_clustermap_logpval.png", format="png")
#
# f = sns.clustermap( sorted_pan_sig.T, figsize=(size1,size2) )
#
# pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
# pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
# pp.setp(f.ax_heatmap.yaxis.get_majorticklabels(), fontsize=12)
# pp.setp(f.ax_heatmap.xaxis.get_majorticklabels(), fontsize=12)
# pp.savefig( dna_dir + "/z_for_dna_clustermap_sig.png", format="png")
#
# pp.close('all')
if __name__ == "__main__":
data_location = sys.argv[1]
results_location = sys.argv[2]
project_location = "projects/tcga_encoder"
main( data_location, project_location, results_location ) | mit |
joewandy/keras-molecules | preprocess.py | 4 | 3491 | import argparse
import pandas
import h5py
import numpy as np
from molecules.utils import one_hot_array, one_hot_index
from sklearn.model_selection import train_test_split
MAX_NUM_ROWS = 500000
SMILES_COL_NAME = 'structure'
def get_arguments():
parser = argparse.ArgumentParser(description='Prepare data for training')
parser.add_argument('infile', type=str, help='Input file name')
parser.add_argument('outfile', type=str, help='Output file name')
parser.add_argument('--length', type=int, metavar='N', default = MAX_NUM_ROWS,
help='Maximum number of rows to include (randomly sampled).')
parser.add_argument('--smiles_column', type=str, default = SMILES_COL_NAME,
help="Name of the column that contains the SMILES strings. Default: %s" % SMILES_COL_NAME)
parser.add_argument('--property_column', type=str,
help="Name of the column that contains the property values to predict. Default: None")
return parser.parse_args()
def chunk_iterator(dataset, chunk_size=1000):
chunk_indices = np.array_split(np.arange(len(dataset)),
len(dataset)/chunk_size)
for chunk_ixs in chunk_indices:
chunk = dataset[chunk_ixs]
yield (chunk_ixs, chunk)
raise StopIteration
def main():
args = get_arguments()
data = pandas.read_hdf(args.infile, 'table')
keys = data[args.smiles_column].map(len) < 121
if args.length <= len(keys):
data = data[keys].sample(n = args.length)
else:
data = data[keys]
structures = data[args.smiles_column].map(lambda x: list(x.ljust(120)))
if args.property_column:
properties = data[args.property_column][keys]
del data
train_idx, test_idx = map(np.array,
train_test_split(structures.index, test_size = 0.20))
charset = list(reduce(lambda x, y: set(y) | x, structures, set()))
one_hot_encoded_fn = lambda row: map(lambda x: one_hot_array(x, len(charset)),
one_hot_index(row, charset))
h5f = h5py.File(args.outfile, 'w')
h5f.create_dataset('charset', data = charset)
def create_chunk_dataset(h5file, dataset_name, dataset, dataset_shape,
chunk_size=1000, apply_fn=None):
new_data = h5file.create_dataset(dataset_name, dataset_shape,
chunks=tuple([chunk_size]+list(dataset_shape[1:])))
for (chunk_ixs, chunk) in chunk_iterator(dataset):
if not apply_fn:
new_data[chunk_ixs, ...] = chunk
else:
new_data[chunk_ixs, ...] = apply_fn(chunk)
create_chunk_dataset(h5f, 'data_train', train_idx,
(len(train_idx), 120, len(charset)),
apply_fn=lambda ch: np.array(map(one_hot_encoded_fn,
structures[ch])))
create_chunk_dataset(h5f, 'data_test', test_idx,
(len(test_idx), 120, len(charset)),
apply_fn=lambda ch: np.array(map(one_hot_encoded_fn,
structures[ch])))
if args.property_column:
h5f.create_dataset('property_train', data = properties[train_idx])
h5f.create_dataset('property_test', data = properties[test_idx])
h5f.close()
if __name__ == '__main__':
main()
| mit |
LedaLima/incubator-spot | spot-setup/migration/migrate_old_proxy_data.py | 7 | 11314 | #!/bin/env python
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF 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.
#
import os
import sys
import subprocess
import fnmatch
import re
import pandas as pd
import datetime
from utilities import util
old_oa_path=sys.argv[1]
staging_db=sys.argv[2]
hdfs_staging_path=sys.argv[3]
dest_db = sys.argv[4]
impala_daemon = sys.argv[5]
# Execution example:
#./migrate_old_proxy_data.py '/home/spotuser/incubator-spot_old/spot-oa' 'spot_migration' '/user/spotuser/spot_migration/' 'migrated' 'node01'
def main():
log = util.get_logger('SPOT.MIGRATE.PROXY')
cur_path = os.path.dirname(os.path.realpath(__file__))
new_spot_path = os.path.split(os.path.split(cur_path)[0])[0]
new_oa_path = '{0}/spot-oa'.format(new_spot_path)
log.info('New Spot OA path: {0}'.format(new_oa_path))
old_spot_path = os.path.split(old_oa_path)[0]
log.info("Creating HDFS paths for Impala tables")
util.create_hdfs_folder('{0}/proxy/scores'.format(hdfs_staging_path),log)
util.create_hdfs_folder('{0}/proxy/edge'.format(hdfs_staging_path),log)
util.create_hdfs_folder('{0}/proxy/summary'.format(hdfs_staging_path),log)
util.create_hdfs_folder('{0}/proxy/storyboard'.format(hdfs_staging_path),log)
util.create_hdfs_folder('{0}/proxy/timeline'.format(hdfs_staging_path),log)
util.create_hdfs_folder('{0}/proxy/iana_rcode'.format(hdfs_staging_path),log)
util.execute_cmd('hdfs dfs -setfacl -R -m user:impala:rwx {0}'.format(hdfs_staging_path),log)
log.info("Creating Staging tables in Impala")
util.execute_cmd('impala-shell -i {0} --var=hpath={1} --var=dbname={2} -c -f create_proxy_migration_tables.hql'.format(impala_daemon, hdfs_staging_path, staging_db),log)
## proxy Ingest Summary
log.info('Processing proxy Ingest Summary')
ing_sum_path='{0}/data/proxy/ingest_summary/'.format(old_oa_path)
pattern='is_??????.csv'
staging_table_name = 'proxy_ingest_summary_tmp'
dest_table_name = 'proxy_ingest_summary'
if os.path.exists(ing_sum_path):
for file in fnmatch.filter(os.listdir(ing_sum_path), pattern):
log.info('Processing file: {0}'.format(file))
filepath='{0}{1}'.format(ing_sum_path, file)
df = pd.read_csv(filepath)
s = df.iloc[:,0]
l_dates = list(s.unique())
l_dates = map(lambda x: x[0:10].strip(), l_dates)
l_dates = filter(lambda x: re.match('\d{4}[-/]\d{2}[-/]\d{1}', x), l_dates)
s_dates = set(l_dates)
for date_str in s_dates:
dt = datetime.datetime.strptime(date_str, '%Y-%m-%d')
log.info('Processing day: {0} {1} {2} {3}'.format(date_str, dt.year, dt.month, dt.day))
records = df[df['date'].str.contains(date_str)]
filename = "ingest_summary_{0}{1}{2}.csv".format(dt.year, dt.month, dt.day)
records.to_csv(filename, index=False)
load_cmd = "LOAD DATA LOCAL INPATH '{0}' OVERWRITE INTO TABLE {1}.{2};".format(filename, staging_db, staging_table_name)
util.execute_hive_cmd(load_cmd, log)
insert_cmd = "INSERT INTO {0}.{1} PARTITION (y={2}, m={3}, d={4}) SELECT tdate, total FROM {5}.{6}".format(dest_db, dest_table_name, dt.year, dt.month, dt.day, staging_db, staging_table_name)
util.execute_hive_cmd(insert_cmd, log)
os.remove(filename)
## Iterating days
days_path='{0}/data/proxy/'.format(old_oa_path)
if os.path.exists(days_path):
for day_folder in fnmatch.filter(os.listdir(days_path), '2*'):
print day_folder
dt = datetime.datetime.strptime(day_folder, '%Y%m%d')
log.info('Processing day: {0} {1} {2} {3}'.format(day_folder, dt.year, dt.month, dt.day))
full_day_path = '{0}{1}'.format(days_path,day_folder)
## proxy Scores and proxy Threat Investigation
filename = '{0}/proxy_scores.tsv'.format(full_day_path)
if os.path.isfile(filename):
log.info("Processing Proxy Scores")
staging_table_name = 'proxy_scores_tmp'
dest_table_name = 'proxy_scores'
load_cmd = "LOAD DATA LOCAL INPATH '{0}' OVERWRITE INTO TABLE {1}.{2};".format(filename, staging_db, staging_table_name)
util.execute_hive_cmd(load_cmd, log)
insert_cmd = "INSERT INTO {0}.{1} PARTITION (y={2}, m={3}, d={4}) SELECT tdate, time, clientip, host, reqmethod, useragent, resconttype, duration, username, webcat, referer, respcode, uriport, uripath, uriquery, serverip, scbytes, csbytes, fulluri, word, ml_score, uri_rep, respcode_name, network_context FROM {5}.{6}".format(dest_db, dest_table_name, dt.year, dt.month, dt.day, staging_db, staging_table_name)
util.execute_hive_cmd(insert_cmd, log)
log.info("Processing Proxy Threat Investigation")
staging_table_name = 'proxy_scores_tmp'
dest_table_name = 'proxy_threat_investigation'
insert_cmd = "INSERT INTO {0}.{1} PARTITION (y={2}, m={3}, d={4}) SELECT tdate, fulluri, uri_sev FROM {5}.{6} WHERE uri_sev > 0;".format(dest_db, dest_table_name, dt.year, dt.month, dt.day, staging_db, staging_table_name)
util.execute_hive_cmd(insert_cmd, log)
## proxy IANA resp codes
log.info("Uploading Proxy IANA resp codes")
iana_table_name = 'proxy_iana_rcode_tmp'
iana_code_csv = '{0}/oa/components/iana/http-rcode.csv'.format(old_oa_path)
load_cmd = "LOAD DATA LOCAL INPATH '{0}' OVERWRITE INTO TABLE {1}.{2};".format(iana_code_csv, staging_db, iana_table_name)
util.execute_hive_cmd(load_cmd, log)
## proxy Edge
log.info("Processing Proxy Edge")
staging_table_name = 'proxy_edge_tmp'
dest_table_name = 'proxy_edge'
pattern = 'edge*.tsv'
edge_files = fnmatch.filter(os.listdir(full_day_path), pattern)
filename = '{0}/{1}'.format(full_day_path, pattern)
if len(edge_files) > 0:
load_cmd = "LOAD DATA LOCAL INPATH '{0}' OVERWRITE INTO TABLE {1}.{2};".format(filename, staging_db, staging_table_name)
util.execute_hive_cmd(load_cmd, log)
insert_cmd = "INSERT INTO {0}.{1} PARTITION (y={2}, m={3}, d={4}) SELECT e.tdate, e.time, e.clientip, e.host, e.webcat, COALESCE(i.respcode,'0'), e.reqmethod, e.useragent, e.resconttype, e.referer, e.uriport, e.serverip, e.scbytes, e.csbytes, e.fulluri, hour(e.time), e.respcode FROM {5}.{6} e left join {5}.{7} i on e.respcode = i.respcode_name ;".format(dest_db, dest_table_name, dt.year, dt.month, dt.day, staging_db, staging_table_name, iana_table_name)
util.execute_hive_cmd(insert_cmd, log)
##proxy_storyboard
log.info("Processing Proxy Storyboard")
staging_table_name = 'proxy_storyboard_tmp'
dest_table_name = 'proxy_storyboard'
filename = '{0}/threats.csv'.format(full_day_path)
if os.path.isfile(filename):
load_cmd = "LOAD DATA LOCAL INPATH '{0}' OVERWRITE INTO TABLE {1}.{2};".format(filename, staging_db, staging_table_name)
util.execute_hive_cmd(load_cmd, log)
insert_cmd = "INSERT INTO {0}.{1} PARTITION (y={2}, m={3}, d={4}) SELECT p_threat, title, text FROM {5}.{6};".format(dest_db, dest_table_name, dt.year, dt.month, dt.day, staging_db, staging_table_name)
util.execute_hive_cmd(insert_cmd, log)
##proxy Timeline
log.info("Processing Proxy Timeline")
staging_table_name = 'proxy_timeline_tmp'
dest_table_name = 'proxy_timeline'
pattern = 'timeline*.tsv'
for file in fnmatch.filter(os.listdir(full_day_path), pattern):
filename = '{0}/{1}'.format(full_day_path, file)
hash_code = re.findall("timeline-(\S+).tsv", file)[0]
extsearch_path = "{0}/es-{1}.csv".format(full_day_path, hash_code)
log.info('File: {0} Hash: {1} Extended Search file: {2}'.format(file, hash_code, extsearch_path))
if os.path.isfile('{0}'.format(extsearch_path)):
log.info("Getting Full URI from extended search file")
es_df = pd.read_csv(extsearch_path, sep='\t')
fulluri = es_df.iloc[0]['fulluri']
log.info('Full URI found: {0}'.format(fulluri))
# Load timeline to staging table
load_cmd = "LOAD DATA LOCAL INPATH '{0}' OVERWRITE INTO TABLE {1}.{2};".format(filename, staging_db, staging_table_name)
util.execute_hive_cmd(load_cmd, log)
# Insert into new table from staging table and adding FullURI value
insert_cmd = "INSERT INTO {0}.{1} PARTITION (y={2}, m={3}, d={4}) SELECT '{5}', t.tstart, t.tend, t.duration, t.clientip, t.respcode, COALESCE(i.respcode_name,'') FROM {6}.{7} t left join {6}.{8} i on t.respcode = i.respcode where cast(tstart as timestamp) is not null;".format(dest_db, dest_table_name, dt.year, dt.month, dt.day, fulluri, staging_db, staging_table_name, iana_table_name)
util.execute_hive_cmd(insert_cmd, log)
else:
print "Extended search file {0} doesn't exist. Timeline on file {1} can't be processed".format(extsearch_path, timeline_path)
log.info("Dropping staging tables")
util.execute_cmd('impala-shell -i {0} --var=dbname={1} -c -f drop_proxy_migration_tables.hql'.format(impala_daemon, staging_db),log)
log.info("Removing staging tables' path in HDFS")
util.execute_cmd('hadoop fs -rm -r {0}/proxy/'.format(hdfs_staging_path),log)
log.info("Moving CSV data to backup folder")
util.execute_cmd('mkdir {0}/data/backup/'.format(old_oa_path),log)
util.execute_cmd('cp -r {0}/data/proxy/ {0}/data/backup/'.format(old_oa_path),log)
util.execute_cmd('rm -r {0}/data/proxy/'.format(old_oa_path),log)
log.info("Invalidating metadata in Impala to refresh tables content")
util.execute_cmd('impala-shell -i {0} -q "INVALIDATE METADATA;"'.format(impala_daemon),log)
log.info("Creating ipynb template structure and copying advanced mode and threat investigation ipynb templates for each pre-existing day in the new Spot location")
ipynb_pipeline_path = '{0}/ipynb/proxy/'.format(old_oa_path)
if os.path.exists(ipynb_pipeline_path):
for folder in os.listdir(ipynb_pipeline_path):
log.info("Creating ipynb proxy folders in new Spot locaiton: {0}".format(folder))
util.execute_cmd('mkdir -p {0}/ipynb/proxy/{1}/'.format(new_oa_path, folder),log)
log.info("Copying advanced mode ipynb template")
util.execute_cmd('cp {0}/oa/proxy/ipynb_templates/Advanced_Mode_master.ipynb {0}/ipynb/proxy/{1}/Advanced_Mode.ipynb'.format(new_oa_path, folder),log)
log.info("Copying threat investigation ipynb template")
util.execute_cmd('cp {0}/oa/proxy/ipynb_templates/Threat_Investigation_master.ipynb {0}/ipynb/proxy/{1}/Threat_Investigation.ipynb'.format(new_oa_path, folder),log)
if __name__=='__main__':
main()
| apache-2.0 |
MohammedWasim/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 65 | 50308 | # Authors: Peter Prettenhofer <[email protected]> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from .base import make_dataset
from ..base import BaseEstimator, RegressorMixin
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import (check_array, check_random_state, check_X_y,
deprecated)
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import _check_partial_fit_first_call
from ..utils.validation import check_is_fitted
from ..externals import six
from .sgd_fast import plain_sgd, average_sgd
from ..utils.fixes import astype
from ..utils import compute_class_weight
from .sgd_fast import Hinge
from .sgd_fast import SquaredHinge
from .sgd_fast import Log
from .sgd_fast import ModifiedHuber
from .sgd_fast import SquaredLoss
from .sgd_fast import Huber
from .sgd_fast import EpsilonInsensitive
from .sgd_fast import SquaredEpsilonInsensitive
LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3,
"pa1": 4, "pa2": 5}
PENALTY_TYPES = {"none": 0, "l2": 2, "l1": 1, "elasticnet": 3}
DEFAULT_EPSILON = 0.1
# Default value of ``epsilon`` parameter.
class BaseSGD(six.with_metaclass(ABCMeta, BaseEstimator, SparseCoefMixin)):
"""Base class for SGD classification and regression."""
def __init__(self, loss, penalty='l2', alpha=0.0001, C=1.0,
l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
verbose=0, epsilon=0.1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
warm_start=False, average=False):
self.loss = loss
self.penalty = penalty
self.learning_rate = learning_rate
self.epsilon = epsilon
self.alpha = alpha
self.C = C
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
self.n_iter = n_iter
self.shuffle = shuffle
self.random_state = random_state
self.verbose = verbose
self.eta0 = eta0
self.power_t = power_t
self.warm_start = warm_start
self.average = average
self._validate_params()
self.coef_ = None
if self.average > 0:
self.standard_coef_ = None
self.average_coef_ = None
# iteration count for learning rate schedule
# must not be int (e.g. if ``learning_rate=='optimal'``)
self.t_ = None
def set_params(self, *args, **kwargs):
super(BaseSGD, self).set_params(*args, **kwargs)
self._validate_params()
return self
@abstractmethod
def fit(self, X, y):
"""Fit model."""
def _validate_params(self):
"""Validate input params. """
if not isinstance(self.shuffle, bool):
raise ValueError("shuffle must be either True or False")
if self.n_iter <= 0:
raise ValueError("n_iter must be > zero")
if not (0.0 <= self.l1_ratio <= 1.0):
raise ValueError("l1_ratio must be in [0, 1]")
if self.alpha < 0.0:
raise ValueError("alpha must be >= 0")
if self.learning_rate in ("constant", "invscaling"):
if self.eta0 <= 0.0:
raise ValueError("eta0 must be > 0")
# raises ValueError if not registered
self._get_penalty_type(self.penalty)
self._get_learning_rate_type(self.learning_rate)
if self.loss not in self.loss_functions:
raise ValueError("The loss %s is not supported. " % self.loss)
def _get_loss_function(self, loss):
"""Get concrete ``LossFunction`` object for str ``loss``. """
try:
loss_ = self.loss_functions[loss]
loss_class, args = loss_[0], loss_[1:]
if loss in ('huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'):
args = (self.epsilon, )
return loss_class(*args)
except KeyError:
raise ValueError("The loss %s is not supported. " % loss)
def _get_learning_rate_type(self, learning_rate):
try:
return LEARNING_RATE_TYPES[learning_rate]
except KeyError:
raise ValueError("learning rate %s "
"is not supported. " % learning_rate)
def _get_penalty_type(self, penalty):
penalty = str(penalty).lower()
try:
return PENALTY_TYPES[penalty]
except KeyError:
raise ValueError("Penalty %s is not supported. " % penalty)
def _validate_sample_weight(self, sample_weight, n_samples):
"""Set the sample weight array."""
if sample_weight is None:
# uniform sample weights
sample_weight = np.ones(n_samples, dtype=np.float64, order='C')
else:
# user-provided array
sample_weight = np.asarray(sample_weight, dtype=np.float64,
order="C")
if sample_weight.shape[0] != n_samples:
raise ValueError("Shapes of X and sample_weight do not match.")
return sample_weight
def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None,
intercept_init=None):
"""Allocate mem for parameters; initialize if provided."""
if n_classes > 2:
# allocate coef_ for multi-class
if coef_init is not None:
coef_init = np.asarray(coef_init, order="C")
if coef_init.shape != (n_classes, n_features):
raise ValueError("Provided ``coef_`` does not match dataset. ")
self.coef_ = coef_init
else:
self.coef_ = np.zeros((n_classes, n_features),
dtype=np.float64, order="C")
# allocate intercept_ for multi-class
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, order="C")
if intercept_init.shape != (n_classes, ):
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init
else:
self.intercept_ = np.zeros(n_classes, dtype=np.float64,
order="C")
else:
# allocate coef_ for binary problem
if coef_init is not None:
coef_init = np.asarray(coef_init, dtype=np.float64,
order="C")
coef_init = coef_init.ravel()
if coef_init.shape != (n_features,):
raise ValueError("Provided coef_init does not "
"match dataset.")
self.coef_ = coef_init
else:
self.coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
# allocate intercept_ for binary problem
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, dtype=np.float64)
if intercept_init.shape != (1,) and intercept_init.shape != ():
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init.reshape(1,)
else:
self.intercept_ = np.zeros(1, dtype=np.float64, order="C")
# initialize average parameters
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = np.zeros(self.coef_.shape,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(self.standard_intercept_.shape,
dtype=np.float64,
order="C")
def _prepare_fit_binary(est, y, i):
"""Initialization for fit_binary.
Returns y, coef, intercept.
"""
y_i = np.ones(y.shape, dtype=np.float64, order="C")
y_i[y != est.classes_[i]] = -1.0
average_intercept = 0
average_coef = None
if len(est.classes_) == 2:
if not est.average:
coef = est.coef_.ravel()
intercept = est.intercept_[0]
else:
coef = est.standard_coef_.ravel()
intercept = est.standard_intercept_[0]
average_coef = est.average_coef_.ravel()
average_intercept = est.average_intercept_[0]
else:
if not est.average:
coef = est.coef_[i]
intercept = est.intercept_[i]
else:
coef = est.standard_coef_[i]
intercept = est.standard_intercept_[i]
average_coef = est.average_coef_[i]
average_intercept = est.average_intercept_[i]
return y_i, coef, intercept, average_coef, average_intercept
def fit_binary(est, i, X, y, alpha, C, learning_rate, n_iter,
pos_weight, neg_weight, sample_weight):
"""Fit a single binary classifier.
The i'th class is considered the "positive" class.
"""
# if average is not true, average_coef, and average_intercept will be
# unused
y_i, coef, intercept, average_coef, average_intercept = \
_prepare_fit_binary(est, y, i)
assert y_i.shape[0] == y.shape[0] == sample_weight.shape[0]
dataset, intercept_decay = make_dataset(X, y_i, sample_weight)
penalty_type = est._get_penalty_type(est.penalty)
learning_rate_type = est._get_learning_rate_type(learning_rate)
# XXX should have random_state_!
random_state = check_random_state(est.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
if not est.average:
return plain_sgd(coef, intercept, est.loss_function,
penalty_type, alpha, C, est.l1_ratio,
dataset, n_iter, int(est.fit_intercept),
int(est.verbose), int(est.shuffle), seed,
pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay)
else:
standard_coef, standard_intercept, average_coef, \
average_intercept = average_sgd(coef, intercept, average_coef,
average_intercept,
est.loss_function, penalty_type,
alpha, C, est.l1_ratio, dataset,
n_iter, int(est.fit_intercept),
int(est.verbose), int(est.shuffle),
seed, pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_,
intercept_decay,
est.average)
if len(est.classes_) == 2:
est.average_intercept_[0] = average_intercept
else:
est.average_intercept_[i] = average_intercept
return standard_coef, standard_intercept
class BaseSGDClassifier(six.with_metaclass(ABCMeta, BaseSGD,
LinearClassifierMixin)):
loss_functions = {
"hinge": (Hinge, 1.0),
"squared_hinge": (SquaredHinge, 1.0),
"perceptron": (Hinge, 0.0),
"log": (Log, ),
"modified_huber": (ModifiedHuber, ),
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, n_iter=5, shuffle=True, verbose=0,
epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
class_weight=None, warm_start=False, average=False):
super(BaseSGDClassifier, self).__init__(loss=loss, penalty=penalty,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept,
n_iter=n_iter, shuffle=shuffle,
verbose=verbose,
epsilon=epsilon,
random_state=random_state,
learning_rate=learning_rate,
eta0=eta0, power_t=power_t,
warm_start=warm_start,
average=average)
self.class_weight = class_weight
self.classes_ = None
self.n_jobs = int(n_jobs)
def _partial_fit(self, X, y, alpha, C,
loss, learning_rate, n_iter,
classes, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")
n_samples, n_features = X.shape
self._validate_params()
_check_partial_fit_first_call(self, classes)
n_classes = self.classes_.shape[0]
# Allocate datastructures from input arguments
self._expanded_class_weight = compute_class_weight(self.class_weight,
self.classes_, y)
sample_weight = self._validate_sample_weight(sample_weight, n_samples)
if self.coef_ is None or coef_init is not None:
self._allocate_parameter_mem(n_classes, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous data %d."
% (n_features, self.coef_.shape[-1]))
self.loss_function = self._get_loss_function(loss)
if self.t_ is None:
self.t_ = 1.0
# delegate to concrete training procedure
if n_classes > 2:
self._fit_multiclass(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight, n_iter=n_iter)
elif n_classes == 2:
self._fit_binary(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight, n_iter=n_iter)
else:
raise ValueError("The number of class labels must be "
"greater than one.")
return self
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
if hasattr(self, "classes_"):
self.classes_ = None
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")
n_samples, n_features = X.shape
# labels can be encoded as float, int, or string literals
# np.unique sorts in asc order; largest class id is positive class
classes = np.unique(y)
if self.warm_start and self.coef_ is not None:
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = None
self._partial_fit(X, y, alpha, C, loss, learning_rate, self.n_iter,
classes, sample_weight, coef_init, intercept_init)
return self
def _fit_binary(self, X, y, alpha, C, sample_weight,
learning_rate, n_iter):
"""Fit a binary classifier on X and y. """
coef, intercept = fit_binary(self, 1, X, y, alpha, C,
learning_rate, n_iter,
self._expanded_class_weight[1],
self._expanded_class_weight[0],
sample_weight)
self.t_ += n_iter * X.shape[0]
# need to be 2d
if self.average > 0:
if self.average <= self.t_ - 1:
self.coef_ = self.average_coef_.reshape(1, -1)
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_.reshape(1, -1)
self.standard_intercept_ = np.atleast_1d(intercept)
self.intercept_ = self.standard_intercept_
else:
self.coef_ = coef.reshape(1, -1)
# intercept is a float, need to convert it to an array of length 1
self.intercept_ = np.atleast_1d(intercept)
def _fit_multiclass(self, X, y, alpha, C, learning_rate,
sample_weight, n_iter):
"""Fit a multi-class classifier by combining binary classifiers
Each binary classifier predicts one class versus all others. This
strategy is called OVA: One Versus All.
"""
# Use joblib to fit OvA in parallel.
result = Parallel(n_jobs=self.n_jobs, backend="threading",
verbose=self.verbose)(
delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate,
n_iter, self._expanded_class_weight[i], 1.,
sample_weight)
for i in range(len(self.classes_)))
for i, (_, intercept) in enumerate(result):
self.intercept_[i] = intercept
self.t_ += n_iter * X.shape[0]
if self.average > 0:
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.standard_intercept_ = np.atleast_1d(intercept)
self.intercept_ = self.standard_intercept_
def partial_fit(self, X, y, classes=None, sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data
y : numpy array, shape (n_samples,)
Subset of the target values
classes : array, shape (n_classes,)
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
if self.class_weight in ['balanced', 'auto']:
raise ValueError("class_weight '{0}' is not supported for "
"partial_fit. In order to use 'balanced' weights, "
"use compute_class_weight('{0}', classes, y). "
"In place of y you can us a large enough sample "
"of the full training set target to properly "
"estimate the class frequency distributions. "
"Pass the resulting weights as the class_weight "
"parameter.".format(self.class_weight))
return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss,
learning_rate=self.learning_rate, n_iter=1,
classes=classes, sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def fit(self, X, y, coef_init=None, intercept_init=None, sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (n_classes,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed. These weights will
be multiplied with class_weight (passed through the
contructor) if class_weight is specified
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init, intercept_init=intercept_init,
sample_weight=sample_weight)
class SGDClassifier(BaseSGDClassifier, _LearntSelectorMixin):
"""Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This estimator implements regularized linear models with stochastic
gradient descent (SGD) learning: the gradient of the loss is estimated
each sample at a time and the model is updated along the way with a
decreasing strength schedule (aka learning rate). SGD allows minibatch
(online/out-of-core) learning, see the partial_fit method.
For best results using the default learning rate schedule, the data should
have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays
of floating point values for the features. The model it fits can be
controlled with the loss parameter; by default, it fits a linear support
vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, 'hinge', 'log', 'modified_huber', 'squared_hinge',\
'perceptron', or a regression loss: 'squared_loss', 'huber',\
'epsilon_insensitive', or 'squared_epsilon_insensitive'
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM.
The 'log' loss gives logistic regression, a probabilistic classifier.
'modified_huber' is another smooth loss that brings tolerance to
outliers as well as probability estimates.
'squared_hinge' is like hinge but is quadratically penalized.
'perceptron' is the linear loss used by the perceptron algorithm.
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
n_iter : int, optional
The number of passes over the training data (aka epochs). The number
of iterations is set to 1 if using partial_fit.
Defaults to 5.
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use when
shuffling the data.
verbose : integer, optional
The verbosity level
epsilon : float
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
n_jobs : integer, optional
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation. -1 means 'all CPUs'. Defaults
to 1.
learning_rate : string, optional
The learning rate schedule:
constant: eta = eta0
optimal: eta = 1.0 / (t + t0) [default]
invscaling: eta = eta0 / pow(t, power_t)
where t0 is chosen by a heuristic proposed by Leon Bottou.
eta0 : double
The initial learning rate for the 'constant' or 'invscaling'
schedules. The default value is 0.0 as eta0 is not used by the
default schedule 'optimal'.
power_t : double
The exponent for inverse scaling learning rate [default 0.5].
class_weight : dict, {class_label: weight} or "balanced" or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes
are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
average : bool or int, optional
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So average=10 will begin averaging after seeing 10 samples.
Attributes
----------
coef_ : array, shape (1, n_features) if n_classes == 2 else (n_classes,\
n_features)
Weights assigned to the features.
intercept_ : array, shape (1,) if n_classes == 2 else (n_classes,)
Constants in decision function.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
>>> clf = linear_model.SGDClassifier()
>>> clf.fit(X, Y)
... #doctest: +NORMALIZE_WHITESPACE
SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,
eta0=0.0, fit_intercept=True, l1_ratio=0.15,
learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,
penalty='l2', power_t=0.5, random_state=None, shuffle=True,
verbose=0, warm_start=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
--------
LinearSVC, LogisticRegression, Perceptron
"""
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, n_iter=5, shuffle=True, verbose=0,
epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
class_weight=None, warm_start=False, average=False):
super(SGDClassifier, self).__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, n_iter=n_iter, shuffle=shuffle,
verbose=verbose, epsilon=epsilon, n_jobs=n_jobs,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, class_weight=class_weight, warm_start=warm_start,
average=average)
def _check_proba(self):
check_is_fitted(self, "t_")
if self.loss not in ("log", "modified_huber"):
raise AttributeError("probability estimates are not available for"
" loss=%r" % self.loss)
@property
def predict_proba(self):
"""Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest)
estimates by simple normalization, as recommended by Zadrozny and
Elkan.
Binary probability estimates for loss="modified_huber" are given by
(clip(decision_function(X), -1, 1) + 1) / 2.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model,
where classes are ordered as they are in `self.classes_`.
References
----------
Zadrozny and Elkan, "Transforming classifier scores into multiclass
probability estimates", SIGKDD'02,
http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss="modified_huber"
case is in the appendix B in:
http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
"""
self._check_proba()
return self._predict_proba
def _predict_proba(self, X):
if self.loss == "log":
return self._predict_proba_lr(X)
elif self.loss == "modified_huber":
binary = (len(self.classes_) == 2)
scores = self.decision_function(X)
if binary:
prob2 = np.ones((scores.shape[0], 2))
prob = prob2[:, 1]
else:
prob = scores
np.clip(scores, -1, 1, prob)
prob += 1.
prob /= 2.
if binary:
prob2[:, 0] -= prob
prob = prob2
else:
# the above might assign zero to all classes, which doesn't
# normalize neatly; work around this to produce uniform
# probabilities
prob_sum = prob.sum(axis=1)
all_zero = (prob_sum == 0)
if np.any(all_zero):
prob[all_zero, :] = 1
prob_sum[all_zero] = len(self.classes_)
# normalize
prob /= prob_sum.reshape((prob.shape[0], -1))
return prob
else:
raise NotImplementedError("predict_(log_)proba only supported when"
" loss='log' or loss='modified_huber' "
"(%r given)" % self.loss)
@property
def predict_log_proba(self):
"""Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss="modified_huber", probability estimates may be hard zeros
and ones, so taking the logarithm is not possible.
See ``predict_proba`` for details.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Returns
-------
T : array-like, shape (n_samples, n_classes)
Returns the log-probability of the sample for each class in the
model, where classes are ordered as they are in
`self.classes_`.
"""
self._check_proba()
return self._predict_log_proba
def _predict_log_proba(self, X):
return np.log(self.predict_proba(X))
class BaseSGDRegressor(BaseSGD, RegressorMixin):
loss_functions = {
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, random_state=None,
learning_rate="invscaling", eta0=0.01, power_t=0.25,
warm_start=False, average=False):
super(BaseSGDRegressor, self).__init__(loss=loss, penalty=penalty,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept,
n_iter=n_iter, shuffle=shuffle,
verbose=verbose,
epsilon=epsilon,
random_state=random_state,
learning_rate=learning_rate,
eta0=eta0, power_t=power_t,
warm_start=warm_start,
average=average)
def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
n_iter, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64)
y = astype(y, np.float64, copy=False)
n_samples, n_features = X.shape
self._validate_params()
# Allocate datastructures from input arguments
sample_weight = self._validate_sample_weight(sample_weight, n_samples)
if self.coef_ is None:
self._allocate_parameter_mem(1, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous data %d."
% (n_features, self.coef_.shape[-1]))
if self.average > 0 and self.average_coef_ is None:
self.average_coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(1,
dtype=np.float64,
order="C")
self._fit_regressor(X, y, alpha, C, loss, learning_rate,
sample_weight, n_iter)
return self
def partial_fit(self, X, y, sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of training data
y : numpy array of shape (n_samples,)
Subset of target values
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
return self._partial_fit(X, y, self.alpha, C=1.0,
loss=self.loss,
learning_rate=self.learning_rate, n_iter=1,
sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
if self.warm_start and self.coef_ is not None:
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_intercept_ = self.intercept_
self.standard_coef_ = self.coef_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = None
return self._partial_fit(X, y, alpha, C, loss, learning_rate,
self.n_iter, sample_weight,
coef_init, intercept_init)
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight)
@deprecated(" and will be removed in 0.19.")
def decision_function(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
return self._decision_function(X)
def _decision_function(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
check_is_fitted(self, ["t_", "coef_", "intercept_"], all_or_any=all)
X = check_array(X, accept_sparse='csr')
scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
return scores.ravel()
def predict(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
return self._decision_function(X)
def _fit_regressor(self, X, y, alpha, C, loss, learning_rate,
sample_weight, n_iter):
dataset, intercept_decay = make_dataset(X, y, sample_weight)
loss_function = self._get_loss_function(loss)
penalty_type = self._get_penalty_type(self.penalty)
learning_rate_type = self._get_learning_rate_type(learning_rate)
if self.t_ is None:
self.t_ = 1.0
random_state = check_random_state(self.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
if self.average > 0:
self.standard_coef_, self.standard_intercept_, \
self.average_coef_, self.average_intercept_ =\
average_sgd(self.standard_coef_,
self.standard_intercept_[0],
self.average_coef_,
self.average_intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
n_iter,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay, self.average)
self.average_intercept_ = np.atleast_1d(self.average_intercept_)
self.standard_intercept_ = np.atleast_1d(self.standard_intercept_)
self.t_ += n_iter * X.shape[0]
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.intercept_ = self.standard_intercept_
else:
self.coef_, self.intercept_ = \
plain_sgd(self.coef_,
self.intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
n_iter,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay)
self.t_ += n_iter * X.shape[0]
self.intercept_ = np.atleast_1d(self.intercept_)
class SGDRegressor(BaseSGDRegressor, _LearntSelectorMixin):
"""Linear model fitted by minimizing a regularized empirical loss with SGD
SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
This implementation works with data represented as dense numpy arrays of
floating point values for the features.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, 'squared_loss', 'huber', 'epsilon_insensitive', \
or 'squared_epsilon_insensitive'
The loss function to be used. Defaults to 'squared_loss' which refers
to the ordinary least squares fit. 'huber' modifies 'squared_loss' to
focus less on getting outliers correct by switching from squared to
linear loss past a distance of epsilon. 'epsilon_insensitive' ignores
errors less than epsilon and is linear past that; this is the loss
function used in SVR. 'squared_epsilon_insensitive' is the same but
becomes squared loss past a tolerance of epsilon.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
n_iter : int, optional
The number of passes over the training data (aka epochs). The number
of iterations is set to 1 if using partial_fit.
Defaults to 5.
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use when
shuffling the data.
verbose : integer, optional
The verbosity level.
epsilon : float
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
learning_rate : string, optional
The learning rate:
constant: eta = eta0
optimal: eta = 1.0/(alpha * t)
invscaling: eta = eta0 / pow(t, power_t) [default]
eta0 : double, optional
The initial learning rate [default 0.01].
power_t : double, optional
The exponent for inverse scaling learning rate [default 0.25].
warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
average : bool or int, optional
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10 will`` begin averaging after seeing 10 samples.
Attributes
----------
coef_ : array, shape (n_features,)
Weights assigned to the features.
intercept_ : array, shape (1,)
The intercept term.
average_coef_ : array, shape (n_features,)
Averaged weights assigned to the features.
average_intercept_ : array, shape (1,)
The averaged intercept term.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = linear_model.SGDRegressor()
>>> clf.fit(X, y)
... #doctest: +NORMALIZE_WHITESPACE
SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,
fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25,
random_state=None, shuffle=True, verbose=0, warm_start=False)
See also
--------
Ridge, ElasticNet, Lasso, SVR
"""
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, random_state=None,
learning_rate="invscaling", eta0=0.01, power_t=0.25,
warm_start=False, average=False):
super(SGDRegressor, self).__init__(loss=loss, penalty=penalty,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept,
n_iter=n_iter, shuffle=shuffle,
verbose=verbose,
epsilon=epsilon,
random_state=random_state,
learning_rate=learning_rate,
eta0=eta0, power_t=power_t,
warm_start=warm_start,
average=average)
| bsd-3-clause |
akrherz/idep | scripts/plots/county_results.py | 2 | 1803 | """County level agg."""
from pyiem.util import get_dbconn
from pyiem.plot.geoplot import MapPlot
from geopandas import read_postgis
from pandas.io.sql import read_sql
def main():
"""Go Main Go."""
years = 12.0 # 2008 - 2019
pgconn = get_dbconn("idep")
postgis = get_dbconn("postgis")
# Get the initial geometries
df = read_postgis(
"""
SELECT ugc, name, geom from ugcs WHERE end_ts is null and
substr(ugc, 1, 3) = 'IAC'
""",
postgis,
index_col="ugc",
crs="EPSG:4326",
)
scenario = 0
df2 = read_sql(
"""WITH data as (
SELECT r.huc_12,
sum(avg_loss) * 4.463 / %s as detach,
sum(avg_delivery) * 4.463 / %s as delivery,
sum(avg_runoff) / 25.4 / %s as runoff
from results_by_huc12 r
, huc12 h WHERE r.huc_12 = h.huc_12 and h.states ~* 'IA'
and r.scenario = %s and h.scenario = 0 and r.valid < '2020-01-01'
and r.valid > '2008-01-01'
GROUP by r.huc_12)
SELECT ugc, avg(detach) as detach, avg(delivery) as delivery,
avg(runoff) as runoff from data d JOIN huc12 h on (d.huc_12 = h.huc_12)
WHERE h.scenario = 0 GROUP by ugc ORDER by delivery desc
""",
pgconn,
params=(years, years, years, scenario),
index_col="ugc",
)
newcols = {
"detach": "det%s" % (0,),
"delivery": "del%s" % (0,),
"runoff": "run%s" % (0,),
}
for key, val in newcols.items():
df[val] = df2[key]
df = df.sort_values("del0", ascending=False)
print(df.head(10))
mp = MapPlot(
title="2008-2019 DEP Top 10 Erosive Counties", logo="dep", caption=""
)
df2 = df.head(10)
mp.fill_ugcs(df2["del0"].to_dict())
mp.postprocess(filename="test.png")
if __name__ == "__main__":
main()
| mit |
saquiba2/numpy2 | numpy/lib/recfunctions.py | 148 | 35012 | """
Collection of utilities to manipulate structured arrays.
Most of these functions were initially implemented by John Hunter for
matplotlib. They have been rewritten and extended for convenience.
"""
from __future__ import division, absolute_import, print_function
import sys
import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.lib._iotools import _is_string_like
from numpy.compat import basestring
if sys.version_info[0] < 3:
from future_builtins import zip
_check_fill_value = np.ma.core._check_fill_value
__all__ = [
'append_fields', 'drop_fields', 'find_duplicates',
'get_fieldstructure', 'join_by', 'merge_arrays',
'rec_append_fields', 'rec_drop_fields', 'rec_join',
'recursive_fill_fields', 'rename_fields', 'stack_arrays',
]
def recursive_fill_fields(input, output):
"""
Fills fields from output with fields from input,
with support for nested structures.
Parameters
----------
input : ndarray
Input array.
output : ndarray
Output array.
Notes
-----
* `output` should be at least the same size as `input`
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', int), ('B', float)])
>>> b = np.zeros((3,), dtype=a.dtype)
>>> rfn.recursive_fill_fields(a, b)
array([(1, 10.0), (2, 20.0), (0, 0.0)],
dtype=[('A', '<i4'), ('B', '<f8')])
"""
newdtype = output.dtype
for field in newdtype.names:
try:
current = input[field]
except ValueError:
continue
if current.dtype.names:
recursive_fill_fields(current, output[field])
else:
output[field][:len(current)] = current
return output
def get_names(adtype):
"""
Returns the field names of the input datatype as a tuple.
Parameters
----------
adtype : dtype
Input datatype
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names(np.empty((1,), dtype=int)) is None
True
>>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]))
('A', 'B')
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names(adtype)
('a', ('b', ('ba', 'bb')))
"""
listnames = []
names = adtype.names
for name in names:
current = adtype[name]
if current.names:
listnames.append((name, tuple(get_names(current))))
else:
listnames.append(name)
return tuple(listnames) or None
def get_names_flat(adtype):
"""
Returns the field names of the input datatype as a tuple. Nested structure
are flattend beforehand.
Parameters
----------
adtype : dtype
Input datatype
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names_flat(np.empty((1,), dtype=int)) is None
True
>>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', float)]))
('A', 'B')
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names_flat(adtype)
('a', 'b', 'ba', 'bb')
"""
listnames = []
names = adtype.names
for name in names:
listnames.append(name)
current = adtype[name]
if current.names:
listnames.extend(get_names_flat(current))
return tuple(listnames) or None
def flatten_descr(ndtype):
"""
Flatten a structured data-type description.
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> ndtype = np.dtype([('a', '<i4'), ('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.flatten_descr(ndtype)
(('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32')))
"""
names = ndtype.names
if names is None:
return ndtype.descr
else:
descr = []
for field in names:
(typ, _) = ndtype.fields[field]
if typ.names:
descr.extend(flatten_descr(typ))
else:
descr.append((field, typ))
return tuple(descr)
def zip_descr(seqarrays, flatten=False):
"""
Combine the dtype description of a series of arrays.
Parameters
----------
seqarrays : sequence of arrays
Sequence of arrays
flatten : {boolean}, optional
Whether to collapse nested descriptions.
"""
newdtype = []
if flatten:
for a in seqarrays:
newdtype.extend(flatten_descr(a.dtype))
else:
for a in seqarrays:
current = a.dtype
names = current.names or ()
if len(names) > 1:
newdtype.append(('', current.descr))
else:
newdtype.extend(current.descr)
return np.dtype(newdtype).descr
def get_fieldstructure(adtype, lastname=None, parents=None,):
"""
Returns a dictionary with fields indexing lists of their parent fields.
This function is used to simplify access to fields nested in other fields.
Parameters
----------
adtype : np.dtype
Input datatype
lastname : optional
Last processed field name (used internally during recursion).
parents : dictionary
Dictionary of parent fields (used interbally during recursion).
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> ndtype = np.dtype([('A', int),
... ('B', [('BA', int),
... ('BB', [('BBA', int), ('BBB', int)])])])
>>> rfn.get_fieldstructure(ndtype)
... # XXX: possible regression, order of BBA and BBB is swapped
{'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}
"""
if parents is None:
parents = {}
names = adtype.names
for name in names:
current = adtype[name]
if current.names:
if lastname:
parents[name] = [lastname, ]
else:
parents[name] = []
parents.update(get_fieldstructure(current, name, parents))
else:
lastparent = [_ for _ in (parents.get(lastname, []) or [])]
if lastparent:
lastparent.append(lastname)
elif lastname:
lastparent = [lastname, ]
parents[name] = lastparent or []
return parents or None
def _izip_fields_flat(iterable):
"""
Returns an iterator of concatenated fields from a sequence of arrays,
collapsing any nested structure.
"""
for element in iterable:
if isinstance(element, np.void):
for f in _izip_fields_flat(tuple(element)):
yield f
else:
yield element
def _izip_fields(iterable):
"""
Returns an iterator of concatenated fields from a sequence of arrays.
"""
for element in iterable:
if (hasattr(element, '__iter__') and
not isinstance(element, basestring)):
for f in _izip_fields(element):
yield f
elif isinstance(element, np.void) and len(tuple(element)) == 1:
for f in _izip_fields(element):
yield f
else:
yield element
def izip_records(seqarrays, fill_value=None, flatten=True):
"""
Returns an iterator of concatenated items from a sequence of arrays.
Parameters
----------
seqarrays : sequence of arrays
Sequence of arrays.
fill_value : {None, integer}
Value used to pad shorter iterables.
flatten : {True, False},
Whether to
"""
# OK, that's a complete ripoff from Python2.6 itertools.izip_longest
def sentinel(counter=([fill_value] * (len(seqarrays) - 1)).pop):
"Yields the fill_value or raises IndexError"
yield counter()
#
fillers = itertools.repeat(fill_value)
iters = [itertools.chain(it, sentinel(), fillers) for it in seqarrays]
# Should we flatten the items, or just use a nested approach
if flatten:
zipfunc = _izip_fields_flat
else:
zipfunc = _izip_fields
#
try:
for tup in zip(*iters):
yield tuple(zipfunc(tup))
except IndexError:
pass
def _fix_output(output, usemask=True, asrecarray=False):
"""
Private function: return a recarray, a ndarray, a MaskedArray
or a MaskedRecords depending on the input parameters
"""
if not isinstance(output, MaskedArray):
usemask = False
if usemask:
if asrecarray:
output = output.view(MaskedRecords)
else:
output = ma.filled(output)
if asrecarray:
output = output.view(recarray)
return output
def _fix_defaults(output, defaults=None):
"""
Update the fill_value and masked data of `output`
from the default given in a dictionary defaults.
"""
names = output.dtype.names
(data, mask, fill_value) = (output.data, output.mask, output.fill_value)
for (k, v) in (defaults or {}).items():
if k in names:
fill_value[k] = v
data[k][mask[k]] = v
return output
def merge_arrays(seqarrays, fill_value=-1, flatten=False,
usemask=False, asrecarray=False):
"""
Merge arrays field by field.
Parameters
----------
seqarrays : sequence of ndarrays
Sequence of arrays
fill_value : {float}, optional
Filling value used to pad missing data on the shorter arrays.
flatten : {False, True}, optional
Whether to collapse nested fields.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : {False, True}, optional
Whether to return a recarray (MaskedRecords) or not.
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
masked_array(data = [(1, 10.0) (2, 20.0) (--, 30.0)],
mask = [(False, False) (False, False) (True, False)],
fill_value = (999999, 1e+20),
dtype = [('f0', '<i4'), ('f1', '<f8')])
>>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])),
... usemask=False)
array([(1, 10.0), (2, 20.0), (-1, 30.0)],
dtype=[('f0', '<i4'), ('f1', '<f8')])
>>> rfn.merge_arrays((np.array([1, 2]).view([('a', int)]),
... np.array([10., 20., 30.])),
... usemask=False, asrecarray=True)
rec.array([(1, 10.0), (2, 20.0), (-1, 30.0)],
dtype=[('a', '<i4'), ('f1', '<f8')])
Notes
-----
* Without a mask, the missing value will be filled with something,
* depending on what its corresponding type:
-1 for integers
-1.0 for floating point numbers
'-' for characters
'-1' for strings
True for boolean values
* XXX: I just obtained these values empirically
"""
# Only one item in the input sequence ?
if (len(seqarrays) == 1):
seqarrays = np.asanyarray(seqarrays[0])
# Do we have a single ndarray as input ?
if isinstance(seqarrays, (ndarray, np.void)):
seqdtype = seqarrays.dtype
if (not flatten) or \
(zip_descr((seqarrays,), flatten=True) == seqdtype.descr):
# Minimal processing needed: just make sure everythng's a-ok
seqarrays = seqarrays.ravel()
# Make sure we have named fields
if not seqdtype.names:
seqdtype = [('', seqdtype)]
# Find what type of array we must return
if usemask:
if asrecarray:
seqtype = MaskedRecords
else:
seqtype = MaskedArray
elif asrecarray:
seqtype = recarray
else:
seqtype = ndarray
return seqarrays.view(dtype=seqdtype, type=seqtype)
else:
seqarrays = (seqarrays,)
else:
# Make sure we have arrays in the input sequence
seqarrays = [np.asanyarray(_m) for _m in seqarrays]
# Find the sizes of the inputs and their maximum
sizes = tuple(a.size for a in seqarrays)
maxlength = max(sizes)
# Get the dtype of the output (flattening if needed)
newdtype = zip_descr(seqarrays, flatten=flatten)
# Initialize the sequences for data and mask
seqdata = []
seqmask = []
# If we expect some kind of MaskedArray, make a special loop.
if usemask:
for (a, n) in zip(seqarrays, sizes):
nbmissing = (maxlength - n)
# Get the data and mask
data = a.ravel().__array__()
mask = ma.getmaskarray(a).ravel()
# Get the filling value (if needed)
if nbmissing:
fval = _check_fill_value(fill_value, a.dtype)
if isinstance(fval, (ndarray, np.void)):
if len(fval.dtype) == 1:
fval = fval.item()[0]
fmsk = True
else:
fval = np.array(fval, dtype=a.dtype, ndmin=1)
fmsk = np.ones((1,), dtype=mask.dtype)
else:
fval = None
fmsk = True
# Store an iterator padding the input to the expected length
seqdata.append(itertools.chain(data, [fval] * nbmissing))
seqmask.append(itertools.chain(mask, [fmsk] * nbmissing))
# Create an iterator for the data
data = tuple(izip_records(seqdata, flatten=flatten))
output = ma.array(np.fromiter(data, dtype=newdtype, count=maxlength),
mask=list(izip_records(seqmask, flatten=flatten)))
if asrecarray:
output = output.view(MaskedRecords)
else:
# Same as before, without the mask we don't need...
for (a, n) in zip(seqarrays, sizes):
nbmissing = (maxlength - n)
data = a.ravel().__array__()
if nbmissing:
fval = _check_fill_value(fill_value, a.dtype)
if isinstance(fval, (ndarray, np.void)):
if len(fval.dtype) == 1:
fval = fval.item()[0]
else:
fval = np.array(fval, dtype=a.dtype, ndmin=1)
else:
fval = None
seqdata.append(itertools.chain(data, [fval] * nbmissing))
output = np.fromiter(tuple(izip_records(seqdata, flatten=flatten)),
dtype=newdtype, count=maxlength)
if asrecarray:
output = output.view(recarray)
# And we're done...
return output
def drop_fields(base, drop_names, usemask=True, asrecarray=False):
"""
Return a new array with fields in `drop_names` dropped.
Nested fields are supported.
Parameters
----------
base : array
Input array
drop_names : string or sequence
String or sequence of strings corresponding to the names of the
fields to drop.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : string or sequence, optional
Whether to return a recarray or a mrecarray (`asrecarray=True`) or
a plain ndarray or masked array with flexible dtype. The default
is False.
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
... dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
>>> rfn.drop_fields(a, 'a')
array([((2.0, 3),), ((5.0, 6),)],
dtype=[('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.drop_fields(a, 'ba')
array([(1, (3,)), (4, (6,))],
dtype=[('a', '<i4'), ('b', [('bb', '<i4')])])
>>> rfn.drop_fields(a, ['ba', 'bb'])
array([(1,), (4,)],
dtype=[('a', '<i4')])
"""
if _is_string_like(drop_names):
drop_names = [drop_names, ]
else:
drop_names = set(drop_names)
def _drop_descr(ndtype, drop_names):
names = ndtype.names
newdtype = []
for name in names:
current = ndtype[name]
if name in drop_names:
continue
if current.names:
descr = _drop_descr(current, drop_names)
if descr:
newdtype.append((name, descr))
else:
newdtype.append((name, current))
return newdtype
newdtype = _drop_descr(base.dtype, drop_names)
if not newdtype:
return None
output = np.empty(base.shape, dtype=newdtype)
output = recursive_fill_fields(base, output)
return _fix_output(output, usemask=usemask, asrecarray=asrecarray)
def rec_drop_fields(base, drop_names):
"""
Returns a new numpy.recarray with fields in `drop_names` dropped.
"""
return drop_fields(base, drop_names, usemask=False, asrecarray=True)
def rename_fields(base, namemapper):
"""
Rename the fields from a flexible-datatype ndarray or recarray.
Nested fields are supported.
Parameters
----------
base : ndarray
Input array whose fields must be modified.
namemapper : dictionary
Dictionary mapping old field names to their new version.
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
... dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
>>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
array([(1, (2.0, [3.0, 30.0])), (4, (5.0, [6.0, 60.0]))],
dtype=[('A', '<i4'), ('b', [('ba', '<f8'), ('BB', '<f8', 2)])])
"""
def _recursive_rename_fields(ndtype, namemapper):
newdtype = []
for name in ndtype.names:
newname = namemapper.get(name, name)
current = ndtype[name]
if current.names:
newdtype.append(
(newname, _recursive_rename_fields(current, namemapper))
)
else:
newdtype.append((newname, current))
return newdtype
newdtype = _recursive_rename_fields(base.dtype, namemapper)
return base.view(newdtype)
def append_fields(base, names, data, dtypes=None,
fill_value=-1, usemask=True, asrecarray=False):
"""
Add new fields to an existing array.
The names of the fields are given with the `names` arguments,
the corresponding values with the `data` arguments.
If a single field is appended, `names`, `data` and `dtypes` do not have
to be lists but just values.
Parameters
----------
base : array
Input array to extend.
names : string, sequence
String or sequence of strings corresponding to the names
of the new fields.
data : array or sequence of arrays
Array or sequence of arrays storing the fields to add to the base.
dtypes : sequence of datatypes, optional
Datatype or sequence of datatypes.
If None, the datatypes are estimated from the `data`.
fill_value : {float}, optional
Filling value used to pad missing data on the shorter arrays.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : {False, True}, optional
Whether to return a recarray (MaskedRecords) or not.
"""
# Check the names
if isinstance(names, (tuple, list)):
if len(names) != len(data):
msg = "The number of arrays does not match the number of names"
raise ValueError(msg)
elif isinstance(names, basestring):
names = [names, ]
data = [data, ]
#
if dtypes is None:
data = [np.array(a, copy=False, subok=True) for a in data]
data = [a.view([(name, a.dtype)]) for (name, a) in zip(names, data)]
else:
if not isinstance(dtypes, (tuple, list)):
dtypes = [dtypes, ]
if len(data) != len(dtypes):
if len(dtypes) == 1:
dtypes = dtypes * len(data)
else:
msg = "The dtypes argument must be None, a dtype, or a list."
raise ValueError(msg)
data = [np.array(a, copy=False, subok=True, dtype=d).view([(n, d)])
for (a, n, d) in zip(data, names, dtypes)]
#
base = merge_arrays(base, usemask=usemask, fill_value=fill_value)
if len(data) > 1:
data = merge_arrays(data, flatten=True, usemask=usemask,
fill_value=fill_value)
else:
data = data.pop()
#
output = ma.masked_all(max(len(base), len(data)),
dtype=base.dtype.descr + data.dtype.descr)
output = recursive_fill_fields(base, output)
output = recursive_fill_fields(data, output)
#
return _fix_output(output, usemask=usemask, asrecarray=asrecarray)
def rec_append_fields(base, names, data, dtypes=None):
"""
Add new fields to an existing array.
The names of the fields are given with the `names` arguments,
the corresponding values with the `data` arguments.
If a single field is appended, `names`, `data` and `dtypes` do not have
to be lists but just values.
Parameters
----------
base : array
Input array to extend.
names : string, sequence
String or sequence of strings corresponding to the names
of the new fields.
data : array or sequence of arrays
Array or sequence of arrays storing the fields to add to the base.
dtypes : sequence of datatypes, optional
Datatype or sequence of datatypes.
If None, the datatypes are estimated from the `data`.
See Also
--------
append_fields
Returns
-------
appended_array : np.recarray
"""
return append_fields(base, names, data=data, dtypes=dtypes,
asrecarray=True, usemask=False)
def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False,
autoconvert=False):
"""
Superposes arrays fields by fields
Parameters
----------
arrays : array or sequence
Sequence of input arrays.
defaults : dictionary, optional
Dictionary mapping field names to the corresponding default values.
usemask : {True, False}, optional
Whether to return a MaskedArray (or MaskedRecords is
`asrecarray==True`) or a ndarray.
asrecarray : {False, True}, optional
Whether to return a recarray (or MaskedRecords if `usemask==True`)
or just a flexible-type ndarray.
autoconvert : {False, True}, optional
Whether automatically cast the type of the field to the maximum.
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> x = np.array([1, 2,])
>>> rfn.stack_arrays(x) is x
True
>>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
>>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
... dtype=[('A', '|S3'), ('B', float), ('C', float)])
>>> test = rfn.stack_arrays((z,zz))
>>> test
masked_array(data = [('A', 1.0, --) ('B', 2.0, --) ('a', 10.0, 100.0) ('b', 20.0, 200.0)
('c', 30.0, 300.0)],
mask = [(False, False, True) (False, False, True) (False, False, False)
(False, False, False) (False, False, False)],
fill_value = ('N/A', 1e+20, 1e+20),
dtype = [('A', '|S3'), ('B', '<f8'), ('C', '<f8')])
"""
if isinstance(arrays, ndarray):
return arrays
elif len(arrays) == 1:
return arrays[0]
seqarrays = [np.asanyarray(a).ravel() for a in arrays]
nrecords = [len(a) for a in seqarrays]
ndtype = [a.dtype for a in seqarrays]
fldnames = [d.names for d in ndtype]
#
dtype_l = ndtype[0]
newdescr = dtype_l.descr
names = [_[0] for _ in newdescr]
for dtype_n in ndtype[1:]:
for descr in dtype_n.descr:
name = descr[0] or ''
if name not in names:
newdescr.append(descr)
names.append(name)
else:
nameidx = names.index(name)
current_descr = newdescr[nameidx]
if autoconvert:
if np.dtype(descr[1]) > np.dtype(current_descr[-1]):
current_descr = list(current_descr)
current_descr[-1] = descr[1]
newdescr[nameidx] = tuple(current_descr)
elif descr[1] != current_descr[-1]:
raise TypeError("Incompatible type '%s' <> '%s'" %
(dict(newdescr)[name], descr[1]))
# Only one field: use concatenate
if len(newdescr) == 1:
output = ma.concatenate(seqarrays)
else:
#
output = ma.masked_all((np.sum(nrecords),), newdescr)
offset = np.cumsum(np.r_[0, nrecords])
seen = []
for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]):
names = a.dtype.names
if names is None:
output['f%i' % len(seen)][i:j] = a
else:
for name in n:
output[name][i:j] = a[name]
if name not in seen:
seen.append(name)
#
return _fix_output(_fix_defaults(output, defaults),
usemask=usemask, asrecarray=asrecarray)
def find_duplicates(a, key=None, ignoremask=True, return_index=False):
"""
Find the duplicates in a structured array along a given key
Parameters
----------
a : array-like
Input array
key : {string, None}, optional
Name of the fields along which to check the duplicates.
If None, the search is performed by records
ignoremask : {True, False}, optional
Whether masked data should be discarded or considered as duplicates.
return_index : {False, True}, optional
Whether to return the indices of the duplicated values.
Examples
--------
>>> from numpy.lib import recfunctions as rfn
>>> ndtype = [('a', int)]
>>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
... mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
>>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
... # XXX: judging by the output, the ignoremask flag has no effect
"""
a = np.asanyarray(a).ravel()
# Get a dictionary of fields
fields = get_fieldstructure(a.dtype)
# Get the sorting data (by selecting the corresponding field)
base = a
if key:
for f in fields[key]:
base = base[f]
base = base[key]
# Get the sorting indices and the sorted data
sortidx = base.argsort()
sortedbase = base[sortidx]
sorteddata = sortedbase.filled()
# Compare the sorting data
flag = (sorteddata[:-1] == sorteddata[1:])
# If masked data must be ignored, set the flag to false where needed
if ignoremask:
sortedmask = sortedbase.recordmask
flag[sortedmask[1:]] = False
flag = np.concatenate(([False], flag))
# We need to take the point on the left as well (else we're missing it)
flag[:-1] = flag[:-1] + flag[1:]
duplicates = a[sortidx][flag]
if return_index:
return (duplicates, sortidx[flag])
else:
return duplicates
def join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2',
defaults=None, usemask=True, asrecarray=False):
"""
Join arrays `r1` and `r2` on key `key`.
The key should be either a string or a sequence of string corresponding
to the fields used to join the array. An exception is raised if the
`key` field cannot be found in the two input arrays. Neither `r1` nor
`r2` should have any duplicates along `key`: the presence of duplicates
will make the output quite unreliable. Note that duplicates are not
looked for by the algorithm.
Parameters
----------
key : {string, sequence}
A string or a sequence of strings corresponding to the fields used
for comparison.
r1, r2 : arrays
Structured arrays.
jointype : {'inner', 'outer', 'leftouter'}, optional
If 'inner', returns the elements common to both r1 and r2.
If 'outer', returns the common elements as well as the elements of
r1 not in r2 and the elements of not in r2.
If 'leftouter', returns the common elements and the elements of r1
not in r2.
r1postfix : string, optional
String appended to the names of the fields of r1 that are present
in r2 but absent of the key.
r2postfix : string, optional
String appended to the names of the fields of r2 that are present
in r1 but absent of the key.
defaults : {dictionary}, optional
Dictionary mapping field names to the corresponding default values.
usemask : {True, False}, optional
Whether to return a MaskedArray (or MaskedRecords is
`asrecarray==True`) or a ndarray.
asrecarray : {False, True}, optional
Whether to return a recarray (or MaskedRecords if `usemask==True`)
or just a flexible-type ndarray.
Notes
-----
* The output is sorted along the key.
* A temporary array is formed by dropping the fields not in the key for
the two arrays and concatenating the result. This array is then
sorted, and the common entries selected. The output is constructed by
filling the fields with the selected entries. Matching is not
preserved if there are some duplicates...
"""
# Check jointype
if jointype not in ('inner', 'outer', 'leftouter'):
raise ValueError(
"The 'jointype' argument should be in 'inner', "
"'outer' or 'leftouter' (got '%s' instead)" % jointype
)
# If we have a single key, put it in a tuple
if isinstance(key, basestring):
key = (key,)
# Check the keys
for name in key:
if name not in r1.dtype.names:
raise ValueError('r1 does not have key field %s' % name)
if name not in r2.dtype.names:
raise ValueError('r2 does not have key field %s' % name)
# Make sure we work with ravelled arrays
r1 = r1.ravel()
r2 = r2.ravel()
# Fixme: nb2 below is never used. Commenting out for pyflakes.
# (nb1, nb2) = (len(r1), len(r2))
nb1 = len(r1)
(r1names, r2names) = (r1.dtype.names, r2.dtype.names)
# Check the names for collision
if (set.intersection(set(r1names), set(r2names)).difference(key) and
not (r1postfix or r2postfix)):
msg = "r1 and r2 contain common names, r1postfix and r2postfix "
msg += "can't be empty"
raise ValueError(msg)
# Make temporary arrays of just the keys
r1k = drop_fields(r1, [n for n in r1names if n not in key])
r2k = drop_fields(r2, [n for n in r2names if n not in key])
# Concatenate the two arrays for comparison
aux = ma.concatenate((r1k, r2k))
idx_sort = aux.argsort(order=key)
aux = aux[idx_sort]
#
# Get the common keys
flag_in = ma.concatenate(([False], aux[1:] == aux[:-1]))
flag_in[:-1] = flag_in[1:] + flag_in[:-1]
idx_in = idx_sort[flag_in]
idx_1 = idx_in[(idx_in < nb1)]
idx_2 = idx_in[(idx_in >= nb1)] - nb1
(r1cmn, r2cmn) = (len(idx_1), len(idx_2))
if jointype == 'inner':
(r1spc, r2spc) = (0, 0)
elif jointype == 'outer':
idx_out = idx_sort[~flag_in]
idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)]))
idx_2 = np.concatenate((idx_2, idx_out[(idx_out >= nb1)] - nb1))
(r1spc, r2spc) = (len(idx_1) - r1cmn, len(idx_2) - r2cmn)
elif jointype == 'leftouter':
idx_out = idx_sort[~flag_in]
idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)]))
(r1spc, r2spc) = (len(idx_1) - r1cmn, 0)
# Select the entries from each input
(s1, s2) = (r1[idx_1], r2[idx_2])
#
# Build the new description of the output array .......
# Start with the key fields
ndtype = [list(_) for _ in r1k.dtype.descr]
# Add the other fields
ndtype.extend(list(_) for _ in r1.dtype.descr if _[0] not in key)
# Find the new list of names (it may be different from r1names)
names = list(_[0] for _ in ndtype)
for desc in r2.dtype.descr:
desc = list(desc)
name = desc[0]
# Have we seen the current name already ?
if name in names:
nameidx = ndtype.index(desc)
current = ndtype[nameidx]
# The current field is part of the key: take the largest dtype
if name in key:
current[-1] = max(desc[1], current[-1])
# The current field is not part of the key: add the suffixes
else:
current[0] += r1postfix
desc[0] += r2postfix
ndtype.insert(nameidx + 1, desc)
#... we haven't: just add the description to the current list
else:
names.extend(desc[0])
ndtype.append(desc)
# Revert the elements to tuples
ndtype = [tuple(_) for _ in ndtype]
# Find the largest nb of common fields :
# r1cmn and r2cmn should be equal, but...
cmn = max(r1cmn, r2cmn)
# Construct an empty array
output = ma.masked_all((cmn + r1spc + r2spc,), dtype=ndtype)
names = output.dtype.names
for f in r1names:
selected = s1[f]
if f not in names or (f in r2names and not r2postfix and f not in key):
f += r1postfix
current = output[f]
current[:r1cmn] = selected[:r1cmn]
if jointype in ('outer', 'leftouter'):
current[cmn:cmn + r1spc] = selected[r1cmn:]
for f in r2names:
selected = s2[f]
if f not in names or (f in r1names and not r1postfix and f not in key):
f += r2postfix
current = output[f]
current[:r2cmn] = selected[:r2cmn]
if (jointype == 'outer') and r2spc:
current[-r2spc:] = selected[r2cmn:]
# Sort and finalize the output
output.sort(order=key)
kwargs = dict(usemask=usemask, asrecarray=asrecarray)
return _fix_output(_fix_defaults(output, defaults), **kwargs)
def rec_join(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2',
defaults=None):
"""
Join arrays `r1` and `r2` on keys.
Alternative to join_by, that always returns a np.recarray.
See Also
--------
join_by : equivalent function
"""
kwargs = dict(jointype=jointype, r1postfix=r1postfix, r2postfix=r2postfix,
defaults=defaults, usemask=False, asrecarray=True)
return join_by(key, r1, r2, **kwargs)
| bsd-3-clause |
nickcdryan/rep | tests/test_factory_regression.py | 4 | 3005 | from __future__ import division, print_function, absolute_import
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
from sklearn.metrics.metrics import mean_squared_error
import numpy
from rep.data import LabeledDataStorage
from rep.metaml import RegressorsFactory
from six.moves import cPickle
from rep.report import RegressionReport
from rep.test.test_estimators import generate_classification_data
__author__ = 'Tatiana Likhomanenko'
# TODO testing of right-classification of estimators
def test_factory():
factory = RegressorsFactory()
try:
from rep.estimators.tmva import TMVARegressor
factory.add_regressor('tmva', TMVARegressor())
except ImportError:
pass
factory.add_regressor('rf', RandomForestRegressor(n_estimators=10))
factory.add_regressor('ada', AdaBoostRegressor(n_estimators=20))
X, y, sample_weight = generate_classification_data()
assert factory == factory.fit(X, y, sample_weight=sample_weight, features=list(X.columns))
values = factory.predict(X)
for cl in factory.values():
assert list(cl.features) == list(X.columns)
for key, val in values.items():
score = mean_squared_error(y, val)
print(score)
assert score < 0.2
for key, iterator in factory.staged_predict(X).items():
assert key != 'tmva', 'tmva does not support staged pp'
for p in iterator:
assert p.shape == (len(X), )
# checking that last iteration coincides with previous
assert numpy.all(p == values[key])
# testing picklability
dump_string = cPickle.dumps(factory)
clf_loaded = cPickle.loads(dump_string)
assert type(factory) == type(clf_loaded)
probs1 = factory.predict(X)
probs2 = clf_loaded.predict(X)
for key, val in probs1.items():
assert numpy.all(val == probs2[key]), 'something strange was loaded'
report = RegressionReport({'rf': factory['rf']}, LabeledDataStorage(X, y, sample_weight))
report.feature_importance_shuffling(mean_squared_mod).plot(new_plot=True, figsize=(18, 3))
report = factory.test_on_lds(LabeledDataStorage(X, y, sample_weight))
report = factory.test_on(X, y, sample_weight=sample_weight)
report.feature_importance()
report.features_correlation_matrix()
report.predictions_scatter()
val = numpy.mean(X['column0'])
report_mask(report, "column0 > %f" % val, X)
report_mask(report, lambda x: numpy.array(x['column0']) < val, X)
report_mask(report, None, X)
def mean_squared_mod(y_true, values, sample_weight=None):
return mean_squared_error(y_true, values, sample_weight=sample_weight)
def report_mask(report, mask, X):
report.features_correlation_matrix(mask=mask).plot()
report.feature_importance().plot()
report.scatter([(X.columns[0], X.columns[2])], mask=mask).plot()
report.predictions_scatter([X.columns[0], X.columns[2]], mask=mask).plot()
report.learning_curve(mean_squared_error, mask=mask).plot()
| apache-2.0 |
bokeh/bokeh | tests/unit/bokeh/core/property/test_pandas.py | 1 | 2931 | #-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2021, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations # isort:skip
import pytest ; pytest
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
# Bokeh imports
from bokeh._testing.util.api import verify_all
from _util_property import _TestHasProps, _TestModel
# Module under test
import bokeh.core.property.pandas as bcpp # isort:skip
#-----------------------------------------------------------------------------
# Setup
#-----------------------------------------------------------------------------
ALL = (
'PandasDataFrame',
'PandasGroupBy',
)
#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------
class Test_PandasDataFrame:
def test_valid(self, pd) -> None:
prop = bcpp.PandasDataFrame()
assert prop.is_valid(pd.DataFrame())
def test_invalid(self) -> None:
prop = bcpp.PandasDataFrame()
assert not prop.is_valid(None)
assert not prop.is_valid(1.0+1.0j)
assert not prop.is_valid(())
assert not prop.is_valid([])
assert not prop.is_valid({})
assert not prop.is_valid(_TestHasProps())
assert not prop.is_valid(_TestModel())
class Test_PandasGroupBy:
def test_valid(self, pd) -> None:
prop = bcpp.PandasGroupBy()
assert prop.is_valid(pd.core.groupby.GroupBy(pd.DataFrame()))
def test_invalid(self) -> None:
prop = bcpp.PandasGroupBy()
assert not prop.is_valid(None)
assert not prop.is_valid(1.0+1.0j)
assert not prop.is_valid(())
assert not prop.is_valid([])
assert not prop.is_valid({})
assert not prop.is_valid(_TestHasProps())
assert not prop.is_valid(_TestModel())
#-----------------------------------------------------------------------------
# Dev API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Private API
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Code
#-----------------------------------------------------------------------------
Test___all__ = verify_all(bcpp, ALL)
| bsd-3-clause |
mhvk/astropy | astropy/visualization/wcsaxes/tests/test_frame.py | 11 | 5290 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
import numpy as np
import matplotlib.pyplot as plt
from astropy.wcs import WCS
from astropy.visualization.wcsaxes import WCSAxes
from astropy.visualization.wcsaxes.frame import BaseFrame
from astropy.tests.image_tests import IMAGE_REFERENCE_DIR
from .test_images import BaseImageTests
class HexagonalFrame(BaseFrame):
spine_names = 'abcdef'
def update_spines(self):
xmin, xmax = self.parent_axes.get_xlim()
ymin, ymax = self.parent_axes.get_ylim()
ymid = 0.5 * (ymin + ymax)
xmid1 = (xmin + xmax) / 4.
xmid2 = (xmin + xmax) * 3. / 4.
self['a'].data = np.array(([xmid1, ymin], [xmid2, ymin]))
self['b'].data = np.array(([xmid2, ymin], [xmax, ymid]))
self['c'].data = np.array(([xmax, ymid], [xmid2, ymax]))
self['d'].data = np.array(([xmid2, ymax], [xmid1, ymax]))
self['e'].data = np.array(([xmid1, ymax], [xmin, ymid]))
self['f'].data = np.array(([xmin, ymid], [xmid1, ymin]))
class TestFrame(BaseImageTests):
@pytest.mark.remote_data(source='astropy')
@pytest.mark.mpl_image_compare(baseline_dir=IMAGE_REFERENCE_DIR,
tolerance=0, style={})
def test_custom_frame(self):
wcs = WCS(self.msx_header)
fig = plt.figure(figsize=(4, 4))
ax = WCSAxes(fig, [0.15, 0.15, 0.7, 0.7],
wcs=wcs,
frame_class=HexagonalFrame)
fig.add_axes(ax)
ax.coords.grid(color='white')
im = ax.imshow(np.ones((149, 149)), vmin=0., vmax=2.,
origin='lower', cmap=plt.cm.gist_heat)
minpad = {}
minpad['a'] = minpad['d'] = 1
minpad['b'] = minpad['c'] = minpad['e'] = minpad['f'] = 2.75
ax.coords['glon'].set_axislabel("Longitude", minpad=minpad)
ax.coords['glon'].set_axislabel_position('ad')
ax.coords['glat'].set_axislabel("Latitude", minpad=minpad)
ax.coords['glat'].set_axislabel_position('bcef')
ax.coords['glon'].set_ticklabel_position('ad')
ax.coords['glat'].set_ticklabel_position('bcef')
# Set limits so that no labels overlap
ax.set_xlim(5.5, 100.5)
ax.set_ylim(5.5, 110.5)
# Clip the image to the frame
im.set_clip_path(ax.coords.frame.patch)
return fig
@pytest.mark.remote_data(source='astropy')
@pytest.mark.mpl_image_compare(baseline_dir=IMAGE_REFERENCE_DIR,
tolerance=0, style={})
def test_update_clip_path_rectangular(self, tmpdir):
fig = plt.figure()
ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8], aspect='equal')
fig.add_axes(ax)
ax.set_xlim(0., 2.)
ax.set_ylim(0., 2.)
# Force drawing, which freezes the clip path returned by WCSAxes
fig.savefig(tmpdir.join('nothing').strpath)
ax.imshow(np.zeros((12, 4)))
ax.set_xlim(-0.5, 3.5)
ax.set_ylim(-0.5, 11.5)
ax.coords[0].set_auto_axislabel(False)
ax.coords[1].set_auto_axislabel(False)
return fig
@pytest.mark.remote_data(source='astropy')
@pytest.mark.mpl_image_compare(baseline_dir=IMAGE_REFERENCE_DIR,
tolerance=0, style={})
def test_update_clip_path_nonrectangular(self, tmpdir):
fig = plt.figure()
ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8], aspect='equal',
frame_class=HexagonalFrame)
fig.add_axes(ax)
ax.set_xlim(0., 2.)
ax.set_ylim(0., 2.)
# Force drawing, which freezes the clip path returned by WCSAxes
fig.savefig(tmpdir.join('nothing').strpath)
ax.imshow(np.zeros((12, 4)))
ax.set_xlim(-0.5, 3.5)
ax.set_ylim(-0.5, 11.5)
return fig
@pytest.mark.remote_data(source='astropy')
@pytest.mark.mpl_image_compare(baseline_dir=IMAGE_REFERENCE_DIR,
tolerance=0, style={})
def test_update_clip_path_change_wcs(self, tmpdir):
# When WCS is changed, a new frame is created, so we need to make sure
# that the path is carried over to the new frame.
fig = plt.figure()
ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8], aspect='equal')
fig.add_axes(ax)
ax.set_xlim(0., 2.)
ax.set_ylim(0., 2.)
# Force drawing, which freezes the clip path returned by WCSAxes
fig.savefig(tmpdir.join('nothing').strpath)
ax.reset_wcs()
ax.imshow(np.zeros((12, 4)))
ax.set_xlim(-0.5, 3.5)
ax.set_ylim(-0.5, 11.5)
ax.coords[0].set_auto_axislabel(False)
ax.coords[1].set_auto_axislabel(False)
return fig
def test_copy_frame_properties_change_wcs(self):
# When WCS is changed, a new frame is created, so we need to make sure
# that the color and linewidth are transferred over
fig = plt.figure()
ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8])
fig.add_axes(ax)
ax.coords.frame.set_linewidth(5)
ax.coords.frame.set_color('purple')
ax.reset_wcs()
assert ax.coords.frame.get_linewidth() == 5
assert ax.coords.frame.get_color() == 'purple'
| bsd-3-clause |
vigilv/scikit-learn | examples/exercises/plot_iris_exercise.py | 323 | 1602 | """
================================
SVM Exercise
================================
A tutorial exercise for using different SVM kernels.
This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
iris = datasets.load_iris()
X = iris.data
y = iris.target
X = X[y != 0, :2]
y = y[y != 0]
n_sample = len(X)
np.random.seed(0)
order = np.random.permutation(n_sample)
X = X[order]
y = y[order].astype(np.float)
X_train = X[:.9 * n_sample]
y_train = y[:.9 * n_sample]
X_test = X[.9 * n_sample:]
y_test = y[.9 * n_sample:]
# fit the model
for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')):
clf = svm.SVC(kernel=kernel, gamma=10)
clf.fit(X_train, y_train)
plt.figure(fig_num)
plt.clf()
plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired)
# Circle out the test data
plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10)
plt.axis('tight')
x_min = X[:, 0].min()
x_max = X[:, 0].max()
y_min = X[:, 1].min()
y_max = X[:, 1].max()
XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
# Put the result into a color plot
Z = Z.reshape(XX.shape)
plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
levels=[-.5, 0, .5])
plt.title(kernel)
plt.show()
| bsd-3-clause |
dallascard/guac | core/experiment/evaluate_fn_fp.py | 1 | 1686 | from optparse import OptionParser
import os
import pandas as pd
import numpy as np
from ..experiment import evaluation
from ..util import dirs
from ..util import file_handling as fh
def main():
usage = "%prog project experiment_dir"
parser = OptionParser(usage=usage)
#parser.add_option('--keyword', dest='key', default=None,
# help='Keyword argument: default=%default')
#parser.add_option('--boolarg', action="store_true", dest="boolarg", default=False,
# help='Keyword argument: default=%default')
(options, args) = parser.parse_args()
project = args[0]
exp_dir = args[1]
evaluate_fn_fp(project, exp_dir)
def evaluate_fn_fp(project, exp_dir):
dirs.make_base_dir(project)
test_file = os.path.join(exp_dir, 'test_errors.csv')
errors_df = pd.read_csv(test_file, header=0, index_col=0)
errors = errors_df['errors'].ravel()
disagreements = np.sum((errors > 0) * (errors < 1))
consensus = len(errors) - disagreements
index = errors_df['errors'].isin([0,1])
true = errors_df.ix[index, 'annotations']
pred = errors_df.ix[index, 'predictions']
f1, acc = evaluation.calc_f1_and_acc_for_column(true, pred)
#false_positive = np.sum((errors == 1) * (errors_df['predictions'].ravel() == 1))
#false_negatives = np.sum((errors == 1) * (errors_df['annotations'].ravel() == 1))
#true_positives = np.sum(errors_df['annotations'].ravel() == 1)
#print 'fp:', false_positive, ' fn:', false_negatives, ' annotator disagreements:', disagreements, ' tp:', true_positives
print "n_items:", consensus, "f1:", f1, "acc:", acc
if __name__ == '__main__':
main()
| apache-2.0 |
WangWenjun559/Weiss | summary/sumy/sklearn/metrics/tests/test_classification.py | 15 | 49665 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
from sklearn.utils.fixes import np_version
from sklearn.utils.validation import check_random_state
from sklearn.utils.testing import assert_raises, clean_warning_registry
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import ignore_warnings
from sklearn.metrics import accuracy_score
from sklearn.metrics import average_precision_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import fbeta_score
from sklearn.metrics import hamming_loss
from sklearn.metrics import hinge_loss
from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import log_loss
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import zero_one_loss
from sklearn.metrics import brier_score_loss
from sklearn.metrics.classification import _check_targets
from sklearn.metrics.base import UndefinedMetricWarning
###############################################################################
# Utilities for testing
def make_prediction(dataset=None, binary=False):
"""Make some classification predictions on a toy dataset using a SVC
If binary is True restrict to a binary classification problem instead of a
multiclass classification problem
"""
if dataset is None:
# import some data to play with
dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
if binary:
# restrict to a binary classification task
X, y = X[y < 2], y[y < 2]
n_samples, n_features = X.shape
p = np.arange(n_samples)
rng = check_random_state(37)
rng.shuffle(p)
X, y = X[p], y[p]
half = int(n_samples / 2)
# add noisy features to make the problem harder and avoid perfect results
rng = np.random.RandomState(0)
X = np.c_[X, rng.randn(n_samples, 200 * n_features)]
# run classifier, get class probabilities and label predictions
clf = svm.SVC(kernel='linear', probability=True, random_state=0)
probas_pred = clf.fit(X[:half], y[:half]).predict_proba(X[half:])
if binary:
# only interested in probabilities of the positive case
# XXX: do we really want a special API for the binary case?
probas_pred = probas_pred[:, 1]
y_pred = clf.predict(X[half:])
y_true = y[half:]
return y_true, y_pred, probas_pred
###############################################################################
# Tests
def test_multilabel_accuracy_score_subset_accuracy():
# Dense label indicator matrix format
y1 = np.array([[0, 1, 1], [1, 0, 1]])
y2 = np.array([[0, 0, 1], [1, 0, 1]])
assert_equal(accuracy_score(y1, y2), 0.5)
assert_equal(accuracy_score(y1, y1), 1)
assert_equal(accuracy_score(y2, y2), 1)
assert_equal(accuracy_score(y2, np.logical_not(y2)), 0)
assert_equal(accuracy_score(y1, np.logical_not(y1)), 0)
assert_equal(accuracy_score(y1, np.zeros(y1.shape)), 0)
assert_equal(accuracy_score(y2, np.zeros(y1.shape)), 0)
with ignore_warnings(): # sequence of sequences is deprecated
# List of tuple of label
y1 = [(1, 2,), (0, 2,)]
y2 = [(2,), (0, 2,)]
assert_equal(accuracy_score(y1, y2), 0.5)
assert_equal(accuracy_score(y1, y1), 1)
assert_equal(accuracy_score(y2, y2), 1)
assert_equal(accuracy_score(y2, [(), ()]), 0)
assert_equal(accuracy_score(y1, y2, normalize=False), 1)
assert_equal(accuracy_score(y1, y1, normalize=False), 2)
assert_equal(accuracy_score(y2, y2, normalize=False), 2)
assert_equal(accuracy_score(y2, [(), ()], normalize=False), 0)
def test_precision_recall_f1_score_binary():
# Test Precision Recall and F1 Score for binary classification task
y_true, y_pred, _ = make_prediction(binary=True)
# detailed measures for each class
p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
assert_array_almost_equal(p, [0.73, 0.85], 2)
assert_array_almost_equal(r, [0.88, 0.68], 2)
assert_array_almost_equal(f, [0.80, 0.76], 2)
assert_array_equal(s, [25, 25])
# individual scoring function that can be used for grid search: in the
# binary class case the score is the value of the measure for the positive
# class (e.g. label == 1). This is deprecated for average != 'binary'.
assert_dep_warning = partial(assert_warns, DeprecationWarning)
for kwargs, my_assert in [({}, assert_no_warnings),
({'average': 'binary'}, assert_no_warnings),
({'average': 'micro'}, assert_dep_warning)]:
ps = my_assert(precision_score, y_true, y_pred, **kwargs)
assert_array_almost_equal(ps, 0.85, 2)
rs = my_assert(recall_score, y_true, y_pred, **kwargs)
assert_array_almost_equal(rs, 0.68, 2)
fs = my_assert(f1_score, y_true, y_pred, **kwargs)
assert_array_almost_equal(fs, 0.76, 2)
assert_almost_equal(my_assert(fbeta_score, y_true, y_pred, beta=2,
**kwargs),
(1 + 2 ** 2) * ps * rs / (2 ** 2 * ps + rs), 2)
@ignore_warnings
def test_precision_recall_f_binary_single_class():
# Test precision, recall and F1 score behave with a single positive or
# negative class
# Such a case may occur with non-stratified cross-validation
assert_equal(1., precision_score([1, 1], [1, 1]))
assert_equal(1., recall_score([1, 1], [1, 1]))
assert_equal(1., f1_score([1, 1], [1, 1]))
assert_equal(0., precision_score([-1, -1], [-1, -1]))
assert_equal(0., recall_score([-1, -1], [-1, -1]))
assert_equal(0., f1_score([-1, -1], [-1, -1]))
def test_average_precision_score_score_non_binary_class():
# Test that average_precision_score function returns an error when trying
# to compute average_precision_score for multiclass task.
rng = check_random_state(404)
y_pred = rng.rand(10)
# y_true contains three different class values
y_true = rng.randint(0, 3, size=10)
assert_raise_message(ValueError, "multiclass format is not supported",
average_precision_score, y_true, y_pred)
def test_average_precision_score_duplicate_values():
# Duplicate values with precision-recall require a different
# processing than when computing the AUC of a ROC, because the
# precision-recall curve is a decreasing curve
# The following situtation corresponds to a perfect
# test statistic, the average_precision_score should be 1
y_true = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
y_score = [0, .1, .1, .4, .5, .6, .6, .9, .9, 1, 1]
assert_equal(average_precision_score(y_true, y_score), 1)
def test_average_precision_score_tied_values():
# Here if we go from left to right in y_true, the 0 values are
# are separated from the 1 values, so it appears that we've
# Correctly sorted our classifications. But in fact the first two
# values have the same score (0.5) and so the first two values
# could be swapped around, creating an imperfect sorting. This
# imperfection should come through in the end score, making it less
# than one.
y_true = [0, 1, 1]
y_score = [.5, .5, .6]
assert_not_equal(average_precision_score(y_true, y_score), 1.)
@ignore_warnings
def test_precision_recall_fscore_support_errors():
y_true, y_pred, _ = make_prediction(binary=True)
# Bad beta
assert_raises(ValueError, precision_recall_fscore_support,
y_true, y_pred, beta=0.0)
# Bad pos_label
assert_raises(ValueError, precision_recall_fscore_support,
y_true, y_pred, pos_label=2, average='macro')
# Bad average option
assert_raises(ValueError, precision_recall_fscore_support,
[0, 1, 2], [1, 2, 0], average='mega')
def test_confusion_matrix_binary():
# Test confusion matrix - binary classification case
y_true, y_pred, _ = make_prediction(binary=True)
def test(y_true, y_pred):
cm = confusion_matrix(y_true, y_pred)
assert_array_equal(cm, [[22, 3], [8, 17]])
tp, fp, fn, tn = cm.flatten()
num = (tp * tn - fp * fn)
den = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
true_mcc = 0 if den == 0 else num / den
mcc = matthews_corrcoef(y_true, y_pred)
assert_array_almost_equal(mcc, true_mcc, decimal=2)
assert_array_almost_equal(mcc, 0.57, decimal=2)
test(y_true, y_pred)
test([str(y) for y in y_true],
[str(y) for y in y_pred])
@ignore_warnings
def test_matthews_corrcoef_nan():
assert_equal(matthews_corrcoef([0], [1]), 0.0)
assert_equal(matthews_corrcoef([0, 0], [0, 1]), 0.0)
def test_precision_recall_f1_score_multiclass():
# Test Precision Recall and F1 Score for multiclass classification task
y_true, y_pred, _ = make_prediction(binary=False)
# compute scores with default labels introspection
p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
assert_array_almost_equal(p, [0.83, 0.33, 0.42], 2)
assert_array_almost_equal(r, [0.79, 0.09, 0.90], 2)
assert_array_almost_equal(f, [0.81, 0.15, 0.57], 2)
assert_array_equal(s, [24, 31, 20])
# averaging tests
ps = precision_score(y_true, y_pred, pos_label=1, average='micro')
assert_array_almost_equal(ps, 0.53, 2)
rs = recall_score(y_true, y_pred, average='micro')
assert_array_almost_equal(rs, 0.53, 2)
fs = f1_score(y_true, y_pred, average='micro')
assert_array_almost_equal(fs, 0.53, 2)
ps = precision_score(y_true, y_pred, average='macro')
assert_array_almost_equal(ps, 0.53, 2)
rs = recall_score(y_true, y_pred, average='macro')
assert_array_almost_equal(rs, 0.60, 2)
fs = f1_score(y_true, y_pred, average='macro')
assert_array_almost_equal(fs, 0.51, 2)
ps = precision_score(y_true, y_pred, average='weighted')
assert_array_almost_equal(ps, 0.51, 2)
rs = recall_score(y_true, y_pred, average='weighted')
assert_array_almost_equal(rs, 0.53, 2)
fs = f1_score(y_true, y_pred, average='weighted')
assert_array_almost_equal(fs, 0.47, 2)
assert_raises(ValueError, precision_score, y_true, y_pred,
average="samples")
assert_raises(ValueError, recall_score, y_true, y_pred, average="samples")
assert_raises(ValueError, f1_score, y_true, y_pred, average="samples")
assert_raises(ValueError, fbeta_score, y_true, y_pred, average="samples",
beta=0.5)
# same prediction but with and explicit label ordering
p, r, f, s = precision_recall_fscore_support(
y_true, y_pred, labels=[0, 2, 1], average=None)
assert_array_almost_equal(p, [0.83, 0.41, 0.33], 2)
assert_array_almost_equal(r, [0.79, 0.90, 0.10], 2)
assert_array_almost_equal(f, [0.81, 0.57, 0.15], 2)
assert_array_equal(s, [24, 20, 31])
def test_precision_refcall_f1_score_multilabel_unordered_labels():
# test that labels need not be sorted in the multilabel case
y_true = np.array([[1, 1, 0, 0]])
y_pred = np.array([[0, 0, 1, 1]])
for average in ['samples', 'micro', 'macro', 'weighted', None]:
p, r, f, s = precision_recall_fscore_support(
y_true, y_pred, labels=[4, 1, 2, 3], warn_for=[], average=average)
assert_array_equal(p, 0)
assert_array_equal(r, 0)
assert_array_equal(f, 0)
if average is None:
assert_array_equal(s, [0, 1, 1, 0])
def test_precision_recall_f1_score_multiclass_pos_label_none():
# Test Precision Recall and F1 Score for multiclass classification task
# GH Issue #1296
# initialize data
y_true = np.array([0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1])
y_pred = np.array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1])
# compute scores with default labels introspection
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
pos_label=None,
average='weighted')
def test_zero_precision_recall():
# Check that pathological cases do not bring NaNs
old_error_settings = np.seterr(all='raise')
try:
y_true = np.array([0, 1, 2, 0, 1, 2])
y_pred = np.array([2, 0, 1, 1, 2, 0])
assert_almost_equal(precision_score(y_true, y_pred,
average='weighted'), 0.0, 2)
assert_almost_equal(recall_score(y_true, y_pred, average='weighted'),
0.0, 2)
assert_almost_equal(f1_score(y_true, y_pred, average='weighted'),
0.0, 2)
finally:
np.seterr(**old_error_settings)
def test_confusion_matrix_multiclass():
# Test confusion matrix - multi-class case
y_true, y_pred, _ = make_prediction(binary=False)
def test(y_true, y_pred, string_type=False):
# compute confusion matrix with default labels introspection
cm = confusion_matrix(y_true, y_pred)
assert_array_equal(cm, [[19, 4, 1],
[4, 3, 24],
[0, 2, 18]])
# compute confusion matrix with explicit label ordering
labels = ['0', '2', '1'] if string_type else [0, 2, 1]
cm = confusion_matrix(y_true,
y_pred,
labels=labels)
assert_array_equal(cm, [[19, 1, 4],
[0, 18, 2],
[4, 24, 3]])
test(y_true, y_pred)
test(list(str(y) for y in y_true),
list(str(y) for y in y_pred),
string_type=True)
def test_confusion_matrix_multiclass_subset_labels():
# Test confusion matrix - multi-class case with subset of labels
y_true, y_pred, _ = make_prediction(binary=False)
# compute confusion matrix with only first two labels considered
cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
assert_array_equal(cm, [[19, 4],
[4, 3]])
# compute confusion matrix with explicit label ordering for only subset
# of labels
cm = confusion_matrix(y_true, y_pred, labels=[2, 1])
assert_array_equal(cm, [[18, 2],
[24, 3]])
def test_classification_report_multiclass():
# Test performance report
iris = datasets.load_iris()
y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)
# print classification report with class names
expected_report = """\
precision recall f1-score support
setosa 0.83 0.79 0.81 24
versicolor 0.33 0.10 0.15 31
virginica 0.42 0.90 0.57 20
avg / total 0.51 0.53 0.47 75
"""
report = classification_report(
y_true, y_pred, labels=np.arange(len(iris.target_names)),
target_names=iris.target_names)
assert_equal(report, expected_report)
# print classification report with label detection
expected_report = """\
precision recall f1-score support
0 0.83 0.79 0.81 24
1 0.33 0.10 0.15 31
2 0.42 0.90 0.57 20
avg / total 0.51 0.53 0.47 75
"""
report = classification_report(y_true, y_pred)
assert_equal(report, expected_report)
def test_classification_report_multiclass_with_digits():
# Test performance report with added digits in floating point values
iris = datasets.load_iris()
y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)
# print classification report with class names
expected_report = """\
precision recall f1-score support
setosa 0.82609 0.79167 0.80851 24
versicolor 0.33333 0.09677 0.15000 31
virginica 0.41860 0.90000 0.57143 20
avg / total 0.51375 0.53333 0.47310 75
"""
report = classification_report(
y_true, y_pred, labels=np.arange(len(iris.target_names)),
target_names=iris.target_names, digits=5)
assert_equal(report, expected_report)
# print classification report with label detection
expected_report = """\
precision recall f1-score support
0 0.83 0.79 0.81 24
1 0.33 0.10 0.15 31
2 0.42 0.90 0.57 20
avg / total 0.51 0.53 0.47 75
"""
report = classification_report(y_true, y_pred)
assert_equal(report, expected_report)
def test_classification_report_multiclass_with_string_label():
y_true, y_pred, _ = make_prediction(binary=False)
y_true = np.array(["blue", "green", "red"])[y_true]
y_pred = np.array(["blue", "green", "red"])[y_pred]
expected_report = """\
precision recall f1-score support
blue 0.83 0.79 0.81 24
green 0.33 0.10 0.15 31
red 0.42 0.90 0.57 20
avg / total 0.51 0.53 0.47 75
"""
report = classification_report(y_true, y_pred)
assert_equal(report, expected_report)
expected_report = """\
precision recall f1-score support
a 0.83 0.79 0.81 24
b 0.33 0.10 0.15 31
c 0.42 0.90 0.57 20
avg / total 0.51 0.53 0.47 75
"""
report = classification_report(y_true, y_pred,
target_names=["a", "b", "c"])
assert_equal(report, expected_report)
def test_classification_report_multiclass_with_unicode_label():
y_true, y_pred, _ = make_prediction(binary=False)
labels = np.array([u"blue\xa2", u"green\xa2", u"red\xa2"])
y_true = labels[y_true]
y_pred = labels[y_pred]
expected_report = u"""\
precision recall f1-score support
blue\xa2 0.83 0.79 0.81 24
green\xa2 0.33 0.10 0.15 31
red\xa2 0.42 0.90 0.57 20
avg / total 0.51 0.53 0.47 75
"""
if np_version[:3] < (1, 7, 0):
expected_message = ("NumPy < 1.7.0 does not implement"
" searchsorted on unicode data correctly.")
assert_raise_message(RuntimeError, expected_message,
classification_report, y_true, y_pred)
else:
report = classification_report(y_true, y_pred)
assert_equal(report, expected_report)
@ignore_warnings # sequence of sequences is deprecated
def test_multilabel_classification_report():
n_classes = 4
n_samples = 50
make_ml = make_multilabel_classification
_, y_true_ll = make_ml(n_features=1, n_classes=n_classes, random_state=0,
n_samples=n_samples)
_, y_pred_ll = make_ml(n_features=1, n_classes=n_classes, random_state=1,
n_samples=n_samples)
expected_report = """\
precision recall f1-score support
0 0.50 0.67 0.57 24
1 0.51 0.74 0.61 27
2 0.29 0.08 0.12 26
3 0.52 0.56 0.54 27
avg / total 0.45 0.51 0.46 104
"""
lb = MultiLabelBinarizer()
lb.fit([range(4)])
y_true_bi = lb.transform(y_true_ll)
y_pred_bi = lb.transform(y_pred_ll)
for y_true, y_pred in [(y_true_ll, y_pred_ll), (y_true_bi, y_pred_bi)]:
report = classification_report(y_true, y_pred)
assert_equal(report, expected_report)
def test_multilabel_zero_one_loss_subset():
# Dense label indicator matrix format
y1 = np.array([[0, 1, 1], [1, 0, 1]])
y2 = np.array([[0, 0, 1], [1, 0, 1]])
assert_equal(zero_one_loss(y1, y2), 0.5)
assert_equal(zero_one_loss(y1, y1), 0)
assert_equal(zero_one_loss(y2, y2), 0)
assert_equal(zero_one_loss(y2, np.logical_not(y2)), 1)
assert_equal(zero_one_loss(y1, np.logical_not(y1)), 1)
assert_equal(zero_one_loss(y1, np.zeros(y1.shape)), 1)
assert_equal(zero_one_loss(y2, np.zeros(y1.shape)), 1)
with ignore_warnings(): # sequence of sequences is deprecated
# List of tuple of label
y1 = [(1, 2,), (0, 2,)]
y2 = [(2,), (0, 2,)]
assert_equal(zero_one_loss(y1, y2), 0.5)
assert_equal(zero_one_loss(y1, y1), 0)
assert_equal(zero_one_loss(y2, y2), 0)
assert_equal(zero_one_loss(y2, [(), ()]), 1)
assert_equal(zero_one_loss(y2, [tuple(), (10, )]), 1)
def test_multilabel_hamming_loss():
# Dense label indicator matrix format
y1 = np.array([[0, 1, 1], [1, 0, 1]])
y2 = np.array([[0, 0, 1], [1, 0, 1]])
assert_equal(hamming_loss(y1, y2), 1 / 6)
assert_equal(hamming_loss(y1, y1), 0)
assert_equal(hamming_loss(y2, y2), 0)
assert_equal(hamming_loss(y2, np.logical_not(y2)), 1)
assert_equal(hamming_loss(y1, np.logical_not(y1)), 1)
assert_equal(hamming_loss(y1, np.zeros(y1.shape)), 4 / 6)
assert_equal(hamming_loss(y2, np.zeros(y1.shape)), 0.5)
with ignore_warnings(): # sequence of sequences is deprecated
# List of tuple of label
y1 = [(1, 2,), (0, 2,)]
y2 = [(2,), (0, 2,)]
assert_equal(hamming_loss(y1, y2), 1 / 6)
assert_equal(hamming_loss(y1, y1), 0)
assert_equal(hamming_loss(y2, y2), 0)
assert_equal(hamming_loss(y2, [(), ()]), 0.75)
assert_equal(hamming_loss(y1, [tuple(), (10, )]), 0.625)
assert_almost_equal(hamming_loss(y2, [tuple(), (10, )],
classes=np.arange(11)), 0.1818, 2)
def test_multilabel_jaccard_similarity_score():
# Dense label indicator matrix format
y1 = np.array([[0, 1, 1], [1, 0, 1]])
y2 = np.array([[0, 0, 1], [1, 0, 1]])
# size(y1 \inter y2) = [1, 2]
# size(y1 \union y2) = [2, 2]
assert_equal(jaccard_similarity_score(y1, y2), 0.75)
assert_equal(jaccard_similarity_score(y1, y1), 1)
assert_equal(jaccard_similarity_score(y2, y2), 1)
assert_equal(jaccard_similarity_score(y2, np.logical_not(y2)), 0)
assert_equal(jaccard_similarity_score(y1, np.logical_not(y1)), 0)
assert_equal(jaccard_similarity_score(y1, np.zeros(y1.shape)), 0)
assert_equal(jaccard_similarity_score(y2, np.zeros(y1.shape)), 0)
with ignore_warnings(): # sequence of sequences is deprecated
# List of tuple of label
y1 = [(1, 2,), (0, 2,)]
y2 = [(2,), (0, 2,)]
assert_equal(jaccard_similarity_score(y1, y2), 0.75)
assert_equal(jaccard_similarity_score(y1, y1), 1)
assert_equal(jaccard_similarity_score(y2, y2), 1)
assert_equal(jaccard_similarity_score(y2, [(), ()]), 0)
# |y3 inter y4 | = [0, 1, 1]
# |y3 union y4 | = [2, 1, 3]
y3 = [(0,), (1,), (3,)]
y4 = [(4,), (4,), (5, 6)]
assert_almost_equal(jaccard_similarity_score(y3, y4), 0)
# |y5 inter y6 | = [0, 1, 1]
# |y5 union y6 | = [2, 1, 3]
y5 = [(0,), (1,), (2, 3)]
y6 = [(1,), (1,), (2, 0)]
assert_almost_equal(jaccard_similarity_score(y5, y6), (1 + 1 / 3) / 3)
@ignore_warnings
def test_precision_recall_f1_score_multilabel_1():
# Test precision_recall_f1_score on a crafted multilabel example
# First crafted example
y_true_ll = [(0,), (1,), (2, 3)]
y_pred_ll = [(1,), (1,), (2, 0)]
lb = LabelBinarizer()
lb.fit([range(4)])
y_true_bi = lb.transform(y_true_ll)
y_pred_bi = lb.transform(y_pred_ll)
for y_true, y_pred in [(y_true_ll, y_pred_ll), (y_true_bi, y_pred_bi)]:
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average=None)
#tp = [0, 1, 1, 0]
#fn = [1, 0, 0, 1]
#fp = [1, 1, 0, 0]
# Check per class
assert_array_almost_equal(p, [0.0, 0.5, 1.0, 0.0], 2)
assert_array_almost_equal(r, [0.0, 1.0, 1.0, 0.0], 2)
assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2)
assert_array_almost_equal(s, [1, 1, 1, 1], 2)
f2 = fbeta_score(y_true, y_pred, beta=2, average=None)
support = s
assert_array_almost_equal(f2, [0, 0.83, 1, 0], 2)
# Check macro
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="macro")
assert_almost_equal(p, 1.5 / 4)
assert_almost_equal(r, 0.5)
assert_almost_equal(f, 2.5 / 1.5 * 0.25)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="macro"),
np.mean(f2))
# Check micro
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="micro")
assert_almost_equal(p, 0.5)
assert_almost_equal(r, 0.5)
assert_almost_equal(f, 0.5)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="micro"),
(1 + 4) * p * r / (4 * p + r))
# Check weigted
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="weighted")
assert_almost_equal(p, 1.5 / 4)
assert_almost_equal(r, 0.5)
assert_almost_equal(f, 2.5 / 1.5 * 0.25)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="weighted"),
np.average(f2, weights=support))
# Check weigted
# |h(x_i) inter y_i | = [0, 1, 1]
# |y_i| = [1, 1, 2]
# |h(x_i)| = [1, 1, 2]
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="samples")
assert_almost_equal(p, 0.5)
assert_almost_equal(r, 0.5)
assert_almost_equal(f, 0.5)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="samples"),
0.5)
@ignore_warnings
def test_precision_recall_f1_score_multilabel_2():
# Test precision_recall_f1_score on a crafted multilabel example 2
# Second crafted example
y_true_ll = [(1,), (2,), (2, 3)]
y_pred_ll = [(4,), (4,), (2, 1)]
lb = LabelBinarizer()
lb.fit([range(1, 5)])
y_true_bi = lb.transform(y_true_ll)
y_pred_bi = lb.transform(y_pred_ll)
for y_true, y_pred in [(y_true_ll, y_pred_ll), (y_true_bi, y_pred_bi)]:
# tp = [ 0. 1. 0. 0.]
# fp = [ 1. 0. 0. 2.]
# fn = [ 1. 1. 1. 0.]
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average=None)
assert_array_almost_equal(p, [0.0, 1.0, 0.0, 0.0], 2)
assert_array_almost_equal(r, [0.0, 0.5, 0.0, 0.0], 2)
assert_array_almost_equal(f, [0.0, 0.66, 0.0, 0.0], 2)
assert_array_almost_equal(s, [1, 2, 1, 0], 2)
f2 = fbeta_score(y_true, y_pred, beta=2, average=None)
support = s
assert_array_almost_equal(f2, [0, 0.55, 0, 0], 2)
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="micro")
assert_almost_equal(p, 0.25)
assert_almost_equal(r, 0.25)
assert_almost_equal(f, 2 * 0.25 * 0.25 / 0.5)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="micro"),
(1 + 4) * p * r / (4 * p + r))
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="macro")
assert_almost_equal(p, 0.25)
assert_almost_equal(r, 0.125)
assert_almost_equal(f, 2 / 12)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="macro"),
np.mean(f2))
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="weighted")
assert_almost_equal(p, 2 / 4)
assert_almost_equal(r, 1 / 4)
assert_almost_equal(f, 2 / 3 * 2 / 4)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="weighted"),
np.average(f2, weights=support))
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="samples")
# Check weigted
# |h(x_i) inter y_i | = [0, 0, 1]
# |y_i| = [1, 1, 2]
# |h(x_i)| = [1, 1, 2]
assert_almost_equal(p, 1 / 6)
assert_almost_equal(r, 1 / 6)
assert_almost_equal(f, 2 / 4 * 1 / 3)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="samples"),
0.1666, 2)
@ignore_warnings
def test_precision_recall_f1_score_with_an_empty_prediction():
y_true_ll = [(1,), (0,), (2, 1,)]
y_pred_ll = [tuple(), (3,), (2, 1)]
lb = LabelBinarizer()
lb.fit([range(4)])
y_true_bi = lb.transform(y_true_ll)
y_pred_bi = lb.transform(y_pred_ll)
for y_true, y_pred in [(y_true_ll, y_pred_ll), (y_true_bi, y_pred_bi)]:
# true_pos = [ 0. 1. 1. 0.]
# false_pos = [ 0. 0. 0. 1.]
# false_neg = [ 1. 1. 0. 0.]
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average=None)
assert_array_almost_equal(p, [0.0, 1.0, 1.0, 0.0], 2)
assert_array_almost_equal(r, [0.0, 0.5, 1.0, 0.0], 2)
assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2)
assert_array_almost_equal(s, [1, 2, 1, 0], 2)
f2 = fbeta_score(y_true, y_pred, beta=2, average=None)
support = s
assert_array_almost_equal(f2, [0, 0.55, 1, 0], 2)
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="macro")
assert_almost_equal(p, 0.5)
assert_almost_equal(r, 1.5 / 4)
assert_almost_equal(f, 2.5 / (4 * 1.5))
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="macro"),
np.mean(f2))
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="micro")
assert_almost_equal(p, 2 / 3)
assert_almost_equal(r, 0.5)
assert_almost_equal(f, 2 / 3 / (2 / 3 + 0.5))
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="micro"),
(1 + 4) * p * r / (4 * p + r))
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="weighted")
assert_almost_equal(p, 3 / 4)
assert_almost_equal(r, 0.5)
assert_almost_equal(f, (2 / 1.5 + 1) / 4)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="weighted"),
np.average(f2, weights=support))
p, r, f, s = precision_recall_fscore_support(y_true, y_pred,
average="samples")
# |h(x_i) inter y_i | = [0, 0, 2]
# |y_i| = [1, 1, 2]
# |h(x_i)| = [0, 1, 2]
assert_almost_equal(p, 1 / 3)
assert_almost_equal(r, 1 / 3)
assert_almost_equal(f, 1 / 3)
assert_equal(s, None)
assert_almost_equal(fbeta_score(y_true, y_pred, beta=2,
average="samples"),
0.333, 2)
def test_precision_recall_f1_no_labels():
y_true = np.zeros((20, 3))
y_pred = np.zeros_like(y_true)
# tp = [0, 0, 0]
# fn = [0, 0, 0]
# fp = [0, 0, 0]
# support = [0, 0, 0]
# |y_hat_i inter y_i | = [0, 0, 0]
# |y_i| = [0, 0, 0]
# |y_hat_i| = [0, 0, 0]
for beta in [1]:
p, r, f, s = assert_warns(UndefinedMetricWarning,
precision_recall_fscore_support,
y_true, y_pred, average=None, beta=beta)
assert_array_almost_equal(p, [0, 0, 0], 2)
assert_array_almost_equal(r, [0, 0, 0], 2)
assert_array_almost_equal(f, [0, 0, 0], 2)
assert_array_almost_equal(s, [0, 0, 0], 2)
fbeta = assert_warns(UndefinedMetricWarning, fbeta_score,
y_true, y_pred, beta=beta, average=None)
assert_array_almost_equal(fbeta, [0, 0, 0], 2)
for average in ["macro", "micro", "weighted", "samples"]:
p, r, f, s = assert_warns(UndefinedMetricWarning,
precision_recall_fscore_support,
y_true, y_pred, average=average,
beta=beta)
assert_almost_equal(p, 0)
assert_almost_equal(r, 0)
assert_almost_equal(f, 0)
assert_equal(s, None)
fbeta = assert_warns(UndefinedMetricWarning, fbeta_score,
y_true, y_pred,
beta=beta, average=average)
assert_almost_equal(fbeta, 0)
def test_prf_warnings():
# average of per-label scores
f, w = precision_recall_fscore_support, UndefinedMetricWarning
my_assert = assert_warns_message
for average in [None, 'weighted', 'macro']:
msg = ('Precision and F-score are ill-defined and '
'being set to 0.0 in labels with no predicted samples.')
my_assert(w, msg, f, [0, 1, 2], [1, 1, 2], average=average)
msg = ('Recall and F-score are ill-defined and '
'being set to 0.0 in labels with no true samples.')
my_assert(w, msg, f, [1, 1, 2], [0, 1, 2], average=average)
# average of per-sample scores
msg = ('Precision and F-score are ill-defined and '
'being set to 0.0 in samples with no predicted labels.')
my_assert(w, msg, f, np.array([[1, 0], [1, 0]]),
np.array([[1, 0], [0, 0]]), average='samples')
msg = ('Recall and F-score are ill-defined and '
'being set to 0.0 in samples with no true labels.')
my_assert(w, msg, f, np.array([[1, 0], [0, 0]]),
np.array([[1, 0], [1, 0]]),
average='samples')
# single score: micro-average
msg = ('Precision and F-score are ill-defined and '
'being set to 0.0 due to no predicted samples.')
my_assert(w, msg, f, np.array([[1, 1], [1, 1]]),
np.array([[0, 0], [0, 0]]), average='micro')
msg = ('Recall and F-score are ill-defined and '
'being set to 0.0 due to no true samples.')
my_assert(w, msg, f, np.array([[0, 0], [0, 0]]),
np.array([[1, 1], [1, 1]]), average='micro')
# single postive label
msg = ('Precision and F-score are ill-defined and '
'being set to 0.0 due to no predicted samples.')
my_assert(w, msg, f, [1, 1], [-1, -1], average='macro')
msg = ('Recall and F-score are ill-defined and '
'being set to 0.0 due to no true samples.')
my_assert(w, msg, f, [-1, -1], [1, 1], average='macro')
def test_recall_warnings():
assert_no_warnings(recall_score,
np.array([[1, 1], [1, 1]]),
np.array([[0, 0], [0, 0]]),
average='micro')
clean_warning_registry()
with warnings.catch_warnings(record=True) as record:
warnings.simplefilter('always')
recall_score(np.array([[0, 0], [0, 0]]),
np.array([[1, 1], [1, 1]]),
average='micro')
assert_equal(str(record.pop().message),
'Recall is ill-defined and '
'being set to 0.0 due to no true samples.')
def test_precision_warnings():
clean_warning_registry()
with warnings.catch_warnings(record=True) as record:
warnings.simplefilter('always')
precision_score(np.array([[1, 1], [1, 1]]),
np.array([[0, 0], [0, 0]]),
average='micro')
assert_equal(str(record.pop().message),
'Precision is ill-defined and '
'being set to 0.0 due to no predicted samples.')
assert_no_warnings(precision_score,
np.array([[0, 0], [0, 0]]),
np.array([[1, 1], [1, 1]]),
average='micro')
def test_fscore_warnings():
clean_warning_registry()
with warnings.catch_warnings(record=True) as record:
warnings.simplefilter('always')
for score in [f1_score, partial(fbeta_score, beta=2)]:
score(np.array([[1, 1], [1, 1]]),
np.array([[0, 0], [0, 0]]),
average='micro')
assert_equal(str(record.pop().message),
'F-score is ill-defined and '
'being set to 0.0 due to no predicted samples.')
score(np.array([[0, 0], [0, 0]]),
np.array([[1, 1], [1, 1]]),
average='micro')
assert_equal(str(record.pop().message),
'F-score is ill-defined and '
'being set to 0.0 due to no true samples.')
def test_prf_average_compat():
# Ensure warning if f1_score et al.'s average is implicit for multiclass
y_true = [1, 2, 3, 3]
y_pred = [1, 2, 3, 1]
y_true_bin = [0, 1, 1]
y_pred_bin = [0, 1, 0]
for metric in [precision_score, recall_score, f1_score,
partial(fbeta_score, beta=2)]:
score = assert_warns(DeprecationWarning, metric, y_true, y_pred)
score_weighted = assert_no_warnings(metric, y_true, y_pred,
average='weighted')
assert_equal(score, score_weighted,
'average does not act like "weighted" by default')
# check binary passes without warning
assert_no_warnings(metric, y_true_bin, y_pred_bin)
# but binary with pos_label=None should behave like multiclass
score = assert_warns(DeprecationWarning, metric,
y_true_bin, y_pred_bin, pos_label=None)
score_weighted = assert_no_warnings(metric, y_true_bin, y_pred_bin,
pos_label=None, average='weighted')
assert_equal(score, score_weighted,
'average does not act like "weighted" by default with '
'binary data and pos_label=None')
@ignore_warnings # sequence of sequences is deprecated
def test__check_targets():
# Check that _check_targets correctly merges target types, squeezes
# output and fails if input lengths differ.
IND = 'multilabel-indicator'
SEQ = 'multilabel-sequences'
MC = 'multiclass'
BIN = 'binary'
CNT = 'continuous'
MMC = 'multiclass-multioutput'
MCN = 'continuous-multioutput'
# all of length 3
EXAMPLES = [
(IND, np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]])),
# must not be considered binary
(IND, np.array([[0, 1], [1, 0], [1, 1]])),
(SEQ, [[2, 3], [1], [3]]),
(MC, [2, 3, 1]),
(BIN, [0, 1, 1]),
(CNT, [0., 1.5, 1.]),
(MC, np.array([[2], [3], [1]])),
(BIN, np.array([[0], [1], [1]])),
(CNT, np.array([[0.], [1.5], [1.]])),
(MMC, np.array([[0, 2], [1, 3], [2, 3]])),
(MCN, np.array([[0.5, 2.], [1.1, 3.], [2., 3.]])),
]
# expected type given input types, or None for error
# (types will be tried in either order)
EXPECTED = {
(IND, IND): IND,
(SEQ, SEQ): IND,
(MC, MC): MC,
(BIN, BIN): BIN,
(IND, SEQ): None,
(MC, SEQ): None,
(BIN, SEQ): None,
(MC, IND): None,
(BIN, IND): None,
(BIN, MC): MC,
# Disallowed types
(CNT, CNT): None,
(MMC, MMC): None,
(MCN, MCN): None,
(IND, CNT): None,
(SEQ, CNT): None,
(MC, CNT): None,
(BIN, CNT): None,
(MMC, CNT): None,
(MCN, CNT): None,
(IND, MMC): None,
(SEQ, MMC): None,
(MC, MMC): None,
(BIN, MMC): None,
(MCN, MMC): None,
(IND, MCN): None,
(SEQ, MCN): None,
(MC, MCN): None,
(BIN, MCN): None,
}
for (type1, y1), (type2, y2) in product(EXAMPLES, repeat=2):
try:
expected = EXPECTED[type1, type2]
except KeyError:
expected = EXPECTED[type2, type1]
if expected is None:
assert_raises(ValueError, _check_targets, y1, y2)
if type1 != type2:
assert_raise_message(
ValueError,
"Can't handle mix of {0} and {1}".format(type1, type2),
_check_targets, y1, y2)
else:
if type1 not in (BIN, MC, SEQ, IND):
assert_raise_message(ValueError,
"{0} is not supported".format(type1),
_check_targets, y1, y2)
else:
merged_type, y1out, y2out = _check_targets(y1, y2)
assert_equal(merged_type, expected)
if merged_type.startswith('multilabel'):
assert_equal(y1out.format, 'csr')
assert_equal(y2out.format, 'csr')
else:
assert_array_equal(y1out, np.squeeze(y1))
assert_array_equal(y2out, np.squeeze(y2))
assert_raises(ValueError, _check_targets, y1[:-1], y2)
def test_hinge_loss_binary():
y_true = np.array([-1, 1, 1, -1])
pred_decision = np.array([-8.5, 0.5, 1.5, -0.3])
assert_equal(hinge_loss(y_true, pred_decision), 1.2 / 4)
y_true = np.array([0, 2, 2, 0])
pred_decision = np.array([-8.5, 0.5, 1.5, -0.3])
assert_equal(hinge_loss(y_true, pred_decision), 1.2 / 4)
def test_hinge_loss_multiclass():
pred_decision = np.array([
[0.36, -0.17, -0.58, -0.99],
[-0.54, -0.37, -0.48, -0.58],
[-1.45, -0.58, -0.38, -0.17],
[-0.54, -0.38, -0.48, -0.58],
[-2.36, -0.79, -0.27, 0.24],
[-1.45, -0.58, -0.38, -0.17]
])
y_true = np.array([0, 1, 2, 1, 3, 2])
dummy_losses = np.array([
1 - pred_decision[0][0] + pred_decision[0][1],
1 - pred_decision[1][1] + pred_decision[1][2],
1 - pred_decision[2][2] + pred_decision[2][3],
1 - pred_decision[3][1] + pred_decision[3][2],
1 - pred_decision[4][3] + pred_decision[4][2],
1 - pred_decision[5][2] + pred_decision[5][3]
])
dummy_losses[dummy_losses <= 0] = 0
dummy_hinge_loss = np.mean(dummy_losses)
assert_equal(hinge_loss(y_true, pred_decision),
dummy_hinge_loss)
def test_hinge_loss_multiclass_missing_labels_with_labels_none():
y_true = np.array([0, 1, 2, 2])
pred_decision = np.array([
[1.27, 0.034, -0.68, -1.40],
[-1.45, -0.58, -0.38, -0.17],
[-2.36, -0.79, -0.27, 0.24],
[-2.36, -0.79, -0.27, 0.24]
])
error_message = ("Please include all labels in y_true "
"or pass labels as third argument")
assert_raise_message(ValueError,
error_message,
hinge_loss, y_true, pred_decision)
def test_hinge_loss_multiclass_with_missing_labels():
pred_decision = np.array([
[0.36, -0.17, -0.58, -0.99],
[-0.55, -0.38, -0.48, -0.58],
[-1.45, -0.58, -0.38, -0.17],
[-0.55, -0.38, -0.48, -0.58],
[-1.45, -0.58, -0.38, -0.17]
])
y_true = np.array([0, 1, 2, 1, 2])
labels = np.array([0, 1, 2, 3])
dummy_losses = np.array([
1 - pred_decision[0][0] + pred_decision[0][1],
1 - pred_decision[1][1] + pred_decision[1][2],
1 - pred_decision[2][2] + pred_decision[2][3],
1 - pred_decision[3][1] + pred_decision[3][2],
1 - pred_decision[4][2] + pred_decision[4][3]
])
dummy_losses[dummy_losses <= 0] = 0
dummy_hinge_loss = np.mean(dummy_losses)
assert_equal(hinge_loss(y_true, pred_decision, labels=labels),
dummy_hinge_loss)
def test_hinge_loss_multiclass_invariance_lists():
# Currently, invariance of string and integer labels cannot be tested
# in common invariance tests because invariance tests for multiclass
# decision functions is not implemented yet.
y_true = ['blue', 'green', 'red',
'green', 'white', 'red']
pred_decision = [
[0.36, -0.17, -0.58, -0.99],
[-0.55, -0.38, -0.48, -0.58],
[-1.45, -0.58, -0.38, -0.17],
[-0.55, -0.38, -0.48, -0.58],
[-2.36, -0.79, -0.27, 0.24],
[-1.45, -0.58, -0.38, -0.17]]
dummy_losses = np.array([
1 - pred_decision[0][0] + pred_decision[0][1],
1 - pred_decision[1][1] + pred_decision[1][2],
1 - pred_decision[2][2] + pred_decision[2][3],
1 - pred_decision[3][1] + pred_decision[3][2],
1 - pred_decision[4][3] + pred_decision[4][2],
1 - pred_decision[5][2] + pred_decision[5][3]
])
dummy_losses[dummy_losses <= 0] = 0
dummy_hinge_loss = np.mean(dummy_losses)
assert_equal(hinge_loss(y_true, pred_decision),
dummy_hinge_loss)
def test_log_loss():
# binary case with symbolic labels ("no" < "yes")
y_true = ["no", "no", "no", "yes", "yes", "yes"]
y_pred = np.array([[0.5, 0.5], [0.1, 0.9], [0.01, 0.99],
[0.9, 0.1], [0.75, 0.25], [0.001, 0.999]])
loss = log_loss(y_true, y_pred)
assert_almost_equal(loss, 1.8817971)
# multiclass case; adapted from http://bit.ly/RJJHWA
y_true = [1, 0, 2]
y_pred = [[0.2, 0.7, 0.1], [0.6, 0.2, 0.2], [0.6, 0.1, 0.3]]
loss = log_loss(y_true, y_pred, normalize=True)
assert_almost_equal(loss, 0.6904911)
# check that we got all the shapes and axes right
# by doubling the length of y_true and y_pred
y_true *= 2
y_pred *= 2
loss = log_loss(y_true, y_pred, normalize=False)
assert_almost_equal(loss, 0.6904911 * 6, decimal=6)
# check eps and handling of absolute zero and one probabilities
y_pred = np.asarray(y_pred) > .5
loss = log_loss(y_true, y_pred, normalize=True, eps=.1)
assert_almost_equal(loss, log_loss(y_true, np.clip(y_pred, .1, .9)))
# raise error if number of classes are not equal.
y_true = [1, 0, 2]
y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1]]
assert_raises(ValueError, log_loss, y_true, y_pred)
# case when y_true is a string array object
y_true = ["ham", "spam", "spam", "ham"]
y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]]
loss = log_loss(y_true, y_pred)
assert_almost_equal(loss, 1.0383217, decimal=6)
def test_brier_score_loss():
# Check brier_score_loss function
y_true = np.array([0, 1, 1, 0, 1, 1])
y_pred = np.array([0.1, 0.8, 0.9, 0.3, 1., 0.95])
true_score = linalg.norm(y_true - y_pred) ** 2 / len(y_true)
assert_almost_equal(brier_score_loss(y_true, y_true), 0.0)
assert_almost_equal(brier_score_loss(y_true, y_pred), true_score)
assert_almost_equal(brier_score_loss(1. + y_true, y_pred),
true_score)
assert_almost_equal(brier_score_loss(2 * y_true - 1, y_pred),
true_score)
assert_raises(ValueError, brier_score_loss, y_true, y_pred[1:])
assert_raises(ValueError, brier_score_loss, y_true, y_pred + 1.)
assert_raises(ValueError, brier_score_loss, y_true, y_pred - 1.)
| apache-2.0 |
CVML/scikit-learn | sklearn/decomposition/nmf.py | 30 | 19208 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck <[email protected]>
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# Author: Anthony Di Franco (original Python and NumPy port)
# License: BSD 3 clause
from __future__ import division
from math import sqrt
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.optimize import nnls
from ..base import BaseEstimator, TransformerMixin
from ..utils import check_random_state, check_array
from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
from ..utils.validation import check_is_fitted
def safe_vstack(Xs):
if any(sp.issparse(X) for X in Xs):
return sp.vstack(Xs)
else:
return np.vstack(Xs)
def norm(x):
"""Dot product-based Euclidean norm implementation
See: http://fseoane.net/blog/2011/computing-the-vector-norm/
"""
return sqrt(squared_norm(x))
def trace_dot(X, Y):
"""Trace of np.dot(X, Y.T)."""
return np.dot(X.ravel(), Y.ravel())
def _sparseness(x):
"""Hoyer's measure of sparsity for a vector"""
sqrt_n = np.sqrt(len(x))
return (sqrt_n - np.linalg.norm(x, 1) / norm(x)) / (sqrt_n - 1)
def check_non_negative(X, whom):
X = X.data if sp.issparse(X) else X
if (X < 0).any():
raise ValueError("Negative values in data passed to %s" % whom)
def _initialize_nmf(X, n_components, variant=None, eps=1e-6,
random_state=None):
"""NNDSVD algorithm for NMF initialization.
Computes a good initial guess for the non-negative
rank k matrix approximation for X: X = WH
Parameters
----------
X : array, [n_samples, n_features]
The data matrix to be decomposed.
n_components : array, [n_components, n_features]
The number of components desired in the approximation.
variant : None | 'a' | 'ar'
The variant of the NNDSVD algorithm.
Accepts None, 'a', 'ar'
None: leaves the zero entries as zero
'a': Fills the zero entries with the average of X
'ar': Fills the zero entries with standard normal random variates.
Default: None
eps: float
Truncate all values less then this in output to zero.
random_state : numpy.RandomState | int, optional
The generator used to fill in the zeros, when using variant='ar'
Default: numpy.random
Returns
-------
(W, H) :
Initial guesses for solving X ~= WH such that
the number of columns in W is n_components.
References
----------
C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
nonnegative matrix factorization - Pattern Recognition, 2008
http://tinyurl.com/nndsvd
"""
check_non_negative(X, "NMF initialization")
if variant not in (None, 'a', 'ar'):
raise ValueError("Invalid variant name")
random_state = check_random_state(random_state)
U, S, V = randomized_svd(X, n_components, random_state=random_state)
W, H = np.zeros(U.shape), np.zeros(V.shape)
# The leading singular triplet is non-negative
# so it can be used as is for initialization.
W[:, 0] = np.sqrt(S[0]) * np.abs(U[:, 0])
H[0, :] = np.sqrt(S[0]) * np.abs(V[0, :])
for j in range(1, n_components):
x, y = U[:, j], V[j, :]
# extract positive and negative parts of column vectors
x_p, y_p = np.maximum(x, 0), np.maximum(y, 0)
x_n, y_n = np.abs(np.minimum(x, 0)), np.abs(np.minimum(y, 0))
# and their norms
x_p_nrm, y_p_nrm = norm(x_p), norm(y_p)
x_n_nrm, y_n_nrm = norm(x_n), norm(y_n)
m_p, m_n = x_p_nrm * y_p_nrm, x_n_nrm * y_n_nrm
# choose update
if m_p > m_n:
u = x_p / x_p_nrm
v = y_p / y_p_nrm
sigma = m_p
else:
u = x_n / x_n_nrm
v = y_n / y_n_nrm
sigma = m_n
lbd = np.sqrt(S[j] * sigma)
W[:, j] = lbd * u
H[j, :] = lbd * v
W[W < eps] = 0
H[H < eps] = 0
if variant == "a":
avg = X.mean()
W[W == 0] = avg
H[H == 0] = avg
elif variant == "ar":
avg = X.mean()
W[W == 0] = abs(avg * random_state.randn(len(W[W == 0])) / 100)
H[H == 0] = abs(avg * random_state.randn(len(H[H == 0])) / 100)
return W, H
def _nls_subproblem(V, W, H, tol, max_iter, sigma=0.01, beta=0.1):
"""Non-negative least square solver
Solves a non-negative least squares subproblem using the
projected gradient descent algorithm.
min || WH - V ||_2
Parameters
----------
V, W : array-like
Constant matrices.
H : array-like
Initial guess for the solution.
tol : float
Tolerance of the stopping condition.
max_iter : int
Maximum number of iterations before timing out.
sigma : float
Constant used in the sufficient decrease condition checked by the line
search. Smaller values lead to a looser sufficient decrease condition,
thus reducing the time taken by the line search, but potentially
increasing the number of iterations of the projected gradient
procedure. 0.01 is a commonly used value in the optimization
literature.
beta : float
Factor by which the step size is decreased (resp. increased) until
(resp. as long as) the sufficient decrease condition is satisfied.
Larger values allow to find a better step size but lead to longer line
search. 0.1 is a commonly used value in the optimization literature.
Returns
-------
H : array-like
Solution to the non-negative least squares problem.
grad : array-like
The gradient.
n_iter : int
The number of iterations done by the algorithm.
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix factorization.
Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
"""
WtV = safe_sparse_dot(W.T, V)
WtW = np.dot(W.T, W)
# values justified in the paper
alpha = 1
for n_iter in range(1, max_iter + 1):
grad = np.dot(WtW, H) - WtV
# The following multiplication with a boolean array is more than twice
# as fast as indexing into grad.
if norm(grad * np.logical_or(grad < 0, H > 0)) < tol:
break
Hp = H
for inner_iter in range(19):
# Gradient step.
Hn = H - alpha * grad
# Projection step.
Hn *= Hn > 0
d = Hn - H
gradd = np.dot(grad.ravel(), d.ravel())
dQd = np.dot(np.dot(WtW, d).ravel(), d.ravel())
suff_decr = (1 - sigma) * gradd + 0.5 * dQd < 0
if inner_iter == 0:
decr_alpha = not suff_decr
if decr_alpha:
if suff_decr:
H = Hn
break
else:
alpha *= beta
elif not suff_decr or (Hp == Hn).all():
H = Hp
break
else:
alpha /= beta
Hp = Hn
if n_iter == max_iter:
warnings.warn("Iteration limit reached in nls subproblem.")
return H, grad, n_iter
class ProjectedGradientNMF(BaseEstimator, TransformerMixin):
"""Non-Negative matrix factorization by Projected Gradient (NMF)
Read more in the :ref:`User Guide <NMF>`.
Parameters
----------
n_components : int or None
Number of components, if n_components is not set all components
are kept
init : 'nndsvd' | 'nndsvda' | 'nndsvdar' | 'random'
Method used to initialize the procedure.
Default: 'nndsvd' if n_components < n_features, otherwise random.
Valid options::
'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
'random': non-negative random matrices
sparseness : 'data' | 'components' | None, default: None
Where to enforce sparsity in the model.
beta : double, default: 1
Degree of sparseness, if sparseness is not None. Larger values mean
more sparseness.
eta : double, default: 0.1
Degree of correctness to maintain, if sparsity is not None. Smaller
values mean larger error.
tol : double, default: 1e-4
Tolerance value used in stopping conditions.
max_iter : int, default: 200
Number of iterations to compute.
nls_max_iter : int, default: 2000
Number of iterations in NLS subproblem.
random_state : int or RandomState
Random number generator seed control.
Attributes
----------
components_ : array, [n_components, n_features]
Non-negative components of the data.
reconstruction_err_ : number
Frobenius norm of the matrix difference between
the training data and the reconstructed data from
the fit produced by the model. ``|| X - WH ||_2``
n_iter_ : int
Number of iterations run.
Examples
--------
>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> from sklearn.decomposition import ProjectedGradientNMF
>>> model = ProjectedGradientNMF(n_components=2, init='random',
... random_state=0)
>>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200,
n_components=2, nls_max_iter=2000, random_state=0, sparseness=None,
tol=0.0001)
>>> model.components_
array([[ 0.77032744, 0.11118662],
[ 0.38526873, 0.38228063]])
>>> model.reconstruction_err_ #doctest: +ELLIPSIS
0.00746...
>>> model = ProjectedGradientNMF(n_components=2,
... sparseness='components', init='random', random_state=0)
>>> model.fit(X) #doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200,
n_components=2, nls_max_iter=2000, random_state=0,
sparseness='components', tol=0.0001)
>>> model.components_
array([[ 1.67481991, 0.29614922],
[ 0. , 0.4681982 ]])
>>> model.reconstruction_err_ #doctest: +ELLIPSIS
0.513...
References
----------
This implements
C.-J. Lin. Projected gradient methods
for non-negative matrix factorization. Neural
Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
P. Hoyer. Non-negative Matrix Factorization with
Sparseness Constraints. Journal of Machine Learning
Research 2004.
NNDSVD is introduced in
C. Boutsidis, E. Gallopoulos: SVD based
initialization: A head start for nonnegative
matrix factorization - Pattern Recognition, 2008
http://tinyurl.com/nndsvd
"""
def __init__(self, n_components=None, init=None, sparseness=None, beta=1,
eta=0.1, tol=1e-4, max_iter=200, nls_max_iter=2000,
random_state=None):
self.n_components = n_components
self.init = init
self.tol = tol
if sparseness not in (None, 'data', 'components'):
raise ValueError(
'Invalid sparseness parameter: got %r instead of one of %r' %
(sparseness, (None, 'data', 'components')))
self.sparseness = sparseness
self.beta = beta
self.eta = eta
self.max_iter = max_iter
self.nls_max_iter = nls_max_iter
self.random_state = random_state
def _init(self, X):
n_samples, n_features = X.shape
init = self.init
if init is None:
if self.n_components_ < n_features:
init = 'nndsvd'
else:
init = 'random'
rng = check_random_state(self.random_state)
if init == 'nndsvd':
W, H = _initialize_nmf(X, self.n_components_, random_state=rng)
elif init == 'nndsvda':
W, H = _initialize_nmf(X, self.n_components_, variant='a',
random_state=rng)
elif init == 'nndsvdar':
W, H = _initialize_nmf(X, self.n_components_, variant='ar',
random_state=rng)
elif init == "random":
W = rng.randn(n_samples, self.n_components_)
# we do not write np.abs(W, out=W) to stay compatible with
# numpy 1.5 and earlier where the 'out' keyword is not
# supported as a kwarg on ufuncs
np.abs(W, W)
H = rng.randn(self.n_components_, n_features)
np.abs(H, H)
else:
raise ValueError(
'Invalid init parameter: got %r instead of one of %r' %
(init, (None, 'nndsvd', 'nndsvda', 'nndsvdar', 'random')))
return W, H
def _update_W(self, X, H, W, tolW):
n_samples, n_features = X.shape
if self.sparseness is None:
W, gradW, iterW = _nls_subproblem(X.T, H.T, W.T, tolW,
self.nls_max_iter)
elif self.sparseness == 'data':
W, gradW, iterW = _nls_subproblem(
safe_vstack([X.T, np.zeros((1, n_samples))]),
safe_vstack([H.T, np.sqrt(self.beta) * np.ones((1,
self.n_components_))]),
W.T, tolW, self.nls_max_iter)
elif self.sparseness == 'components':
W, gradW, iterW = _nls_subproblem(
safe_vstack([X.T,
np.zeros((self.n_components_, n_samples))]),
safe_vstack([H.T,
np.sqrt(self.eta) * np.eye(self.n_components_)]),
W.T, tolW, self.nls_max_iter)
return W.T, gradW.T, iterW
def _update_H(self, X, H, W, tolH):
n_samples, n_features = X.shape
if self.sparseness is None:
H, gradH, iterH = _nls_subproblem(X, W, H, tolH,
self.nls_max_iter)
elif self.sparseness == 'data':
H, gradH, iterH = _nls_subproblem(
safe_vstack([X, np.zeros((self.n_components_, n_features))]),
safe_vstack([W,
np.sqrt(self.eta) * np.eye(self.n_components_)]),
H, tolH, self.nls_max_iter)
elif self.sparseness == 'components':
H, gradH, iterH = _nls_subproblem(
safe_vstack([X, np.zeros((1, n_features))]),
safe_vstack([W,
np.sqrt(self.beta)
* np.ones((1, self.n_components_))]),
H, tolH, self.nls_max_iter)
return H, gradH, iterH
def fit_transform(self, X, y=None):
"""Learn a NMF model for the data X and returns the transformed data.
This is more efficient than calling fit followed by transform.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data matrix to be decomposed
Returns
-------
data: array, [n_samples, n_components]
Transformed data
"""
X = check_array(X, accept_sparse='csr')
check_non_negative(X, "NMF.fit")
n_samples, n_features = X.shape
if not self.n_components:
self.n_components_ = n_features
else:
self.n_components_ = self.n_components
W, H = self._init(X)
gradW = (np.dot(W, np.dot(H, H.T))
- safe_sparse_dot(X, H.T, dense_output=True))
gradH = (np.dot(np.dot(W.T, W), H)
- safe_sparse_dot(W.T, X, dense_output=True))
init_grad = norm(np.r_[gradW, gradH.T])
tolW = max(0.001, self.tol) * init_grad # why max?
tolH = tolW
tol = self.tol * init_grad
for n_iter in range(1, self.max_iter + 1):
# stopping condition
# as discussed in paper
proj_norm = norm(np.r_[gradW[np.logical_or(gradW < 0, W > 0)],
gradH[np.logical_or(gradH < 0, H > 0)]])
if proj_norm < tol:
break
# update W
W, gradW, iterW = self._update_W(X, H, W, tolW)
if iterW == 1:
tolW = 0.1 * tolW
# update H
H, gradH, iterH = self._update_H(X, H, W, tolH)
if iterH == 1:
tolH = 0.1 * tolH
if not sp.issparse(X):
error = norm(X - np.dot(W, H))
else:
sqnorm_X = np.dot(X.data, X.data)
norm_WHT = trace_dot(np.dot(np.dot(W.T, W), H), H)
cross_prod = trace_dot((X * H.T), W)
error = sqrt(sqnorm_X + norm_WHT - 2. * cross_prod)
self.reconstruction_err_ = error
self.comp_sparseness_ = _sparseness(H.ravel())
self.data_sparseness_ = _sparseness(W.ravel())
H[H == 0] = 0 # fix up negative zeros
self.components_ = H
if n_iter == self.max_iter:
warnings.warn("Iteration limit reached during fit. Solving for W exactly.")
return self.transform(X)
self.n_iter_ = n_iter
return W
def fit(self, X, y=None, **params):
"""Learn a NMF model for the data X.
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data matrix to be decomposed
Returns
-------
self
"""
self.fit_transform(X, **params)
return self
def transform(self, X):
"""Transform the data X according to the fitted NMF model
Parameters
----------
X: {array-like, sparse matrix}, shape = [n_samples, n_features]
Data matrix to be transformed by the model
Returns
-------
data: array, [n_samples, n_components]
Transformed data
"""
check_is_fitted(self, 'n_components_')
X = check_array(X, accept_sparse='csc')
Wt = np.zeros((self.n_components_, X.shape[0]))
check_non_negative(X, "ProjectedGradientNMF.transform")
if sp.issparse(X):
Wt, _, _ = _nls_subproblem(X.T, self.components_.T, Wt,
tol=self.tol,
max_iter=self.nls_max_iter)
else:
for j in range(0, X.shape[0]):
Wt[:, j], _ = nnls(self.components_.T, X[j, :])
return Wt.T
class NMF(ProjectedGradientNMF):
__doc__ = ProjectedGradientNMF.__doc__
pass
| bsd-3-clause |
mjfarmer/scada_py | env/lib/python2.7/site-packages/IPython/core/interactiveshell.py | 6 | 132557 | # -*- coding: utf-8 -*-
"""Main IPython class."""
#-----------------------------------------------------------------------------
# Copyright (C) 2001 Janko Hauser <[email protected]>
# Copyright (C) 2001-2007 Fernando Perez. <[email protected]>
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-----------------------------------------------------------------------------
from __future__ import absolute_import, print_function
import __future__
import abc
import ast
import atexit
import functools
import os
import re
import runpy
import sys
import tempfile
import traceback
import types
import subprocess
import warnings
from io import open as io_open
from pickleshare import PickleShareDB
from traitlets.config.configurable import SingletonConfigurable
from IPython.core import debugger, oinspect
from IPython.core import magic
from IPython.core import page
from IPython.core import prefilter
from IPython.core import shadowns
from IPython.core import ultratb
from IPython.core.alias import Alias, AliasManager
from IPython.core.autocall import ExitAutocall
from IPython.core.builtin_trap import BuiltinTrap
from IPython.core.events import EventManager, available_events
from IPython.core.compilerop import CachingCompiler, check_linecache_ipython
from IPython.core.display_trap import DisplayTrap
from IPython.core.displayhook import DisplayHook
from IPython.core.displaypub import DisplayPublisher
from IPython.core.error import InputRejected, UsageError
from IPython.core.extensions import ExtensionManager
from IPython.core.formatters import DisplayFormatter
from IPython.core.history import HistoryManager
from IPython.core.inputsplitter import IPythonInputSplitter, ESC_MAGIC, ESC_MAGIC2
from IPython.core.logger import Logger
from IPython.core.macro import Macro
from IPython.core.payload import PayloadManager
from IPython.core.prefilter import PrefilterManager
from IPython.core.profiledir import ProfileDir
from IPython.core.prompts import PromptManager
from IPython.core.usage import default_banner
from IPython.testing.skipdoctest import skip_doctest
from IPython.utils import PyColorize
from IPython.utils import io
from IPython.utils import py3compat
from IPython.utils import openpy
from IPython.utils.contexts import NoOpContext
from IPython.utils.decorators import undoc
from IPython.utils.io import ask_yes_no
from IPython.utils.ipstruct import Struct
from IPython.paths import get_ipython_dir
from IPython.utils.path import get_home_dir, get_py_filename, unquote_filename, ensure_dir_exists
from IPython.utils.process import system, getoutput
from IPython.utils.py3compat import (builtin_mod, unicode_type, string_types,
with_metaclass, iteritems)
from IPython.utils.strdispatch import StrDispatch
from IPython.utils.syspathcontext import prepended_to_syspath
from IPython.utils.text import (format_screen, LSString, SList,
DollarFormatter)
from traitlets import (Integer, Bool, CBool, CaselessStrEnum, Enum,
List, Dict, Unicode, Instance, Type)
from IPython.utils.warn import warn, error
import IPython.core.hooks
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
# compiled regexps for autoindent management
dedent_re = re.compile(r'^\s+raise|^\s+return|^\s+pass')
#-----------------------------------------------------------------------------
# Utilities
#-----------------------------------------------------------------------------
@undoc
def softspace(file, newvalue):
"""Copied from code.py, to remove the dependency"""
oldvalue = 0
try:
oldvalue = file.softspace
except AttributeError:
pass
try:
file.softspace = newvalue
except (AttributeError, TypeError):
# "attribute-less object" or "read-only attributes"
pass
return oldvalue
@undoc
def no_op(*a, **kw): pass
class SpaceInInput(Exception): pass
@undoc
class Bunch: pass
def get_default_colors():
if sys.platform=='darwin':
return "LightBG"
elif os.name=='nt':
return 'Linux'
else:
return 'Linux'
class SeparateUnicode(Unicode):
r"""A Unicode subclass to validate separate_in, separate_out, etc.
This is a Unicode based trait that converts '0'->'' and ``'\\n'->'\n'``.
"""
def validate(self, obj, value):
if value == '0': value = ''
value = value.replace('\\n','\n')
return super(SeparateUnicode, self).validate(obj, value)
@undoc
class DummyMod(object):
"""A dummy module used for IPython's interactive module when
a namespace must be assigned to the module's __dict__."""
pass
class ExecutionResult(object):
"""The result of a call to :meth:`InteractiveShell.run_cell`
Stores information about what took place.
"""
execution_count = None
error_before_exec = None
error_in_exec = None
result = None
@property
def success(self):
return (self.error_before_exec is None) and (self.error_in_exec is None)
def raise_error(self):
"""Reraises error if `success` is `False`, otherwise does nothing"""
if self.error_before_exec is not None:
raise self.error_before_exec
if self.error_in_exec is not None:
raise self.error_in_exec
class InteractiveShell(SingletonConfigurable):
"""An enhanced, interactive shell for Python."""
_instance = None
ast_transformers = List([], config=True, help=
"""
A list of ast.NodeTransformer subclass instances, which will be applied
to user input before code is run.
"""
)
autocall = Enum((0,1,2), default_value=0, config=True, help=
"""
Make IPython automatically call any callable object even if you didn't
type explicit parentheses. For example, 'str 43' becomes 'str(43)'
automatically. The value can be '0' to disable the feature, '1' for
'smart' autocall, where it is not applied if there are no more
arguments on the line, and '2' for 'full' autocall, where all callable
objects are automatically called (even if no arguments are present).
"""
)
# TODO: remove all autoindent logic and put into frontends.
# We can't do this yet because even runlines uses the autoindent.
autoindent = CBool(True, config=True, help=
"""
Autoindent IPython code entered interactively.
"""
)
automagic = CBool(True, config=True, help=
"""
Enable magic commands to be called without the leading %.
"""
)
banner1 = Unicode(default_banner, config=True,
help="""The part of the banner to be printed before the profile"""
)
banner2 = Unicode('', config=True,
help="""The part of the banner to be printed after the profile"""
)
cache_size = Integer(1000, config=True, help=
"""
Set the size of the output cache. The default is 1000, you can
change it permanently in your config file. Setting it to 0 completely
disables the caching system, and the minimum value accepted is 20 (if
you provide a value less than 20, it is reset to 0 and a warning is
issued). This limit is defined because otherwise you'll spend more
time re-flushing a too small cache than working
"""
)
color_info = CBool(True, config=True, help=
"""
Use colors for displaying information about objects. Because this
information is passed through a pager (like 'less'), and some pagers
get confused with color codes, this capability can be turned off.
"""
)
colors = CaselessStrEnum(('NoColor','LightBG','Linux'),
default_value=get_default_colors(), config=True,
help="Set the color scheme (NoColor, Linux, or LightBG)."
)
colors_force = CBool(False, help=
"""
Force use of ANSI color codes, regardless of OS and readline
availability.
"""
# FIXME: This is essentially a hack to allow ZMQShell to show colors
# without readline on Win32. When the ZMQ formatting system is
# refactored, this should be removed.
)
debug = CBool(False, config=True)
deep_reload = CBool(False, config=True, help=
"""
**Deprecated**
Will be removed in IPython 6.0
Enable deep (recursive) reloading by default. IPython can use the
deep_reload module which reloads changes in modules recursively (it
replaces the reload() function, so you don't need to change anything to
use it). `deep_reload` forces a full reload of modules whose code may
have changed, which the default reload() function does not. When
deep_reload is off, IPython will use the normal reload(), but
deep_reload will still be available as dreload().
"""
)
disable_failing_post_execute = CBool(False, config=True,
help="Don't call post-execute functions that have failed in the past."
)
display_formatter = Instance(DisplayFormatter, allow_none=True)
displayhook_class = Type(DisplayHook)
display_pub_class = Type(DisplayPublisher)
data_pub_class = None
exit_now = CBool(False)
exiter = Instance(ExitAutocall)
def _exiter_default(self):
return ExitAutocall(self)
# Monotonically increasing execution counter
execution_count = Integer(1)
filename = Unicode("<ipython console>")
ipython_dir= Unicode('', config=True) # Set to get_ipython_dir() in __init__
# Input splitter, to transform input line by line and detect when a block
# is ready to be executed.
input_splitter = Instance('IPython.core.inputsplitter.IPythonInputSplitter',
(), {'line_input_checker': True})
# This InputSplitter instance is used to transform completed cells before
# running them. It allows cell magics to contain blank lines.
input_transformer_manager = Instance('IPython.core.inputsplitter.IPythonInputSplitter',
(), {'line_input_checker': False})
logstart = CBool(False, config=True, help=
"""
Start logging to the default log file in overwrite mode.
Use `logappend` to specify a log file to **append** logs to.
"""
)
logfile = Unicode('', config=True, help=
"""
The name of the logfile to use.
"""
)
logappend = Unicode('', config=True, help=
"""
Start logging to the given file in append mode.
Use `logfile` to specify a log file to **overwrite** logs to.
"""
)
object_info_string_level = Enum((0,1,2), default_value=0,
config=True)
pdb = CBool(False, config=True, help=
"""
Automatically call the pdb debugger after every exception.
"""
)
multiline_history = CBool(sys.platform != 'win32', config=True,
help="Save multi-line entries as one entry in readline history"
)
display_page = Bool(False, config=True,
help="""If True, anything that would be passed to the pager
will be displayed as regular output instead."""
)
# deprecated prompt traits:
prompt_in1 = Unicode('In [\\#]: ', config=True,
help="Deprecated, will be removed in IPython 5.0, use PromptManager.in_template")
prompt_in2 = Unicode(' .\\D.: ', config=True,
help="Deprecated, will be removed in IPython 5.0, use PromptManager.in2_template")
prompt_out = Unicode('Out[\\#]: ', config=True,
help="Deprecated, will be removed in IPython 5.0, use PromptManager.out_template")
prompts_pad_left = CBool(True, config=True,
help="Deprecated, will be removed in IPython 5.0, use PromptManager.justify")
def _prompt_trait_changed(self, name, old, new):
table = {
'prompt_in1' : 'in_template',
'prompt_in2' : 'in2_template',
'prompt_out' : 'out_template',
'prompts_pad_left' : 'justify',
}
warn("InteractiveShell.{name} is deprecated, use PromptManager.{newname}".format(
name=name, newname=table[name])
)
# protect against weird cases where self.config may not exist:
if self.config is not None:
# propagate to corresponding PromptManager trait
setattr(self.config.PromptManager, table[name], new)
_prompt_in1_changed = _prompt_trait_changed
_prompt_in2_changed = _prompt_trait_changed
_prompt_out_changed = _prompt_trait_changed
_prompt_pad_left_changed = _prompt_trait_changed
show_rewritten_input = CBool(True, config=True,
help="Show rewritten input, e.g. for autocall."
)
quiet = CBool(False, config=True)
history_length = Integer(10000, config=True)
history_load_length = Integer(1000, config=True, help=
"""
The number of saved history entries to be loaded
into the readline buffer at startup.
"""
)
# The readline stuff will eventually be moved to the terminal subclass
# but for now, we can't do that as readline is welded in everywhere.
readline_use = CBool(True, config=True)
readline_remove_delims = Unicode('-/~', config=True)
readline_delims = Unicode() # set by init_readline()
# don't use \M- bindings by default, because they
# conflict with 8-bit encodings. See gh-58,gh-88
readline_parse_and_bind = List([
'tab: complete',
'"\C-l": clear-screen',
'set show-all-if-ambiguous on',
'"\C-o": tab-insert',
'"\C-r": reverse-search-history',
'"\C-s": forward-search-history',
'"\C-p": history-search-backward',
'"\C-n": history-search-forward',
'"\e[A": history-search-backward',
'"\e[B": history-search-forward',
'"\C-k": kill-line',
'"\C-u": unix-line-discard',
], config=True)
_custom_readline_config = False
def _readline_parse_and_bind_changed(self, name, old, new):
# notice that readline config is customized
# indicates that it should have higher priority than inputrc
self._custom_readline_config = True
ast_node_interactivity = Enum(['all', 'last', 'last_expr', 'none'],
default_value='last_expr', config=True,
help="""
'all', 'last', 'last_expr' or 'none', specifying which nodes should be
run interactively (displaying output from expressions).""")
# TODO: this part of prompt management should be moved to the frontends.
# Use custom TraitTypes that convert '0'->'' and '\\n'->'\n'
separate_in = SeparateUnicode('\n', config=True)
separate_out = SeparateUnicode('', config=True)
separate_out2 = SeparateUnicode('', config=True)
wildcards_case_sensitive = CBool(True, config=True)
xmode = CaselessStrEnum(('Context','Plain', 'Verbose'),
default_value='Context', config=True)
# Subcomponents of InteractiveShell
alias_manager = Instance('IPython.core.alias.AliasManager', allow_none=True)
prefilter_manager = Instance('IPython.core.prefilter.PrefilterManager', allow_none=True)
builtin_trap = Instance('IPython.core.builtin_trap.BuiltinTrap', allow_none=True)
display_trap = Instance('IPython.core.display_trap.DisplayTrap', allow_none=True)
extension_manager = Instance('IPython.core.extensions.ExtensionManager', allow_none=True)
payload_manager = Instance('IPython.core.payload.PayloadManager', allow_none=True)
history_manager = Instance('IPython.core.history.HistoryAccessorBase', allow_none=True)
magics_manager = Instance('IPython.core.magic.MagicsManager', allow_none=True)
profile_dir = Instance('IPython.core.application.ProfileDir', allow_none=True)
@property
def profile(self):
if self.profile_dir is not None:
name = os.path.basename(self.profile_dir.location)
return name.replace('profile_','')
# Private interface
_post_execute = Dict()
# Tracks any GUI loop loaded for pylab
pylab_gui_select = None
def __init__(self, ipython_dir=None, profile_dir=None,
user_module=None, user_ns=None,
custom_exceptions=((), None), **kwargs):
# This is where traits with a config_key argument are updated
# from the values on config.
super(InteractiveShell, self).__init__(**kwargs)
self.configurables = [self]
# These are relatively independent and stateless
self.init_ipython_dir(ipython_dir)
self.init_profile_dir(profile_dir)
self.init_instance_attrs()
self.init_environment()
# Check if we're in a virtualenv, and set up sys.path.
self.init_virtualenv()
# Create namespaces (user_ns, user_global_ns, etc.)
self.init_create_namespaces(user_module, user_ns)
# This has to be done after init_create_namespaces because it uses
# something in self.user_ns, but before init_sys_modules, which
# is the first thing to modify sys.
# TODO: When we override sys.stdout and sys.stderr before this class
# is created, we are saving the overridden ones here. Not sure if this
# is what we want to do.
self.save_sys_module_state()
self.init_sys_modules()
# While we're trying to have each part of the code directly access what
# it needs without keeping redundant references to objects, we have too
# much legacy code that expects ip.db to exist.
self.db = PickleShareDB(os.path.join(self.profile_dir.location, 'db'))
self.init_history()
self.init_encoding()
self.init_prefilter()
self.init_syntax_highlighting()
self.init_hooks()
self.init_events()
self.init_pushd_popd_magic()
# self.init_traceback_handlers use to be here, but we moved it below
# because it and init_io have to come after init_readline.
self.init_user_ns()
self.init_logger()
self.init_builtins()
# The following was in post_config_initialization
self.init_inspector()
# init_readline() must come before init_io(), because init_io uses
# readline related things.
self.init_readline()
# We save this here in case user code replaces raw_input, but it needs
# to be after init_readline(), because PyPy's readline works by replacing
# raw_input.
if py3compat.PY3:
self.raw_input_original = input
else:
self.raw_input_original = raw_input
# init_completer must come after init_readline, because it needs to
# know whether readline is present or not system-wide to configure the
# completers, since the completion machinery can now operate
# independently of readline (e.g. over the network)
self.init_completer()
# TODO: init_io() needs to happen before init_traceback handlers
# because the traceback handlers hardcode the stdout/stderr streams.
# This logic in in debugger.Pdb and should eventually be changed.
self.init_io()
self.init_traceback_handlers(custom_exceptions)
self.init_prompts()
self.init_display_formatter()
self.init_display_pub()
self.init_data_pub()
self.init_displayhook()
self.init_magics()
self.init_alias()
self.init_logstart()
self.init_pdb()
self.init_extension_manager()
self.init_payload()
self.init_deprecation_warnings()
self.hooks.late_startup_hook()
self.events.trigger('shell_initialized', self)
atexit.register(self.atexit_operations)
def get_ipython(self):
"""Return the currently running IPython instance."""
return self
#-------------------------------------------------------------------------
# Trait changed handlers
#-------------------------------------------------------------------------
def _ipython_dir_changed(self, name, new):
ensure_dir_exists(new)
def set_autoindent(self,value=None):
"""Set the autoindent flag, checking for readline support.
If called with no arguments, it acts as a toggle."""
if value != 0 and not self.has_readline:
if os.name == 'posix':
warn("The auto-indent feature requires the readline library")
self.autoindent = 0
return
if value is None:
self.autoindent = not self.autoindent
else:
self.autoindent = value
#-------------------------------------------------------------------------
# init_* methods called by __init__
#-------------------------------------------------------------------------
def init_ipython_dir(self, ipython_dir):
if ipython_dir is not None:
self.ipython_dir = ipython_dir
return
self.ipython_dir = get_ipython_dir()
def init_profile_dir(self, profile_dir):
if profile_dir is not None:
self.profile_dir = profile_dir
return
self.profile_dir =\
ProfileDir.create_profile_dir_by_name(self.ipython_dir, 'default')
def init_instance_attrs(self):
self.more = False
# command compiler
self.compile = CachingCompiler()
# Make an empty namespace, which extension writers can rely on both
# existing and NEVER being used by ipython itself. This gives them a
# convenient location for storing additional information and state
# their extensions may require, without fear of collisions with other
# ipython names that may develop later.
self.meta = Struct()
# Temporary files used for various purposes. Deleted at exit.
self.tempfiles = []
self.tempdirs = []
# Keep track of readline usage (later set by init_readline)
self.has_readline = False
# keep track of where we started running (mainly for crash post-mortem)
# This is not being used anywhere currently.
self.starting_dir = py3compat.getcwd()
# Indentation management
self.indent_current_nsp = 0
# Dict to track post-execution functions that have been registered
self._post_execute = {}
def init_environment(self):
"""Any changes we need to make to the user's environment."""
pass
def init_encoding(self):
# Get system encoding at startup time. Certain terminals (like Emacs
# under Win32 have it set to None, and we need to have a known valid
# encoding to use in the raw_input() method
try:
self.stdin_encoding = sys.stdin.encoding or 'ascii'
except AttributeError:
self.stdin_encoding = 'ascii'
def init_syntax_highlighting(self):
# Python source parser/formatter for syntax highlighting
pyformat = PyColorize.Parser().format
self.pycolorize = lambda src: pyformat(src,'str',self.colors)
def init_pushd_popd_magic(self):
# for pushd/popd management
self.home_dir = get_home_dir()
self.dir_stack = []
def init_logger(self):
self.logger = Logger(self.home_dir, logfname='ipython_log.py',
logmode='rotate')
def init_logstart(self):
"""Initialize logging in case it was requested at the command line.
"""
if self.logappend:
self.magic('logstart %s append' % self.logappend)
elif self.logfile:
self.magic('logstart %s' % self.logfile)
elif self.logstart:
self.magic('logstart')
def init_deprecation_warnings(self):
"""
register default filter for deprecation warning.
This will allow deprecation warning of function used interactively to show
warning to users, and still hide deprecation warning from libraries import.
"""
warnings.filterwarnings("default", category=DeprecationWarning, module=self.user_ns.get("__name__"))
def init_builtins(self):
# A single, static flag that we set to True. Its presence indicates
# that an IPython shell has been created, and we make no attempts at
# removing on exit or representing the existence of more than one
# IPython at a time.
builtin_mod.__dict__['__IPYTHON__'] = True
# In 0.11 we introduced '__IPYTHON__active' as an integer we'd try to
# manage on enter/exit, but with all our shells it's virtually
# impossible to get all the cases right. We're leaving the name in for
# those who adapted their codes to check for this flag, but will
# eventually remove it after a few more releases.
builtin_mod.__dict__['__IPYTHON__active'] = \
'Deprecated, check for __IPYTHON__'
self.builtin_trap = BuiltinTrap(shell=self)
def init_inspector(self):
# Object inspector
self.inspector = oinspect.Inspector(oinspect.InspectColors,
PyColorize.ANSICodeColors,
'NoColor',
self.object_info_string_level)
def init_io(self):
# This will just use sys.stdout and sys.stderr. If you want to
# override sys.stdout and sys.stderr themselves, you need to do that
# *before* instantiating this class, because io holds onto
# references to the underlying streams.
if (sys.platform == 'win32' or sys.platform == 'cli') and self.has_readline:
io.stdout = io.stderr = io.IOStream(self.readline._outputfile)
else:
io.stdout = io.IOStream(sys.stdout)
io.stderr = io.IOStream(sys.stderr)
def init_prompts(self):
self.prompt_manager = PromptManager(shell=self, parent=self)
self.configurables.append(self.prompt_manager)
# Set system prompts, so that scripts can decide if they are running
# interactively.
sys.ps1 = 'In : '
sys.ps2 = '...: '
sys.ps3 = 'Out: '
def init_display_formatter(self):
self.display_formatter = DisplayFormatter(parent=self)
self.configurables.append(self.display_formatter)
def init_display_pub(self):
self.display_pub = self.display_pub_class(parent=self)
self.configurables.append(self.display_pub)
def init_data_pub(self):
if not self.data_pub_class:
self.data_pub = None
return
self.data_pub = self.data_pub_class(parent=self)
self.configurables.append(self.data_pub)
def init_displayhook(self):
# Initialize displayhook, set in/out prompts and printing system
self.displayhook = self.displayhook_class(
parent=self,
shell=self,
cache_size=self.cache_size,
)
self.configurables.append(self.displayhook)
# This is a context manager that installs/revmoes the displayhook at
# the appropriate time.
self.display_trap = DisplayTrap(hook=self.displayhook)
def init_virtualenv(self):
"""Add a virtualenv to sys.path so the user can import modules from it.
This isn't perfect: it doesn't use the Python interpreter with which the
virtualenv was built, and it ignores the --no-site-packages option. A
warning will appear suggesting the user installs IPython in the
virtualenv, but for many cases, it probably works well enough.
Adapted from code snippets online.
http://blog.ufsoft.org/2009/1/29/ipython-and-virtualenv
"""
if 'VIRTUAL_ENV' not in os.environ:
# Not in a virtualenv
return
# venv detection:
# stdlib venv may symlink sys.executable, so we can't use realpath.
# but others can symlink *to* the venv Python, so we can't just use sys.executable.
# So we just check every item in the symlink tree (generally <= 3)
p = os.path.normcase(sys.executable)
paths = [p]
while os.path.islink(p):
p = os.path.normcase(os.path.join(os.path.dirname(p), os.readlink(p)))
paths.append(p)
p_venv = os.path.normcase(os.environ['VIRTUAL_ENV'])
if any(p.startswith(p_venv) for p in paths):
# Running properly in the virtualenv, don't need to do anything
return
warn("Attempting to work in a virtualenv. If you encounter problems, please "
"install IPython inside the virtualenv.")
if sys.platform == "win32":
virtual_env = os.path.join(os.environ['VIRTUAL_ENV'], 'Lib', 'site-packages')
else:
virtual_env = os.path.join(os.environ['VIRTUAL_ENV'], 'lib',
'python%d.%d' % sys.version_info[:2], 'site-packages')
import site
sys.path.insert(0, virtual_env)
site.addsitedir(virtual_env)
#-------------------------------------------------------------------------
# Things related to injections into the sys module
#-------------------------------------------------------------------------
def save_sys_module_state(self):
"""Save the state of hooks in the sys module.
This has to be called after self.user_module is created.
"""
self._orig_sys_module_state = {'stdin': sys.stdin,
'stdout': sys.stdout,
'stderr': sys.stderr,
'excepthook': sys.excepthook}
self._orig_sys_modules_main_name = self.user_module.__name__
self._orig_sys_modules_main_mod = sys.modules.get(self.user_module.__name__)
def restore_sys_module_state(self):
"""Restore the state of the sys module."""
try:
for k, v in iteritems(self._orig_sys_module_state):
setattr(sys, k, v)
except AttributeError:
pass
# Reset what what done in self.init_sys_modules
if self._orig_sys_modules_main_mod is not None:
sys.modules[self._orig_sys_modules_main_name] = self._orig_sys_modules_main_mod
#-------------------------------------------------------------------------
# Things related to the banner
#-------------------------------------------------------------------------
@property
def banner(self):
banner = self.banner1
if self.profile and self.profile != 'default':
banner += '\nIPython profile: %s\n' % self.profile
if self.banner2:
banner += '\n' + self.banner2
return banner
def show_banner(self, banner=None):
if banner is None:
banner = self.banner
self.write(banner)
#-------------------------------------------------------------------------
# Things related to hooks
#-------------------------------------------------------------------------
def init_hooks(self):
# hooks holds pointers used for user-side customizations
self.hooks = Struct()
self.strdispatchers = {}
# Set all default hooks, defined in the IPython.hooks module.
hooks = IPython.core.hooks
for hook_name in hooks.__all__:
# default hooks have priority 100, i.e. low; user hooks should have
# 0-100 priority
self.set_hook(hook_name,getattr(hooks,hook_name), 100, _warn_deprecated=False)
if self.display_page:
self.set_hook('show_in_pager', page.as_hook(page.display_page), 90)
def set_hook(self,name,hook, priority=50, str_key=None, re_key=None,
_warn_deprecated=True):
"""set_hook(name,hook) -> sets an internal IPython hook.
IPython exposes some of its internal API as user-modifiable hooks. By
adding your function to one of these hooks, you can modify IPython's
behavior to call at runtime your own routines."""
# At some point in the future, this should validate the hook before it
# accepts it. Probably at least check that the hook takes the number
# of args it's supposed to.
f = types.MethodType(hook,self)
# check if the hook is for strdispatcher first
if str_key is not None:
sdp = self.strdispatchers.get(name, StrDispatch())
sdp.add_s(str_key, f, priority )
self.strdispatchers[name] = sdp
return
if re_key is not None:
sdp = self.strdispatchers.get(name, StrDispatch())
sdp.add_re(re.compile(re_key), f, priority )
self.strdispatchers[name] = sdp
return
dp = getattr(self.hooks, name, None)
if name not in IPython.core.hooks.__all__:
print("Warning! Hook '%s' is not one of %s" % \
(name, IPython.core.hooks.__all__ ))
if _warn_deprecated and (name in IPython.core.hooks.deprecated):
alternative = IPython.core.hooks.deprecated[name]
warn("Hook {} is deprecated. Use {} instead.".format(name, alternative))
if not dp:
dp = IPython.core.hooks.CommandChainDispatcher()
try:
dp.add(f,priority)
except AttributeError:
# it was not commandchain, plain old func - replace
dp = f
setattr(self.hooks,name, dp)
#-------------------------------------------------------------------------
# Things related to events
#-------------------------------------------------------------------------
def init_events(self):
self.events = EventManager(self, available_events)
self.events.register("pre_execute", self._clear_warning_registry)
def register_post_execute(self, func):
"""DEPRECATED: Use ip.events.register('post_run_cell', func)
Register a function for calling after code execution.
"""
warn("ip.register_post_execute is deprecated, use "
"ip.events.register('post_run_cell', func) instead.")
self.events.register('post_run_cell', func)
def _clear_warning_registry(self):
# clear the warning registry, so that different code blocks with
# overlapping line number ranges don't cause spurious suppression of
# warnings (see gh-6611 for details)
if "__warningregistry__" in self.user_global_ns:
del self.user_global_ns["__warningregistry__"]
#-------------------------------------------------------------------------
# Things related to the "main" module
#-------------------------------------------------------------------------
def new_main_mod(self, filename, modname):
"""Return a new 'main' module object for user code execution.
``filename`` should be the path of the script which will be run in the
module. Requests with the same filename will get the same module, with
its namespace cleared.
``modname`` should be the module name - normally either '__main__' or
the basename of the file without the extension.
When scripts are executed via %run, we must keep a reference to their
__main__ module around so that Python doesn't
clear it, rendering references to module globals useless.
This method keeps said reference in a private dict, keyed by the
absolute path of the script. This way, for multiple executions of the
same script we only keep one copy of the namespace (the last one),
thus preventing memory leaks from old references while allowing the
objects from the last execution to be accessible.
"""
filename = os.path.abspath(filename)
try:
main_mod = self._main_mod_cache[filename]
except KeyError:
main_mod = self._main_mod_cache[filename] = types.ModuleType(
py3compat.cast_bytes_py2(modname),
doc="Module created for script run in IPython")
else:
main_mod.__dict__.clear()
main_mod.__name__ = modname
main_mod.__file__ = filename
# It seems pydoc (and perhaps others) needs any module instance to
# implement a __nonzero__ method
main_mod.__nonzero__ = lambda : True
return main_mod
def clear_main_mod_cache(self):
"""Clear the cache of main modules.
Mainly for use by utilities like %reset.
Examples
--------
In [15]: import IPython
In [16]: m = _ip.new_main_mod(IPython.__file__, 'IPython')
In [17]: len(_ip._main_mod_cache) > 0
Out[17]: True
In [18]: _ip.clear_main_mod_cache()
In [19]: len(_ip._main_mod_cache) == 0
Out[19]: True
"""
self._main_mod_cache.clear()
#-------------------------------------------------------------------------
# Things related to debugging
#-------------------------------------------------------------------------
def init_pdb(self):
# Set calling of pdb on exceptions
# self.call_pdb is a property
self.call_pdb = self.pdb
def _get_call_pdb(self):
return self._call_pdb
def _set_call_pdb(self,val):
if val not in (0,1,False,True):
raise ValueError('new call_pdb value must be boolean')
# store value in instance
self._call_pdb = val
# notify the actual exception handlers
self.InteractiveTB.call_pdb = val
call_pdb = property(_get_call_pdb,_set_call_pdb,None,
'Control auto-activation of pdb at exceptions')
def debugger(self,force=False):
"""Call the pydb/pdb debugger.
Keywords:
- force(False): by default, this routine checks the instance call_pdb
flag and does not actually invoke the debugger if the flag is false.
The 'force' option forces the debugger to activate even if the flag
is false.
"""
if not (force or self.call_pdb):
return
if not hasattr(sys,'last_traceback'):
error('No traceback has been produced, nothing to debug.')
return
# use pydb if available
if debugger.has_pydb:
from pydb import pm
else:
# fallback to our internal debugger
pm = lambda : self.InteractiveTB.debugger(force=True)
with self.readline_no_record:
pm()
#-------------------------------------------------------------------------
# Things related to IPython's various namespaces
#-------------------------------------------------------------------------
default_user_namespaces = True
def init_create_namespaces(self, user_module=None, user_ns=None):
# Create the namespace where the user will operate. user_ns is
# normally the only one used, and it is passed to the exec calls as
# the locals argument. But we do carry a user_global_ns namespace
# given as the exec 'globals' argument, This is useful in embedding
# situations where the ipython shell opens in a context where the
# distinction between locals and globals is meaningful. For
# non-embedded contexts, it is just the same object as the user_ns dict.
# FIXME. For some strange reason, __builtins__ is showing up at user
# level as a dict instead of a module. This is a manual fix, but I
# should really track down where the problem is coming from. Alex
# Schmolck reported this problem first.
# A useful post by Alex Martelli on this topic:
# Re: inconsistent value from __builtins__
# Von: Alex Martelli <[email protected]>
# Datum: Freitag 01 Oktober 2004 04:45:34 nachmittags/abends
# Gruppen: comp.lang.python
# Michael Hohn <[email protected]> wrote:
# > >>> print type(builtin_check.get_global_binding('__builtins__'))
# > <type 'dict'>
# > >>> print type(__builtins__)
# > <type 'module'>
# > Is this difference in return value intentional?
# Well, it's documented that '__builtins__' can be either a dictionary
# or a module, and it's been that way for a long time. Whether it's
# intentional (or sensible), I don't know. In any case, the idea is
# that if you need to access the built-in namespace directly, you
# should start with "import __builtin__" (note, no 's') which will
# definitely give you a module. Yeah, it's somewhat confusing:-(.
# These routines return a properly built module and dict as needed by
# the rest of the code, and can also be used by extension writers to
# generate properly initialized namespaces.
if (user_ns is not None) or (user_module is not None):
self.default_user_namespaces = False
self.user_module, self.user_ns = self.prepare_user_module(user_module, user_ns)
# A record of hidden variables we have added to the user namespace, so
# we can list later only variables defined in actual interactive use.
self.user_ns_hidden = {}
# Now that FakeModule produces a real module, we've run into a nasty
# problem: after script execution (via %run), the module where the user
# code ran is deleted. Now that this object is a true module (needed
# so doctest and other tools work correctly), the Python module
# teardown mechanism runs over it, and sets to None every variable
# present in that module. Top-level references to objects from the
# script survive, because the user_ns is updated with them. However,
# calling functions defined in the script that use other things from
# the script will fail, because the function's closure had references
# to the original objects, which are now all None. So we must protect
# these modules from deletion by keeping a cache.
#
# To avoid keeping stale modules around (we only need the one from the
# last run), we use a dict keyed with the full path to the script, so
# only the last version of the module is held in the cache. Note,
# however, that we must cache the module *namespace contents* (their
# __dict__). Because if we try to cache the actual modules, old ones
# (uncached) could be destroyed while still holding references (such as
# those held by GUI objects that tend to be long-lived)>
#
# The %reset command will flush this cache. See the cache_main_mod()
# and clear_main_mod_cache() methods for details on use.
# This is the cache used for 'main' namespaces
self._main_mod_cache = {}
# A table holding all the namespaces IPython deals with, so that
# introspection facilities can search easily.
self.ns_table = {'user_global':self.user_module.__dict__,
'user_local':self.user_ns,
'builtin':builtin_mod.__dict__
}
@property
def user_global_ns(self):
return self.user_module.__dict__
def prepare_user_module(self, user_module=None, user_ns=None):
"""Prepare the module and namespace in which user code will be run.
When IPython is started normally, both parameters are None: a new module
is created automatically, and its __dict__ used as the namespace.
If only user_module is provided, its __dict__ is used as the namespace.
If only user_ns is provided, a dummy module is created, and user_ns
becomes the global namespace. If both are provided (as they may be
when embedding), user_ns is the local namespace, and user_module
provides the global namespace.
Parameters
----------
user_module : module, optional
The current user module in which IPython is being run. If None,
a clean module will be created.
user_ns : dict, optional
A namespace in which to run interactive commands.
Returns
-------
A tuple of user_module and user_ns, each properly initialised.
"""
if user_module is None and user_ns is not None:
user_ns.setdefault("__name__", "__main__")
user_module = DummyMod()
user_module.__dict__ = user_ns
if user_module is None:
user_module = types.ModuleType("__main__",
doc="Automatically created module for IPython interactive environment")
# We must ensure that __builtin__ (without the final 's') is always
# available and pointing to the __builtin__ *module*. For more details:
# http://mail.python.org/pipermail/python-dev/2001-April/014068.html
user_module.__dict__.setdefault('__builtin__', builtin_mod)
user_module.__dict__.setdefault('__builtins__', builtin_mod)
if user_ns is None:
user_ns = user_module.__dict__
return user_module, user_ns
def init_sys_modules(self):
# We need to insert into sys.modules something that looks like a
# module but which accesses the IPython namespace, for shelve and
# pickle to work interactively. Normally they rely on getting
# everything out of __main__, but for embedding purposes each IPython
# instance has its own private namespace, so we can't go shoving
# everything into __main__.
# note, however, that we should only do this for non-embedded
# ipythons, which really mimic the __main__.__dict__ with their own
# namespace. Embedded instances, on the other hand, should not do
# this because they need to manage the user local/global namespaces
# only, but they live within a 'normal' __main__ (meaning, they
# shouldn't overtake the execution environment of the script they're
# embedded in).
# This is overridden in the InteractiveShellEmbed subclass to a no-op.
main_name = self.user_module.__name__
sys.modules[main_name] = self.user_module
def init_user_ns(self):
"""Initialize all user-visible namespaces to their minimum defaults.
Certain history lists are also initialized here, as they effectively
act as user namespaces.
Notes
-----
All data structures here are only filled in, they are NOT reset by this
method. If they were not empty before, data will simply be added to
therm.
"""
# This function works in two parts: first we put a few things in
# user_ns, and we sync that contents into user_ns_hidden so that these
# initial variables aren't shown by %who. After the sync, we add the
# rest of what we *do* want the user to see with %who even on a new
# session (probably nothing, so they really only see their own stuff)
# The user dict must *always* have a __builtin__ reference to the
# Python standard __builtin__ namespace, which must be imported.
# This is so that certain operations in prompt evaluation can be
# reliably executed with builtins. Note that we can NOT use
# __builtins__ (note the 's'), because that can either be a dict or a
# module, and can even mutate at runtime, depending on the context
# (Python makes no guarantees on it). In contrast, __builtin__ is
# always a module object, though it must be explicitly imported.
# For more details:
# http://mail.python.org/pipermail/python-dev/2001-April/014068.html
ns = dict()
# make global variables for user access to the histories
ns['_ih'] = self.history_manager.input_hist_parsed
ns['_oh'] = self.history_manager.output_hist
ns['_dh'] = self.history_manager.dir_hist
ns['_sh'] = shadowns
# user aliases to input and output histories. These shouldn't show up
# in %who, as they can have very large reprs.
ns['In'] = self.history_manager.input_hist_parsed
ns['Out'] = self.history_manager.output_hist
# Store myself as the public api!!!
ns['get_ipython'] = self.get_ipython
ns['exit'] = self.exiter
ns['quit'] = self.exiter
# Sync what we've added so far to user_ns_hidden so these aren't seen
# by %who
self.user_ns_hidden.update(ns)
# Anything put into ns now would show up in %who. Think twice before
# putting anything here, as we really want %who to show the user their
# stuff, not our variables.
# Finally, update the real user's namespace
self.user_ns.update(ns)
@property
def all_ns_refs(self):
"""Get a list of references to all the namespace dictionaries in which
IPython might store a user-created object.
Note that this does not include the displayhook, which also caches
objects from the output."""
return [self.user_ns, self.user_global_ns, self.user_ns_hidden] + \
[m.__dict__ for m in self._main_mod_cache.values()]
def reset(self, new_session=True):
"""Clear all internal namespaces, and attempt to release references to
user objects.
If new_session is True, a new history session will be opened.
"""
# Clear histories
self.history_manager.reset(new_session)
# Reset counter used to index all histories
if new_session:
self.execution_count = 1
# Flush cached output items
if self.displayhook.do_full_cache:
self.displayhook.flush()
# The main execution namespaces must be cleared very carefully,
# skipping the deletion of the builtin-related keys, because doing so
# would cause errors in many object's __del__ methods.
if self.user_ns is not self.user_global_ns:
self.user_ns.clear()
ns = self.user_global_ns
drop_keys = set(ns.keys())
drop_keys.discard('__builtin__')
drop_keys.discard('__builtins__')
drop_keys.discard('__name__')
for k in drop_keys:
del ns[k]
self.user_ns_hidden.clear()
# Restore the user namespaces to minimal usability
self.init_user_ns()
# Restore the default and user aliases
self.alias_manager.clear_aliases()
self.alias_manager.init_aliases()
# Flush the private list of module references kept for script
# execution protection
self.clear_main_mod_cache()
def del_var(self, varname, by_name=False):
"""Delete a variable from the various namespaces, so that, as
far as possible, we're not keeping any hidden references to it.
Parameters
----------
varname : str
The name of the variable to delete.
by_name : bool
If True, delete variables with the given name in each
namespace. If False (default), find the variable in the user
namespace, and delete references to it.
"""
if varname in ('__builtin__', '__builtins__'):
raise ValueError("Refusing to delete %s" % varname)
ns_refs = self.all_ns_refs
if by_name: # Delete by name
for ns in ns_refs:
try:
del ns[varname]
except KeyError:
pass
else: # Delete by object
try:
obj = self.user_ns[varname]
except KeyError:
raise NameError("name '%s' is not defined" % varname)
# Also check in output history
ns_refs.append(self.history_manager.output_hist)
for ns in ns_refs:
to_delete = [n for n, o in iteritems(ns) if o is obj]
for name in to_delete:
del ns[name]
# displayhook keeps extra references, but not in a dictionary
for name in ('_', '__', '___'):
if getattr(self.displayhook, name) is obj:
setattr(self.displayhook, name, None)
def reset_selective(self, regex=None):
"""Clear selective variables from internal namespaces based on a
specified regular expression.
Parameters
----------
regex : string or compiled pattern, optional
A regular expression pattern that will be used in searching
variable names in the users namespaces.
"""
if regex is not None:
try:
m = re.compile(regex)
except TypeError:
raise TypeError('regex must be a string or compiled pattern')
# Search for keys in each namespace that match the given regex
# If a match is found, delete the key/value pair.
for ns in self.all_ns_refs:
for var in ns:
if m.search(var):
del ns[var]
def push(self, variables, interactive=True):
"""Inject a group of variables into the IPython user namespace.
Parameters
----------
variables : dict, str or list/tuple of str
The variables to inject into the user's namespace. If a dict, a
simple update is done. If a str, the string is assumed to have
variable names separated by spaces. A list/tuple of str can also
be used to give the variable names. If just the variable names are
give (list/tuple/str) then the variable values looked up in the
callers frame.
interactive : bool
If True (default), the variables will be listed with the ``who``
magic.
"""
vdict = None
# We need a dict of name/value pairs to do namespace updates.
if isinstance(variables, dict):
vdict = variables
elif isinstance(variables, string_types+(list, tuple)):
if isinstance(variables, string_types):
vlist = variables.split()
else:
vlist = variables
vdict = {}
cf = sys._getframe(1)
for name in vlist:
try:
vdict[name] = eval(name, cf.f_globals, cf.f_locals)
except:
print('Could not get variable %s from %s' %
(name,cf.f_code.co_name))
else:
raise ValueError('variables must be a dict/str/list/tuple')
# Propagate variables to user namespace
self.user_ns.update(vdict)
# And configure interactive visibility
user_ns_hidden = self.user_ns_hidden
if interactive:
for name in vdict:
user_ns_hidden.pop(name, None)
else:
user_ns_hidden.update(vdict)
def drop_by_id(self, variables):
"""Remove a dict of variables from the user namespace, if they are the
same as the values in the dictionary.
This is intended for use by extensions: variables that they've added can
be taken back out if they are unloaded, without removing any that the
user has overwritten.
Parameters
----------
variables : dict
A dictionary mapping object names (as strings) to the objects.
"""
for name, obj in iteritems(variables):
if name in self.user_ns and self.user_ns[name] is obj:
del self.user_ns[name]
self.user_ns_hidden.pop(name, None)
#-------------------------------------------------------------------------
# Things related to object introspection
#-------------------------------------------------------------------------
def _ofind(self, oname, namespaces=None):
"""Find an object in the available namespaces.
self._ofind(oname) -> dict with keys: found,obj,ospace,ismagic
Has special code to detect magic functions.
"""
oname = oname.strip()
#print '1- oname: <%r>' % oname # dbg
if not oname.startswith(ESC_MAGIC) and \
not oname.startswith(ESC_MAGIC2) and \
not py3compat.isidentifier(oname, dotted=True):
return dict(found=False)
if namespaces is None:
# Namespaces to search in:
# Put them in a list. The order is important so that we
# find things in the same order that Python finds them.
namespaces = [ ('Interactive', self.user_ns),
('Interactive (global)', self.user_global_ns),
('Python builtin', builtin_mod.__dict__),
]
# initialize results to 'null'
found = False; obj = None; ospace = None;
ismagic = False; isalias = False; parent = None
# We need to special-case 'print', which as of python2.6 registers as a
# function but should only be treated as one if print_function was
# loaded with a future import. In this case, just bail.
if (oname == 'print' and not py3compat.PY3 and not \
(self.compile.compiler_flags & __future__.CO_FUTURE_PRINT_FUNCTION)):
return {'found':found, 'obj':obj, 'namespace':ospace,
'ismagic':ismagic, 'isalias':isalias, 'parent':parent}
# Look for the given name by splitting it in parts. If the head is
# found, then we look for all the remaining parts as members, and only
# declare success if we can find them all.
oname_parts = oname.split('.')
oname_head, oname_rest = oname_parts[0],oname_parts[1:]
for nsname,ns in namespaces:
try:
obj = ns[oname_head]
except KeyError:
continue
else:
#print 'oname_rest:', oname_rest # dbg
for idx, part in enumerate(oname_rest):
try:
parent = obj
# The last part is looked up in a special way to avoid
# descriptor invocation as it may raise or have side
# effects.
if idx == len(oname_rest) - 1:
obj = self._getattr_property(obj, part)
else:
obj = getattr(obj, part)
except:
# Blanket except b/c some badly implemented objects
# allow __getattr__ to raise exceptions other than
# AttributeError, which then crashes IPython.
break
else:
# If we finish the for loop (no break), we got all members
found = True
ospace = nsname
break # namespace loop
# Try to see if it's magic
if not found:
obj = None
if oname.startswith(ESC_MAGIC2):
oname = oname.lstrip(ESC_MAGIC2)
obj = self.find_cell_magic(oname)
elif oname.startswith(ESC_MAGIC):
oname = oname.lstrip(ESC_MAGIC)
obj = self.find_line_magic(oname)
else:
# search without prefix, so run? will find %run?
obj = self.find_line_magic(oname)
if obj is None:
obj = self.find_cell_magic(oname)
if obj is not None:
found = True
ospace = 'IPython internal'
ismagic = True
isalias = isinstance(obj, Alias)
# Last try: special-case some literals like '', [], {}, etc:
if not found and oname_head in ["''",'""','[]','{}','()']:
obj = eval(oname_head)
found = True
ospace = 'Interactive'
return {'found':found, 'obj':obj, 'namespace':ospace,
'ismagic':ismagic, 'isalias':isalias, 'parent':parent}
@staticmethod
def _getattr_property(obj, attrname):
"""Property-aware getattr to use in object finding.
If attrname represents a property, return it unevaluated (in case it has
side effects or raises an error.
"""
if not isinstance(obj, type):
try:
# `getattr(type(obj), attrname)` is not guaranteed to return
# `obj`, but does so for property:
#
# property.__get__(self, None, cls) -> self
#
# The universal alternative is to traverse the mro manually
# searching for attrname in class dicts.
attr = getattr(type(obj), attrname)
except AttributeError:
pass
else:
# This relies on the fact that data descriptors (with both
# __get__ & __set__ magic methods) take precedence over
# instance-level attributes:
#
# class A(object):
# @property
# def foobar(self): return 123
# a = A()
# a.__dict__['foobar'] = 345
# a.foobar # == 123
#
# So, a property may be returned right away.
if isinstance(attr, property):
return attr
# Nothing helped, fall back.
return getattr(obj, attrname)
def _object_find(self, oname, namespaces=None):
"""Find an object and return a struct with info about it."""
return Struct(self._ofind(oname, namespaces))
def _inspect(self, meth, oname, namespaces=None, **kw):
"""Generic interface to the inspector system.
This function is meant to be called by pdef, pdoc & friends."""
info = self._object_find(oname, namespaces)
if info.found:
pmethod = getattr(self.inspector, meth)
formatter = format_screen if info.ismagic else None
if meth == 'pdoc':
pmethod(info.obj, oname, formatter)
elif meth == 'pinfo':
pmethod(info.obj, oname, formatter, info, **kw)
else:
pmethod(info.obj, oname)
else:
print('Object `%s` not found.' % oname)
return 'not found' # so callers can take other action
def object_inspect(self, oname, detail_level=0):
"""Get object info about oname"""
with self.builtin_trap:
info = self._object_find(oname)
if info.found:
return self.inspector.info(info.obj, oname, info=info,
detail_level=detail_level
)
else:
return oinspect.object_info(name=oname, found=False)
def object_inspect_text(self, oname, detail_level=0):
"""Get object info as formatted text"""
with self.builtin_trap:
info = self._object_find(oname)
if info.found:
return self.inspector._format_info(info.obj, oname, info=info,
detail_level=detail_level
)
else:
raise KeyError(oname)
#-------------------------------------------------------------------------
# Things related to history management
#-------------------------------------------------------------------------
def init_history(self):
"""Sets up the command history, and starts regular autosaves."""
self.history_manager = HistoryManager(shell=self, parent=self)
self.configurables.append(self.history_manager)
#-------------------------------------------------------------------------
# Things related to exception handling and tracebacks (not debugging)
#-------------------------------------------------------------------------
def init_traceback_handlers(self, custom_exceptions):
# Syntax error handler.
self.SyntaxTB = ultratb.SyntaxTB(color_scheme='NoColor')
# The interactive one is initialized with an offset, meaning we always
# want to remove the topmost item in the traceback, which is our own
# internal code. Valid modes: ['Plain','Context','Verbose']
self.InteractiveTB = ultratb.AutoFormattedTB(mode = 'Plain',
color_scheme='NoColor',
tb_offset = 1,
check_cache=check_linecache_ipython)
# The instance will store a pointer to the system-wide exception hook,
# so that runtime code (such as magics) can access it. This is because
# during the read-eval loop, it may get temporarily overwritten.
self.sys_excepthook = sys.excepthook
# and add any custom exception handlers the user may have specified
self.set_custom_exc(*custom_exceptions)
# Set the exception mode
self.InteractiveTB.set_mode(mode=self.xmode)
def set_custom_exc(self, exc_tuple, handler):
"""set_custom_exc(exc_tuple,handler)
Set a custom exception handler, which will be called if any of the
exceptions in exc_tuple occur in the mainloop (specifically, in the
run_code() method).
Parameters
----------
exc_tuple : tuple of exception classes
A *tuple* of exception classes, for which to call the defined
handler. It is very important that you use a tuple, and NOT A
LIST here, because of the way Python's except statement works. If
you only want to trap a single exception, use a singleton tuple::
exc_tuple == (MyCustomException,)
handler : callable
handler must have the following signature::
def my_handler(self, etype, value, tb, tb_offset=None):
...
return structured_traceback
Your handler must return a structured traceback (a list of strings),
or None.
This will be made into an instance method (via types.MethodType)
of IPython itself, and it will be called if any of the exceptions
listed in the exc_tuple are caught. If the handler is None, an
internal basic one is used, which just prints basic info.
To protect IPython from crashes, if your handler ever raises an
exception or returns an invalid result, it will be immediately
disabled.
WARNING: by putting in your own exception handler into IPython's main
execution loop, you run a very good chance of nasty crashes. This
facility should only be used if you really know what you are doing."""
assert type(exc_tuple)==type(()) , \
"The custom exceptions must be given AS A TUPLE."
def dummy_handler(self,etype,value,tb,tb_offset=None):
print('*** Simple custom exception handler ***')
print('Exception type :',etype)
print('Exception value:',value)
print('Traceback :',tb)
#print 'Source code :','\n'.join(self.buffer)
def validate_stb(stb):
"""validate structured traceback return type
return type of CustomTB *should* be a list of strings, but allow
single strings or None, which are harmless.
This function will *always* return a list of strings,
and will raise a TypeError if stb is inappropriate.
"""
msg = "CustomTB must return list of strings, not %r" % stb
if stb is None:
return []
elif isinstance(stb, string_types):
return [stb]
elif not isinstance(stb, list):
raise TypeError(msg)
# it's a list
for line in stb:
# check every element
if not isinstance(line, string_types):
raise TypeError(msg)
return stb
if handler is None:
wrapped = dummy_handler
else:
def wrapped(self,etype,value,tb,tb_offset=None):
"""wrap CustomTB handler, to protect IPython from user code
This makes it harder (but not impossible) for custom exception
handlers to crash IPython.
"""
try:
stb = handler(self,etype,value,tb,tb_offset=tb_offset)
return validate_stb(stb)
except:
# clear custom handler immediately
self.set_custom_exc((), None)
print("Custom TB Handler failed, unregistering", file=io.stderr)
# show the exception in handler first
stb = self.InteractiveTB.structured_traceback(*sys.exc_info())
print(self.InteractiveTB.stb2text(stb), file=io.stdout)
print("The original exception:", file=io.stdout)
stb = self.InteractiveTB.structured_traceback(
(etype,value,tb), tb_offset=tb_offset
)
return stb
self.CustomTB = types.MethodType(wrapped,self)
self.custom_exceptions = exc_tuple
def excepthook(self, etype, value, tb):
"""One more defense for GUI apps that call sys.excepthook.
GUI frameworks like wxPython trap exceptions and call
sys.excepthook themselves. I guess this is a feature that
enables them to keep running after exceptions that would
otherwise kill their mainloop. This is a bother for IPython
which excepts to catch all of the program exceptions with a try:
except: statement.
Normally, IPython sets sys.excepthook to a CrashHandler instance, so if
any app directly invokes sys.excepthook, it will look to the user like
IPython crashed. In order to work around this, we can disable the
CrashHandler and replace it with this excepthook instead, which prints a
regular traceback using our InteractiveTB. In this fashion, apps which
call sys.excepthook will generate a regular-looking exception from
IPython, and the CrashHandler will only be triggered by real IPython
crashes.
This hook should be used sparingly, only in places which are not likely
to be true IPython errors.
"""
self.showtraceback((etype, value, tb), tb_offset=0)
def _get_exc_info(self, exc_tuple=None):
"""get exc_info from a given tuple, sys.exc_info() or sys.last_type etc.
Ensures sys.last_type,value,traceback hold the exc_info we found,
from whichever source.
raises ValueError if none of these contain any information
"""
if exc_tuple is None:
etype, value, tb = sys.exc_info()
else:
etype, value, tb = exc_tuple
if etype is None:
if hasattr(sys, 'last_type'):
etype, value, tb = sys.last_type, sys.last_value, \
sys.last_traceback
if etype is None:
raise ValueError("No exception to find")
# Now store the exception info in sys.last_type etc.
# WARNING: these variables are somewhat deprecated and not
# necessarily safe to use in a threaded environment, but tools
# like pdb depend on their existence, so let's set them. If we
# find problems in the field, we'll need to revisit their use.
sys.last_type = etype
sys.last_value = value
sys.last_traceback = tb
return etype, value, tb
def show_usage_error(self, exc):
"""Show a short message for UsageErrors
These are special exceptions that shouldn't show a traceback.
"""
self.write_err("UsageError: %s" % exc)
def get_exception_only(self, exc_tuple=None):
"""
Return as a string (ending with a newline) the exception that
just occurred, without any traceback.
"""
etype, value, tb = self._get_exc_info(exc_tuple)
msg = traceback.format_exception_only(etype, value)
return ''.join(msg)
def showtraceback(self, exc_tuple=None, filename=None, tb_offset=None,
exception_only=False):
"""Display the exception that just occurred.
If nothing is known about the exception, this is the method which
should be used throughout the code for presenting user tracebacks,
rather than directly invoking the InteractiveTB object.
A specific showsyntaxerror() also exists, but this method can take
care of calling it if needed, so unless you are explicitly catching a
SyntaxError exception, don't try to analyze the stack manually and
simply call this method."""
try:
try:
etype, value, tb = self._get_exc_info(exc_tuple)
except ValueError:
self.write_err('No traceback available to show.\n')
return
if issubclass(etype, SyntaxError):
# Though this won't be called by syntax errors in the input
# line, there may be SyntaxError cases with imported code.
self.showsyntaxerror(filename)
elif etype is UsageError:
self.show_usage_error(value)
else:
if exception_only:
stb = ['An exception has occurred, use %tb to see '
'the full traceback.\n']
stb.extend(self.InteractiveTB.get_exception_only(etype,
value))
else:
try:
# Exception classes can customise their traceback - we
# use this in IPython.parallel for exceptions occurring
# in the engines. This should return a list of strings.
stb = value._render_traceback_()
except Exception:
stb = self.InteractiveTB.structured_traceback(etype,
value, tb, tb_offset=tb_offset)
self._showtraceback(etype, value, stb)
if self.call_pdb:
# drop into debugger
self.debugger(force=True)
return
# Actually show the traceback
self._showtraceback(etype, value, stb)
except KeyboardInterrupt:
self.write_err('\n' + self.get_exception_only())
def _showtraceback(self, etype, evalue, stb):
"""Actually show a traceback.
Subclasses may override this method to put the traceback on a different
place, like a side channel.
"""
print(self.InteractiveTB.stb2text(stb), file=io.stdout)
def showsyntaxerror(self, filename=None):
"""Display the syntax error that just occurred.
This doesn't display a stack trace because there isn't one.
If a filename is given, it is stuffed in the exception instead
of what was there before (because Python's parser always uses
"<string>" when reading from a string).
"""
etype, value, last_traceback = self._get_exc_info()
if filename and issubclass(etype, SyntaxError):
try:
value.filename = filename
except:
# Not the format we expect; leave it alone
pass
stb = self.SyntaxTB.structured_traceback(etype, value, [])
self._showtraceback(etype, value, stb)
# This is overridden in TerminalInteractiveShell to show a message about
# the %paste magic.
def showindentationerror(self):
"""Called by run_cell when there's an IndentationError in code entered
at the prompt.
This is overridden in TerminalInteractiveShell to show a message about
the %paste magic."""
self.showsyntaxerror()
#-------------------------------------------------------------------------
# Things related to readline
#-------------------------------------------------------------------------
def init_readline(self):
"""Moved to terminal subclass, here only to simplify the init logic."""
self.readline = None
# Set a number of methods that depend on readline to be no-op
self.readline_no_record = NoOpContext()
self.set_readline_completer = no_op
self.set_custom_completer = no_op
@skip_doctest
def set_next_input(self, s, replace=False):
""" Sets the 'default' input string for the next command line.
Example::
In [1]: _ip.set_next_input("Hello Word")
In [2]: Hello Word_ # cursor is here
"""
self.rl_next_input = py3compat.cast_bytes_py2(s)
def _indent_current_str(self):
"""return the current level of indentation as a string"""
return self.input_splitter.indent_spaces * ' '
#-------------------------------------------------------------------------
# Things related to text completion
#-------------------------------------------------------------------------
def init_completer(self):
"""Initialize the completion machinery.
This creates completion machinery that can be used by client code,
either interactively in-process (typically triggered by the readline
library), programmatically (such as in test suites) or out-of-process
(typically over the network by remote frontends).
"""
from IPython.core.completer import IPCompleter
from IPython.core.completerlib import (module_completer,
magic_run_completer, cd_completer, reset_completer)
self.Completer = IPCompleter(shell=self,
namespace=self.user_ns,
global_namespace=self.user_global_ns,
use_readline=self.has_readline,
parent=self,
)
self.configurables.append(self.Completer)
# Add custom completers to the basic ones built into IPCompleter
sdisp = self.strdispatchers.get('complete_command', StrDispatch())
self.strdispatchers['complete_command'] = sdisp
self.Completer.custom_completers = sdisp
self.set_hook('complete_command', module_completer, str_key = 'import')
self.set_hook('complete_command', module_completer, str_key = 'from')
self.set_hook('complete_command', module_completer, str_key = '%aimport')
self.set_hook('complete_command', magic_run_completer, str_key = '%run')
self.set_hook('complete_command', cd_completer, str_key = '%cd')
self.set_hook('complete_command', reset_completer, str_key = '%reset')
def complete(self, text, line=None, cursor_pos=None):
"""Return the completed text and a list of completions.
Parameters
----------
text : string
A string of text to be completed on. It can be given as empty and
instead a line/position pair are given. In this case, the
completer itself will split the line like readline does.
line : string, optional
The complete line that text is part of.
cursor_pos : int, optional
The position of the cursor on the input line.
Returns
-------
text : string
The actual text that was completed.
matches : list
A sorted list with all possible completions.
The optional arguments allow the completion to take more context into
account, and are part of the low-level completion API.
This is a wrapper around the completion mechanism, similar to what
readline does at the command line when the TAB key is hit. By
exposing it as a method, it can be used by other non-readline
environments (such as GUIs) for text completion.
Simple usage example:
In [1]: x = 'hello'
In [2]: _ip.complete('x.l')
Out[2]: ('x.l', ['x.ljust', 'x.lower', 'x.lstrip'])
"""
# Inject names into __builtin__ so we can complete on the added names.
with self.builtin_trap:
return self.Completer.complete(text, line, cursor_pos)
def set_custom_completer(self, completer, pos=0):
"""Adds a new custom completer function.
The position argument (defaults to 0) is the index in the completers
list where you want the completer to be inserted."""
newcomp = types.MethodType(completer,self.Completer)
self.Completer.matchers.insert(pos,newcomp)
def set_completer_frame(self, frame=None):
"""Set the frame of the completer."""
if frame:
self.Completer.namespace = frame.f_locals
self.Completer.global_namespace = frame.f_globals
else:
self.Completer.namespace = self.user_ns
self.Completer.global_namespace = self.user_global_ns
#-------------------------------------------------------------------------
# Things related to magics
#-------------------------------------------------------------------------
def init_magics(self):
from IPython.core import magics as m
self.magics_manager = magic.MagicsManager(shell=self,
parent=self,
user_magics=m.UserMagics(self))
self.configurables.append(self.magics_manager)
# Expose as public API from the magics manager
self.register_magics = self.magics_manager.register
self.define_magic = self.magics_manager.define_magic
self.register_magics(m.AutoMagics, m.BasicMagics, m.CodeMagics,
m.ConfigMagics, m.DeprecatedMagics, m.DisplayMagics, m.ExecutionMagics,
m.ExtensionMagics, m.HistoryMagics, m.LoggingMagics,
m.NamespaceMagics, m.OSMagics, m.PylabMagics, m.ScriptMagics,
)
# Register Magic Aliases
mman = self.magics_manager
# FIXME: magic aliases should be defined by the Magics classes
# or in MagicsManager, not here
mman.register_alias('ed', 'edit')
mman.register_alias('hist', 'history')
mman.register_alias('rep', 'recall')
mman.register_alias('SVG', 'svg', 'cell')
mman.register_alias('HTML', 'html', 'cell')
mman.register_alias('file', 'writefile', 'cell')
# FIXME: Move the color initialization to the DisplayHook, which
# should be split into a prompt manager and displayhook. We probably
# even need a centralize colors management object.
self.magic('colors %s' % self.colors)
# Defined here so that it's included in the documentation
@functools.wraps(magic.MagicsManager.register_function)
def register_magic_function(self, func, magic_kind='line', magic_name=None):
self.magics_manager.register_function(func,
magic_kind=magic_kind, magic_name=magic_name)
def run_line_magic(self, magic_name, line):
"""Execute the given line magic.
Parameters
----------
magic_name : str
Name of the desired magic function, without '%' prefix.
line : str
The rest of the input line as a single string.
"""
fn = self.find_line_magic(magic_name)
if fn is None:
cm = self.find_cell_magic(magic_name)
etpl = "Line magic function `%%%s` not found%s."
extra = '' if cm is None else (' (But cell magic `%%%%%s` exists, '
'did you mean that instead?)' % magic_name )
error(etpl % (magic_name, extra))
else:
# Note: this is the distance in the stack to the user's frame.
# This will need to be updated if the internal calling logic gets
# refactored, or else we'll be expanding the wrong variables.
stack_depth = 2
magic_arg_s = self.var_expand(line, stack_depth)
# Put magic args in a list so we can call with f(*a) syntax
args = [magic_arg_s]
kwargs = {}
# Grab local namespace if we need it:
if getattr(fn, "needs_local_scope", False):
kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
with self.builtin_trap:
result = fn(*args,**kwargs)
return result
def run_cell_magic(self, magic_name, line, cell):
"""Execute the given cell magic.
Parameters
----------
magic_name : str
Name of the desired magic function, without '%' prefix.
line : str
The rest of the first input line as a single string.
cell : str
The body of the cell as a (possibly multiline) string.
"""
fn = self.find_cell_magic(magic_name)
if fn is None:
lm = self.find_line_magic(magic_name)
etpl = "Cell magic `%%{0}` not found{1}."
extra = '' if lm is None else (' (But line magic `%{0}` exists, '
'did you mean that instead?)'.format(magic_name))
error(etpl.format(magic_name, extra))
elif cell == '':
message = '%%{0} is a cell magic, but the cell body is empty.'.format(magic_name)
if self.find_line_magic(magic_name) is not None:
message += ' Did you mean the line magic %{0} (single %)?'.format(magic_name)
raise UsageError(message)
else:
# Note: this is the distance in the stack to the user's frame.
# This will need to be updated if the internal calling logic gets
# refactored, or else we'll be expanding the wrong variables.
stack_depth = 2
magic_arg_s = self.var_expand(line, stack_depth)
with self.builtin_trap:
result = fn(magic_arg_s, cell)
return result
def find_line_magic(self, magic_name):
"""Find and return a line magic by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics['line'].get(magic_name)
def find_cell_magic(self, magic_name):
"""Find and return a cell magic by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics['cell'].get(magic_name)
def find_magic(self, magic_name, magic_kind='line'):
"""Find and return a magic of the given type by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics[magic_kind].get(magic_name)
def magic(self, arg_s):
"""DEPRECATED. Use run_line_magic() instead.
Call a magic function by name.
Input: a string containing the name of the magic function to call and
any additional arguments to be passed to the magic.
magic('name -opt foo bar') is equivalent to typing at the ipython
prompt:
In[1]: %name -opt foo bar
To call a magic without arguments, simply use magic('name').
This provides a proper Python function to call IPython's magics in any
valid Python code you can type at the interpreter, including loops and
compound statements.
"""
# TODO: should we issue a loud deprecation warning here?
magic_name, _, magic_arg_s = arg_s.partition(' ')
magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
return self.run_line_magic(magic_name, magic_arg_s)
#-------------------------------------------------------------------------
# Things related to macros
#-------------------------------------------------------------------------
def define_macro(self, name, themacro):
"""Define a new macro
Parameters
----------
name : str
The name of the macro.
themacro : str or Macro
The action to do upon invoking the macro. If a string, a new
Macro object is created by passing the string to it.
"""
from IPython.core import macro
if isinstance(themacro, string_types):
themacro = macro.Macro(themacro)
if not isinstance(themacro, macro.Macro):
raise ValueError('A macro must be a string or a Macro instance.')
self.user_ns[name] = themacro
#-------------------------------------------------------------------------
# Things related to the running of system commands
#-------------------------------------------------------------------------
def system_piped(self, cmd):
"""Call the given cmd in a subprocess, piping stdout/err
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported. Should not be a command that expects input
other than simple text.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
# We do not support backgrounding processes because we either use
# pexpect or pipes to read from. Users can always just call
# os.system() or use ip.system=ip.system_raw
# if they really want a background process.
raise OSError("Background processes not supported.")
# we explicitly do NOT return the subprocess status code, because
# a non-None value would trigger :func:`sys.displayhook` calls.
# Instead, we store the exit_code in user_ns.
self.user_ns['_exit_code'] = system(self.var_expand(cmd, depth=1))
def system_raw(self, cmd):
"""Call the given cmd in a subprocess using os.system on Windows or
subprocess.call using the system shell on other platforms.
Parameters
----------
cmd : str
Command to execute.
"""
cmd = self.var_expand(cmd, depth=1)
# protect os.system from UNC paths on Windows, which it can't handle:
if sys.platform == 'win32':
from IPython.utils._process_win32 import AvoidUNCPath
with AvoidUNCPath() as path:
if path is not None:
cmd = '"pushd %s &&"%s' % (path, cmd)
cmd = py3compat.unicode_to_str(cmd)
try:
ec = os.system(cmd)
except KeyboardInterrupt:
self.write_err('\n' + self.get_exception_only())
ec = -2
else:
cmd = py3compat.unicode_to_str(cmd)
# For posix the result of the subprocess.call() below is an exit
# code, which by convention is zero for success, positive for
# program failure. Exit codes above 128 are reserved for signals,
# and the formula for converting a signal to an exit code is usually
# signal_number+128. To more easily differentiate between exit
# codes and signals, ipython uses negative numbers. For instance
# since control-c is signal 2 but exit code 130, ipython's
# _exit_code variable will read -2. Note that some shells like
# csh and fish don't follow sh/bash conventions for exit codes.
executable = os.environ.get('SHELL', None)
try:
# Use env shell instead of default /bin/sh
ec = subprocess.call(cmd, shell=True, executable=executable)
except KeyboardInterrupt:
# intercept control-C; a long traceback is not useful here
self.write_err('\n' + self.get_exception_only())
ec = 130
if ec > 128:
ec = -(ec - 128)
# We explicitly do NOT return the subprocess status code, because
# a non-None value would trigger :func:`sys.displayhook` calls.
# Instead, we store the exit_code in user_ns. Note the semantics
# of _exit_code: for control-c, _exit_code == -signal.SIGNIT,
# but raising SystemExit(_exit_code) will give status 254!
self.user_ns['_exit_code'] = ec
# use piped system by default, because it is better behaved
system = system_piped
def getoutput(self, cmd, split=True, depth=0):
"""Get output (possibly including stderr) from a subprocess.
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported.
split : bool, optional
If True, split the output into an IPython SList. Otherwise, an
IPython LSString is returned. These are objects similar to normal
lists and strings, with a few convenience attributes for easier
manipulation of line-based output. You can use '?' on them for
details.
depth : int, optional
How many frames above the caller are the local variables which should
be expanded in the command string? The default (0) assumes that the
expansion variables are in the stack frame calling this function.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
raise OSError("Background processes not supported.")
out = getoutput(self.var_expand(cmd, depth=depth+1))
if split:
out = SList(out.splitlines())
else:
out = LSString(out)
return out
#-------------------------------------------------------------------------
# Things related to aliases
#-------------------------------------------------------------------------
def init_alias(self):
self.alias_manager = AliasManager(shell=self, parent=self)
self.configurables.append(self.alias_manager)
#-------------------------------------------------------------------------
# Things related to extensions
#-------------------------------------------------------------------------
def init_extension_manager(self):
self.extension_manager = ExtensionManager(shell=self, parent=self)
self.configurables.append(self.extension_manager)
#-------------------------------------------------------------------------
# Things related to payloads
#-------------------------------------------------------------------------
def init_payload(self):
self.payload_manager = PayloadManager(parent=self)
self.configurables.append(self.payload_manager)
#-------------------------------------------------------------------------
# Things related to the prefilter
#-------------------------------------------------------------------------
def init_prefilter(self):
self.prefilter_manager = PrefilterManager(shell=self, parent=self)
self.configurables.append(self.prefilter_manager)
# Ultimately this will be refactored in the new interpreter code, but
# for now, we should expose the main prefilter method (there's legacy
# code out there that may rely on this).
self.prefilter = self.prefilter_manager.prefilter_lines
def auto_rewrite_input(self, cmd):
"""Print to the screen the rewritten form of the user's command.
This shows visual feedback by rewriting input lines that cause
automatic calling to kick in, like::
/f x
into::
------> f(x)
after the user's input prompt. This helps the user understand that the
input line was transformed automatically by IPython.
"""
if not self.show_rewritten_input:
return
rw = self.prompt_manager.render('rewrite') + cmd
try:
# plain ascii works better w/ pyreadline, on some machines, so
# we use it and only print uncolored rewrite if we have unicode
rw = str(rw)
print(rw, file=io.stdout)
except UnicodeEncodeError:
print("------> " + cmd)
#-------------------------------------------------------------------------
# Things related to extracting values/expressions from kernel and user_ns
#-------------------------------------------------------------------------
def _user_obj_error(self):
"""return simple exception dict
for use in user_expressions
"""
etype, evalue, tb = self._get_exc_info()
stb = self.InteractiveTB.get_exception_only(etype, evalue)
exc_info = {
u'status' : 'error',
u'traceback' : stb,
u'ename' : unicode_type(etype.__name__),
u'evalue' : py3compat.safe_unicode(evalue),
}
return exc_info
def _format_user_obj(self, obj):
"""format a user object to display dict
for use in user_expressions
"""
data, md = self.display_formatter.format(obj)
value = {
'status' : 'ok',
'data' : data,
'metadata' : md,
}
return value
def user_expressions(self, expressions):
"""Evaluate a dict of expressions in the user's namespace.
Parameters
----------
expressions : dict
A dict with string keys and string values. The expression values
should be valid Python expressions, each of which will be evaluated
in the user namespace.
Returns
-------
A dict, keyed like the input expressions dict, with the rich mime-typed
display_data of each value.
"""
out = {}
user_ns = self.user_ns
global_ns = self.user_global_ns
for key, expr in iteritems(expressions):
try:
value = self._format_user_obj(eval(expr, global_ns, user_ns))
except:
value = self._user_obj_error()
out[key] = value
return out
#-------------------------------------------------------------------------
# Things related to the running of code
#-------------------------------------------------------------------------
def ex(self, cmd):
"""Execute a normal python statement in user namespace."""
with self.builtin_trap:
exec(cmd, self.user_global_ns, self.user_ns)
def ev(self, expr):
"""Evaluate python expression expr in user namespace.
Returns the result of evaluation
"""
with self.builtin_trap:
return eval(expr, self.user_global_ns, self.user_ns)
def safe_execfile(self, fname, *where, **kw):
"""A safe version of the builtin execfile().
This version will never throw an exception, but instead print
helpful error messages to the screen. This only works on pure
Python files with the .py extension.
Parameters
----------
fname : string
The name of the file to be executed.
where : tuple
One or two namespaces, passed to execfile() as (globals,locals).
If only one is given, it is passed as both.
exit_ignore : bool (False)
If True, then silence SystemExit for non-zero status (it is always
silenced for zero status, as it is so common).
raise_exceptions : bool (False)
If True raise exceptions everywhere. Meant for testing.
shell_futures : bool (False)
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
"""
kw.setdefault('exit_ignore', False)
kw.setdefault('raise_exceptions', False)
kw.setdefault('shell_futures', False)
fname = os.path.abspath(os.path.expanduser(fname))
# Make sure we can open the file
try:
with open(fname):
pass
except:
warn('Could not open file <%s> for safe execution.' % fname)
return
# Find things also in current directory. This is needed to mimic the
# behavior of running a script from the system command line, where
# Python inserts the script's directory into sys.path
dname = os.path.dirname(fname)
with prepended_to_syspath(dname):
try:
glob, loc = (where + (None, ))[:2]
py3compat.execfile(
fname, glob, loc,
self.compile if kw['shell_futures'] else None)
except SystemExit as status:
# If the call was made with 0 or None exit status (sys.exit(0)
# or sys.exit() ), don't bother showing a traceback, as both of
# these are considered normal by the OS:
# > python -c'import sys;sys.exit(0)'; echo $?
# 0
# > python -c'import sys;sys.exit()'; echo $?
# 0
# For other exit status, we show the exception unless
# explicitly silenced, but only in short form.
if status.code:
if kw['raise_exceptions']:
raise
if not kw['exit_ignore']:
self.showtraceback(exception_only=True)
except:
if kw['raise_exceptions']:
raise
# tb offset is 2 because we wrap execfile
self.showtraceback(tb_offset=2)
def safe_execfile_ipy(self, fname, shell_futures=False, raise_exceptions=False):
"""Like safe_execfile, but for .ipy or .ipynb files with IPython syntax.
Parameters
----------
fname : str
The name of the file to execute. The filename must have a
.ipy or .ipynb extension.
shell_futures : bool (False)
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
raise_exceptions : bool (False)
If True raise exceptions everywhere. Meant for testing.
"""
fname = os.path.abspath(os.path.expanduser(fname))
# Make sure we can open the file
try:
with open(fname):
pass
except:
warn('Could not open file <%s> for safe execution.' % fname)
return
# Find things also in current directory. This is needed to mimic the
# behavior of running a script from the system command line, where
# Python inserts the script's directory into sys.path
dname = os.path.dirname(fname)
def get_cells():
"""generator for sequence of code blocks to run"""
if fname.endswith('.ipynb'):
from nbformat import read
with io_open(fname) as f:
nb = read(f, as_version=4)
if not nb.cells:
return
for cell in nb.cells:
if cell.cell_type == 'code':
yield cell.source
else:
with open(fname) as f:
yield f.read()
with prepended_to_syspath(dname):
try:
for cell in get_cells():
result = self.run_cell(cell, silent=True, shell_futures=shell_futures)
if raise_exceptions:
result.raise_error()
elif not result.success:
break
except:
if raise_exceptions:
raise
self.showtraceback()
warn('Unknown failure executing file: <%s>' % fname)
def safe_run_module(self, mod_name, where):
"""A safe version of runpy.run_module().
This version will never throw an exception, but instead print
helpful error messages to the screen.
`SystemExit` exceptions with status code 0 or None are ignored.
Parameters
----------
mod_name : string
The name of the module to be executed.
where : dict
The globals namespace.
"""
try:
try:
where.update(
runpy.run_module(str(mod_name), run_name="__main__",
alter_sys=True)
)
except SystemExit as status:
if status.code:
raise
except:
self.showtraceback()
warn('Unknown failure executing module: <%s>' % mod_name)
def run_cell(self, raw_cell, store_history=False, silent=False, shell_futures=True):
"""Run a complete IPython cell.
Parameters
----------
raw_cell : str
The code (including IPython code such as %magic functions) to run.
store_history : bool
If True, the raw and translated cell will be stored in IPython's
history. For user code calling back into IPython's machinery, this
should be set to False.
silent : bool
If True, avoid side-effects, such as implicit displayhooks and
and logging. silent=True forces store_history=False.
shell_futures : bool
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
Returns
-------
result : :class:`ExecutionResult`
"""
result = ExecutionResult()
if (not raw_cell) or raw_cell.isspace():
return result
if silent:
store_history = False
if store_history:
result.execution_count = self.execution_count
def error_before_exec(value):
result.error_before_exec = value
return result
self.events.trigger('pre_execute')
if not silent:
self.events.trigger('pre_run_cell')
# If any of our input transformation (input_transformer_manager or
# prefilter_manager) raises an exception, we store it in this variable
# so that we can display the error after logging the input and storing
# it in the history.
preprocessing_exc_tuple = None
try:
# Static input transformations
cell = self.input_transformer_manager.transform_cell(raw_cell)
except SyntaxError:
preprocessing_exc_tuple = sys.exc_info()
cell = raw_cell # cell has to exist so it can be stored/logged
else:
if len(cell.splitlines()) == 1:
# Dynamic transformations - only applied for single line commands
with self.builtin_trap:
try:
# use prefilter_lines to handle trailing newlines
# restore trailing newline for ast.parse
cell = self.prefilter_manager.prefilter_lines(cell) + '\n'
except Exception:
# don't allow prefilter errors to crash IPython
preprocessing_exc_tuple = sys.exc_info()
# Store raw and processed history
if store_history:
self.history_manager.store_inputs(self.execution_count,
cell, raw_cell)
if not silent:
self.logger.log(cell, raw_cell)
# Display the exception if input processing failed.
if preprocessing_exc_tuple is not None:
self.showtraceback(preprocessing_exc_tuple)
if store_history:
self.execution_count += 1
return error_before_exec(preprocessing_exc_tuple[2])
# Our own compiler remembers the __future__ environment. If we want to
# run code with a separate __future__ environment, use the default
# compiler
compiler = self.compile if shell_futures else CachingCompiler()
with self.builtin_trap:
cell_name = self.compile.cache(cell, self.execution_count)
with self.display_trap:
# Compile to bytecode
try:
code_ast = compiler.ast_parse(cell, filename=cell_name)
except IndentationError as e:
self.showindentationerror()
if store_history:
self.execution_count += 1
return error_before_exec(e)
except (OverflowError, SyntaxError, ValueError, TypeError,
MemoryError) as e:
self.showsyntaxerror()
if store_history:
self.execution_count += 1
return error_before_exec(e)
# Apply AST transformations
try:
code_ast = self.transform_ast(code_ast)
except InputRejected as e:
self.showtraceback()
if store_history:
self.execution_count += 1
return error_before_exec(e)
# Give the displayhook a reference to our ExecutionResult so it
# can fill in the output value.
self.displayhook.exec_result = result
# Execute the user code
interactivity = "none" if silent else self.ast_node_interactivity
self.run_ast_nodes(code_ast.body, cell_name,
interactivity=interactivity, compiler=compiler, result=result)
# Reset this so later displayed values do not modify the
# ExecutionResult
self.displayhook.exec_result = None
self.events.trigger('post_execute')
if not silent:
self.events.trigger('post_run_cell')
if store_history:
# Write output to the database. Does nothing unless
# history output logging is enabled.
self.history_manager.store_output(self.execution_count)
# Each cell is a *single* input, regardless of how many lines it has
self.execution_count += 1
return result
def transform_ast(self, node):
"""Apply the AST transformations from self.ast_transformers
Parameters
----------
node : ast.Node
The root node to be transformed. Typically called with the ast.Module
produced by parsing user input.
Returns
-------
An ast.Node corresponding to the node it was called with. Note that it
may also modify the passed object, so don't rely on references to the
original AST.
"""
for transformer in self.ast_transformers:
try:
node = transformer.visit(node)
except InputRejected:
# User-supplied AST transformers can reject an input by raising
# an InputRejected. Short-circuit in this case so that we
# don't unregister the transform.
raise
except Exception:
warn("AST transformer %r threw an error. It will be unregistered." % transformer)
self.ast_transformers.remove(transformer)
if self.ast_transformers:
ast.fix_missing_locations(node)
return node
def run_ast_nodes(self, nodelist, cell_name, interactivity='last_expr',
compiler=compile, result=None):
"""Run a sequence of AST nodes. The execution mode depends on the
interactivity parameter.
Parameters
----------
nodelist : list
A sequence of AST nodes to run.
cell_name : str
Will be passed to the compiler as the filename of the cell. Typically
the value returned by ip.compile.cache(cell).
interactivity : str
'all', 'last', 'last_expr' or 'none', specifying which nodes should be
run interactively (displaying output from expressions). 'last_expr'
will run the last node interactively only if it is an expression (i.e.
expressions in loops or other blocks are not displayed. Other values
for this parameter will raise a ValueError.
compiler : callable
A function with the same interface as the built-in compile(), to turn
the AST nodes into code objects. Default is the built-in compile().
result : ExecutionResult, optional
An object to store exceptions that occur during execution.
Returns
-------
True if an exception occurred while running code, False if it finished
running.
"""
if not nodelist:
return
if interactivity == 'last_expr':
if isinstance(nodelist[-1], ast.Expr):
interactivity = "last"
else:
interactivity = "none"
if interactivity == 'none':
to_run_exec, to_run_interactive = nodelist, []
elif interactivity == 'last':
to_run_exec, to_run_interactive = nodelist[:-1], nodelist[-1:]
elif interactivity == 'all':
to_run_exec, to_run_interactive = [], nodelist
else:
raise ValueError("Interactivity was %r" % interactivity)
try:
for i, node in enumerate(to_run_exec):
mod = ast.Module([node])
code = compiler(mod, cell_name, "exec")
if self.run_code(code, result):
return True
for i, node in enumerate(to_run_interactive):
mod = ast.Interactive([node])
code = compiler(mod, cell_name, "single")
if self.run_code(code, result):
return True
# Flush softspace
if softspace(sys.stdout, 0):
print()
except:
# It's possible to have exceptions raised here, typically by
# compilation of odd code (such as a naked 'return' outside a
# function) that did parse but isn't valid. Typically the exception
# is a SyntaxError, but it's safest just to catch anything and show
# the user a traceback.
# We do only one try/except outside the loop to minimize the impact
# on runtime, and also because if any node in the node list is
# broken, we should stop execution completely.
if result:
result.error_before_exec = sys.exc_info()[1]
self.showtraceback()
return True
return False
def run_code(self, code_obj, result=None):
"""Execute a code object.
When an exception occurs, self.showtraceback() is called to display a
traceback.
Parameters
----------
code_obj : code object
A compiled code object, to be executed
result : ExecutionResult, optional
An object to store exceptions that occur during execution.
Returns
-------
False : successful execution.
True : an error occurred.
"""
# Set our own excepthook in case the user code tries to call it
# directly, so that the IPython crash handler doesn't get triggered
old_excepthook, sys.excepthook = sys.excepthook, self.excepthook
# we save the original sys.excepthook in the instance, in case config
# code (such as magics) needs access to it.
self.sys_excepthook = old_excepthook
outflag = 1 # happens in more places, so it's easier as default
try:
try:
self.hooks.pre_run_code_hook()
#rprint('Running code', repr(code_obj)) # dbg
exec(code_obj, self.user_global_ns, self.user_ns)
finally:
# Reset our crash handler in place
sys.excepthook = old_excepthook
except SystemExit as e:
if result is not None:
result.error_in_exec = e
self.showtraceback(exception_only=True)
warn("To exit: use 'exit', 'quit', or Ctrl-D.", level=1)
except self.custom_exceptions:
etype, value, tb = sys.exc_info()
if result is not None:
result.error_in_exec = value
self.CustomTB(etype, value, tb)
except:
if result is not None:
result.error_in_exec = sys.exc_info()[1]
self.showtraceback()
else:
outflag = 0
return outflag
# For backwards compatibility
runcode = run_code
#-------------------------------------------------------------------------
# Things related to GUI support and pylab
#-------------------------------------------------------------------------
def enable_gui(self, gui=None):
raise NotImplementedError('Implement enable_gui in a subclass')
def enable_matplotlib(self, gui=None):
"""Enable interactive matplotlib and inline figure support.
This takes the following steps:
1. select the appropriate eventloop and matplotlib backend
2. set up matplotlib for interactive use with that backend
3. configure formatters for inline figure display
4. enable the selected gui eventloop
Parameters
----------
gui : optional, string
If given, dictates the choice of matplotlib GUI backend to use
(should be one of IPython's supported backends, 'qt', 'osx', 'tk',
'gtk', 'wx' or 'inline'), otherwise we use the default chosen by
matplotlib (as dictated by the matplotlib build-time options plus the
user's matplotlibrc configuration file). Note that not all backends
make sense in all contexts, for example a terminal ipython can't
display figures inline.
"""
from IPython.core import pylabtools as pt
gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
if gui != 'inline':
# If we have our first gui selection, store it
if self.pylab_gui_select is None:
self.pylab_gui_select = gui
# Otherwise if they are different
elif gui != self.pylab_gui_select:
print ('Warning: Cannot change to a different GUI toolkit: %s.'
' Using %s instead.' % (gui, self.pylab_gui_select))
gui, backend = pt.find_gui_and_backend(self.pylab_gui_select)
pt.activate_matplotlib(backend)
pt.configure_inline_support(self, backend)
# Now we must activate the gui pylab wants to use, and fix %run to take
# plot updates into account
self.enable_gui(gui)
self.magics_manager.registry['ExecutionMagics'].default_runner = \
pt.mpl_runner(self.safe_execfile)
return gui, backend
def enable_pylab(self, gui=None, import_all=True, welcome_message=False):
"""Activate pylab support at runtime.
This turns on support for matplotlib, preloads into the interactive
namespace all of numpy and pylab, and configures IPython to correctly
interact with the GUI event loop. The GUI backend to be used can be
optionally selected with the optional ``gui`` argument.
This method only adds preloading the namespace to InteractiveShell.enable_matplotlib.
Parameters
----------
gui : optional, string
If given, dictates the choice of matplotlib GUI backend to use
(should be one of IPython's supported backends, 'qt', 'osx', 'tk',
'gtk', 'wx' or 'inline'), otherwise we use the default chosen by
matplotlib (as dictated by the matplotlib build-time options plus the
user's matplotlibrc configuration file). Note that not all backends
make sense in all contexts, for example a terminal ipython can't
display figures inline.
import_all : optional, bool, default: True
Whether to do `from numpy import *` and `from pylab import *`
in addition to module imports.
welcome_message : deprecated
This argument is ignored, no welcome message will be displayed.
"""
from IPython.core.pylabtools import import_pylab
gui, backend = self.enable_matplotlib(gui)
# We want to prevent the loading of pylab to pollute the user's
# namespace as shown by the %who* magics, so we execute the activation
# code in an empty namespace, and we update *both* user_ns and
# user_ns_hidden with this information.
ns = {}
import_pylab(ns, import_all)
# warn about clobbered names
ignored = {"__builtins__"}
both = set(ns).intersection(self.user_ns).difference(ignored)
clobbered = [ name for name in both if self.user_ns[name] is not ns[name] ]
self.user_ns.update(ns)
self.user_ns_hidden.update(ns)
return gui, backend, clobbered
#-------------------------------------------------------------------------
# Utilities
#-------------------------------------------------------------------------
def var_expand(self, cmd, depth=0, formatter=DollarFormatter()):
"""Expand python variables in a string.
The depth argument indicates how many frames above the caller should
be walked to look for the local namespace where to expand variables.
The global namespace for expansion is always the user's interactive
namespace.
"""
ns = self.user_ns.copy()
try:
frame = sys._getframe(depth+1)
except ValueError:
# This is thrown if there aren't that many frames on the stack,
# e.g. if a script called run_line_magic() directly.
pass
else:
ns.update(frame.f_locals)
try:
# We have to use .vformat() here, because 'self' is a valid and common
# name, and expanding **ns for .format() would make it collide with
# the 'self' argument of the method.
cmd = formatter.vformat(cmd, args=[], kwargs=ns)
except Exception:
# if formatter couldn't format, just let it go untransformed
pass
return cmd
def mktempfile(self, data=None, prefix='ipython_edit_'):
"""Make a new tempfile and return its filename.
This makes a call to tempfile.mkstemp (created in a tempfile.mkdtemp),
but it registers the created filename internally so ipython cleans it up
at exit time.
Optional inputs:
- data(None): if data is given, it gets written out to the temp file
immediately, and the file is closed again."""
dirname = tempfile.mkdtemp(prefix=prefix)
self.tempdirs.append(dirname)
handle, filename = tempfile.mkstemp('.py', prefix, dir=dirname)
os.close(handle) # On Windows, there can only be one open handle on a file
self.tempfiles.append(filename)
if data:
tmp_file = open(filename,'w')
tmp_file.write(data)
tmp_file.close()
return filename
# TODO: This should be removed when Term is refactored.
def write(self,data):
"""Write a string to the default output"""
io.stdout.write(data)
# TODO: This should be removed when Term is refactored.
def write_err(self,data):
"""Write a string to the default error output"""
io.stderr.write(data)
def ask_yes_no(self, prompt, default=None, interrupt=None):
if self.quiet:
return True
return ask_yes_no(prompt,default,interrupt)
def show_usage(self):
"""Show a usage message"""
page.page(IPython.core.usage.interactive_usage)
def extract_input_lines(self, range_str, raw=False):
"""Return as a string a set of input history slices.
Parameters
----------
range_str : string
The set of slices is given as a string, like "~5/6-~4/2 4:8 9",
since this function is for use by magic functions which get their
arguments as strings. The number before the / is the session
number: ~n goes n back from the current session.
raw : bool, optional
By default, the processed input is used. If this is true, the raw
input history is used instead.
Notes
-----
Slices can be described with two notations:
* ``N:M`` -> standard python form, means including items N...(M-1).
* ``N-M`` -> include items N..M (closed endpoint).
"""
lines = self.history_manager.get_range_by_str(range_str, raw=raw)
return "\n".join(x for _, _, x in lines)
def find_user_code(self, target, raw=True, py_only=False, skip_encoding_cookie=True, search_ns=False):
"""Get a code string from history, file, url, or a string or macro.
This is mainly used by magic functions.
Parameters
----------
target : str
A string specifying code to retrieve. This will be tried respectively
as: ranges of input history (see %history for syntax), url,
corresponding .py file, filename, or an expression evaluating to a
string or Macro in the user namespace.
raw : bool
If true (default), retrieve raw history. Has no effect on the other
retrieval mechanisms.
py_only : bool (default False)
Only try to fetch python code, do not try alternative methods to decode file
if unicode fails.
Returns
-------
A string of code.
ValueError is raised if nothing is found, and TypeError if it evaluates
to an object of another type. In each case, .args[0] is a printable
message.
"""
code = self.extract_input_lines(target, raw=raw) # Grab history
if code:
return code
utarget = unquote_filename(target)
try:
if utarget.startswith(('http://', 'https://')):
return openpy.read_py_url(utarget, skip_encoding_cookie=skip_encoding_cookie)
except UnicodeDecodeError:
if not py_only :
# Deferred import
try:
from urllib.request import urlopen # Py3
except ImportError:
from urllib import urlopen
response = urlopen(target)
return response.read().decode('latin1')
raise ValueError(("'%s' seem to be unreadable.") % utarget)
potential_target = [target]
try :
potential_target.insert(0,get_py_filename(target))
except IOError:
pass
for tgt in potential_target :
if os.path.isfile(tgt): # Read file
try :
return openpy.read_py_file(tgt, skip_encoding_cookie=skip_encoding_cookie)
except UnicodeDecodeError :
if not py_only :
with io_open(tgt,'r', encoding='latin1') as f :
return f.read()
raise ValueError(("'%s' seem to be unreadable.") % target)
elif os.path.isdir(os.path.expanduser(tgt)):
raise ValueError("'%s' is a directory, not a regular file." % target)
if search_ns:
# Inspect namespace to load object source
object_info = self.object_inspect(target, detail_level=1)
if object_info['found'] and object_info['source']:
return object_info['source']
try: # User namespace
codeobj = eval(target, self.user_ns)
except Exception:
raise ValueError(("'%s' was not found in history, as a file, url, "
"nor in the user namespace.") % target)
if isinstance(codeobj, string_types):
return codeobj
elif isinstance(codeobj, Macro):
return codeobj.value
raise TypeError("%s is neither a string nor a macro." % target,
codeobj)
#-------------------------------------------------------------------------
# Things related to IPython exiting
#-------------------------------------------------------------------------
def atexit_operations(self):
"""This will be executed at the time of exit.
Cleanup operations and saving of persistent data that is done
unconditionally by IPython should be performed here.
For things that may depend on startup flags or platform specifics (such
as having readline or not), register a separate atexit function in the
code that has the appropriate information, rather than trying to
clutter
"""
# Close the history session (this stores the end time and line count)
# this must be *before* the tempfile cleanup, in case of temporary
# history db
self.history_manager.end_session()
# Cleanup all tempfiles and folders left around
for tfile in self.tempfiles:
try:
os.unlink(tfile)
except OSError:
pass
for tdir in self.tempdirs:
try:
os.rmdir(tdir)
except OSError:
pass
# Clear all user namespaces to release all references cleanly.
self.reset(new_session=False)
# Run user hooks
self.hooks.shutdown_hook()
def cleanup(self):
self.restore_sys_module_state()
class InteractiveShellABC(with_metaclass(abc.ABCMeta, object)):
"""An abstract base class for InteractiveShell."""
InteractiveShellABC.register(InteractiveShell)
| gpl-3.0 |
victorbergelin/scikit-learn | examples/covariance/plot_covariance_estimation.py | 250 | 5070 | """
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================
When working with covariance estimation, the usual approach is to use
a maximum likelihood estimator, such as the
:class:`sklearn.covariance.EmpiricalCovariance`. It is unbiased, i.e. it
converges to the true (population) covariance when given many
observations. However, it can also be beneficial to regularize it, in
order to reduce its variance; this, in turn, introduces some bias. This
example illustrates the simple regularization used in
:ref:`shrunk_covariance` estimators. In particular, it focuses on how to
set the amount of regularization, i.e. how to choose the bias-variance
trade-off.
Here we compare 3 approaches:
* Setting the parameter by cross-validating the likelihood on three folds
according to a grid of potential shrinkage parameters.
* A close formula proposed by Ledoit and Wolf to compute
the asymptotically optimal regularization parameter (minimizing a MSE
criterion), yielding the :class:`sklearn.covariance.LedoitWolf`
covariance estimate.
* An improvement of the Ledoit-Wolf shrinkage, the
:class:`sklearn.covariance.OAS`, proposed by Chen et al. Its
convergence is significantly better under the assumption that the data
are Gaussian, in particular for small samples.
To quantify estimation error, we plot the likelihood of unseen data for
different values of the shrinkage parameter. We also show the choices by
cross-validation, or with the LedoitWolf and OAS estimates.
Note that the maximum likelihood estimate corresponds to no shrinkage,
and thus performs poorly. The Ledoit-Wolf estimate performs really well,
as it is close to the optimal and is computational not costly. In this
example, the OAS estimate is a bit further away. Interestingly, both
approaches outperform cross-validation, which is significantly most
computationally costly.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from scipy import linalg
from sklearn.covariance import LedoitWolf, OAS, ShrunkCovariance, \
log_likelihood, empirical_covariance
from sklearn.grid_search import GridSearchCV
###############################################################################
# Generate sample data
n_features, n_samples = 40, 20
np.random.seed(42)
base_X_train = np.random.normal(size=(n_samples, n_features))
base_X_test = np.random.normal(size=(n_samples, n_features))
# Color samples
coloring_matrix = np.random.normal(size=(n_features, n_features))
X_train = np.dot(base_X_train, coloring_matrix)
X_test = np.dot(base_X_test, coloring_matrix)
###############################################################################
# Compute the likelihood on test data
# spanning a range of possible shrinkage coefficient values
shrinkages = np.logspace(-2, 0, 30)
negative_logliks = [-ShrunkCovariance(shrinkage=s).fit(X_train).score(X_test)
for s in shrinkages]
# under the ground-truth model, which we would not have access to in real
# settings
real_cov = np.dot(coloring_matrix.T, coloring_matrix)
emp_cov = empirical_covariance(X_train)
loglik_real = -log_likelihood(emp_cov, linalg.inv(real_cov))
###############################################################################
# Compare different approaches to setting the parameter
# GridSearch for an optimal shrinkage coefficient
tuned_parameters = [{'shrinkage': shrinkages}]
cv = GridSearchCV(ShrunkCovariance(), tuned_parameters)
cv.fit(X_train)
# Ledoit-Wolf optimal shrinkage coefficient estimate
lw = LedoitWolf()
loglik_lw = lw.fit(X_train).score(X_test)
# OAS coefficient estimate
oa = OAS()
loglik_oa = oa.fit(X_train).score(X_test)
###############################################################################
# Plot results
fig = plt.figure()
plt.title("Regularized covariance: likelihood and shrinkage coefficient")
plt.xlabel('Regularizaton parameter: shrinkage coefficient')
plt.ylabel('Error: negative log-likelihood on test data')
# range shrinkage curve
plt.loglog(shrinkages, negative_logliks, label="Negative log-likelihood")
plt.plot(plt.xlim(), 2 * [loglik_real], '--r',
label="Real covariance likelihood")
# adjust view
lik_max = np.amax(negative_logliks)
lik_min = np.amin(negative_logliks)
ymin = lik_min - 6. * np.log((plt.ylim()[1] - plt.ylim()[0]))
ymax = lik_max + 10. * np.log(lik_max - lik_min)
xmin = shrinkages[0]
xmax = shrinkages[-1]
# LW likelihood
plt.vlines(lw.shrinkage_, ymin, -loglik_lw, color='magenta',
linewidth=3, label='Ledoit-Wolf estimate')
# OAS likelihood
plt.vlines(oa.shrinkage_, ymin, -loglik_oa, color='purple',
linewidth=3, label='OAS estimate')
# best CV estimator likelihood
plt.vlines(cv.best_estimator_.shrinkage, ymin,
-cv.best_estimator_.score(X_test), color='cyan',
linewidth=3, label='Cross-validation best estimate')
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
plt.legend()
plt.show()
| bsd-3-clause |
gdementen/larray | larray/random.py | 2 | 20880 | # Portions (part of the docstrings) of this file come from numpy/random/mtrand/mtrand.pyx
# that file (and thus those portions) are licensed under the terms below
# mtrand.pyx -- A Pyrex wrapper of Jean-Sebastien Roy's RandomKit
#
# Copyright 2005 Robert Kern ([email protected])
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import numpy as np
from larray.core.axis import Axis, AxisCollection
from larray.core.array import Array, asarray
from larray.core.array import raw_broadcastable
import larray as la
__all__ = ['randint', 'normal', 'uniform', 'permutation', 'choice']
def generic_random(np_func, args, min_axes, meta):
args, res_axes = raw_broadcastable(args, min_axes=min_axes)
res_data = np_func(*args, size=res_axes.shape)
return Array(res_data, res_axes, meta=meta)
# We choose to place the axes argument in place of the numpy size argument, instead of having axes as the first
# argument, because that would make it ugly for scalars. As a consequence, it is slightly ugly when arguments
# before axes are not required.
def randint(low, high=None, axes=None, dtype='l', meta=None):
r"""Return random integers from `low` (inclusive) to `high` (exclusive).
Return random integers from the "discrete uniform" distribution of the specified dtype in the "half-open" interval
[`low`, `high`). If `high` is None (the default), then results are from [0, `low`).
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution (unless ``high=None``, in which case this parameter
is one above the *highest* such integer).
high : int, optional
If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior
if ``high=None``).
axes : int, tuple of int, str, Axis or tuple/list/AxisCollection of Axis, optional
Axes (or shape) of the resulting array. If ``axes`` is None (the default), a single value is returned.
Otherwise, if the resulting axes have a shape of, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn.
dtype : data-type, optional
Desired dtype of the result. All dtypes are determined by their name, i.e., 'int64', 'int', etc, so byteorder
is not available and a specific precision may have different C types depending on the platform.
The default value is 'np.int'.
meta : list of pairs or dict or OrderedDict or Metadata, optional
Metadata (title, description, author, creation_date, ...) associated with the array.
Keys must be strings. Values must be of type string, int, float, date, time or datetime.
Returns
-------
Array
Examples
--------
Generate a single int between 0 and 9, inclusive:
>>> la.random.randint(10) # doctest: +SKIP
6
Generate an array of 10 ints between 1 and 5, inclusive:
>>> la.random.randint(1, 6, 10) # doctest: +SKIP
{0}* 0 1 2 3 4 5 6 7 8 9
1 1 5 1 5 4 3 4 2 1
Generate a 2 x 3 array of ints between 0 and 4, inclusive:
>>> la.random.randint(5, axes=(2, 3)) # doctest: +SKIP
{0}*\{1}* 0 1 2
0 4 4 1
1 1 2 2
>>> la.random.randint(5, axes='a=a0,a1;b=b0..b2') # doctest: +SKIP
a\b b0 b1 b2
a0 0 3 1
a1 4 0 1
"""
# TODO: support broadcasting arguments when np.randint supports it (https://github.com/numpy/numpy/issues/6745)
# to do that, uncommenting the following code should be enough:
# return generic_random(np.random.randint, (low, high), axes, meta)
axes = AxisCollection(axes)
return Array(np.random.randint(low, high, axes.shape, dtype), axes, meta=meta)
def normal(loc=0.0, scale=1.0, axes=None, meta=None):
r"""
Draw random samples from a normal (Gaussian) distribution.
Its probability density function is often called the bell curve because of its characteristic shape (see the
example below)
Parameters
----------
loc : float or array_like of floats
Mean ("centre") of the distribution.
scale : float or array_like of floats
Standard deviation (spread or "width") of the distribution.
axes : int, tuple of int, str, Axis or tuple/list/AxisCollection of Axis, optional
Minimum axes the resulting array must have. Defaults to None. The resulting array axes will be the union of
those mentioned in ``axes`` and those of ``loc`` and ``scale``. If ``loc`` and ``scale`` are scalars and
``axes`` is None, a single value is returned. Otherwise, if the resulting axes have a shape of, e.g.,
``(m, n, k)``, then ``m * n * k`` samples are drawn.
meta : list of pairs or dict or OrderedDict or Metadata, optional
Metadata (title, description, author, creation_date, ...) associated with the array.
Keys must be strings. Values must be of type string, int, float, date, time or datetime.
Returns
-------
Array or scalar
Drawn samples from the parameterized normal distribution.
Notes
-----
The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of
samples influenced by a large number of tiny, random disturbances, each with its own unique distribution [2]_.
The probability density function for the Gaussian distribution, first derived by De Moivre and 200 years later by
both Gauss and Laplace independently [2]_, is
.. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
where :math:`\mu` is the mean and :math:`\sigma` the standard deviation. The square of the standard deviation,
:math:`\sigma^2`, is called the variance.
The function has its peak at the mean, and its "spread" increases with the standard deviation (the function reaches
0.607 times its maximum at :math:`x + \sigma` and :math:`x - \sigma` [2]_). This implies that
`la.random.normal` is more likely to return samples lying close to the mean, rather than those far away.
References
----------
.. [1] Wikipedia, "Normal distribution",
http://en.wikipedia.org/wiki/Normal_distribution
.. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
Random Variables and Random Signal Principles", 4th ed., 2001, pp. 51, 51, 125.
Examples
--------
Generate a 2 x 3 array with numbers drawn from the distribution:
>>> la.random.normal(0, 1, axes=(2, 3)) # doctest: +SKIP
{0}*\{1}* 0 1 2
0 0.3564325741877542 0.8944149721039006 1.7206904920773107
1 0.6904447654719367 -0.09395966570976753 0.185136309092257
With named and labelled axes
>>> la.random.normal(0, 1, axes='a=a0,a1;b=b0..b2') # doctest: +SKIP
a\b b0 b1 b2
a0 2.3096106652701827 -0.4269082412118316 -1.0862791566867225
a1 0.8598817639620348 -2.386411240813283 0.10116503197279443
With varying loc and scale (each depending on a different axis)
>>> a = la.Axis('a=a0,a1')
>>> b = la.Axis('b=b0..b2')
>>> mu = la.sequence(a, initial=5, inc=5)
>>> mu
a a0 a1
5 10
>>> sigma = la.sequence(b, initial=1)
>>> sigma
b b0 b1 b2
1 2 3
>>> la.random.normal(mu, sigma) # doctest: +SKIP
a\b b0 b1 b2
a0 5.939369790854615 2.5043856460438403 8.33560126941519
a1 10.759526714752091 10.093213549397403 11.705881778249683
Draw 1000 samples from the distribution:
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> sample = la.random.normal(mu, sigma, 1000)
Verify the mean and the variance:
>>> abs(mu - la.mean(sample)) < 0.01
True
>>> abs(sigma - la.std(sample, ddof=1)) < 0.01
True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt # doctest: +SKIP
>>> count, bins, ignored = plt.hist(sample, 30, normed=True) # doctest: +SKIP
>>> pdf = 1 / (sigma * la.sqrt(2 * la.pi)) \
... * la.exp(- (bins - mu) ** 2 / (2 * sigma ** 2)) # doctest: +SKIP
>>> _ = plt.plot(bins, pdf, linewidth=2, color='r') # doctest: +SKIP
>>> plt.show() # doctest: +SKIP
"""
return generic_random(np.random.normal, (loc, scale), axes, meta)
def uniform(low=0.0, high=1.0, axes=None, meta=None):
r"""
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval ``[low, high)`` (includes low, but excludes high).
In other words, any value within the given interval is equally likely to be drawn by `uniform`.
Parameters
----------
low : float or array_like of floats, optional
Lower boundary of the output interval. All values generated will be greater than or equal to low.
Defaults to 0.0.
high : float or array_like of floats, optional
Upper boundary of the output interval. All values generated will be less than high.
Defaults to 1.0.
axes : int, tuple of int, str, Axis or tuple/list/AxisCollection of Axis, optional
Minimum axes the resulting array must have. Defaults to None. The resulting array axes will be the union of
those mentioned in ``axes`` and those of ``low`` and ``high``. If ``low`` and ``high`` are scalars and
``axes`` is None, a single value is returned. Otherwise, if the resulting axes have a shape of, e.g.,
``(m, n, k)``, then ``m * n * k`` samples are drawn.
meta : list of pairs or dict or OrderedDict or Metadata, optional
Metadata (title, description, author, creation_date, ...) associated with the array.
Keys must be strings. Values must be of type string, int, float, date, time or datetime.
Returns
-------
Array or scalar
Drawn samples from the parameterized uniform distribution.
See Also
--------
randint : Discrete uniform distribution, yielding integers.
Notes
-----
The probability density function of the uniform distribution is
.. math:: p(x) = \frac{1}{b - a}
anywhere within the interval ``[a, b)``, and zero elsewhere.
When ``high`` == ``low``, values of ``low`` will be returned.
If ``high`` < ``low``, the results are officially undefined and may eventually raise an error, i.e. do not rely on
this function to behave when passed arguments satisfying that inequality condition.
Examples
--------
Generate a single sample from the distribution:
>>> la.random.uniform() # doctest: +SKIP
0.4616049008844396
Generate a 2 x 3 array with numbers drawn from the distribution:
>>> la.random.uniform(0, 5, axes=(2, 3)) # doctest: +SKIP
{0}*\{1}* 0 1 2
0 3.4951791043804192 3.888533056628081 4.347461073315136
1 2.146211610940853 0.509146487437932 2.790852715735223
With named and labelled axes
>>> la.random.uniform(1, 2, axes='a=a0,a1;b=b0..b2') # doctest: +SKIP
a\b b0 b1 b2
a0 1.4167729850467825 1.6953091052066793 1.2321770607672526
a1 1.4386221912579358 1.8480607144284926 1.1726213637670433
With varying low and high (each depending on a different axis)
>>> a = la.Axis('a=a0,a1')
>>> b = la.Axis('b=b0..b2')
>>> low = la.sequence(a)
>>> low
a a0 a1
0 1
>>> high = la.sequence(b, initial=1, inc=0.5)
>>> high
b b0 b1 b2
1.0 1.5 2.0
>>> la.random.uniform(low, high) # doctest: +SKIP
a\b b0 b1 b2
a0 0.44608671494167573 0.948315996350121 1.74189664009661
a1 1.0 1.1099944474264194 1.1362792569316835
Draw 1000 samples from the distribution:
>>> s = la.random.uniform(-1, 0, 1000)
All values are within the given interval:
>>> la.all(s >= -1)
True
>>> la.all(s < 0)
True
Display the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt # doctest: +SKIP
>>> count, bins, ignored = plt.hist(s, 15, normed=True) # doctest: +SKIP
>>> _ = plt.plot(bins, np.ones_like(bins), linewidth=2, color='r') # doctest: +SKIP
>>> plt.show() # doctest: +SKIP
"""
return generic_random(np.random.uniform, (low, high), axes, meta)
def permutation(x, axis=0):
r"""
Randomly permute a sequence along an axis, or return a permuted range.
Parameters
----------
x : int or array_like
If `x` is an integer, randomly permute ``sequence(x)``.
If `x` is an array, returns a randomly shuffled copy.
axis : int, str or Axis, optional
Axis along which to permute. Defaults to the first axis.
Returns
-------
Array
Permuted sequence or array range.
Examples
--------
>>> la.random.permutation(10) # doctest: +SKIP
{0}* 0 1 2 3 4 5 6 7 8 9
6 8 0 9 4 7 1 5 3 2
>>> la.random.permutation([1, 4, 9, 12, 15]) # doctest: +SKIP
{0}* 0 1 2 3 4
1 15 12 9 4
>>> la.random.permutation(la.ndtest(5)) # doctest: +SKIP
a a3 a1 a2 a4 a0
3 1 2 4 0
>>> arr = la.ndtest((3, 3)) # doctest: +SKIP
>>> la.random.permutation(arr) # doctest: +SKIP
a\b b0 b1 b2
a1 3 4 5
a2 6 7 8
a0 0 1 2
>>> la.random.permutation(arr, axis='b') # doctest: +SKIP
a\b b1 b2 b0
a0 1 2 0
a1 4 5 3
a2 7 8 6
"""
if isinstance(x, (int, np.integer)):
return Array(np.random.permutation(x))
else:
x = asarray(x)
axis = x.axes[axis]
g = axis.i[np.random.permutation(len(axis))]
return x[g]
def choice(choices=None, axes=None, replace=True, p=None, meta=None):
r"""
Generates a random sample from given choices
Parameters
----------
choices : 1-D array-like or int, optional
Values to choose from.
If an array, a random sample is generated from its elements.
If an int n, the random sample is generated as if choices was la.sequence(n)
If p is a 1-D Array, choices are taken from its axis.
axes : int, tuple of int, str, Axis or tuple/list/AxisCollection of Axis, optional
Axes (or shape) of the resulting array. If ``axes`` is None (the default), a single value is returned.
Otherwise, if the resulting axes have a shape of, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn.
replace : boolean, optional
Whether the sample is with or without replacement.
p : array-like, optional
The probabilities associated with each entry in choices.
If p is a 1-D Array, choices are taken from its axis labels. If p is an N-D Array, each cell represents the
probability that the combination of labels will occur.
If not given the sample assumes a uniform distribution over all entries in choices.
meta : list of pairs or dict or OrderedDict or Metadata, optional
Metadata (title, description, author, creation_date, ...) associated with the array.
Keys must be strings. Values must be of type string, int, float, date, time or datetime.
Returns
-------
Array or scalar
The generated random samples with given ``axes`` (or shape).
Raises
------
ValueError
If choices is an int and less than zero, if choices or p are not 1-dimensional,
if choices is an array-like of size 0, if p is not a vector of probabilities,
if choices and p have different lengths, or
if replace=False and the sample size is greater than the population size.
See Also
--------
randint, permutation
Examples
--------
Generate one random value out of given choices (each choice has the same probability of occurring):
>>> la.random.choice(['hello', 'world', '!']) # doctest: +SKIP
hello
With given probabilities:
>>> la.random.choice(['hello', 'world', '!'], p=[0.1, 0.8, 0.1]) # doctest: +SKIP
world
Generate a 2 x 3 array with given axes and values drawn from the given choices using given probabilities:
>>> la.random.choice([5, 10, 15], p=[0.3, 0.5, 0.2], axes='a=a0,a1;b=b0..b2') # doctest: +SKIP
a\b b0 b1 b2
a0 15 10 10
a1 10 5 10
Same as above with labels and probabilities given as a one dimensional Array
>>> proba = Array([0.3, 0.5, 0.2], Axis([5, 10, 15], 'outcome')) # doctest: +SKIP
>>> proba # doctest: +SKIP
outcome 5 10 15
0.3 0.5 0.2
>>> choice(p=proba, axes='a=a0,a1;b=b0..b2') # doctest: +SKIP
a\b b0 b1 b2
a0 10 15 5
a1 10 5 10
Generate a uniform random sample of size 3 from la.sequence(5):
>>> la.random.choice(5, 3) # doctest: +SKIP
{0}* 0 1 2
3 2 0
>>> # This is equivalent to la.random.randint(0, 5, 3)
Generate a non-uniform random sample of size 3 from the given choices without replacement:
>>> la.random.choice(['hello', 'world', '!'], 3, replace=False, p=[0.1, 0.6, 0.3]) # doctest: +SKIP
{0}* 0 1 2
world ! hello
Using an N-dimensional array as probabilities:
>>> proba = Array([[0.15, 0.25, 0.10],
... [0.20, 0.10, 0.20]], 'a=a0,a1;b=b0..b2') # doctest: +SKIP
>>> proba # doctest: +SKIP
a\b b0 b1 b2
a0 0.15 0.25 0.1
a1 0.2 0.1 0.2
>>> choice(p=proba, axes='draw=d0..d5') # doctest: +SKIP
draw\axis a b
d0 a1 b2
d1 a1 b1
d2 a0 b1
d3 a0 b0
d4 a1 b2
d5 a0 b1
"""
axes = AxisCollection(axes)
if isinstance(p, Array):
if choices is not None:
raise ValueError("choices argument cannot be used when p argument is an Array")
if p.ndim > 1:
flat_p = p.data.reshape(-1)
flat_indices = choice(p.size, axes=axes, replace=replace, p=flat_p)
return p.axes._flat_lookup(flat_indices)
else:
choices = p.axes[0].labels
p = p.data
if choices is None:
raise ValueError("choices argument must be provided unless p is an Array")
return Array(np.random.choice(choices, axes.shape, replace, p), axes, meta=meta)
| gpl-3.0 |
waylonflinn/bquery | bquery/ctable.py | 1 | 23323 | # internal imports
from bquery import ctable_ext
# external imports
import numpy as np
import bcolz
import os
from bquery.ctable_ext import \
SUM, COUNT, COUNT_NA, COUNT_DISTINCT, SORTED_COUNT_DISTINCT, \
MEAN, STDEV
class ctable(bcolz.ctable):
def cache_valid(self, col):
"""
Checks whether the column has a factorization that exists and is not older than the source
:param col:
:return:
"""
if self.rootdir:
col_org_file_check = self[col].rootdir + '/__attrs__'
col_values_file_check = self[col].rootdir + '.values/__attrs__'
if not os.path.exists(col_org_file_check):
raise KeyError(str(col) + ' does not exist')
if os.path.exists(col_values_file_check):
return os.path.getctime(col_org_file_check) < os.path.getctime(col_values_file_check)
else:
return False
else:
return False
def cache_factor(self, col_list, refresh=False):
"""
Existing todos here are: these should be hidden helper carrays
As in: not normal columns that you would normally see as a user
The factor (label index) carray is as long as the original carray
(and the rest of the table therefore)
But the (unique) values carray is not as long (as long as the number
of unique values)
:param col_list:
:param refresh:
:return:
"""
if not self.rootdir:
raise TypeError('Only out-of-core ctables can have '
'factorization caching at the moment')
if not isinstance(col_list, list):
col_list = [col_list]
for col in col_list:
# create cache if needed
if refresh or not self.cache_valid(col):
col_rootdir = self[col].rootdir
col_factor_rootdir = col_rootdir + '.factor'
col_values_rootdir = col_rootdir + '.values'
carray_factor = \
bcolz.carray([], dtype='int64', expectedlen=self.size,
rootdir=col_factor_rootdir, mode='w')
_, values = \
ctable_ext.factorize(self[col], labels=carray_factor)
carray_factor.flush()
carray_values = \
bcolz.carray(np.fromiter(values.values(), dtype=self[col].dtype),
rootdir=col_values_rootdir, mode='w')
carray_values.flush()
def unique(self, col_or_col_list):
"""
Return a list of unique values of a column or a list of lists of column list
:param col_or_col_list: a column or a list of columns
:return:
"""
if isinstance(col_or_col_list, list):
col_is_list = True
col_list = col_or_col_list
else:
col_is_list = False
col_list = [col_or_col_list]
output = []
for col in col_list:
if self.cache_valid(col):
# retrieve values from existing disk-based factorization
col_values_rootdir = self[col].rootdir + '.values'
carray_values = bcolz.carray(rootdir=col_values_rootdir, mode='r')
values = list(carray_values)
else:
# factorize on-the-fly
_, values = ctable_ext.factorize(self[col])
values = values.values()
output.append(values)
if not col_is_list:
output = output[0]
return output
def aggregate_groups(self, ct_agg, nr_groups, skip_key,
factor_carray, groupby_cols, output_agg_ops,
dtype_dict, bool_arr=None):
'''Perform aggregation and place the result in the given ctable.
Args:
ct_agg (ctable): the table to hold the aggregation
nr_groups (int): the number of groups (number of rows in output table)
skip_key (int): index of the output row to remove from results (used for filtering)
factor_carray: the carray for each row in the table a reference to the the unique group index
groupby_cols: the list of 'dimension' columns that are used to perform the groupby over
output_agg_ops (list): list of tuples of the form: (input_col, agg_op)
input_col (string): name of the column to act on
agg_op (int): aggregation operation to perform
bool_arr: a boolean array containing the filter
'''
# this creates the groupby columns
for col in groupby_cols:
result_array = ctable_ext.groupby_value(self[col], factor_carray,
nr_groups, skip_key)
if bool_arr is not None:
result_array = np.delete(result_array, skip_key)
ct_agg.addcol(result_array, name=col)
del result_array
# this creates the aggregation columns
for input_col_name, output_col_name, agg_op in output_agg_ops:
input_col = self[input_col_name]
output_col_dtype = dtype_dict[output_col_name]
input_buffer = np.empty(input_col.chunklen, dtype=input_col.dtype)
output_buffer = np.zeros(nr_groups, dtype=output_col_dtype)
try:
ctable_ext.aggregate(input_col, factor_carray, nr_groups,
skip_key, input_buffer, output_buffer,
agg_op)
except TypeError:
raise NotImplementedError(
'Column dtype ({0}) not supported for aggregation yet '
'(only int32, int64 & float64)'.format(str(input_col.dtype)))
except Exception as e:
raise e
if bool_arr is not None:
output_buffer = np.delete(output_buffer, skip_key)
ct_agg.addcol(output_buffer, name=output_col_name)
del output_buffer
ct_agg.delcol('tmp_col_bquery__')
def groupby(self, groupby_cols, agg_list, bool_arr=None, rootdir=None):
"""
Aggregate the ctable
groupby_cols: a list of columns to groupby over
agg_list: the aggregation operations, which can be:
- a list of column names (output has same name and sum is performed)
['m1', 'm2', ...]
- a list of lists, each list contains input column name and operation
[['m1', 'sum'], ['m2', 'mean'], ...]
- a list of lists, each list contains input column name, operation and
output column name
[['m1', 'sum', 'm1_sum'], ['m1', 'mean', 'm1_mean'], ...]
Currently supported aggregation operations are:
- 'sum'
- 'count'
- 'count_na'
- 'count_distinct'
- 'sorted_count_distinct', data should have been
previously presorted
- 'mean', arithmetic mean (average)
- 'std', standard deviation
boolarr: to be added (filtering the groupby factorization input)
rootdir: the aggregation ctable rootdir
"""
if not agg_list:
raise AttributeError('One or more aggregation operations '
'need to be defined')
factor_list, values_list = self.factorize_groupby_cols(groupby_cols)
factor_carray, nr_groups, skip_key = \
self.make_group_index(factor_list, values_list, groupby_cols,
len(self), bool_arr)
# check if the bool_arr actually filters
if bool_arr is not None and np.all(bool_arr):
bool_arr = None
if bool_arr is None:
expectedlen = nr_groups
else:
expectedlen = nr_groups - 1
ct_agg, dtype_dict, agg_ops = \
self.create_agg_ctable(groupby_cols, agg_list, expectedlen, rootdir)
# perform aggregation
self.aggregate_groups(ct_agg, nr_groups, skip_key,
factor_carray, groupby_cols,
agg_ops, dtype_dict,
bool_arr=bool_arr)
return ct_agg
# groupby helper functions
def factorize_groupby_cols(self, groupby_cols):
"""
:type self: ctable
"""
# first check if the factorized arrays already exist
# unless we need to refresh the cache
factor_list = []
values_list = []
# factorize the groupby columns
for col in groupby_cols:
if self.cache_valid(col):
col_rootdir = self[col].rootdir
col_factor_rootdir = col_rootdir + '.factor'
col_values_rootdir = col_rootdir + '.values'
col_factor_carray = \
bcolz.carray(rootdir=col_factor_rootdir, mode='r')
col_values_carray = \
bcolz.carray(rootdir=col_values_rootdir, mode='r')
else:
col_factor_carray, values = ctable_ext.factorize(self[col])
col_values_carray = \
bcolz.carray(np.fromiter(values.values(), dtype=self[col].dtype))
factor_list.append(col_factor_carray)
values_list.append(col_values_carray)
return factor_list, values_list
def make_group_index(self, factor_list, values_list, groupby_cols,
array_length, bool_arr):
'''Create unique groups for groupby loop
Args:
factor_list:
values_list:
groupby_cols:
array_length:
bool_arr:
Returns:
carray: (factor_carray)
int: (nr_groups) the number of resulting groups
int: (skip_key)
'''
def _create_eval_str(groupby_cols, values_list, check_overflow=True):
eval_list = []
eval_str = ''
col_list = []
previous_value = 1
# Sort evaluated columns by length
col_len_list = [(col, values) for col, values in zip(groupby_cols, values_list)]
col_len_list.sort(key=lambda x: len(x[1]))
groupby_cols = [col for col, _ in col_len_list]
values_list = [values for _, values in col_len_list]
for col, values \
in zip(groupby_cols, values_list):
# check for overflow
if check_overflow:
if previous_value * len(values) > 4294967295:
eval_list.append((eval_str, col_list))
# reset
eval_str = ''
col_list = []
previous_value = 1
if eval_str:
eval_str += ' + '
else:
eval_str += '-2147483648 + '
eval_str += str(previous_value) + '*' + col
col_list.append(col)
previous_value *= len(values)
eval_list.append((eval_str, col_list))
return eval_list
def _calc_group_index(eval_list, factor_set, vm=None):
factorize_list = []
for eval_node in eval_list:
# calculate the cartesian group index for each row
factor_input = bcolz.eval(eval_node[0], user_dict=factor_set, vm=vm)
# now factorize the unique groupby combinations
sub_factor_carray, sub_values = ctable_ext.factorize(factor_input)
factorize_list.append((sub_factor_carray, sub_values))
return factorize_list
def _is_reducible(eval_list):
for eval_node in eval_list:
if len(eval_node[1]) > 1:
return True
return False
def calc_index(groupby_cols, values_list, factor_set, vm=None):
# Initialize eval list
eval_list = _create_eval_str(groupby_cols, values_list)
# Reduce expression as possible
while _is_reducible(eval_list):
del groupby_cols
del values_list
factorize_list = _calc_group_index(eval_list, factor_set)
factor_set = {'g' + str(i): x[0] for i, x in enumerate(factorize_list)}
groupby_cols = ['g' + str(i) for i, x in enumerate(factorize_list)]
values_list = [x[1] for i, x in enumerate(factorize_list)]
eval_list = _create_eval_str(groupby_cols, values_list)
# If we have multiple expressions that cannot be reduced anymore, rewrite as a single one and use Python vm
if len(eval_list) > 1:
eval_list = _create_eval_str(groupby_cols, values_list, check_overflow=False)
vm = 'python'
del groupby_cols
del values_list
# Now we have a single expression, factorize it
return _calc_group_index(eval_list, factor_set, vm=vm)[0]
# create unique groups for groupby loop
if len(factor_list) == 0:
# no columns to groupby over, so directly aggregate the measure
# columns to 1 total (index 0/zero)
factor_carray = bcolz.zeros(array_length, dtype='int64')
values = ['Total']
elif len(factor_list) == 1:
# single column groupby, the groupby output column
# here is 1:1 to the values
factor_carray = factor_list[0]
values = values_list[0]
else:
# multi column groupby
# nb: this might also be cached in the future
# first combine the factorized columns to single values
factor_set = {x: y for x, y in zip(groupby_cols, factor_list)}
# create a numexpr expression that calculates the place on
# a cartesian join index
factor_carray, values = calc_index(groupby_cols, values_list, factor_set)
skip_key = None
if bool_arr is not None:
# make all non relevant combinations -1
factor_carray = bcolz.eval(
'(factor + 1) * bool - 1',
user_dict={'factor': factor_carray, 'bool': bool_arr})
# now check how many unique values there are left
factor_carray, values = ctable_ext.factorize(factor_carray)
# values might contain one value too much (-1) (no direct lookup
# possible because values is a reversed dict)
filter_check = \
[key for key, value in values.items() if value == -1]
if filter_check:
skip_key = filter_check[0]
# using nr_groups as a total length might be one one off due to the skip_key
# (skipping a row in aggregation)
# but that is okay normally
nr_groups = len(values)
if skip_key is None:
# if we shouldn't skip a row, set it at the first row after the total number of groups
skip_key = nr_groups
return factor_carray, nr_groups, skip_key
def create_agg_ctable(self, groupby_cols, agg_list, expectedlen, rootdir):
'''Create a container for the output table, a dictionary describing it's
columns and a list of tuples describing aggregation
operations to perform.
Args:
groupby_cols (list): a list of columns to groupby over
agg_list (list): the aggregation operations (see groupby for more info)
expectedlen (int): expected length of output table
rootdir (string): the directory to write the table to
Returns:
ctable: A table in the correct format for containing the output of
the specified aggregation operations.
dict: (dtype_dict) dictionary describing columns to create
list: (agg_ops) list of tuples of the form:
(input_col_name, output_col_name, agg_op)
input_col_name (string): name of the column to act on
output_col_name (string): name of the column to output to
agg_op (int): aggregation operation to perform
'''
dtype_dict = {}
# include all the groupby columns
for col in groupby_cols:
dtype_dict[col] = self[col].dtype
agg_ops = []
op_translation = {
'sum': SUM,
'count': COUNT,
'count_na': COUNT_NA,
'count_distinct': COUNT_DISTINCT,
'sorted_count_distinct': SORTED_COUNT_DISTINCT,
'mean': MEAN,
'std': STDEV
}
for agg_info in agg_list:
if not isinstance(agg_info, list):
# example: ['m1', 'm2', ...]
# default operation (sum) and default output column name (same is input)
output_col_name = agg_info
input_col_name = agg_info
agg_op = SUM
else:
input_col_name = agg_info[0]
agg_op_input = agg_info[1]
if len(agg_info) == 2:
# example: [['m1', 'sum'], ['m2', 'mean], ...]
# default output column name
output_col_name = input_col_name
else:
# example: [['m1', 'sum', 'mnew1'], ['m1, 'mean','mnew2'], ...]
# fully specified
output_col_name = agg_info[2]
if agg_op_input not in op_translation:
raise NotImplementedError(
'Unknown Aggregation Type: ' + unicode(agg_op_input))
agg_op = op_translation[agg_op_input]
# choose output column dtype based on aggregation operation and
# input column dtype
# TODO: check if the aggregation columns is numeric
# NB: we could build a concatenation for strings like pandas, but I would really prefer to see that as a
# separate operation
if agg_op in (COUNT, COUNT_NA, COUNT_DISTINCT, SORTED_COUNT_DISTINCT):
output_col_dtype = np.dtype(np.int64)
elif agg_op in (MEAN, STDEV):
output_col_dtype = np.dtype(np.float64)
else:
output_col_dtype = self[input_col_name].dtype
dtype_dict[output_col_name] = output_col_dtype
# save output
agg_ops.append((input_col_name, output_col_name, agg_op))
# create aggregation table
ct_agg = bcolz.ctable(
np.zeros(expectedlen, [('tmp_col_bquery__', np.bool)]),
expectedlen=expectedlen,
rootdir=rootdir)
return ct_agg, dtype_dict, agg_ops
def where_terms(self, term_list):
"""
TEMPORARY WORKAROUND TILL NUMEXPR WORKS WITH IN
where_terms(term_list, outcols=None, limit=None, skip=0)
Iterate over rows where `term_list` is true.
A terms list has a [(col, operator, value), ..] construction.
Eg. [('sales', '>', 2), ('state', 'in', ['IL', 'AR'])]
:param term_list:
:param outcols:
:param limit:
:param skip:
:return: :raise ValueError:
"""
if type(term_list) not in [list, set, tuple]:
raise ValueError("Only term lists are supported")
eval_string = ''
eval_list = []
for term in term_list:
filter_col = term[0]
filter_operator = term[1].lower()
filter_value = term[2]
if filter_operator not in ['in', 'not in']:
# direct filters should be added to the eval_string
# add and logic if not the first term
if eval_string:
eval_string += ' & '
eval_string += '(' + filter_col + ' ' \
+ filter_operator + ' ' \
+ str(filter_value) + ')'
elif filter_operator in ['in', 'not in']:
# Check input
if type(filter_value) not in [list, set, tuple]:
raise ValueError("In selections need lists, sets or tuples")
if len(filter_value) < 1:
raise ValueError("A value list needs to have values")
elif len(filter_value) == 1:
# handle as eval
# add and logic if not the first term
if eval_string:
eval_string += ' & '
if filter_operator == 'not in':
filter_operator = '!='
else:
filter_operator = '=='
eval_string += '(' + filter_col + ' ' + \
filter_operator
filter_value = filter_value[0]
if type(filter_value) == str:
filter_value = '"' + filter_value + '"'
else:
filter_value = str(filter_value)
eval_string += filter_value + ') '
else:
if type(filter_value) in [list, tuple]:
filter_value = set(filter_value)
eval_list.append(
(filter_col, filter_operator, filter_value)
)
else:
raise ValueError(
"Input not correctly formatted for eval or list filtering"
)
# (1) Evaluate terms in eval
# return eval_string, eval_list
if eval_string:
boolarr = self.eval(eval_string)
if eval_list:
# convert to numpy array for array_is_in
boolarr = boolarr[:]
else:
boolarr = np.ones(self.size, dtype=bool)
# (2) Evaluate other terms like 'in' or 'not in' ...
for term in eval_list:
name = term[0]
col = self.cols[name]
operator = term[1]
if operator.lower() == 'not in':
reverse = True
elif operator.lower() == 'in':
reverse = False
else:
raise ValueError(
"Input not correctly formatted for list filtering"
)
value_set = set(term[2])
ctable_ext.carray_is_in(col, value_set, boolarr, reverse)
if eval_list:
# convert boolarr back to carray
boolarr = bcolz.carray(boolarr)
return boolarr
def is_in_ordered_subgroups(self, basket_col=None, bool_arr=None,
_max_len_subgroup=1000):
""""""
assert basket_col is not None
if bool_arr is None:
bool_arr = bcolz.ones(self.len)
return \
ctable_ext.is_in_ordered_subgroups(
self[basket_col], bool_arr=bool_arr,
_max_len_subgroup=_max_len_subgroup)
| bsd-3-clause |
poryfly/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solves the problem
know as 'normalized graph cuts': the image is seen as a graph of
connected voxels, and the spectral clustering algorithm amounts to
choosing graph cuts defining regions while minimizing the ratio of the
gradient along the cut, and the volume of the region.
As the algorithm tries to balance the volume (ie balance the region
sizes), if we take circles with different sizes, the segmentation fails.
In addition, as there is no useful information in the intensity of the image,
or its gradient, we choose to perform the spectral clustering on a graph
that is only weakly informed by the gradient. This is close to performing
a Voronoi partition of the graph.
In addition, we use the mask of the objects to restrict the graph to the
outline of the objects. In this example, we are interested in
separating the objects one from the other, and not from the background.
"""
print(__doc__)
# Authors: Emmanuelle Gouillart <[email protected]>
# Gael Varoquaux <[email protected]>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
###############################################################################
l = 100
x, y = np.indices((l, l))
center1 = (28, 24)
center2 = (40, 50)
center3 = (67, 58)
center4 = (24, 70)
radius1, radius2, radius3, radius4 = 16, 14, 15, 14
circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2
circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2
circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2
circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2
###############################################################################
# 4 circles
img = circle1 + circle2 + circle3 + circle4
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(img, mask=mask)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())
# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
plt.matshow(img)
plt.matshow(label_im)
###############################################################################
# 2 circles
img = circle1 + circle2
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
graph = image.img_to_graph(img, mask=mask)
graph.data = np.exp(-graph.data / graph.data.std())
labels = spectral_clustering(graph, n_clusters=2, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
plt.matshow(img)
plt.matshow(label_im)
plt.show()
| bsd-3-clause |
larsmans/scikit-learn | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number of dimensions.
In both cases, only 10% of the features are informative.
"""
import gc
from time import time
import numpy as np
from sklearn.datasets.samples_generator import make_regression
def compute_bench(alpha, n_samples, n_features, precompute):
lasso_results = []
lars_lasso_results = []
it = 0
for ns in n_samples:
for nf in n_features:
it += 1
print('==================')
print('Iteration %s of %s' % (it, max(len(n_samples),
len(n_features))))
print('==================')
n_informative = nf // 10
X, Y, coef_ = make_regression(n_samples=ns, n_features=nf,
n_informative=n_informative,
noise=0.1, coef=True)
X /= np.sqrt(np.sum(X ** 2, axis=0)) # Normalize data
gc.collect()
print("- benchmarking Lasso")
clf = Lasso(alpha=alpha, fit_intercept=False,
precompute=precompute)
tstart = time()
clf.fit(X, Y)
lasso_results.append(time() - tstart)
gc.collect()
print("- benchmarking LassoLars")
clf = LassoLars(alpha=alpha, fit_intercept=False,
normalize=False, precompute=precompute)
tstart = time()
clf.fit(X, Y)
lars_lasso_results.append(time() - tstart)
return lasso_results, lars_lasso_results
if __name__ == '__main__':
from sklearn.linear_model import Lasso, LassoLars
import pylab as pl
alpha = 0.01 # regularization parameter
n_features = 10
list_n_samples = np.linspace(100, 1000000, 5).astype(np.int)
lasso_results, lars_lasso_results = compute_bench(alpha, list_n_samples,
[n_features], precompute=True)
pl.figure('scikit-learn LASSO benchmark results')
pl.subplot(211)
pl.plot(list_n_samples, lasso_results, 'b-',
label='Lasso')
pl.plot(list_n_samples, lars_lasso_results, 'r-',
label='LassoLars')
pl.title('precomputed Gram matrix, %d features, alpha=%s' % (n_features, alpha))
pl.legend(loc='upper left')
pl.xlabel('number of samples')
pl.ylabel('Time (s)')
pl.axis('tight')
n_samples = 2000
list_n_features = np.linspace(500, 3000, 5).astype(np.int)
lasso_results, lars_lasso_results = compute_bench(alpha, [n_samples],
list_n_features, precompute=False)
pl.subplot(212)
pl.plot(list_n_features, lasso_results, 'b-', label='Lasso')
pl.plot(list_n_features, lars_lasso_results, 'r-', label='LassoLars')
pl.title('%d samples, alpha=%s' % (n_samples, alpha))
pl.legend(loc='upper left')
pl.xlabel('number of features')
pl.ylabel('Time (s)')
pl.axis('tight')
pl.show()
| bsd-3-clause |
Titan-C/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting.py | 21 | 41305 | """
Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
"""
import warnings
import numpy as np
from itertools import product
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import coo_matrix
from sklearn import datasets
from sklearn.base import clone
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble.gradient_boosting import ZeroEstimator
from sklearn.metrics import mean_squared_error
from sklearn.utils import check_random_state, tosequence
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import skip_if_32bit
from sklearn.exceptions import DataConversionWarning
from sklearn.exceptions import NotFittedError
# toy sample
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
y = [-1, -1, -1, 1, 1, 1]
T = [[-1, -1], [2, 2], [3, 2]]
true_result = [-1, 1, 1]
rng = np.random.RandomState(0)
# also load the boston dataset
# and randomly permute it
boston = datasets.load_boston()
perm = rng.permutation(boston.target.size)
boston.data = boston.data[perm]
boston.target = boston.target[perm]
# also load the iris dataset
# and randomly permute it
iris = datasets.load_iris()
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
def check_classification_toy(presort, loss):
# Check classification on a toy dataset.
clf = GradientBoostingClassifier(loss=loss, n_estimators=10,
random_state=1, presort=presort)
assert_raises(ValueError, clf.predict, T)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(10, len(clf.estimators_))
deviance_decrease = (clf.train_score_[:-1] - clf.train_score_[1:])
assert_true(np.any(deviance_decrease >= 0.0))
leaves = clf.apply(X)
assert_equal(leaves.shape, (6, 10, 1))
def test_classification_toy():
for presort, loss in product(('auto', True, False),
('deviance', 'exponential')):
yield check_classification_toy, presort, loss
def test_parameter_checks():
# Check input parameter validation.
assert_raises(ValueError,
GradientBoostingClassifier(n_estimators=0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(n_estimators=-1).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(learning_rate=0.0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(learning_rate=-1.0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(loss='foobar').fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_samples_split=0.0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_samples_split=-1.0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_samples_split=1.1).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_samples_leaf=0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_samples_leaf=-1.0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_weight_fraction_leaf=-1.).fit,
X, y)
assert_raises(ValueError,
GradientBoostingClassifier(min_weight_fraction_leaf=0.6).fit,
X, y)
assert_raises(ValueError,
GradientBoostingClassifier(subsample=0.0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(subsample=1.1).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(subsample=-0.1).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(max_depth=-0.1).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(max_depth=0).fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(init={}).fit, X, y)
# test fit before feature importance
assert_raises(ValueError,
lambda: GradientBoostingClassifier().feature_importances_)
# deviance requires ``n_classes >= 2``.
assert_raises(ValueError,
lambda X, y: GradientBoostingClassifier(
loss='deviance').fit(X, y),
X, [0, 0, 0, 0])
def test_loss_function():
assert_raises(ValueError,
GradientBoostingClassifier(loss='ls').fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(loss='lad').fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(loss='quantile').fit, X, y)
assert_raises(ValueError,
GradientBoostingClassifier(loss='huber').fit, X, y)
assert_raises(ValueError,
GradientBoostingRegressor(loss='deviance').fit, X, y)
assert_raises(ValueError,
GradientBoostingRegressor(loss='exponential').fit, X, y)
def check_classification_synthetic(presort, loss):
# Test GradientBoostingClassifier on synthetic dataset used by
# Hastie et al. in ESLII Example 12.7.
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
X_train, X_test = X[:2000], X[2000:]
y_train, y_test = y[:2000], y[2000:]
gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=2,
max_depth=1, loss=loss,
learning_rate=1.0, random_state=0)
gbrt.fit(X_train, y_train)
error_rate = (1.0 - gbrt.score(X_test, y_test))
assert_less(error_rate, 0.09)
gbrt = GradientBoostingClassifier(n_estimators=200, min_samples_split=2,
max_depth=1, loss=loss,
learning_rate=1.0, subsample=0.5,
random_state=0,
presort=presort)
gbrt.fit(X_train, y_train)
error_rate = (1.0 - gbrt.score(X_test, y_test))
assert_less(error_rate, 0.08)
def test_classification_synthetic():
for presort, loss in product(('auto', True, False), ('deviance', 'exponential')):
yield check_classification_synthetic, presort, loss
def check_boston(presort, loss, subsample):
# Check consistency on dataset boston house prices with least squares
# and least absolute deviation.
ones = np.ones(len(boston.target))
last_y_pred = None
for sample_weight in None, ones, 2 * ones:
clf = GradientBoostingRegressor(n_estimators=100,
loss=loss,
max_depth=4,
subsample=subsample,
min_samples_split=2,
random_state=1,
presort=presort)
assert_raises(ValueError, clf.predict, boston.data)
clf.fit(boston.data, boston.target,
sample_weight=sample_weight)
leaves = clf.apply(boston.data)
assert_equal(leaves.shape, (506, 100))
y_pred = clf.predict(boston.data)
mse = mean_squared_error(boston.target, y_pred)
assert_less(mse, 6.0)
if last_y_pred is not None:
assert_array_almost_equal(last_y_pred, y_pred)
last_y_pred = y_pred
def test_boston():
for presort, loss, subsample in product(('auto', True, False),
('ls', 'lad', 'huber'),
(1.0, 0.5)):
yield check_boston, presort, loss, subsample
def check_iris(presort, subsample, sample_weight):
# Check consistency on dataset iris.
clf = GradientBoostingClassifier(n_estimators=100,
loss='deviance',
random_state=1,
subsample=subsample,
presort=presort)
clf.fit(iris.data, iris.target, sample_weight=sample_weight)
score = clf.score(iris.data, iris.target)
assert_greater(score, 0.9)
leaves = clf.apply(iris.data)
assert_equal(leaves.shape, (150, 100, 3))
def test_iris():
ones = np.ones(len(iris.target))
for presort, subsample, sample_weight in product(('auto', True, False),
(1.0, 0.5),
(None, ones)):
yield check_iris, presort, subsample, sample_weight
def test_regression_synthetic():
# Test on synthetic regression datasets used in Leo Breiman,
# `Bagging Predictors?. Machine Learning 24(2): 123-140 (1996).
random_state = check_random_state(1)
regression_params = {'n_estimators': 100, 'max_depth': 4,
'min_samples_split': 2, 'learning_rate': 0.1,
'loss': 'ls'}
# Friedman1
X, y = datasets.make_friedman1(n_samples=1200,
random_state=random_state,
noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
for presort in True, False:
clf = GradientBoostingRegressor(presort=presort)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert_less(mse, 5.0)
# Friedman2
X, y = datasets.make_friedman2(n_samples=1200, random_state=random_state)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
for presort in True, False:
regression_params['presort'] = presort
clf = GradientBoostingRegressor(**regression_params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert_less(mse, 1700.0)
# Friedman3
X, y = datasets.make_friedman3(n_samples=1200, random_state=random_state)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
for presort in True, False:
regression_params['presort'] = presort
clf = GradientBoostingRegressor(**regression_params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
assert_less(mse, 0.015)
def test_feature_importances():
X = np.array(boston.data, dtype=np.float32)
y = np.array(boston.target, dtype=np.float32)
for presort in True, False:
clf = GradientBoostingRegressor(n_estimators=100, max_depth=5,
min_samples_split=2, random_state=1,
presort=presort)
clf.fit(X, y)
assert_true(hasattr(clf, 'feature_importances_'))
def test_probability_log():
# Predict probabilities.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.predict_proba, T)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
# check if probabilities are in [0, 1].
y_proba = clf.predict_proba(T)
assert_true(np.all(y_proba >= 0.0))
assert_true(np.all(y_proba <= 1.0))
# derive predictions from probabilities
y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
assert_array_equal(y_pred, true_result)
def test_check_inputs():
# Test input checks (shape and type of X and y).
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.fit, X, y + [0, 1])
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
assert_raises(ValueError, clf.fit, X, y,
sample_weight=([1] * len(y)) + [0, 1])
def test_check_inputs_predict():
# X has wrong shape
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y)
x = np.array([1.0, 2.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
x = np.array([[]])
assert_raises(ValueError, clf.predict, x)
x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
clf.fit(X, rng.rand(len(X)))
x = np.array([1.0, 2.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
x = np.array([[]])
assert_raises(ValueError, clf.predict, x)
x = np.array([1.0, 2.0, 3.0])[:, np.newaxis]
assert_raises(ValueError, clf.predict, x)
def test_check_max_features():
# test if max_features is valid.
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
max_features=0)
assert_raises(ValueError, clf.fit, X, y)
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
max_features=(len(X[0]) + 1))
assert_raises(ValueError, clf.fit, X, y)
clf = GradientBoostingRegressor(n_estimators=100, random_state=1,
max_features=-0.1)
assert_raises(ValueError, clf.fit, X, y)
def test_max_feature_regression():
# Test to make sure random state is set properly.
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
X_train, X_test = X[:2000], X[2000:]
y_train, y_test = y[:2000], y[2000:]
gbrt = GradientBoostingClassifier(n_estimators=100, min_samples_split=5,
max_depth=2, learning_rate=.1,
max_features=2, random_state=1)
gbrt.fit(X_train, y_train)
deviance = gbrt.loss_(y_test, gbrt.decision_function(X_test))
assert_true(deviance < 0.5, "GB failed with deviance %.4f" % deviance)
def test_max_feature_auto():
# Test if max features is set properly for floats and str.
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
_, n_features = X.shape
X_train = X[:2000]
y_train = y[:2000]
gbrt = GradientBoostingClassifier(n_estimators=1, max_features='auto')
gbrt.fit(X_train, y_train)
assert_equal(gbrt.max_features_, int(np.sqrt(n_features)))
gbrt = GradientBoostingRegressor(n_estimators=1, max_features='auto')
gbrt.fit(X_train, y_train)
assert_equal(gbrt.max_features_, n_features)
gbrt = GradientBoostingRegressor(n_estimators=1, max_features=0.3)
gbrt.fit(X_train, y_train)
assert_equal(gbrt.max_features_, int(n_features * 0.3))
gbrt = GradientBoostingRegressor(n_estimators=1, max_features='sqrt')
gbrt.fit(X_train, y_train)
assert_equal(gbrt.max_features_, int(np.sqrt(n_features)))
gbrt = GradientBoostingRegressor(n_estimators=1, max_features='log2')
gbrt.fit(X_train, y_train)
assert_equal(gbrt.max_features_, int(np.log2(n_features)))
gbrt = GradientBoostingRegressor(n_estimators=1,
max_features=0.01 / X.shape[1])
gbrt.fit(X_train, y_train)
assert_equal(gbrt.max_features_, 1)
def test_staged_predict():
# Test whether staged decision function eventually gives
# the same prediction.
X, y = datasets.make_friedman1(n_samples=1200,
random_state=1, noise=1.0)
X_train, y_train = X[:200], y[:200]
X_test = X[200:]
clf = GradientBoostingRegressor()
# test raise ValueError if not fitted
assert_raises(ValueError, lambda X: np.fromiter(
clf.staged_predict(X), dtype=np.float64), X_test)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# test if prediction for last stage equals ``predict``
for y in clf.staged_predict(X_test):
assert_equal(y.shape, y_pred.shape)
assert_array_equal(y_pred, y)
def test_staged_predict_proba():
# Test whether staged predict proba eventually gives
# the same prediction.
X, y = datasets.make_hastie_10_2(n_samples=1200,
random_state=1)
X_train, y_train = X[:200], y[:200]
X_test, y_test = X[200:], y[200:]
clf = GradientBoostingClassifier(n_estimators=20)
# test raise NotFittedError if not fitted
assert_raises(NotFittedError, lambda X: np.fromiter(
clf.staged_predict_proba(X), dtype=np.float64), X_test)
clf.fit(X_train, y_train)
# test if prediction for last stage equals ``predict``
for y_pred in clf.staged_predict(X_test):
assert_equal(y_test.shape, y_pred.shape)
assert_array_equal(clf.predict(X_test), y_pred)
# test if prediction for last stage equals ``predict_proba``
for staged_proba in clf.staged_predict_proba(X_test):
assert_equal(y_test.shape[0], staged_proba.shape[0])
assert_equal(2, staged_proba.shape[1])
assert_array_equal(clf.predict_proba(X_test), staged_proba)
def test_staged_functions_defensive():
# test that staged_functions make defensive copies
rng = np.random.RandomState(0)
X = rng.uniform(size=(10, 3))
y = (4 * X[:, 0]).astype(np.int) + 1 # don't predict zeros
for estimator in [GradientBoostingRegressor(),
GradientBoostingClassifier()]:
estimator.fit(X, y)
for func in ['predict', 'decision_function', 'predict_proba']:
staged_func = getattr(estimator, "staged_" + func, None)
if staged_func is None:
# regressor has no staged_predict_proba
continue
with warnings.catch_warnings(record=True):
staged_result = list(staged_func(X))
staged_result[1][:] = 0
assert_true(np.all(staged_result[0] != 0))
def test_serialization():
# Check model serialization.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
try:
import cPickle as pickle
except ImportError:
import pickle
serialized_clf = pickle.dumps(clf, protocol=pickle.HIGHEST_PROTOCOL)
clf = None
clf = pickle.loads(serialized_clf)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
def test_degenerate_targets():
# Check if we can fit even though all targets are equal.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
# classifier should raise exception
assert_raises(ValueError, clf.fit, X, np.ones(len(X)))
clf = GradientBoostingRegressor(n_estimators=100, random_state=1)
clf.fit(X, np.ones(len(X)))
clf.predict([rng.rand(2)])
assert_array_equal(np.ones((1,), dtype=np.float64),
clf.predict([rng.rand(2)]))
def test_quantile_loss():
# Check if quantile loss with alpha=0.5 equals lad.
clf_quantile = GradientBoostingRegressor(n_estimators=100, loss='quantile',
max_depth=4, alpha=0.5,
random_state=7)
clf_quantile.fit(boston.data, boston.target)
y_quantile = clf_quantile.predict(boston.data)
clf_lad = GradientBoostingRegressor(n_estimators=100, loss='lad',
max_depth=4, random_state=7)
clf_lad.fit(boston.data, boston.target)
y_lad = clf_lad.predict(boston.data)
assert_array_almost_equal(y_quantile, y_lad, decimal=4)
def test_symbol_labels():
# Test with non-integer class labels.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
symbol_y = tosequence(map(str, y))
clf.fit(X, symbol_y)
assert_array_equal(clf.predict(T), tosequence(map(str, true_result)))
assert_equal(100, len(clf.estimators_))
def test_float_class_labels():
# Test with float class labels.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
float_y = np.asarray(y, dtype=np.float32)
clf.fit(X, float_y)
assert_array_equal(clf.predict(T),
np.asarray(true_result, dtype=np.float32))
assert_equal(100, len(clf.estimators_))
def test_shape_y():
# Test with float class labels.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
y_ = np.asarray(y, dtype=np.int32)
y_ = y_[:, np.newaxis]
# This will raise a DataConversionWarning that we want to
# "always" raise, elsewhere the warnings gets ignored in the
# later tests, and the tests that check for this warning fail
assert_warns(DataConversionWarning, clf.fit, X, y_)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
def test_mem_layout():
# Test with different memory layouts of X and y
X_ = np.asfortranarray(X)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X_, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
X_ = np.ascontiguousarray(X)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X_, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
y_ = np.asarray(y, dtype=np.int32)
y_ = np.ascontiguousarray(y_)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y_)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
y_ = np.asarray(y, dtype=np.int32)
y_ = np.asfortranarray(y_)
clf = GradientBoostingClassifier(n_estimators=100, random_state=1)
clf.fit(X, y_)
assert_array_equal(clf.predict(T), true_result)
assert_equal(100, len(clf.estimators_))
def test_oob_improvement():
# Test if oob improvement has correct shape and regression test.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
subsample=0.5)
clf.fit(X, y)
assert_equal(clf.oob_improvement_.shape[0], 100)
# hard-coded regression test - change if modification in OOB computation
assert_array_almost_equal(clf.oob_improvement_[:5],
np.array([0.19, 0.15, 0.12, -0.12, -0.11]),
decimal=2)
def test_oob_improvement_raise():
# Test if oob improvement has correct shape.
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
subsample=1.0)
clf.fit(X, y)
assert_raises(AttributeError, lambda: clf.oob_improvement_)
def test_oob_multilcass_iris():
# Check OOB improvement on multi-class dataset.
clf = GradientBoostingClassifier(n_estimators=100, loss='deviance',
random_state=1, subsample=0.5)
clf.fit(iris.data, iris.target)
score = clf.score(iris.data, iris.target)
assert_greater(score, 0.9)
assert_equal(clf.oob_improvement_.shape[0], clf.n_estimators)
# hard-coded regression test - change if modification in OOB computation
# FIXME: the following snippet does not yield the same results on 32 bits
# assert_array_almost_equal(clf.oob_improvement_[:5],
# np.array([12.68, 10.45, 8.18, 6.43, 5.13]),
# decimal=2)
def test_verbose_output():
# Check verbose=1 does not cause error.
from sklearn.externals.six.moves import cStringIO as StringIO
import sys
old_stdout = sys.stdout
sys.stdout = StringIO()
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
verbose=1, subsample=0.8)
clf.fit(X, y)
verbose_output = sys.stdout
sys.stdout = old_stdout
# check output
verbose_output.seek(0)
header = verbose_output.readline().rstrip()
# with OOB
true_header = ' '.join(['%10s'] + ['%16s'] * 3) % (
'Iter', 'Train Loss', 'OOB Improve', 'Remaining Time')
assert_equal(true_header, header)
n_lines = sum(1 for l in verbose_output.readlines())
# one for 1-10 and then 9 for 20-100
assert_equal(10 + 9, n_lines)
def test_more_verbose_output():
# Check verbose=2 does not cause error.
from sklearn.externals.six.moves import cStringIO as StringIO
import sys
old_stdout = sys.stdout
sys.stdout = StringIO()
clf = GradientBoostingClassifier(n_estimators=100, random_state=1,
verbose=2)
clf.fit(X, y)
verbose_output = sys.stdout
sys.stdout = old_stdout
# check output
verbose_output.seek(0)
header = verbose_output.readline().rstrip()
# no OOB
true_header = ' '.join(['%10s'] + ['%16s'] * 2) % (
'Iter', 'Train Loss', 'Remaining Time')
assert_equal(true_header, header)
n_lines = sum(1 for l in verbose_output.readlines())
# 100 lines for n_estimators==100
assert_equal(100, n_lines)
def test_warm_start():
# Test if warm start equals fit.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=200, max_depth=1)
est.fit(X, y)
est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
est_ws.fit(X, y)
est_ws.set_params(n_estimators=200)
est_ws.fit(X, y)
assert_array_almost_equal(est_ws.predict(X), est.predict(X))
def test_warm_start_n_estimators():
# Test if warm start equals fit - set n_estimators.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=300, max_depth=1)
est.fit(X, y)
est_ws = Cls(n_estimators=100, max_depth=1, warm_start=True)
est_ws.fit(X, y)
est_ws.set_params(n_estimators=300)
est_ws.fit(X, y)
assert_array_almost_equal(est_ws.predict(X), est.predict(X))
def test_warm_start_max_depth():
# Test if possible to fit trees of different depth in ensemble.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
est.fit(X, y)
est.set_params(n_estimators=110, max_depth=2)
est.fit(X, y)
# last 10 trees have different depth
assert_equal(est.estimators_[0, 0].max_depth, 1)
for i in range(1, 11):
assert_equal(est.estimators_[-i, 0].max_depth, 2)
def test_warm_start_clear():
# Test if fit clears state.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=100, max_depth=1)
est.fit(X, y)
est_2 = Cls(n_estimators=100, max_depth=1, warm_start=True)
est_2.fit(X, y) # inits state
est_2.set_params(warm_start=False)
est_2.fit(X, y) # clears old state and equals est
assert_array_almost_equal(est_2.predict(X), est.predict(X))
def test_warm_start_zero_n_estimators():
# Test if warm start with zero n_estimators raises error
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
est.fit(X, y)
est.set_params(n_estimators=0)
assert_raises(ValueError, est.fit, X, y)
def test_warm_start_smaller_n_estimators():
# Test if warm start with smaller n_estimators raises error
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
est.fit(X, y)
est.set_params(n_estimators=99)
assert_raises(ValueError, est.fit, X, y)
def test_warm_start_equal_n_estimators():
# Test if warm start with equal n_estimators does nothing
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=100, max_depth=1)
est.fit(X, y)
est2 = clone(est)
est2.set_params(n_estimators=est.n_estimators, warm_start=True)
est2.fit(X, y)
assert_array_almost_equal(est2.predict(X), est.predict(X))
def test_warm_start_oob_switch():
# Test if oob can be turned on during warm start.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=100, max_depth=1, warm_start=True)
est.fit(X, y)
est.set_params(n_estimators=110, subsample=0.5)
est.fit(X, y)
assert_array_equal(est.oob_improvement_[:100], np.zeros(100))
# the last 10 are not zeros
assert_array_equal(est.oob_improvement_[-10:] == 0.0,
np.zeros(10, dtype=np.bool))
def test_warm_start_oob():
# Test if warm start OOB equals fit.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=200, max_depth=1, subsample=0.5,
random_state=1)
est.fit(X, y)
est_ws = Cls(n_estimators=100, max_depth=1, subsample=0.5,
random_state=1, warm_start=True)
est_ws.fit(X, y)
est_ws.set_params(n_estimators=200)
est_ws.fit(X, y)
assert_array_almost_equal(est_ws.oob_improvement_[:100],
est.oob_improvement_[:100])
def early_stopping_monitor(i, est, locals):
"""Returns True on the 10th iteration. """
if i == 9:
return True
else:
return False
def test_monitor_early_stopping():
# Test if monitor return value works.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
for Cls in [GradientBoostingRegressor, GradientBoostingClassifier]:
est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5)
est.fit(X, y, monitor=early_stopping_monitor)
assert_equal(est.n_estimators, 20) # this is not altered
assert_equal(est.estimators_.shape[0], 10)
assert_equal(est.train_score_.shape[0], 10)
assert_equal(est.oob_improvement_.shape[0], 10)
# try refit
est.set_params(n_estimators=30)
est.fit(X, y)
assert_equal(est.n_estimators, 30)
assert_equal(est.estimators_.shape[0], 30)
assert_equal(est.train_score_.shape[0], 30)
est = Cls(n_estimators=20, max_depth=1, random_state=1, subsample=0.5,
warm_start=True)
est.fit(X, y, monitor=early_stopping_monitor)
assert_equal(est.n_estimators, 20)
assert_equal(est.estimators_.shape[0], 10)
assert_equal(est.train_score_.shape[0], 10)
assert_equal(est.oob_improvement_.shape[0], 10)
# try refit
est.set_params(n_estimators=30, warm_start=False)
est.fit(X, y)
assert_equal(est.n_estimators, 30)
assert_equal(est.train_score_.shape[0], 30)
assert_equal(est.estimators_.shape[0], 30)
assert_equal(est.oob_improvement_.shape[0], 30)
def test_complete_classification():
# Test greedy trees with max_depth + 1 leafs.
from sklearn.tree._tree import TREE_LEAF
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
k = 4
est = GradientBoostingClassifier(n_estimators=20, max_depth=None,
random_state=1, max_leaf_nodes=k + 1)
est.fit(X, y)
tree = est.estimators_[0, 0].tree_
assert_equal(tree.max_depth, k)
assert_equal(tree.children_left[tree.children_left == TREE_LEAF].shape[0],
k + 1)
def test_complete_regression():
# Test greedy trees with max_depth + 1 leafs.
from sklearn.tree._tree import TREE_LEAF
k = 4
est = GradientBoostingRegressor(n_estimators=20, max_depth=None,
random_state=1, max_leaf_nodes=k + 1)
est.fit(boston.data, boston.target)
tree = est.estimators_[-1, 0].tree_
assert_equal(tree.children_left[tree.children_left == TREE_LEAF].shape[0],
k + 1)
def test_zero_estimator_reg():
# Test if ZeroEstimator works for regression.
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
random_state=1, init=ZeroEstimator())
est.fit(boston.data, boston.target)
y_pred = est.predict(boston.data)
mse = mean_squared_error(boston.target, y_pred)
assert_almost_equal(mse, 33.0, decimal=0)
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
random_state=1, init='zero')
est.fit(boston.data, boston.target)
y_pred = est.predict(boston.data)
mse = mean_squared_error(boston.target, y_pred)
assert_almost_equal(mse, 33.0, decimal=0)
est = GradientBoostingRegressor(n_estimators=20, max_depth=1,
random_state=1, init='foobar')
assert_raises(ValueError, est.fit, boston.data, boston.target)
def test_zero_estimator_clf():
# Test if ZeroEstimator works for classification.
X = iris.data
y = np.array(iris.target)
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
random_state=1, init=ZeroEstimator())
est.fit(X, y)
assert_greater(est.score(X, y), 0.96)
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
random_state=1, init='zero')
est.fit(X, y)
assert_greater(est.score(X, y), 0.96)
# binary clf
mask = y != 0
y[mask] = 1
y[~mask] = 0
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
random_state=1, init='zero')
est.fit(X, y)
assert_greater(est.score(X, y), 0.96)
est = GradientBoostingClassifier(n_estimators=20, max_depth=1,
random_state=1, init='foobar')
assert_raises(ValueError, est.fit, X, y)
def test_max_leaf_nodes_max_depth():
# Test precedence of max_leaf_nodes over max_depth.
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
all_estimators = [GradientBoostingRegressor,
GradientBoostingClassifier]
k = 4
for GBEstimator in all_estimators:
est = GBEstimator(max_depth=1, max_leaf_nodes=k).fit(X, y)
tree = est.estimators_[0, 0].tree_
assert_greater(tree.max_depth, 1)
est = GBEstimator(max_depth=1).fit(X, y)
tree = est.estimators_[0, 0].tree_
assert_equal(tree.max_depth, 1)
def test_min_impurity_split():
# Test if min_impurity_split of base estimators is set
# Regression test for #8006
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
all_estimators = [GradientBoostingRegressor, GradientBoostingClassifier]
for GBEstimator in all_estimators:
est = GBEstimator(min_impurity_split=0.1)
est = assert_warns_message(DeprecationWarning, "min_impurity_decrease",
est.fit, X, y)
for tree in est.estimators_.flat:
assert_equal(tree.min_impurity_split, 0.1)
def test_min_impurity_decrease():
X, y = datasets.make_hastie_10_2(n_samples=100, random_state=1)
all_estimators = [GradientBoostingRegressor, GradientBoostingClassifier]
for GBEstimator in all_estimators:
est = GBEstimator(min_impurity_decrease=0.1)
est.fit(X, y)
for tree in est.estimators_.flat:
# Simply check if the parameter is passed on correctly. Tree tests
# will suffice for the actual working of this param
assert_equal(tree.min_impurity_decrease, 0.1)
def test_warm_start_wo_nestimators_change():
# Test if warm_start does nothing if n_estimators is not changed.
# Regression test for #3513.
clf = GradientBoostingClassifier(n_estimators=10, warm_start=True)
clf.fit([[0, 1], [2, 3]], [0, 1])
assert_equal(clf.estimators_.shape[0], 10)
clf.fit([[0, 1], [2, 3]], [0, 1])
assert_equal(clf.estimators_.shape[0], 10)
def test_probability_exponential():
# Predict probabilities.
clf = GradientBoostingClassifier(loss='exponential',
n_estimators=100, random_state=1)
assert_raises(ValueError, clf.predict_proba, T)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
# check if probabilities are in [0, 1].
y_proba = clf.predict_proba(T)
assert_true(np.all(y_proba >= 0.0))
assert_true(np.all(y_proba <= 1.0))
score = clf.decision_function(T).ravel()
assert_array_almost_equal(y_proba[:, 1],
1.0 / (1.0 + np.exp(-2 * score)))
# derive predictions from probabilities
y_pred = clf.classes_.take(y_proba.argmax(axis=1), axis=0)
assert_array_equal(y_pred, true_result)
def test_non_uniform_weights_toy_edge_case_reg():
X = [[1, 0],
[1, 0],
[1, 0],
[0, 1]]
y = [0, 0, 1, 0]
# ignore the first 2 training samples by setting their weight to 0
sample_weight = [0, 0, 1, 1]
for loss in ('huber', 'ls', 'lad', 'quantile'):
gb = GradientBoostingRegressor(learning_rate=1.0, n_estimators=2,
loss=loss)
gb.fit(X, y, sample_weight=sample_weight)
assert_greater(gb.predict([[1, 0]])[0], 0.5)
def test_non_uniform_weights_toy_edge_case_clf():
X = [[1, 0],
[1, 0],
[1, 0],
[0, 1]]
y = [0, 0, 1, 0]
# ignore the first 2 training samples by setting their weight to 0
sample_weight = [0, 0, 1, 1]
for loss in ('deviance', 'exponential'):
gb = GradientBoostingClassifier(n_estimators=5, loss=loss)
gb.fit(X, y, sample_weight=sample_weight)
assert_array_equal(gb.predict([[1, 0]]), [1])
def check_sparse_input(EstimatorClass, X, X_sparse, y):
dense = EstimatorClass(n_estimators=10, random_state=0,
max_depth=2).fit(X, y)
sparse = EstimatorClass(n_estimators=10, random_state=0, max_depth=2,
presort=False).fit(X_sparse, y)
auto = EstimatorClass(n_estimators=10, random_state=0, max_depth=2,
presort='auto').fit(X_sparse, y)
assert_array_almost_equal(sparse.apply(X), dense.apply(X))
assert_array_almost_equal(sparse.predict(X), dense.predict(X))
assert_array_almost_equal(sparse.feature_importances_,
dense.feature_importances_)
assert_array_almost_equal(sparse.apply(X), auto.apply(X))
assert_array_almost_equal(sparse.predict(X), auto.predict(X))
assert_array_almost_equal(sparse.feature_importances_,
auto.feature_importances_)
assert_array_almost_equal(sparse.predict(X_sparse), dense.predict(X))
assert_array_almost_equal(dense.predict(X_sparse), sparse.predict(X))
if isinstance(EstimatorClass, GradientBoostingClassifier):
assert_array_almost_equal(sparse.predict_proba(X),
dense.predict_proba(X))
assert_array_almost_equal(sparse.predict_log_proba(X),
dense.predict_log_proba(X))
assert_array_almost_equal(sparse.predict_proba(X),
auto.predict_proba(X))
assert_array_almost_equal(sparse.predict_log_proba(X),
auto.predict_log_proba(X))
assert_array_almost_equal(sparse.decision_function(X_sparse),
sparse.decision_function(X))
assert_array_almost_equal(dense.decision_function(X_sparse),
sparse.decision_function(X))
assert_array_almost_equal(
np.array(sparse.staged_decision_function(X_sparse)),
np.array(sparse.staged_decision_function(X)))
@skip_if_32bit
def test_sparse_input():
ests = (GradientBoostingClassifier, GradientBoostingRegressor)
sparse_matrices = (csr_matrix, csc_matrix, coo_matrix)
y, X = datasets.make_multilabel_classification(random_state=0,
n_samples=50,
n_features=1,
n_classes=20)
y = y[:, 0]
for EstimatorClass, sparse_matrix in product(ests, sparse_matrices):
yield check_sparse_input, EstimatorClass, X, sparse_matrix(X), y
| bsd-3-clause |
vsmolyakov/cv | gan/keras_dcgan_cifar10.py | 1 | 4808 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import keras
from keras import optimizers
from keras import backend as K
from keras import regularizers
from keras.models import Model
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.layers import Input, Reshape, Conv2D, Conv2DTranspose, LeakyReLU
from keras.utils import np_utils
from keras.utils import plot_model
from keras.models import load_model
from keras.preprocessing import image
from keras.callbacks import ModelCheckpoint
from keras.callbacks import TensorBoard
from keras.callbacks import LearningRateScheduler
from keras.callbacks import EarlyStopping
import os, math
from tqdm import tqdm
from sklearn.metrics import accuracy_score
sns.set_style("whitegrid")
FIGURES_PATH = "./figures/"
def plot_images(generated_images, step, name, num_img=16, dim=(4,4), figsize=(10,10)):
plt.figure()
for i in range(generated_images.shape[0]):
plt.subplot(dim[0], dim[1], i+1)
img = generated_images[i,:,:,:]
plt.imshow(img)
plt.axis('off')
#end for
plt.tight_layout()
plt.savefig(FIGURES_PATH + '/' + name + '_cifar10_' + str(step) + '.png')
return None
#load data
print "loading data..."
(x_train, y_train), (_, _) = keras.datasets.cifar10.load_data()
x_train = x_train[y_train.flatten() == 7] #select horse images
x_train = x_train.reshape((x_train.shape[0],) + (32, 32, 3)).astype('float32')/255.0
#training params
num_iter = 10000
batch_size = 20
#model params
latent_dim = 32
height, width, channels = 32, 32, 3
#generator architecture
generator_input = Input(shape=(latent_dim, ))
x = Dense(128 * 16 * 16)(generator_input)
x = LeakyReLU()(x)
x = Reshape((16, 16, 128))(x)
x = Conv2D(256, 5, padding='same')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(256, 4, strides=2, padding='same')(x)
x = LeakyReLU()(x)
x = Conv2D(256, 5, padding='same')(x)
x = LeakyReLU()(x)
x = Conv2D(256, 5, padding='same')(x)
x = LeakyReLU()(x)
x = Conv2D(channels, 7, activation='tanh', padding='same')(x) #NOTE: tanh
generator = Model(generator_input, x)
generator.summary()
#discriminator architecture
discriminator_input = Input(shape=(height, width, channels))
x = Conv2D(128, 3)(discriminator_input)
x = LeakyReLU()(x)
x = Conv2D(128, 4, strides=2)(x)
x = LeakyReLU()(x)
x = Conv2D(128, 4, strides=2)(x)
x = LeakyReLU()(x)
x = Conv2D(128, 4, strides=2)(x)
x = LeakyReLU()(x)
x = Flatten()(x)
x = Dropout(0.4)(x) #important
x = Dense(1, activation='sigmoid')(x)
discriminator = Model(discriminator_input, x)
discriminator.summary()
discriminator_optimizer = optimizers.RMSprop(lr=0.0008, clipvalue=1.0, decay=1e-8)
discriminator.compile(optimizer=discriminator_optimizer, loss='binary_crossentropy')
#GAN architecture
discriminator.trainable = False #applies to GAN only (since discriminator is compiled)
gan_input = Input(shape=(latent_dim, ))
gan_output = discriminator(generator(gan_input))
gan = Model(gan_input, gan_output)
gan_optimizer = optimizers.RMSprop(lr=0.0004, clipvalue=1.0, decay=1e-8)
gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy')
#GAN training
start = 0
training_loss_dis = []
training_loss_gan = []
for step in tqdm(range(num_iter)):
random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))
generated_images = generator.predict(random_latent_vectors)
stop = start + batch_size
real_images = x_train[start:stop]
combined_images = np.concatenate([generated_images, real_images])
labels = np.concatenate([np.ones((batch_size, 1)), np.zeros((batch_size, 1))])
labels += 0.05 * np.random.random(labels.shape) #important
d_loss = discriminator.train_on_batch(combined_images, labels)
random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))
misleading_targets = np.zeros((batch_size, 1)) #says all images are real
a_loss = gan.train_on_batch(random_latent_vectors, misleading_targets)
start += batch_size
if start > len(x_train) - batch_size:
start = 0
training_loss_dis.append(d_loss)
training_loss_gan.append(a_loss)
if step % 1000 == 0:
#gan.save_weights('gan.h5')
print "step ", step
print "discriminator loss: ", d_loss
print "adversarial loss: ", a_loss
plot_images(generated_images[:16,:,:,:], step, name='generated')
plot_images(real_images[:16,:,:,:], step, name='real')
#end if
#end for
plt.figure()
plt.plot(training_loss_dis, c='b', lw=2.0, label='discriminator')
plt.plot(training_loss_gan, c='r', lw=2.0, label='GAN')
plt.title('DC-GAN CIFAR10')
plt.xlabel('Batches')
plt.ylabel('Training Loss')
plt.legend(loc='upper right')
plt.savefig('./figures/dcgan_training_loss_cifar10.png')
| mit |
victorbergelin/scikit-learn | sklearn/linear_model/ridge.py | 89 | 39360 | """
Ridge regression
"""
# Author: Mathieu Blondel <[email protected]>
# Reuben Fletcher-Costin <[email protected]>
# Fabian Pedregosa <[email protected]>
# Michael Eickenberg <[email protected]>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy import linalg
from scipy import sparse
from scipy.sparse import linalg as sp_linalg
from .base import LinearClassifierMixin, LinearModel
from ..base import RegressorMixin
from ..utils.extmath import safe_sparse_dot
from ..utils import check_X_y
from ..utils import compute_sample_weight
from ..utils import column_or_1d
from ..preprocessing import LabelBinarizer
from ..grid_search import GridSearchCV
from ..externals import six
from ..metrics.scorer import check_scoring
def _solve_sparse_cg(X, y, alpha, max_iter=None, tol=1e-3, verbose=0):
n_samples, n_features = X.shape
X1 = sp_linalg.aslinearoperator(X)
coefs = np.empty((y.shape[1], n_features))
if n_features > n_samples:
def create_mv(curr_alpha):
def _mv(x):
return X1.matvec(X1.rmatvec(x)) + curr_alpha * x
return _mv
else:
def create_mv(curr_alpha):
def _mv(x):
return X1.rmatvec(X1.matvec(x)) + curr_alpha * x
return _mv
for i in range(y.shape[1]):
y_column = y[:, i]
mv = create_mv(alpha[i])
if n_features > n_samples:
# kernel ridge
# w = X.T * inv(X X^t + alpha*Id) y
C = sp_linalg.LinearOperator(
(n_samples, n_samples), matvec=mv, dtype=X.dtype)
coef, info = sp_linalg.cg(C, y_column, tol=tol)
coefs[i] = X1.rmatvec(coef)
else:
# linear ridge
# w = inv(X^t X + alpha*Id) * X.T y
y_column = X1.rmatvec(y_column)
C = sp_linalg.LinearOperator(
(n_features, n_features), matvec=mv, dtype=X.dtype)
coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter,
tol=tol)
if info < 0:
raise ValueError("Failed with error code %d" % info)
if max_iter is None and info > 0 and verbose:
warnings.warn("sparse_cg did not converge after %d iterations." %
info)
return coefs
def _solve_lsqr(X, y, alpha, max_iter=None, tol=1e-3):
n_samples, n_features = X.shape
coefs = np.empty((y.shape[1], n_features))
# According to the lsqr documentation, alpha = damp^2.
sqrt_alpha = np.sqrt(alpha)
for i in range(y.shape[1]):
y_column = y[:, i]
coefs[i] = sp_linalg.lsqr(X, y_column, damp=sqrt_alpha[i],
atol=tol, btol=tol, iter_lim=max_iter)[0]
return coefs
def _solve_cholesky(X, y, alpha):
# w = inv(X^t X + alpha*Id) * X.T y
n_samples, n_features = X.shape
n_targets = y.shape[1]
A = safe_sparse_dot(X.T, X, dense_output=True)
Xy = safe_sparse_dot(X.T, y, dense_output=True)
one_alpha = np.array_equal(alpha, len(alpha) * [alpha[0]])
if one_alpha:
A.flat[::n_features + 1] += alpha[0]
return linalg.solve(A, Xy, sym_pos=True,
overwrite_a=True).T
else:
coefs = np.empty([n_targets, n_features])
for coef, target, current_alpha in zip(coefs, Xy.T, alpha):
A.flat[::n_features + 1] += current_alpha
coef[:] = linalg.solve(A, target, sym_pos=True,
overwrite_a=False).ravel()
A.flat[::n_features + 1] -= current_alpha
return coefs
def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False):
# dual_coef = inv(X X^t + alpha*Id) y
n_samples = K.shape[0]
n_targets = y.shape[1]
if copy:
K = K.copy()
alpha = np.atleast_1d(alpha)
one_alpha = (alpha == alpha[0]).all()
has_sw = isinstance(sample_weight, np.ndarray) \
or sample_weight not in [1.0, None]
if has_sw:
# Unlike other solvers, we need to support sample_weight directly
# because K might be a pre-computed kernel.
sw = np.sqrt(np.atleast_1d(sample_weight))
y = y * sw[:, np.newaxis]
K *= np.outer(sw, sw)
if one_alpha:
# Only one penalty, we can solve multi-target problems in one time.
K.flat[::n_samples + 1] += alpha[0]
try:
# Note: we must use overwrite_a=False in order to be able to
# use the fall-back solution below in case a LinAlgError
# is raised
dual_coef = linalg.solve(K, y, sym_pos=True,
overwrite_a=False)
except np.linalg.LinAlgError:
warnings.warn("Singular matrix in solving dual problem. Using "
"least-squares solution instead.")
dual_coef = linalg.lstsq(K, y)[0]
# K is expensive to compute and store in memory so change it back in
# case it was user-given.
K.flat[::n_samples + 1] -= alpha[0]
if has_sw:
dual_coef *= sw[:, np.newaxis]
return dual_coef
else:
# One penalty per target. We need to solve each target separately.
dual_coefs = np.empty([n_targets, n_samples])
for dual_coef, target, current_alpha in zip(dual_coefs, y.T, alpha):
K.flat[::n_samples + 1] += current_alpha
dual_coef[:] = linalg.solve(K, target, sym_pos=True,
overwrite_a=False).ravel()
K.flat[::n_samples + 1] -= current_alpha
if has_sw:
dual_coefs *= sw[np.newaxis, :]
return dual_coefs.T
def _solve_svd(X, y, alpha):
U, s, Vt = linalg.svd(X, full_matrices=False)
idx = s > 1e-15 # same default value as scipy.linalg.pinv
s_nnz = s[idx][:, np.newaxis]
UTy = np.dot(U.T, y)
d = np.zeros((s.size, alpha.size))
d[idx] = s_nnz / (s_nnz ** 2 + alpha)
d_UT_y = d * UTy
return np.dot(Vt.T, d_UT_y).T
def _rescale_data(X, y, sample_weight):
"""Rescale data so as to support sample_weight"""
n_samples = X.shape[0]
sample_weight = sample_weight * np.ones(n_samples)
sample_weight = np.sqrt(sample_weight)
sw_matrix = sparse.dia_matrix((sample_weight, 0),
shape=(n_samples, n_samples))
X = safe_sparse_dot(sw_matrix, X)
y = safe_sparse_dot(sw_matrix, y)
return X, y
def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
max_iter=None, tol=1e-3, verbose=0):
"""Solve the ridge equation by the method of normal equations.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
X : {array-like, sparse matrix, LinearOperator},
shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
alpha : {float, array-like},
shape = [n_targets] if array-like
The l_2 penalty to be used. If an array is passed, penalties are
assumed to be specific to targets
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample. If sample_weight is set, then
the solver will automatically be set to 'cholesky'
solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg'}
Solver to use in the computational routines:
- 'auto' chooses the solver automatically based on the type of data.
- 'svd' uses a Singular Value Decomposition of X to compute the Ridge
coefficients. More stable for singular matrices than
'cholesky'.
- 'cholesky' uses the standard scipy.linalg.solve function to
obtain a closed-form solution via a Cholesky decomposition of
dot(X.T, X)
- 'sparse_cg' uses the conjugate gradient solver as found in
scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
more appropriate than 'cholesky' for large-scale data
(possibility to set `tol` and `max_iter`).
- 'lsqr' uses the dedicated regularized least-squares routine
scipy.sparse.linalg.lsqr. It is the fatest but may not be available
in old scipy versions. It also uses an iterative procedure.
All three solvers support both dense and sparse data.
tol : float
Precision of the solution.
verbose : int
Verbosity level. Setting verbose > 0 will display additional information
depending on the solver used.
Returns
-------
coef : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
Notes
-----
This function won't compute the intercept.
"""
n_samples, n_features = X.shape
if y.ndim > 2:
raise ValueError("Target y has the wrong shape %s" % str(y.shape))
ravel = False
if y.ndim == 1:
y = y.reshape(-1, 1)
ravel = True
n_samples_, n_targets = y.shape
if n_samples != n_samples_:
raise ValueError("Number of samples in X and y does not correspond:"
" %d != %d" % (n_samples, n_samples_))
has_sw = sample_weight is not None
if solver == 'auto':
# cholesky if it's a dense array and cg in
# any other case
if not sparse.issparse(X) or has_sw:
solver = 'cholesky'
else:
solver = 'sparse_cg'
elif solver == 'lsqr' and not hasattr(sp_linalg, 'lsqr'):
warnings.warn("""lsqr not available on this machine, falling back
to sparse_cg.""")
solver = 'sparse_cg'
if has_sw:
if np.atleast_1d(sample_weight).ndim > 1:
raise ValueError("Sample weights must be 1D array or scalar")
# Sample weight can be implemented via a simple rescaling.
X, y = _rescale_data(X, y, sample_weight)
# There should be either 1 or n_targets penalties
alpha = np.asarray(alpha).ravel()
if alpha.size not in [1, n_targets]:
raise ValueError("Number of targets and number of penalties "
"do not correspond: %d != %d"
% (alpha.size, n_targets))
if alpha.size == 1 and n_targets > 1:
alpha = np.repeat(alpha, n_targets)
if solver not in ('sparse_cg', 'cholesky', 'svd', 'lsqr'):
raise ValueError('Solver %s not understood' % solver)
if solver == 'sparse_cg':
coef = _solve_sparse_cg(X, y, alpha, max_iter, tol, verbose)
elif solver == "lsqr":
coef = _solve_lsqr(X, y, alpha, max_iter, tol)
elif solver == 'cholesky':
if n_features > n_samples:
K = safe_sparse_dot(X, X.T, dense_output=True)
try:
dual_coef = _solve_cholesky_kernel(K, y, alpha)
coef = safe_sparse_dot(X.T, dual_coef, dense_output=True).T
except linalg.LinAlgError:
# use SVD solver if matrix is singular
solver = 'svd'
else:
try:
coef = _solve_cholesky(X, y, alpha)
except linalg.LinAlgError:
# use SVD solver if matrix is singular
solver = 'svd'
if solver == 'svd':
if sparse.issparse(X):
raise TypeError('SVD solver does not support sparse'
' inputs currently')
coef = _solve_svd(X, y, alpha)
if ravel:
# When y was passed as a 1d-array, we flatten the coefficients.
coef = coef.ravel()
return coef
class _BaseRidge(six.with_metaclass(ABCMeta, LinearModel)):
@abstractmethod
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=1e-3, solver="auto"):
self.alpha = alpha
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.max_iter = max_iter
self.tol = tol
self.solver = solver
def fit(self, X, y, sample_weight=None):
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float,
multi_output=True, y_numeric=True)
if ((sample_weight is not None) and
np.atleast_1d(sample_weight).ndim > 1):
raise ValueError("Sample weights must be 1D array or scalar")
X, y, X_mean, y_mean, X_std = self._center_data(
X, y, self.fit_intercept, self.normalize, self.copy_X,
sample_weight=sample_weight)
self.coef_ = ridge_regression(X, y,
alpha=self.alpha,
sample_weight=sample_weight,
max_iter=self.max_iter,
tol=self.tol,
solver=self.solver)
self._set_intercept(X_mean, y_mean, X_std)
return self
class Ridge(_BaseRidge, RegressorMixin):
"""Linear least squares with l2 regularization.
This model solves a regression model where the loss function is
the linear least squares function and regularization is given by
the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alpha : {float, array-like}
shape = [n_targets]
Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
``(2*C)^-1`` in other linear models such as LogisticRegression or
LinearSVC. If an array is passed, penalties are assumed to be specific
to the targets. Hence they must correspond in number.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg'}
Solver to use in the computational routines:
- 'auto' chooses the solver automatically based on the type of data.
- 'svd' uses a Singular Value Decomposition of X to compute the Ridge
coefficients. More stable for singular matrices than
'cholesky'.
- 'cholesky' uses the standard scipy.linalg.solve function to
obtain a closed-form solution.
- 'sparse_cg' uses the conjugate gradient solver as found in
scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
more appropriate than 'cholesky' for large-scale data
(possibility to set `tol` and `max_iter`).
- 'lsqr' uses the dedicated regularized least-squares routine
scipy.sparse.linalg.lsqr. It is the fatest but may not be available
in old scipy versions. It also uses an iterative procedure.
All three solvers support both dense and sparse data.
tol : float
Precision of the solution.
Attributes
----------
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
See also
--------
RidgeClassifier, RidgeCV, KernelRidge
Examples
--------
>>> from sklearn.linear_model import Ridge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = Ridge(alpha=1.0)
>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, solver='auto', tol=0.001)
"""
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=1e-3, solver="auto"):
super(Ridge, self).__init__(alpha=alpha, fit_intercept=fit_intercept,
normalize=normalize, copy_X=copy_X,
max_iter=max_iter, tol=tol, solver=solver)
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample
Returns
-------
self : returns an instance of self.
"""
return super(Ridge, self).fit(X, y, sample_weight=sample_weight)
class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
"""Classifier using Ridge regression.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alpha : float
Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
``(2*C)^-1`` in other linear models such as LogisticRegression or
LinearSVC.
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set to false, no
intercept will be used in calculations (e.g. data is expected to be
already centered).
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg'}
Solver to use in the computational
routines. 'svd' will use a Singular value decomposition to obtain
the solution, 'cholesky' will use the standard
scipy.linalg.solve function, 'sparse_cg' will use the
conjugate gradient solver as found in
scipy.sparse.linalg.cg while 'auto' will chose the most
appropriate depending on the matrix X. 'lsqr' uses
a direct regularized least-squares routine provided by scipy.
tol : float
Precision of the solution.
Attributes
----------
coef_ : array, shape = [n_features] or [n_classes, n_features]
Weight vector(s).
See also
--------
Ridge, RidgeClassifierCV
Notes
-----
For multi-class classification, n_class classifiers are trained in
a one-versus-all approach. Concretely, this is implemented by taking
advantage of the multi-variate response support in Ridge.
"""
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=1e-3, class_weight=None,
solver="auto"):
super(RidgeClassifier, self).__init__(
alpha=alpha, fit_intercept=fit_intercept, normalize=normalize,
copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver)
self.class_weight = class_weight
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples,n_features]
Training data
y : array-like, shape = [n_samples]
Target values
sample_weight : float or numpy array of shape (n_samples,)
Sample weight.
Returns
-------
self : returns an instance of self.
"""
self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
Y = self._label_binarizer.fit_transform(y)
if not self._label_binarizer.y_type_.startswith('multilabel'):
y = column_or_1d(y, warn=True)
if self.class_weight:
if sample_weight is None:
sample_weight = 1.
# modify the sample weights with the corresponding class weight
sample_weight = (sample_weight *
compute_sample_weight(self.class_weight, y))
super(RidgeClassifier, self).fit(X, Y, sample_weight=sample_weight)
return self
@property
def classes_(self):
return self._label_binarizer.classes_
class _RidgeGCV(LinearModel):
"""Ridge regression with built-in Generalized Cross-Validation
It allows efficient Leave-One-Out cross-validation.
This class is not intended to be used directly. Use RidgeCV instead.
Notes
-----
We want to solve (K + alpha*Id)c = y,
where K = X X^T is the kernel matrix.
Let G = (K + alpha*Id)^-1.
Dual solution: c = Gy
Primal solution: w = X^T c
Compute eigendecomposition K = Q V Q^T.
Then G = Q (V + alpha*Id)^-1 Q^T,
where (V + alpha*Id) is diagonal.
It is thus inexpensive to inverse for many alphas.
Let loov be the vector of prediction values for each example
when the model was fitted with all examples but this example.
loov = (KGY - diag(KG)Y) / diag(I-KG)
Let looe be the vector of prediction errors for each example
when the model was fitted with all examples but this example.
looe = y - loov = c / diag(G)
References
----------
http://cbcl.mit.edu/projects/cbcl/publications/ps/MIT-CSAIL-TR-2007-025.pdf
http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
"""
def __init__(self, alphas=(0.1, 1.0, 10.0),
fit_intercept=True, normalize=False,
scoring=None, copy_X=True,
gcv_mode=None, store_cv_values=False):
self.alphas = np.asarray(alphas)
self.fit_intercept = fit_intercept
self.normalize = normalize
self.scoring = scoring
self.copy_X = copy_X
self.gcv_mode = gcv_mode
self.store_cv_values = store_cv_values
def _pre_compute(self, X, y):
# even if X is very sparse, K is usually very dense
K = safe_sparse_dot(X, X.T, dense_output=True)
v, Q = linalg.eigh(K)
QT_y = np.dot(Q.T, y)
return v, Q, QT_y
def _decomp_diag(self, v_prime, Q):
# compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T))
return (v_prime * Q ** 2).sum(axis=-1)
def _diag_dot(self, D, B):
# compute dot(diag(D), B)
if len(B.shape) > 1:
# handle case where B is > 1-d
D = D[(slice(None), ) + (np.newaxis, ) * (len(B.shape) - 1)]
return D * B
def _errors(self, alpha, y, v, Q, QT_y):
# don't construct matrix G, instead compute action on y & diagonal
w = 1.0 / (v + alpha)
c = np.dot(Q, self._diag_dot(w, QT_y))
G_diag = self._decomp_diag(w, Q)
# handle case where y is 2-d
if len(y.shape) != 1:
G_diag = G_diag[:, np.newaxis]
return (c / G_diag) ** 2, c
def _values(self, alpha, y, v, Q, QT_y):
# don't construct matrix G, instead compute action on y & diagonal
w = 1.0 / (v + alpha)
c = np.dot(Q, self._diag_dot(w, QT_y))
G_diag = self._decomp_diag(w, Q)
# handle case where y is 2-d
if len(y.shape) != 1:
G_diag = G_diag[:, np.newaxis]
return y - (c / G_diag), c
def _pre_compute_svd(self, X, y):
if sparse.issparse(X):
raise TypeError("SVD not supported for sparse matrices")
U, s, _ = linalg.svd(X, full_matrices=0)
v = s ** 2
UT_y = np.dot(U.T, y)
return v, U, UT_y
def _errors_svd(self, alpha, y, v, U, UT_y):
w = ((v + alpha) ** -1) - (alpha ** -1)
c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha ** -1) * y
G_diag = self._decomp_diag(w, U) + (alpha ** -1)
if len(y.shape) != 1:
# handle case where y is 2-d
G_diag = G_diag[:, np.newaxis]
return (c / G_diag) ** 2, c
def _values_svd(self, alpha, y, v, U, UT_y):
w = ((v + alpha) ** -1) - (alpha ** -1)
c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha ** -1) * y
G_diag = self._decomp_diag(w, U) + (alpha ** -1)
if len(y.shape) != 1:
# handle case when y is 2-d
G_diag = G_diag[:, np.newaxis]
return y - (c / G_diag), c
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or array-like of shape [n_samples]
Sample weight
Returns
-------
self : Returns self.
"""
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float,
multi_output=True, y_numeric=True)
n_samples, n_features = X.shape
X, y, X_mean, y_mean, X_std = LinearModel._center_data(
X, y, self.fit_intercept, self.normalize, self.copy_X,
sample_weight=sample_weight)
gcv_mode = self.gcv_mode
with_sw = len(np.shape(sample_weight))
if gcv_mode is None or gcv_mode == 'auto':
if sparse.issparse(X) or n_features > n_samples or with_sw:
gcv_mode = 'eigen'
else:
gcv_mode = 'svd'
elif gcv_mode == "svd" and with_sw:
# FIXME non-uniform sample weights not yet supported
warnings.warn("non-uniform sample weights unsupported for svd, "
"forcing usage of eigen")
gcv_mode = 'eigen'
if gcv_mode == 'eigen':
_pre_compute = self._pre_compute
_errors = self._errors
_values = self._values
elif gcv_mode == 'svd':
# assert n_samples >= n_features
_pre_compute = self._pre_compute_svd
_errors = self._errors_svd
_values = self._values_svd
else:
raise ValueError('bad gcv_mode "%s"' % gcv_mode)
v, Q, QT_y = _pre_compute(X, y)
n_y = 1 if len(y.shape) == 1 else y.shape[1]
cv_values = np.zeros((n_samples * n_y, len(self.alphas)))
C = []
scorer = check_scoring(self, scoring=self.scoring, allow_none=True)
error = scorer is None
for i, alpha in enumerate(self.alphas):
weighted_alpha = (sample_weight * alpha
if sample_weight is not None
else alpha)
if error:
out, c = _errors(weighted_alpha, y, v, Q, QT_y)
else:
out, c = _values(weighted_alpha, y, v, Q, QT_y)
cv_values[:, i] = out.ravel()
C.append(c)
if error:
best = cv_values.mean(axis=0).argmin()
else:
# The scorer want an object that will make the predictions but
# they are already computed efficiently by _RidgeGCV. This
# identity_estimator will just return them
def identity_estimator():
pass
identity_estimator.decision_function = lambda y_predict: y_predict
identity_estimator.predict = lambda y_predict: y_predict
out = [scorer(identity_estimator, y.ravel(), cv_values[:, i])
for i in range(len(self.alphas))]
best = np.argmax(out)
self.alpha_ = self.alphas[best]
self.dual_coef_ = C[best]
self.coef_ = safe_sparse_dot(self.dual_coef_.T, X)
self._set_intercept(X_mean, y_mean, X_std)
if self.store_cv_values:
if len(y.shape) == 1:
cv_values_shape = n_samples, len(self.alphas)
else:
cv_values_shape = n_samples, n_y, len(self.alphas)
self.cv_values_ = cv_values.reshape(cv_values_shape)
return self
class _BaseRidgeCV(LinearModel):
def __init__(self, alphas=(0.1, 1.0, 10.0),
fit_intercept=True, normalize=False, scoring=None,
cv=None, gcv_mode=None,
store_cv_values=False):
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.scoring = scoring
self.cv = cv
self.gcv_mode = gcv_mode
self.store_cv_values = store_cv_values
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or array-like of shape [n_samples]
Sample weight
Returns
-------
self : Returns self.
"""
if self.cv is None:
estimator = _RidgeGCV(self.alphas,
fit_intercept=self.fit_intercept,
normalize=self.normalize,
scoring=self.scoring,
gcv_mode=self.gcv_mode,
store_cv_values=self.store_cv_values)
estimator.fit(X, y, sample_weight=sample_weight)
self.alpha_ = estimator.alpha_
if self.store_cv_values:
self.cv_values_ = estimator.cv_values_
else:
if self.store_cv_values:
raise ValueError("cv!=None and store_cv_values=True "
" are incompatible")
parameters = {'alpha': self.alphas}
fit_params = {'sample_weight' : sample_weight}
gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept),
parameters, fit_params=fit_params, cv=self.cv)
gs.fit(X, y)
estimator = gs.best_estimator_
self.alpha_ = gs.best_estimator_.alpha
self.coef_ = estimator.coef_
self.intercept_ = estimator.intercept_
return self
class RidgeCV(_BaseRidgeCV, RegressorMixin):
"""Ridge regression with built-in cross-validation.
By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alphas : numpy array of shape [n_alphas]
Array of alpha values to try.
Small positive values of alpha improve the conditioning of the
problem and reduce the variance of the estimates.
Alpha corresponds to ``(2*C)^-1`` in other linear models such as
LogisticRegression or LinearSVC.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
cv : integer or cross-validation generator, optional
If None, Generalized Cross-Validation (efficient Leave-One-Out)
will be used.
If an integer is passed, it is the number of folds for KFold cross
validation. Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects
gcv_mode : {None, 'auto', 'svd', eigen'}, optional
Flag indicating which strategy to use when performing
Generalized Cross-Validation. Options are::
'auto' : use svd if n_samples > n_features or when X is a sparse
matrix, otherwise use eigen
'svd' : force computation via singular value decomposition of X
(does not work for sparse matrices)
'eigen' : force computation via eigendecomposition of X^T X
The 'auto' mode is the default and is intended to pick the cheaper
option of the two depending upon the shape and format of the training
data.
store_cv_values : boolean, default=False
Flag indicating if the cross-validation values corresponding to
each alpha should be stored in the `cv_values_` attribute (see
below). This flag is only compatible with `cv=None` (i.e. using
Generalized Cross-Validation).
Attributes
----------
cv_values_ : array, shape = [n_samples, n_alphas] or \
shape = [n_samples, n_targets, n_alphas], optional
Cross-validation values for each alpha (if `store_cv_values=True` and \
`cv=None`). After `fit()` has been called, this attribute will \
contain the mean squared errors (by default) or the values of the \
`{loss,score}_func` function (if provided in the constructor).
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
alpha_ : float
Estimated regularization parameter.
intercept_ : float | array, shape = (n_targets,)
Independent term in decision function. Set to 0.0 if
``fit_intercept = False``.
See also
--------
Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeClassifierCV: Ridge classifier with built-in cross validation
"""
pass
class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
"""Ridge classifier with built-in cross-validation.
By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation. Currently, only the n_features >
n_samples case is handled efficiently.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alphas : numpy array of shape [n_alphas]
Array of alpha values to try.
Small positive values of alpha improve the conditioning of the
problem and reduce the variance of the estimates.
Alpha corresponds to ``(2*C)^-1`` in other linear models such as
LogisticRegression or LinearSVC.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
cv : cross-validation generator, optional
If None, Generalized Cross-Validation (efficient Leave-One-Out)
will be used.
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
Attributes
----------
cv_values_ : array, shape = [n_samples, n_alphas] or \
shape = [n_samples, n_responses, n_alphas], optional
Cross-validation values for each alpha (if `store_cv_values=True` and
`cv=None`). After `fit()` has been called, this attribute will contain \
the mean squared errors (by default) or the values of the \
`{loss,score}_func` function (if provided in the constructor).
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
alpha_ : float
Estimated regularization parameter
See also
--------
Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeCV: Ridge regression with built-in cross validation
Notes
-----
For multi-class classification, n_class classifiers are trained in
a one-versus-all approach. Concretely, this is implemented by taking
advantage of the multi-variate response support in Ridge.
"""
def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,
normalize=False, scoring=None, cv=None, class_weight=None):
super(RidgeClassifierCV, self).__init__(
alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,
scoring=scoring, cv=cv)
self.class_weight = class_weight
def fit(self, X, y, sample_weight=None):
"""Fit the ridge classifier.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
sample_weight : float or numpy array of shape (n_samples,)
Sample weight.
Returns
-------
self : object
Returns self.
"""
self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
Y = self._label_binarizer.fit_transform(y)
if not self._label_binarizer.y_type_.startswith('multilabel'):
y = column_or_1d(y, warn=True)
if self.class_weight:
if sample_weight is None:
sample_weight = 1.
# modify the sample weights with the corresponding class weight
sample_weight = (sample_weight *
compute_sample_weight(self.class_weight, y))
_BaseRidgeCV.fit(self, X, Y, sample_weight=sample_weight)
return self
@property
def classes_(self):
return self._label_binarizer.classes_
| bsd-3-clause |
lancezlin/ml_template_py | lib/python2.7/site-packages/matplotlib/tests/test_patheffects.py | 7 | 5067 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import numpy as np
from matplotlib.testing.decorators import (image_comparison, cleanup,
knownfailureif)
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
try:
# mock in python 3.3+
from unittest import mock
except ImportError:
import mock
from nose.tools import assert_equal
@image_comparison(baseline_images=['patheffect1'], remove_text=True)
def test_patheffect1():
ax1 = plt.subplot(111)
ax1.imshow([[1, 2], [2, 3]])
txt = ax1.annotate("test", (1., 1.), (0., 0),
arrowprops=dict(arrowstyle="->",
connectionstyle="angle3", lw=2),
size=20, ha="center",
path_effects=[path_effects.withStroke(linewidth=3,
foreground="w")])
txt.arrow_patch.set_path_effects([path_effects.Stroke(linewidth=5,
foreground="w"),
path_effects.Normal()])
ax1.grid(True, linestyle="-")
pe = [path_effects.withStroke(linewidth=3, foreground="w")]
for l in ax1.get_xgridlines() + ax1.get_ygridlines():
l.set_path_effects(pe)
@image_comparison(baseline_images=['patheffect2'], remove_text=True)
def test_patheffect2():
ax2 = plt.subplot(111)
arr = np.arange(25).reshape((5, 5))
ax2.imshow(arr)
cntr = ax2.contour(arr, colors="k")
plt.setp(cntr.collections,
path_effects=[path_effects.withStroke(linewidth=3,
foreground="w")])
clbls = ax2.clabel(cntr, fmt="%2.0f", use_clabeltext=True)
plt.setp(clbls,
path_effects=[path_effects.withStroke(linewidth=3,
foreground="w")])
@image_comparison(baseline_images=['patheffect3'])
def test_patheffect3():
p1, = plt.plot([1, 3, 5, 4, 3], 'o-b', lw=4)
p1.set_path_effects([path_effects.SimpleLineShadow(),
path_effects.Normal()])
plt.title(r'testing$^{123}$',
path_effects=[path_effects.withStroke(linewidth=1, foreground="r")])
leg = plt.legend([p1], [r'Line 1$^2$'], fancybox=True, loc=2)
leg.legendPatch.set_path_effects([path_effects.withSimplePatchShadow()])
text = plt.text(2, 3, 'Drop test', color='white',
bbox={'boxstyle': 'circle,pad=0.1', 'color': 'red'})
pe = [path_effects.Stroke(linewidth=3.75, foreground='k'),
path_effects.withSimplePatchShadow((6, -3), shadow_rgbFace='blue')]
text.set_path_effects(pe)
text.get_bbox_patch().set_path_effects(pe)
pe = [path_effects.PathPatchEffect(offset=(4, -4), hatch='xxxx',
facecolor='gray'),
path_effects.PathPatchEffect(edgecolor='white', facecolor='black',
lw=1.1)]
t = plt.gcf().text(0.02, 0.1, 'Hatch shadow', fontsize=75, weight=1000,
va='center')
t.set_path_effects(pe)
@cleanup
@knownfailureif(True)
def test_PathEffect_points_to_pixels():
fig = plt.figure(dpi=150)
p1, = plt.plot(range(10))
p1.set_path_effects([path_effects.SimpleLineShadow(),
path_effects.Normal()])
renderer = fig.canvas.get_renderer()
pe_renderer = path_effects.SimpleLineShadow().get_proxy_renderer(renderer)
assert isinstance(pe_renderer, path_effects.PathEffectRenderer), (
'Expected a PathEffectRendere instance, got '
'a {0} instance.'.format(type(pe_renderer)))
# Confirm that using a path effects renderer maintains point sizes
# appropriately. Otherwise rendered font would be the wrong size.
assert_equal(renderer.points_to_pixels(15),
pe_renderer.points_to_pixels(15))
def test_SimplePatchShadow_offset():
pe = path_effects.SimplePatchShadow(offset=(4, 5))
assert_equal(pe._offset, (4, 5))
@image_comparison(baseline_images=['collection'])
def test_collection():
x, y = np.meshgrid(np.linspace(0, 10, 150), np.linspace(-5, 5, 100))
data = np.sin(x) + np.cos(y)
cs = plt.contour(data)
pe = [path_effects.PathPatchEffect(edgecolor='black', facecolor='none',
linewidth=12),
path_effects.Stroke(linewidth=5)]
for collection in cs.collections:
collection.set_path_effects(pe)
for text in plt.clabel(cs, colors='white'):
text.set_path_effects([path_effects.withStroke(foreground='k',
linewidth=3)])
text.set_bbox({'boxstyle': 'sawtooth', 'facecolor': 'none',
'edgecolor': 'blue'})
if __name__ == '__main__':
import nose
nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
| mit |
backupManager/pyflag | src/plugins/Tools/MatlibPlot.py | 6 | 1309 | """ This is a module which exports a set of plotters based on
matplotlib. Yoy will need to have matplotlib installed using something
like:
apt-get install python-matplotlib
"""
import pyflag.DB as DB
import pyflag.Graph as Graph
try:
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import numpy as np
import matplotlib.image as image
import matplotlib.figure as figure
import StringIO
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import tempfile
except ImportError:
active = False
class LinePlot(Graph.GenericGraph):
name = 'Line Plot'
def form(self, query, result):
pass
def plot(self, gen, query, result):
fig = figure.Figure()
ax = fig.add_subplot(111)
x=[]
y=[]
for a,b in gen:
x.append(a)
y.append(b)
ax.plot(x,y , '.')
ax.grid()
## Make a temporary file name:
fd = tempfile.TemporaryFile()
canvas=FigureCanvas(fig)
canvas.print_figure(fd)
fd.seek(0)
result.generator.content_type = "image/png"
result.generator.generator = [ fd.read(), ]
| gpl-2.0 |
and2egg/philharmonic | philharmonic/energy_meter/simple_visualiser.py | 2 | 1414 | '''
Created on Jun 15, 2012
@author: kermit
'''
#from pylab import *
import matplotlib.pyplot as plt
import numpy as np
from haley_api import Wattmeter
from continuous_energy_meter import ContinuousEnergyMeter
from runner_til_keypressed import RunnerTilKeypressed
machines = ["snowwhite", "grumpy"]
metrics = ["active_power", "apparent_power"]
def draw_current_state():
plt.hold(True)
en_meter = Wattmeter()
colors = ["r", "b"]
#machines = ["grumpy", "snowwhite", "sneezy"]
results = en_meter.multiple(machines, metrics)
mat = np.matrix(results[1:])
positions = np.arange(0,len(machines)*len(metrics),len(metrics))
print(mat)
rows = len(np.array(mat[:,0].T)[0])
for i in range(rows):
data_row = np.array(mat[i, :])[0]
plt.bar(positions, data_row, color = colors[i])
positions = [position+1 for position in positions]
plt.show()
def draw_state_long():
# gather data
#machines = ["grumpy", "snowwhite", "sneezy"]
interval = 1 # seconds
cont_en_meter = ContinuousEnergyMeter(machines, metrics, interval)
runner = RunnerTilKeypressed()
runner.run(cont_en_meter)
# draw it
print ("Gonna draw this:")
data = cont_en_meter.get_all_data()
print(data)
#plt.plot(data)
plt.figure()
data.plot()
#plt.show()
if __name__ == '__main__':
#draw_current_state()
draw_state_long() | gpl-3.0 |
DuCorey/bokeh | bokeh/core/properties.py | 2 | 64367 | ''' Properties are objects that can be assigned as class attributes on Bokeh
models, to provide automatic serialization, validation, and documentation.
This documentation is broken down into the following sections:
.. contents::
:local:
Overview
--------
There are many property types defined in the module, for example ``Int`` to
represent integral values, ``Seq`` to represent sequences (e.g. lists or
tuples, etc.). Properties can also be combined: ``Seq(Float)`` represents
a sequence of floating point values.
For example, the following defines a model that has integer, string, and
list[float] properties:
.. code-block:: python
class SomeModel(Model):
foo = Int
bar = String(default="something")
baz = List(Float, help="docs for baz prop")
As seen, properties can be declared as just the property type, e.g.
``foo = Int``, in which case the properties are automatically instantiated
on new Model objects. Or the property can be instantiated on the class,
and configured with default values and help strings.
The properties of this class can be initialized by specifying keyword
arguments to the initializer:
.. code-block:: python
m = SomeModel(foo=10, bar="a str", baz=[1,2,3,4])
But also by setting the attributes on an instance:
.. code-block:: python
m.foo = 20
Attempts to set a property to a value of the wrong type will
result in a ``ValueError`` exception:
.. code-block:: python
>>> m.foo = 2.3
Traceback (most recent call last):
<< traceback omitted >>
ValueError: expected a value of type Integral, got 2.3 of type float
Models with properties know how to serialize themselves, to be understood
by BokehJS. Additionally, any help strings provided on properties can be
easily and automatically extracted with the Sphinx extensions in the
:ref:`bokeh.sphinxext` module.
Basic Properties
----------------
{basic_properties}
Container Properties
--------------------
{container_properties}
DataSpec Properties
-------------------
{dataspec_properties}
Helpers
~~~~~~~
.. autofunction:: field
.. autofunction:: value
Special Properties
------------------
.. autoclass:: Include
.. autoclass:: Override
'''
from __future__ import absolute_import, print_function
import logging
logger = logging.getLogger(__name__)
import collections
from copy import copy
import datetime
import dateutil.parser
from importlib import import_module
import numbers
import re
from six import string_types, iteritems
from ..colors import RGB
from ..util.dependencies import import_optional
from ..util.serialization import convert_datetime_type, decode_base64_dict, transform_column_source_data
from ..util.string import nice_join, format_docstring
from .property.bases import ContainerProperty, DeserializationError, ParameterizedProperty, Property, PrimitiveProperty
from .property.descriptor_factory import PropertyDescriptorFactory
from .property.descriptors import BasicPropertyDescriptor, DataSpecPropertyDescriptor, UnitsSpecPropertyDescriptor
from . import enums
pd = import_optional('pandas')
bokeh_bool_types = (bool,)
try:
import numpy as np
bokeh_bool_types += (np.bool8,)
except ImportError:
pass
bokeh_integer_types = (numbers.Integral,)
class Bool(PrimitiveProperty):
''' Accept boolean values.
Args:
default (obj or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class BoolModel(HasProps):
... prop = Bool(default=False)
...
>>> m = BoolModel()
>>> m.prop = True
>>> m.prop = False
>>> m.prop = 10 # ValueError !!
'''
_underlying_type = bokeh_bool_types
class Int(PrimitiveProperty):
''' Accept signed integer values.
Args:
default (int or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class IntModel(HasProps):
... prop = Int()
...
>>> m = IntModel()
>>> m.prop = 10
>>> m.prop = -200
>>> m.prop = 10.3 # ValueError !!
'''
_underlying_type = bokeh_integer_types
class Float(PrimitiveProperty):
''' Accept floating point values.
Args:
default (float or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class FloatModel(HasProps):
... prop = Float()
...
>>> m = FloatModel()
>>> m.prop = 10
>>> m.prop = 10.3
>>> m.prop = "foo" # ValueError !!
'''
_underlying_type = (numbers.Real,)
class Complex(PrimitiveProperty):
''' Accept complex floating point values.
Args:
default (complex or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
'''
_underlying_type = (numbers.Complex,)
class String(PrimitiveProperty):
''' Accept string values.
Args:
default (string or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class StringModel(HasProps):
... prop = String()
...
>>> m = StringModel()
>>> m.prop = "foo"
>>> m.prop = 10.3 # ValueError !!
>>> m.prop = [1, 2, 3] # ValueError !!
'''
_underlying_type = string_types
class Regex(String):
''' Accept strings that match a given regular expression.
Args:
default (string or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class RegexModel(HasProps):
... prop = Regex("foo[0-9]+bar")
...
>>> m = RegexModel()
>>> m.prop = "foo123bar"
>>> m.prop = "foo" # ValueError !!
>>> m.prop = [1, 2, 3] # ValueError !!
'''
def __init__(self, regex, default=None, help=None):
self.regex = re.compile(regex)
super(Regex, self).__init__(default=default, help=help)
def __str__(self):
return "%s(%r)" % (self.__class__.__name__, self.regex.pattern)
def validate(self, value):
super(Regex, self).validate(value)
if not (value is None or self.regex.match(value) is not None):
raise ValueError("expected a string matching %r pattern, got %r" % (self.regex.pattern, value))
class JSON(String):
''' Accept JSON string values.
The value is transmitted and received by BokehJS as a *string*
containing JSON content. i.e., you must use ``JSON.parse`` to unpack
the value into a JavaScript hash.
Args:
default (string or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
'''
def validate(self, value):
super(JSON, self).validate(value)
if value is None: return
try:
import json
json.loads(value)
except ValueError:
raise ValueError("expected JSON text, got %r" % value)
class Instance(Property):
''' Accept values that are instances of |HasProps|.
'''
def __init__(self, instance_type, default=None, help=None):
if not isinstance(instance_type, (type,) + string_types):
raise ValueError("expected a type or string, got %s" % instance_type)
from .has_props import HasProps
if isinstance(instance_type, type) and not issubclass(instance_type, HasProps):
raise ValueError("expected a subclass of HasProps, got %s" % instance_type)
self._instance_type = instance_type
super(Instance, self).__init__(default=default, help=help)
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, self.instance_type.__name__)
@property
def has_ref(self):
return True
@property
def instance_type(self):
if isinstance(self._instance_type, str):
module, name = self._instance_type.rsplit(".", 1)
self._instance_type = getattr(import_module(module, "bokeh"), name)
return self._instance_type
def from_json(self, json, models=None):
if json is None:
return None
elif isinstance(json, dict):
from ..model import Model
if issubclass(self.instance_type, Model):
if models is None:
raise DeserializationError("%s can't deserialize without models" % self)
else:
model = models.get(json["id"])
if model is not None:
return model
else:
raise DeserializationError("%s failed to deserialize reference to %s" % (self, json))
else:
attrs = {}
for name, value in iteritems(json):
prop_descriptor = self.instance_type.lookup(name).property
attrs[name] = prop_descriptor.from_json(value, models)
# XXX: this doesn't work when Instance(Superclass) := Subclass()
# Serialization dict must carry type information to resolve this.
return self.instance_type(**attrs)
else:
raise DeserializationError("%s expected a dict or None, got %s" % (self, json))
def validate(self, value):
super(Instance, self).validate(value)
if value is not None:
if not isinstance(value, self.instance_type):
raise ValueError("expected an instance of type %s, got %s of type %s" %
(self.instance_type.__name__, value, type(value).__name__))
def _may_have_unstable_default(self):
# because the instance value is mutable
return True
def _sphinx_type(self):
fullname = "%s.%s" % (self.instance_type.__module__, self.instance_type.__name__)
return self._sphinx_prop_link() + "( %s )" % self._sphinx_model_link(fullname)
class Any(Property):
''' Accept all values.
The ``Any`` property does not do any validation or transformation.
Args:
default (obj or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class AnyModel(HasProps):
... prop = Any()
...
>>> m = AnyModel()
>>> m.prop = True
>>> m.prop = 10
>>> m.prop = 3.14
>>> m.prop = "foo"
>>> m.prop = [1, 2, 3]
'''
pass
class Interval(ParameterizedProperty):
''' Accept numeric values that are contained within a given interval.
Args:
interval_type (numeric property):
numeric types for the range, e.g. ``Int``, ``Float``
start (number) :
A minimum allowable value for the range. Values less than
``start`` will result in validation errors.
end (number) :
A maximum allowable value for the range. Values greater than
``end`` will result in validation errors.
Example:
.. code-block:: python
>>> class RangeModel(HasProps):
... prop = Range(Float, 10, 20)
...
>>> m = RangeModel()
>>> m.prop = 10
>>> m.prop = 20
>>> m.prop = 15
>>> m.prop = 2 # ValueError !!
>>> m.prop = 22 # ValueError !!
>>> m.prop = "foo" # ValueError !!
'''
def __init__(self, interval_type, start, end, default=None, help=None):
self.interval_type = self._validate_type_param(interval_type)
# Make up a property name for validation purposes
self.interval_type.validate(start)
self.interval_type.validate(end)
self.start = start
self.end = end
super(Interval, self).__init__(default=default, help=help)
def __str__(self):
return "%s(%s, %r, %r)" % (self.__class__.__name__, self.interval_type, self.start, self.end)
@property
def type_params(self):
return [self.interval_type]
def validate(self, value):
super(Interval, self).validate(value)
if not (value is None or self.interval_type.is_valid(value) and value >= self.start and value <= self.end):
raise ValueError("expected a value of type %s in range [%s, %s], got %r" % (self.interval_type, self.start, self.end, value))
class Byte(Interval):
''' Accept integral byte values (0-255).
Example:
.. code-block:: python
>>> class ByteModel(HasProps):
... prop = Byte(default=0)
...
>>> m = ByteModel()
>>> m.prop = 255
>>> m.prop = 256 # ValueError !!
>>> m.prop = 10.3 # ValueError !!
'''
def __init__(self, default=0, help=None):
super(Byte, self).__init__(Int, 0, 255, default=default, help=help)
class Either(ParameterizedProperty):
''' Accept values according to a sequence of other property types.
Example:
.. code-block:: python
>>> class EitherModel(HasProps):
... prop = Either(Bool, Int, Auto)
...
>>> m = EitherModel()
>>> m.prop = True
>>> m.prop = 10
>>> m.prop = "auto"
>>> m.prop = 10.3 # ValueError !!
>>> m.prop = "foo" # ValueError !!
'''
def __init__(self, tp1, tp2, *type_params, **kwargs):
self._type_params = list(map(self._validate_type_param, (tp1, tp2) + type_params))
help = kwargs.get("help")
def choose_default():
return self._type_params[0]._raw_default()
default = kwargs.get("default", choose_default)
super(Either, self).__init__(default=default, help=help)
self.alternatives = []
for tp in self._type_params:
self.alternatives.extend(tp.alternatives)
# TODO (bev) get rid of this?
def __or__(self, other):
return self.__class__(*(self.type_params + [other]), default=self._default, help=self.help)
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, ", ".join(map(str, self.type_params)))
@property
def type_params(self):
return self._type_params
def from_json(self, json, models=None):
for tp in self.type_params:
try:
return tp.from_json(json, models)
except DeserializationError:
pass
else:
raise DeserializationError("%s couldn't deserialize %s" % (self, json))
def transform(self, value):
for param in self.type_params:
try:
return param.transform(value)
except ValueError:
pass
raise ValueError("Could not transform %r" % value)
def validate(self, value):
super(Either, self).validate(value)
if not (value is None or any(param.is_valid(value) for param in self.type_params)):
raise ValueError("expected an element of either %s, got %r" % (nice_join(self.type_params), value))
# TODO (bev) implement this
# def _may_have_unstable_default(self):
# return any(tp._may_have_unstable_default() for tp in self.type_params)
def _sphinx_type(self):
return self._sphinx_prop_link() + "( %s )" % ", ".join(x._sphinx_type() for x in self.type_params)
class Enum(String):
''' Accept values from enumerations.
The first value in enumeration is used as the default value, unless the
``default`` keyword argument is used.
See :ref:`bokeh.core.enums` for more information.
'''
def __init__(self, enum, *values, **kwargs):
if not (not values and isinstance(enum, enums.Enumeration)):
enum = enums.enumeration(enum, *values)
self._enum = enum
default = kwargs.get("default", enum._default)
help = kwargs.get("help")
super(Enum, self).__init__(default=default, help=help)
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, ", ".join(map(repr, self.allowed_values)))
@property
def allowed_values(self):
return self._enum._values
def validate(self, value):
super(Enum, self).validate(value)
if not (value is None or value in self._enum):
raise ValueError("invalid value: %r; allowed values are %s" % (value, nice_join(self.allowed_values)))
def _sphinx_type(self):
# try to return a link to a proper enum in bokeh.core.enums if possible
if self._enum in enums.__dict__.values():
for name, obj in enums.__dict__.items():
if self._enum is obj:
val = self._sphinx_model_link("%s.%s" % (self._enum.__module__, name))
else:
val = str(self._enum)
return self._sphinx_prop_link() + "( %s )" % val
class Auto(Enum):
''' Accepts only the string "auto".
Useful for properties that can be configured to behave "automatically".
Example:
This property is often most useful in conjunction with the
:class:`~bokeh.core.properties.Either` property.
.. code-block:: python
>>> class AutoModel(HasProps):
... prop = Either(Float, Auto)
...
>>> m = AutoModel()
>>> m.prop = 10.2
>>> m.prop = "auto"
>>> m.prop = "foo" # ValueError !!
>>> m.prop = [1, 2, 3] # ValueError !!
'''
def __init__(self):
super(Auto, self).__init__("auto")
def __str__(self):
return self.__class__.__name__
def _sphinx_type(self):
return self._sphinx_prop_link()
# Properties useful for defining visual attributes
class Color(Either):
''' Accept color values in a variety of ways.
For colors, because we support named colors and hex values prefaced
with a "#", when we are handed a string value, there is a little
interpretation: if the value is one of the 147 SVG named colors or
it starts with a "#", then it is interpreted as a value.
If a 3-tuple is provided, then it is treated as an RGB (0..255).
If a 4-tuple is provided, then it is treated as an RGBa (0..255), with
alpha as a float between 0 and 1. (This follows the HTML5 Canvas API.)
Example:
.. code-block:: python
>>> class ColorModel(HasProps):
... prop = Color()
...
>>> m = ColorModel()
>>> m.prop = "firebrick"
>>> m.prop = "#a240a2"
>>> m.prop = (100, 100, 255)
>>> m.prop = (100, 100, 255, 0.5)
>>> m.prop = "junk" # ValueError !!
>>> m.prop = (100.2, 57.3, 10.2) # ValueError !!
'''
def __init__(self, default=None, help=None):
types = (Enum(enums.NamedColor),
Regex("^#[0-9a-fA-F]{6}$"),
Tuple(Byte, Byte, Byte),
Tuple(Byte, Byte, Byte, Percent))
super(Color, self).__init__(*types, default=default, help=help)
def __str__(self):
return self.__class__.__name__
def transform(self, value):
if isinstance(value, tuple):
value = RGB(*value).to_css()
return value
def _sphinx_type(self):
return self._sphinx_prop_link()
class MinMaxBounds(Either):
''' Accept (min, max) bounds tuples for use with Ranges.
Bounds are provided as a tuple of ``(min, max)`` so regardless of whether your range is
increasing or decreasing, the first item should be the minimum value of the range and the
second item should be the maximum. Setting min > max will result in a ``ValueError``.
Setting bounds to None will allow your plot to pan/zoom as far as you want. If you only
want to constrain one end of the plot, you can set min or max to
``None`` e.g. ``DataRange1d(bounds=(None, 12))`` '''
def __init__(self, accept_datetime=False, default='auto', help=None):
if accept_datetime:
types = (
Auto,
Tuple(Float, Float),
Tuple(Datetime, Datetime),
)
else:
types = (
Auto,
Tuple(Float, Float),
)
super(MinMaxBounds, self).__init__(*types, default=default, help=help)
def validate(self, value):
super(MinMaxBounds, self).validate(value)
if value is None:
pass
elif value[0] is None or value[1] is None:
pass
elif value[0] >= value[1]:
raise ValueError('Invalid bounds: maximum smaller than minimum. Correct usage: bounds=(min, max)')
return True
def _sphinx_type(self):
return self._sphinx_prop_link()
class DashPattern(Either):
''' Accept line dash specifications.
Express patterns that describe line dashes. ``DashPattern`` values
can be specified in a variety of ways:
* An enum: "solid", "dashed", "dotted", "dotdash", "dashdot"
* a tuple or list of integers in the `HTML5 Canvas dash specification style`_.
Note that if the list of integers has an odd number of elements, then
it is duplicated, and that duplicated list becomes the new dash list.
To indicate that dashing is turned off (solid lines), specify the empty
list [].
.. _HTML5 Canvas dash specification style: http://www.w3.org/html/wg/drafts/2dcontext/html5_canvas/#dash-list
'''
_dash_patterns = {
"solid": [],
"dashed": [6],
"dotted": [2,4],
"dotdash": [2,4,6,4],
"dashdot": [6,4,2,4],
}
def __init__(self, default=[], help=None):
types = Enum(enums.DashPattern), Regex(r"^(\d+(\s+\d+)*)?$"), Seq(Int)
super(DashPattern, self).__init__(*types, default=default, help=help)
def __str__(self):
return self.__class__.__name__
def transform(self, value):
value = super(DashPattern, self).transform(value)
if isinstance(value, string_types):
try:
return self._dash_patterns[value]
except KeyError:
return [int(x) for x in value.split()]
else:
return value
def _sphinx_type(self):
return self._sphinx_prop_link()
class Size(Float):
''' Accept non-negative numeric values.
Args:
default (float or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class SizeModel(HasProps):
... prop = Size()
...
>>> m = SizeModel()
>>> m.prop = 0
>>> m.prop = 10e6
>>> m.prop = -10 # ValueError !!
>>> m.prop = "foo" # ValueError !!
'''
def validate(self, value):
super(Size, self).validate(value)
if not (value is None or 0.0 <= value):
raise ValueError("expected a non-negative number, got %r" % value)
class Percent(Float):
''' Accept floating point percentage values.
``Percent`` can be useful and semantically meaningful for specifying
things like alpha values and extents.
Args:
default (float or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
Example:
.. code-block:: python
>>> class PercentModel(HasProps):
... prop = Percent()
...
>>> m = PercentModel()
>>> m.prop = 0.0
>>> m.prop = 0.2
>>> m.prop = 1.0
>>> m.prop = -2 # ValueError !!
>>> m.prop = 5 # ValueError !!
'''
def validate(self, value):
super(Percent, self).validate(value)
if not (value is None or 0.0 <= value <= 1.0):
raise ValueError("expected a value in range [0, 1], got %r" % value)
class Angle(Float):
''' Accept floating point angle values.
``Angle`` is equivalent to :class:`~bokeh.core.properties.Float` but is
provided for cases when it is more semantically meaningful.
Args:
default (float or None, optional) :
A default value for attributes created from this property to
have (default: None)
help (str or None, optional) :
A documentation string for this property. It will be automatically
used by the :ref:`bokeh.sphinxext.bokeh_prop` extension when
generating Spinx documentation. (default: None)
serialized (bool, optional) :
Whether attributes created from this property should be included
in serialization (default: True)
readonly (bool, optional) :
Whether attributes created from this property are read-only.
(default: False)
'''
pass
class Date(Property):
''' Accept Date (but not DateTime) values.
'''
def __init__(self, default=None, help=None):
super(Date, self).__init__(default=default, help=help)
def transform(self, value):
value = super(Date, self).transform(value)
if isinstance(value, (float,) + bokeh_integer_types):
try:
value = datetime.date.fromtimestamp(value)
except ValueError:
value = datetime.date.fromtimestamp(value/1000)
elif isinstance(value, string_types):
value = dateutil.parser.parse(value).date()
return value
def validate(self, value):
super(Date, self).validate(value)
if not (value is None or isinstance(value, (datetime.date,) + string_types + (float,) + bokeh_integer_types)):
raise ValueError("expected a date, string or timestamp, got %r" % value)
class Datetime(Property):
''' Accept Datetime values.
'''
def __init__(self, default=datetime.date.today(), help=None):
super(Datetime, self).__init__(default=default, help=help)
def transform(self, value):
value = super(Datetime, self).transform(value)
return value
# Handled by serialization in protocol.py for now
def validate(self, value):
super(Datetime, self).validate(value)
datetime_types = (datetime.datetime, datetime.date)
try:
import numpy as np
datetime_types += (np.datetime64,)
except (ImportError, AttributeError) as e:
if e.args == ("'module' object has no attribute 'datetime64'",):
import sys
if 'PyPy' in sys.version:
pass
else:
raise e
else:
pass
if (isinstance(value, datetime_types)):
return
if pd and isinstance(value, (pd.Timestamp)):
return
raise ValueError("Expected a datetime instance, got %r" % value)
class TimeDelta(Property):
''' Accept TimeDelta values.
'''
def __init__(self, default=datetime.timedelta(), help=None):
super(TimeDelta, self).__init__(default=default, help=help)
def transform(self, value):
value = super(TimeDelta, self).transform(value)
return value
# Handled by serialization in protocol.py for now
def validate(self, value):
super(TimeDelta, self).validate(value)
timedelta_types = (datetime.timedelta,)
try:
import numpy as np
timedelta_types += (np.timedelta64,)
except (ImportError, AttributeError) as e:
if e.args == ("'module' object has no attribute 'timedelta64'",):
import sys
if 'PyPy' in sys.version:
pass
else:
raise e
else:
pass
if (isinstance(value, timedelta_types)):
return
if pd and isinstance(value, (pd.Timedelta)):
return
raise ValueError("Expected a timedelta instance, got %r" % value)
#------------------------------------------------------------------------------
# Container properties
#------------------------------------------------------------------------------
class Seq(ContainerProperty):
''' Accept non-string ordered sequences of values, e.g. list, tuple, array.
'''
def __init__(self, item_type, default=None, help=None):
self.item_type = self._validate_type_param(item_type)
super(Seq, self).__init__(default=default, help=help)
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, self.item_type)
@property
def type_params(self):
return [self.item_type]
def from_json(self, json, models=None):
if json is None:
return None
elif isinstance(json, list):
return self._new_instance([ self.item_type.from_json(item, models) for item in json ])
else:
raise DeserializationError("%s expected a list or None, got %s" % (self, json))
def validate(self, value):
super(Seq, self).validate(value)
if value is not None:
if not (self._is_seq(value) and all(self.item_type.is_valid(item) for item in value)):
if self._is_seq(value):
invalid = []
for item in value:
if not self.item_type.is_valid(item):
invalid.append(item)
raise ValueError("expected an element of %s, got seq with invalid items %r" % (self, invalid))
else:
raise ValueError("expected an element of %s, got %r" % (self, value))
@classmethod
def _is_seq(cls, value):
return ((isinstance(value, collections.Sequence) or cls._is_seq_like(value)) and
not isinstance(value, string_types))
@classmethod
def _is_seq_like(cls, value):
return (isinstance(value, (collections.Container, collections.Sized, collections.Iterable))
and hasattr(value, "__getitem__") # NOTE: this is what makes it disallow set type
and not isinstance(value, collections.Mapping))
def _new_instance(self, value):
return value
def _sphinx_type(self):
return self._sphinx_prop_link() + "( %s )" % self.item_type._sphinx_type()
class List(Seq):
''' Accept Python list values.
'''
def __init__(self, item_type, default=[], help=None):
# todo: refactor to not use mutable objects as default values.
# Left in place for now because we want to allow None to express
# optional values. Also in Dict.
super(List, self).__init__(item_type, default=default, help=help)
@classmethod
def _is_seq(cls, value):
return isinstance(value, list)
class Array(Seq):
''' Accept NumPy array values.
'''
@classmethod
def _is_seq(cls, value):
import numpy as np
return isinstance(value, np.ndarray)
def _new_instance(self, value):
import numpy as np
return np.array(value)
class Dict(ContainerProperty):
''' Accept Python dict values.
If a default value is passed in, then a shallow copy of it will be
used for each new use of this property.
'''
def __init__(self, keys_type, values_type, default={}, help=None):
self.keys_type = self._validate_type_param(keys_type)
self.values_type = self._validate_type_param(values_type)
super(Dict, self).__init__(default=default, help=help)
def __str__(self):
return "%s(%s, %s)" % (self.__class__.__name__, self.keys_type, self.values_type)
@property
def type_params(self):
return [self.keys_type, self.values_type]
def from_json(self, json, models=None):
if json is None:
return None
elif isinstance(json, dict):
return { self.keys_type.from_json(key, models): self.values_type.from_json(value, models) for key, value in iteritems(json) }
else:
raise DeserializationError("%s expected a dict or None, got %s" % (self, json))
def validate(self, value):
super(Dict, self).validate(value)
if value is not None:
if not (isinstance(value, dict) and \
all(self.keys_type.is_valid(key) and self.values_type.is_valid(val) for key, val in iteritems(value))):
raise ValueError("expected an element of %s, got %r" % (self, value))
def _sphinx_type(self):
return self._sphinx_prop_link() + "( %s, %s )" % (self.keys_type._sphinx_type(), self.values_type._sphinx_type())
class ColumnData(Dict):
''' Accept a Python dictionary suitable as the ``data`` attribute of a
:class:`~bokeh.models.sources.ColumnDataSource`.
This class is a specialization of ``Dict`` that handles efficiently
encoding columns that are NumPy arrays.
'''
def from_json(self, json, models=None):
''' Decodes column source data encoded as lists or base64 strings.
'''
if json is None:
return None
elif not isinstance(json, dict):
raise DeserializationError("%s expected a dict or None, got %s" % (self, json))
new_data = {}
for key, value in json.items():
key = self.keys_type.from_json(key, models)
if isinstance(value, dict) and '__ndarray__' in value:
new_data[key] = decode_base64_dict(value)
elif isinstance(value, list) and any(isinstance(el, dict) and '__ndarray__' in el for el in value):
new_list = []
for el in value:
if isinstance(el, dict) and '__ndarray__' in el:
el = decode_base64_dict(el)
elif isinstance(el, list):
el = self.values_type.from_json(el)
new_list.append(el)
new_data[key] = new_list
else:
new_data[key] = self.values_type.from_json(value, models)
return new_data
def serialize_value(self, value):
return transform_column_source_data(value)
class Tuple(ContainerProperty):
''' Accept Python tuple values.
'''
def __init__(self, tp1, tp2, *type_params, **kwargs):
self._type_params = list(map(self._validate_type_param, (tp1, tp2) + type_params))
super(Tuple, self).__init__(default=kwargs.get("default"), help=kwargs.get("help"))
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, ", ".join(map(str, self.type_params)))
@property
def type_params(self):
return self._type_params
def from_json(self, json, models=None):
if json is None:
return None
elif isinstance(json, list):
return tuple(type_param.from_json(item, models) for type_param, item in zip(self.type_params, json))
else:
raise DeserializationError("%s expected a list or None, got %s" % (self, json))
def validate(self, value):
super(Tuple, self).validate(value)
if value is not None:
if not (isinstance(value, (tuple, list)) and len(self.type_params) == len(value) and \
all(type_param.is_valid(item) for type_param, item in zip(self.type_params, value))):
raise ValueError("expected an element of %s, got %r" % (self, value))
def _sphinx_type(self):
return self._sphinx_prop_link() + "( %s )" % ", ".join(x._sphinx_type() for x in self.type_params)
class RelativeDelta(Dict):
''' Accept RelativeDelta dicts for time delta values.
'''
def __init__(self, default={}, help=None):
keys = Enum("years", "months", "days", "hours", "minutes", "seconds", "microseconds")
values = Int
super(RelativeDelta, self).__init__(keys, values, default=default, help=help)
def __str__(self):
return self.__class__.__name__
#------------------------------------------------------------------------------
# DataSpec properties
#------------------------------------------------------------------------------
class DataSpec(Either):
''' Base class for properties that accept either a fixed value, or a
string name that references a column in a
:class:`~bokeh.models.sources.ColumnDataSource`.
Many Bokeh models have properties that a user might want to set either
to a single fixed value, or to have the property take values from some
column in a data source. As a concrete example consider a glyph with
an ``x`` property for location. We might want to set all the glyphs
that get drawn to have the same location, say ``x=10``. It would be
convenient to just be able to write:
.. code-block:: python
glyph.x = 10
Alternatively, maybe the each glyph that gets drawn should have a
different location, according to the "pressure" column of a data
source. In this case we would like to be able to write:
.. code-block:: python
glyph.x = "pressure"
Bokeh ``DataSpec`` properties (and subclasses) afford this ease of
and consistency of expression. Ultimately, all ``DataSpec`` properties
resolve to dictionary values, with either a ``"value"`` key, or a
``"field"`` key, depending on how it is set.
For instance:
.. code-block:: python
glyph.x = 10 # => { 'value': 10 }
glyph.x = "pressure" # => { 'field': 'pressure' }
When these underlying dictionary dictionary values are received in
the browser, BokehJS knows how to interpret them and take the correct,
expected action (i.e., draw the glyph at ``x=10``, or draw the glyph
with ``x`` coordinates from the "pressure" column). In this way, both
use-cases may be expressed easily in python, without having to handle
anything differently, from the user perspective.
It is worth noting that ``DataSpec`` properties can also be set directly
with properly formed dictionary values:
.. code-block:: python
glyph.x = { 'value': 10 } # same as glyph.x = 10
glyph.x = { 'field': 'pressure' } # same as glyph.x = "pressure"
Setting the property directly as a dict can be useful in certain
situations. For instance some ``DataSpec`` subclasses also add a
``"units"`` key to the dictionary. This key is often set automatically,
but the dictionary format provides a direct mechanism to override as
necessary. Additionally, ``DataSpec`` can have a ``"transform"`` key,
that specifies a client-side transform that should be applied to any
fixed or field values before they are uses. As an example, you might want
to apply a ``Jitter`` transform to the ``x`` values:
.. code-block:: python
glyph.x = { 'value': 10, 'transform': Jitter(width=0.4) }
Note that ``DataSpec`` is not normally useful on its own. Typically,
a model will define properties using one of the sublclasses such
as :class:`~bokeh.core.properties.NumberSpec` or
:class:`~bokeh.core.properties.ColorSpec`. For example, a Bokeh
model with ``x``, ``y`` and ``color`` properties that can handle
fixed values or columns automatically might look like:
.. code-block:: python
class SomeModel(Model):
x = NumberSpec(default=0, help="docs for x")
y = NumberSpec(default=0, help="docs for y")
color = ColorSpec(help="docs for color") # defaults to None
'''
def __init__(self, key_type, value_type, default, help=None):
super(DataSpec, self).__init__(
String,
Dict(
key_type,
Either(
String,
Instance('bokeh.models.transforms.Transform'),
Instance('bokeh.models.expressions.Expression'),
value_type)),
value_type,
default=default,
help=help
)
self._type = self._validate_type_param(value_type)
# TODO (bev) add stricter validation on keys
def make_descriptors(self, base_name):
''' Return a list of ``DataSpecPropertyDescriptor`` instances to
install on a class, in order to delegate attribute access to this
property.
Args:
base_name (str) : the name of the property these descriptors are for
Returns:
list[DataSpecPropertyDescriptor]
The descriptors returned are collected by the ``MetaHasProps``
metaclass and added to ``HasProps`` subclasses during class creation.
'''
return [ DataSpecPropertyDescriptor(base_name, self) ]
def to_serializable(self, obj, name, val):
# Check for None value; this means "the whole thing is
# unset," not "the value is None."
if val is None:
return None
# Check for spec type value
try:
self._type.validate(val)
return dict(value=val)
except ValueError:
pass
# Check for data source field name
if isinstance(val, string_types):
return dict(field=val)
# Must be dict, return a new dict
return dict(val)
def _sphinx_type(self):
return self._sphinx_prop_link()
_ExprFieldValueTransform = Enum("expr", "field", "value", "transform")
class NumberSpec(DataSpec):
''' A |DataSpec| property that accepts numeric and datetime fixed values.
By default, date and datetime values are immediately converted to
milliseconds since epoch. It it possible to disable processing of datetime
values by passing ``accept_datetime=False``.
Timedelta values are interpreted as absolute milliseconds.
.. code-block:: python
m.location = 10.3 # value
m.location = "foo" # field
'''
def __init__(self, default=None, help=None, key_type=_ExprFieldValueTransform, accept_datetime=True):
super(NumberSpec, self).__init__(key_type, Float, default=default, help=help)
self.accepts(TimeDelta, convert_datetime_type)
if accept_datetime:
self.accepts(Datetime, convert_datetime_type)
class StringSpec(DataSpec):
''' A |DataSpec| property that accepts string fixed values.
Because acceptable fixed values and field names are both strings, it can
be necessary explicitly to disambiguate these possibilities. By default,
string values are interpreted as fields, but the |value| function can be
used to specify that a string should interpreted as a value:
.. code-block:: python
m.title = value("foo") # value
m.title = "foo" # field
'''
def __init__(self, default, help=None, key_type=_ExprFieldValueTransform):
super(StringSpec, self).__init__(key_type, List(String), default=default, help=help)
def prepare_value(self, cls, name, value):
if isinstance(value, list):
if len(value) != 1:
raise TypeError("StringSpec convenience list values must have length 1")
value = dict(value=value[0])
return super(StringSpec, self).prepare_value(cls, name, value)
class FontSizeSpec(DataSpec):
''' A |DataSpec| property that accepts font-size fixed values.
The ``FontSizeSpec`` property attempts to first interpret string values as
font sizes (i.e. valid CSS length values). Otherwise string values are
interpreted as field names. For example:
.. code-block:: python
m.font_size = "10pt" # value
m.font_size = "1.5em" # value
m.font_size = "foo" # field
A full list of all valid CSS length units can be found here:
https://drafts.csswg.org/css-values/#lengths
'''
_font_size_re = re.compile(r"^[0-9]+(.[0-9]+)?(%|em|ex|ch|ic|rem|vw|vh|vi|vb|vmin|vmax|cm|mm|q|in|pc|pt|px)$", re.I)
def __init__(self, default, help=None, key_type=_ExprFieldValueTransform):
super(FontSizeSpec, self).__init__(key_type, List(String), default=default, help=help)
def prepare_value(self, cls, name, value):
if isinstance(value, string_types) and self._font_size_re.match(value) is not None:
value = dict(value=value)
return super(FontSizeSpec, self).prepare_value(cls, name, value)
def validate(self, value):
super(FontSizeSpec, self).validate(value)
if isinstance(value, dict) and 'value' in value:
value = value['value']
if isinstance(value, string_types):
if len(value) == 0:
raise ValueError("empty string is not a valid font size value")
elif value[0].isdigit() and self._font_size_re.match(value) is None:
raise ValueError("%r is not a valid font size value" % value)
_ExprFieldValueTransformUnits = Enum("expr", "field", "value", "transform", "units")
class UnitsSpec(NumberSpec):
''' A |DataSpec| property that accepts numeric fixed values, and also
provides an associated units property to store units information.
'''
def __init__(self, default, units_type, units_default, help=None):
super(UnitsSpec, self).__init__(default=default, help=help, key_type=_ExprFieldValueTransformUnits)
self._units_type = self._validate_type_param(units_type)
# this is a hack because we already constructed units_type
self._units_type.validate(units_default)
self._units_type._default = units_default
# this is sort of a hack because we don't have a
# serialized= kwarg on every Property subtype
self._units_type._serialized = False
def __str__(self):
return "%s(units_default=%r)" % (self.__class__.__name__, self._units_type._default)
def make_descriptors(self, base_name):
''' Return a list of ``PropertyDescriptor`` instances to install on a
class, in order to delegate attribute access to this property.
Unlike simpler property types, ``UnitsSpec`` returns multiple
descriptors to install. In particular, descriptors for the base
property as well as the associated units property are returned.
Args:
name (str) : the name of the property these descriptors are for
Returns:
list[PropertyDescriptor]
The descriptors returned are collected by the ``MetaHasProps``
metaclass and added to ``HasProps`` subclasses during class creation.
'''
units_name = base_name + "_units"
units_props = self._units_type.make_descriptors(units_name)
return units_props + [ UnitsSpecPropertyDescriptor(base_name, self, units_props[0]) ]
def to_serializable(self, obj, name, val):
d = super(UnitsSpec, self).to_serializable(obj, name, val)
if d is not None and 'units' not in d:
# d is a PropertyValueDict at this point, we need to convert it to
# a plain dict if we are going to modify its value, otherwise a
# notify_change that should not happen will be triggered
d = dict(d)
d["units"] = getattr(obj, name+"_units")
return d
class AngleSpec(UnitsSpec):
''' A |DataSpec| property that accepts numeric fixed values, and also
provides an associated units property to store angle units.
Acceptable values for units are ``"rad"`` and ``"deg"``.
'''
def __init__(self, default=None, units_default="rad", help=None):
super(AngleSpec, self).__init__(default=default, units_type=Enum(enums.AngleUnits), units_default=units_default, help=help)
class DistanceSpec(UnitsSpec):
''' A |DataSpec| property that accepts numeric fixed values or strings
that refer to columns in a :class:`~bokeh.models.sources.ColumnDataSource`,
and also provides an associated units property to store units information.
Acceptable values for units are ``"screen"`` and ``"data"``.
'''
def __init__(self, default=None, units_default="data", help=None):
super(DistanceSpec, self).__init__(default=default, units_type=Enum(enums.SpatialUnits), units_default=units_default, help=help)
def prepare_value(self, cls, name, value):
try:
if value is not None and value < 0:
raise ValueError("Distances must be positive or None!")
except TypeError:
pass
return super(DistanceSpec, self).prepare_value(cls, name, value)
class ScreenDistanceSpec(NumberSpec):
''' A |DataSpec| property that accepts numeric fixed values for screen
distances, and also provides an associated units property that reports
``"screen"`` as the units.
.. note::
Units are always ``"screen"``.
'''
def prepare_value(self, cls, name, value):
try:
if value is not None and value < 0:
raise ValueError("Distances must be positive or None!")
except TypeError:
pass
return super(ScreenDistanceSpec, self).prepare_value(cls, name, value)
def to_serializable(self, obj, name, val):
d = super(ScreenDistanceSpec, self).to_serializable(obj, name, val)
d["units"] = "screen"
return d
class DataDistanceSpec(NumberSpec):
''' A |DataSpec| property that accepts numeric fixed values for data-space
distances, and also provides an associated units property that reports
``"data"`` as the units.
.. note::
Units are always ``"data"``.
'''
def prepare_value(self, cls, name, value):
try:
if value is not None and value < 0:
raise ValueError("Distances must be positive or None!")
except TypeError:
pass
return super(DataDistanceSpec, self).prepare_value(cls, name, value)
def to_serializable(self, obj, name, val):
d = super(DataDistanceSpec, self).to_serializable(obj, name, val)
d["units"] = "data"
return d
class ColorSpec(DataSpec):
''' A |DataSpec| property that accepts |Color| fixed values.
The ``ColorSpec`` property attempts to first interpret string values as
colors. Otherwise, string values are interpreted as field names. For
example:
.. code-block:: python
m.color = "#a4225f" # value (hex color string)
m.color = "firebrick" # value (named CSS color string)
m.color = "foo" # field (named "foo")
This automatic interpretation can be override using the dict format
directly, or by using the |field| function:
.. code-block:: python
m.color = { "field": "firebrick" } # field (named "firebrick")
m.color = field("firebrick") # field (named "firebrick")
'''
def __init__(self, default, help=None, key_type=_ExprFieldValueTransform):
super(ColorSpec, self).__init__(key_type, Color, default=default, help=help)
@classmethod
def isconst(cls, val):
''' Whether the value is a string color literal.
Checks for a well-formed hexadecimal color value or a named color.
Args:
val (str) : the value to check
Returns:
True, if the value is a string color literal
'''
return isinstance(val, string_types) and \
((len(val) == 7 and val[0] == "#") or val in enums.NamedColor)
def to_serializable(self, obj, name, val):
if val is None:
return dict(value=None)
# Check for hexadecimal or named color
if self.isconst(val):
return dict(value=val)
# Check for RGB or RGBa tuple
if isinstance(val, tuple):
return dict(value=RGB(*val).to_css())
# Check for data source field name
if isinstance(val, string_types):
return dict(field=val)
# Must be dict, return new dict
return dict(val)
def prepare_value(self, cls, name, value):
# Some explanation is in order. We want to accept tuples like
# (12.0, 100.0, 52.0) i.e. that have "float" byte values. The
# ColorSpec has a transform to adapt values like this to tuples
# of integers, but Property validation happens before the
# transform step, so values like that will fail Color validation
# at this point, since Color is very strict about only accepting
# tuples of (integer) bytes. This conditions tuple values to only
# have integer RGB components
if isinstance(value, tuple):
# TODO (bev) verify that all original floats are integer values?
value = tuple(int(v) if i < 3 else v for i, v in enumerate(value))
return super(ColorSpec, self).prepare_value(cls, name, value)
#------------------------------------------------------------------------------
# DataSpec helpers
#------------------------------------------------------------------------------
def expr(expression, transform=None):
''' Convenience function to explicitly return an "expr" specification for
a Bokeh :class:`~bokeh.core.properties.DataSpec` property.
Args:
expression (Expression) : a computed expression for a
``DataSpec`` property.
transform (Transform, optional) : a transform to apply (default: None)
Returns:
dict : ``{ "expr": expression }``
.. note::
This function is included for completeness. String values for
property specifications are by default interpreted as field names.
'''
if transform:
return dict(expr=expression, transform=transform)
return dict(expr=expression)
def field(name, transform=None):
''' Convenience function to explicitly return a "field" specification for
a Bokeh :class:`~bokeh.core.properties.DataSpec` property.
Args:
name (str) : name of a data source field to reference for a
``DataSpec`` property.
transform (Transform, optional) : a transform to apply (default: None)
Returns:
dict : ``{ "field": name }``
.. note::
This function is included for completeness. String values for
property specifications are by default interpreted as field names.
'''
if transform:
return dict(field=name, transform=transform)
return dict(field=name)
def value(val, transform=None):
''' Convenience function to explicitly return a "value" specification for
a Bokeh :class:`~bokeh.core.properties.DataSpec` property.
Args:
val (any) : a fixed value to specify for a ``DataSpec`` property.
transform (Transform, optional) : a transform to apply (default: None)
Returns:
dict : ``{ "value": name }``
.. note::
String values for property specifications are by default interpreted
as field names. This function is especially useful when you want to
specify a fixed value with text properties.
Example:
.. code-block:: python
# The following will take text values to render from a data source
# column "text_column", but use a fixed value "12pt" for font size
p.text("x", "y", text="text_column",
text_font_size=value("12pt"), source=source)
'''
if transform:
return dict(value=val, transform=transform)
return dict(value=val)
#------------------------------------------------------------------------------
# Special Properties
#------------------------------------------------------------------------------
# intentional transitive import to put Override in this module, DO NOT REMOVE
from .property.override import Override ; Override
class Include(PropertyDescriptorFactory):
''' Include "mix-in" property collection in a Bokeh model.
See :ref:`bokeh.core.property_mixins` for more details.
'''
def __init__(self, delegate, help="", use_prefix=True):
from .has_props import HasProps
if not (isinstance(delegate, type) and issubclass(delegate, HasProps)):
raise ValueError("expected a subclass of HasProps, got %r" % delegate)
self.delegate = delegate
self.help = help
self.use_prefix = use_prefix
def make_descriptors(self, base_name):
descriptors = []
delegate = self.delegate
if self.use_prefix:
prefix = re.sub("_props$", "", base_name) + "_"
else:
prefix = ""
# it would be better if we kept the original generators from
# the delegate and built our Include props from those, perhaps.
for subpropname in delegate.properties(with_bases=False):
fullpropname = prefix + subpropname
subprop_descriptor = delegate.lookup(subpropname)
if isinstance(subprop_descriptor, BasicPropertyDescriptor):
prop = copy(subprop_descriptor.property)
if "%s" in self.help:
doc = self.help % subpropname.replace('_', ' ')
else:
doc = self.help
prop.__doc__ = doc
descriptors += prop.make_descriptors(fullpropname)
return descriptors
# Everything below is just to update the module docstring
_all_props = set(x for x in globals().values() if isinstance(x, type) and issubclass(x, Property))
_all_props.remove(Property)
_all_props.remove(PrimitiveProperty)
_all_props.remove(ParameterizedProperty)
_all_props.remove(ContainerProperty)
def _find_and_remove(typ):
global _all_props
sub = set(x for x in _all_props if issubclass(x, typ))
_all_props -= sub
return sub
_data_specs = "\n".join(sorted(".. autoclass:: %s" % x.__name__ for x in _find_and_remove(DataSpec)))
_containers = "\n".join(sorted(".. autoclass:: %s" % x.__name__ for x in _find_and_remove(ContainerProperty)))
_basic = "\n".join(sorted(".. autoclass:: %s" % x.__name__ for x in _all_props))
__doc__ = format_docstring(__doc__, basic_properties=_basic, container_properties=_containers, dataspec_properties=_data_specs)
del _all_props, _data_specs, _containers, _basic, _find_and_remove
| bsd-3-clause |
jyeatman/dipy | doc/examples/linear_fascicle_evaluation.py | 8 | 11596 | """
=================================================
Linear fascicle evaluation (LiFE)
=================================================
Evaluating the results of tractography algorithms is one of the biggest
challenges for diffusion MRI. One proposal for evaluation of tractography
results is to use a forward model that predicts the signal from each of a set of
streamlines, and then fit a linear model to these simultaneous predictions
[Pestilli2014]_.
We will use streamlines generated using probabilistic tracking on CSA
peaks. For brevity, we will include in this example only streamlines going
through the corpus callosum connecting left to right superior frontal
cortex. The process of tracking and finding these streamlines is fully
demonstrated in the `streamline_tools.py` example. If this example has been
run, we can read the streamlines from file. Otherwise, we'll run that example
first, by importing it. This provides us with all of the variables that were
created in that example:
"""
import numpy as np
import os.path as op
import nibabel as nib
import dipy.core.optimize as opt
if not op.exists('lr-superiorfrontal.trk'):
from streamline_tools import *
else:
# We'll need to know where the corpus callosum is from these variables:
from dipy.data import (read_stanford_labels,
fetch_stanford_t1,
read_stanford_t1)
hardi_img, gtab, labels_img = read_stanford_labels()
labels = labels_img.get_data()
cc_slice = labels == 2
fetch_stanford_t1()
t1 = read_stanford_t1()
t1_data = t1.get_data()
data = hardi_img.get_data()
# Read the candidates from file in voxel space:
candidate_sl = [s[0] for s in nib.trackvis.read('lr-superiorfrontal.trk',
points_space='voxel')[0]]
"""
The streamlines that are entered into the model are termed 'candidate
streamliness' (or a 'candidate connectome'):
"""
"""
Let's visualize the initial candidate group of streamlines in 3D, relative to the
anatomical structure of this brain:
"""
from dipy.viz.colormap import line_colors
from dipy.viz import fvtk
candidate_streamlines_actor = fvtk.streamtube(candidate_sl,
line_colors(candidate_sl))
cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)],
opacities=[1.])
vol_actor = fvtk.slicer(t1_data, voxsz=(1.0, 1.0, 1.0), plane_i=[40],
plane_j=None, plane_k=[35], outline=False)
# Add display objects to canvas
ren = fvtk.ren()
fvtk.add(ren, candidate_streamlines_actor)
fvtk.add(ren, cc_ROI_actor)
fvtk.add(ren, vol_actor)
fvtk.record(ren, n_frames=1, out_path='life_candidates.png',
size=(800, 800))
"""
.. figure:: life_candidates.png
:align: center
**Candidate connectome before life optimization**
"""
"""
Next, we initialize a LiFE model. We import the `dipy.tracking.life` module,
which contains the classes and functions that implement the model:
"""
import dipy.tracking.life as life
fiber_model = life.FiberModel(gtab)
"""
Since we read the streamlines from a file, already in the voxel space, we do not
need to transform them into this space. Otherwise, if the streamline coordinates
were in the world space (relative to the scanner iso-center, or relative to the
mid-point of the AC-PC-connecting line), we would use this::
inv_affine = np.linalg.inv(hardi_img.get_affine())
the inverse transformation from world space to the voxel space as the affine for
the following model fit.
The next step is to fit the model, producing a `FiberFit` class instance, that
stores the data, as well as the results of the fitting procedure.
The LiFE model posits that the signal in the diffusion MRI volume can be
explained by the streamlines, by the equation
.. math::
y = X\beta
Where $y$ is the diffusion MRI signal, $\beta$ are a set of weights on the
streamlines and $X$ is a design matrix. This matrix has the dimensions $m$ by
$n$, where $m=n_{voxels} \cdot n_{directions}$, and $n_{voxels}$ is the set of
voxels in the ROI that contains the streamlines considered in this model. The
$i^{th}$ column of the matrix contains the expected contributions of the
$i^{th}$ streamline (arbitrarly ordered) to each of the voxels. $X$ is a sparse
matrix, because each streamline traverses only a small percentage of the
voxels. The expected contributions of the streamline are calculated using a
forward model, where each node of the streamline is modeled as a cylindrical
fiber compartment with Gaussian diffusion, using the diffusion tensor model. See
[Pestilli2014]_ for more detail on the model, and variations of this model.
"""
fiber_fit = fiber_model.fit(data, candidate_sl, affine=np.eye(4))
"""
The `FiberFit` class instance holds various properties of the model fit. For
example, it has the weights $\beta$, that are assigned to each streamline. In
most cases, a tractography through some region will include redundant
streamlines, and these streamlines will have $\beta_i$ that are 0.
"""
import matplotlib.pyplot as plt
import matplotlib
fig, ax = plt.subplots(1)
ax.hist(fiber_fit.beta, bins=100, histtype='step')
ax.set_xlabel('Fiber weights')
ax.set_ylabel('# fibers')
fig.savefig('beta_histogram.png')
"""
.. figure:: beta_histogram.png
:align: center
**LiFE streamline weights**
"""
"""
We use $\beta$ to filter out these redundant streamlines, and generate an
optimized group of streamlines:
"""
optimized_sl = list(np.array(candidate_sl)[np.where(fiber_fit.beta>0)[0]])
ren = fvtk.ren()
fvtk.add(ren, fvtk.streamtube(optimized_sl, line_colors(optimized_sl)))
fvtk.add(ren, cc_ROI_actor)
fvtk.add(ren, vol_actor)
fvtk.record(ren, n_frames=1, out_path='life_optimized.png',
size=(800, 800))
"""
.. figure:: life_optimized.png
:align: center
**Streamlines selected via LiFE optimization**
"""
"""
The new set of streamlines should do well in fitting the data, and redundant
streamlines have presumably been removed (in this case, about 50% of the
streamlines).
But how well does the model do in explaining the diffusion data? We can
quantify that: the `FiberFit` class instance has a `predict` method, which can
be used to invert the model and predict back either the data that was used to
fit the model, or other unseen data (e.g. in cross-validation, see
:ref:`kfold_xval`).
Without arguments, the `.predict()` method will predict the diffusion signal
for the same gradient table that was used in the fit data, but `gtab` and `S0`
key-word arguments can be used to predict for other acquisition schemes and
other baseline non-diffusion-weighted signals.
"""
model_predict = fiber_fit.predict()
"""
We will focus on the error in prediction of the diffusion-weighted data, and
calculate the root of the mean squared error.
"""
model_error = model_predict - fiber_fit.data
model_rmse = np.sqrt(np.mean(model_error[:, 10:] ** 2, -1))
"""
As a baseline against which we can compare, we calculate another error term. In
this case, we assume that the weight for each streamline is equal
to zero. This produces the naive prediction of the mean of the signal in each
voxel.
"""
beta_baseline = np.zeros(fiber_fit.beta.shape[0])
pred_weighted = np.reshape(opt.spdot(fiber_fit.life_matrix, beta_baseline),
(fiber_fit.vox_coords.shape[0],
np.sum(~gtab.b0s_mask)))
mean_pred = np.empty((fiber_fit.vox_coords.shape[0], gtab.bvals.shape[0]))
S0 = fiber_fit.b0_signal
"""
Since the fitting is done in the demeaned S/S0 domain, we need
to add back the mean and then multiply by S0 in every voxel:
"""
mean_pred[..., gtab.b0s_mask] = S0[:, None]
mean_pred[..., ~gtab.b0s_mask] =\
(pred_weighted + fiber_fit.mean_signal[:, None]) * S0[:, None]
mean_error = mean_pred - fiber_fit.data
mean_rmse = np.sqrt(np.mean(mean_error ** 2, -1))
"""
First, we can compare the overall distribution of errors between these two
alternative models of the ROI. We show the distribution of differences in error
(improvement through model fitting, relative to the baseline model). Here,
positive values denote an improvement in error with model fit, relative to
without the model fit.
"""
fig, ax = plt.subplots(1)
ax.hist(mean_rmse - model_rmse, bins=100, histtype='step')
ax.text(0.2, 0.9,'Median RMSE, mean model: %.2f' % np.median(mean_rmse),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
ax.text(0.2, 0.8,'Median RMSE, LiFE: %.2f' % np.median(model_rmse),
horizontalalignment='left',
verticalalignment='center', transform=ax.transAxes)
ax.set_xlabel('RMS Error')
ax.set_ylabel('# voxels')
fig.savefig('error_histograms.png')
"""
.. figure:: error_histograms.png
:align: center
**Improvement in error with fitting of the LiFE model**.
"""
"""
Second, we can show the spatial distribution of the two error terms,
and of the improvement with the model fit:
"""
vol_model = np.ones(data.shape[:3]) * np.nan
vol_model[fiber_fit.vox_coords[:, 0],
fiber_fit.vox_coords[:, 1],
fiber_fit.vox_coords[:, 2]] = model_rmse
vol_mean = np.ones(data.shape[:3]) * np.nan
vol_mean[fiber_fit.vox_coords[:, 0],
fiber_fit.vox_coords[:, 1],
fiber_fit.vox_coords[:, 2]] = mean_rmse
vol_improve = np.ones(data.shape[:3]) * np.nan
vol_improve[fiber_fit.vox_coords[:, 0],
fiber_fit.vox_coords[:, 1],
fiber_fit.vox_coords[:, 2]] = mean_rmse - model_rmse
sl_idx = 49
from mpl_toolkits.axes_grid1 import AxesGrid
fig = plt.figure()
fig.subplots_adjust(left=0.05, right=0.95)
ax = AxesGrid(fig, 111,
nrows_ncols = (1, 3),
label_mode = "1",
share_all = True,
cbar_location="top",
cbar_mode="each",
cbar_size="10%",
cbar_pad="5%")
ax[0].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
im = ax[0].matshow(np.rot90(vol_model[sl_idx, :, :]), cmap=matplotlib.cm.hot)
ax.cbar_axes[0].colorbar(im)
ax[1].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
im = ax[1].matshow(np.rot90(vol_mean[sl_idx, :, :]), cmap=matplotlib.cm.hot)
ax.cbar_axes[1].colorbar(im)
ax[2].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)
im = ax[2].matshow(np.rot90(vol_improve[sl_idx, :, :]), cmap=matplotlib.cm.RdBu)
ax.cbar_axes[2].colorbar(im)
for lax in ax:
lax.set_xticks([])
lax.set_yticks([])
fig.savefig("spatial_errors.png")
"""
.. figure:: spatial_errors.png
:align: center
**Spatial distribution of error and improvement**
"""
"""
This image demonstrates that in many places, fitting the LiFE model results in
substantial reduction of the error.
Note that for full-brain tractographies *LiFE* can require large amounts of
memory. For detailed memory profiling of the algorithm, based on the
streamlines generated in :ref:`example_probabilistic_fiber_tracking`, see `this
IPython notebook
<http://nbviewer.ipython.org/gist/arokem/bc29f34ebc97510d9def>`_.
For the Matlab implementation of LiFE, head over to `Franco Pestilli's github
webpage <http://francopestilli.github.io/life/>`_.
References
~~~~~~~~~~~~~~~~~~~~~~
.. [Pestilli2014] Pestilli, F., Yeatman, J, Rokem, A. Kay, K. and Wandell
B.A. (2014). Validation and statistical inference in living
connectomes. Nature Methods 11:
1058-1063. doi:10.1038/nmeth.3098
.. include:: ../links_names.inc
"""
| bsd-3-clause |
ilo10/scikit-learn | sklearn/preprocessing/tests/test_label.py | 35 | 18559 | import numpy as np
from scipy.sparse import issparse
from scipy.sparse import coo_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
from sklearn.utils.multiclass import type_of_target
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import ignore_warnings
from sklearn.preprocessing.label import LabelBinarizer
from sklearn.preprocessing.label import MultiLabelBinarizer
from sklearn.preprocessing.label import LabelEncoder
from sklearn.preprocessing.label import label_binarize
from sklearn.preprocessing.label import _inverse_binarize_thresholding
from sklearn.preprocessing.label import _inverse_binarize_multiclass
from sklearn import datasets
iris = datasets.load_iris()
def toarray(a):
if hasattr(a, "toarray"):
a = a.toarray()
return a
def test_label_binarizer():
lb = LabelBinarizer()
# one-class case defaults to negative label
inp = ["pos", "pos", "pos", "pos"]
expected = np.array([[0, 0, 0, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["pos"])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
# two-class case
inp = ["neg", "pos", "pos", "neg"]
expected = np.array([[0, 1, 1, 0]]).T
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ["neg", "pos"])
assert_array_equal(expected, got)
to_invert = np.array([[1, 0],
[0, 1],
[0, 1],
[1, 0]])
assert_array_equal(lb.inverse_transform(to_invert), inp)
# multi-class case
inp = ["spam", "ham", "eggs", "ham", "0"]
expected = np.array([[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[1, 0, 0, 0]])
got = lb.fit_transform(inp)
assert_array_equal(lb.classes_, ['0', 'eggs', 'ham', 'spam'])
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
def test_label_binarizer_unseen_labels():
lb = LabelBinarizer()
expected = np.array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
got = lb.fit_transform(['b', 'd', 'e'])
assert_array_equal(expected, got)
expected = np.array([[0, 0, 0],
[1, 0, 0],
[0, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]])
got = lb.transform(['a', 'b', 'c', 'd', 'e', 'f'])
assert_array_equal(expected, got)
@ignore_warnings
def test_label_binarizer_column_y():
# first for binary classification vs multi-label with 1 possible class
# lists are multi-label, array is multi-class :-/
inp_list = [[1], [2], [1]]
inp_array = np.array(inp_list)
multilabel_indicator = np.array([[1, 0], [0, 1], [1, 0]])
binaryclass_array = np.array([[0], [1], [0]])
lb_1 = LabelBinarizer()
out_1 = lb_1.fit_transform(inp_list)
lb_2 = LabelBinarizer()
out_2 = lb_2.fit_transform(inp_array)
assert_array_equal(out_1, multilabel_indicator)
assert_array_equal(out_2, binaryclass_array)
# second for multiclass classification vs multi-label with multiple
# classes
inp_list = [[1], [2], [1], [3]]
inp_array = np.array(inp_list)
# the indicator matrix output is the same in this case
indicator = np.array([[1, 0, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1]])
lb_1 = LabelBinarizer()
out_1 = lb_1.fit_transform(inp_list)
lb_2 = LabelBinarizer()
out_2 = lb_2.fit_transform(inp_array)
assert_array_equal(out_1, out_2)
assert_array_equal(out_2, indicator)
def test_label_binarizer_set_label_encoding():
lb = LabelBinarizer(neg_label=-2, pos_label=0)
# two-class case with pos_label=0
inp = np.array([0, 1, 1, 0])
expected = np.array([[-2, 0, 0, -2]]).T
got = lb.fit_transform(inp)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
lb = LabelBinarizer(neg_label=-2, pos_label=2)
# multi-class case
inp = np.array([3, 2, 1, 2, 0])
expected = np.array([[-2, -2, -2, +2],
[-2, -2, +2, -2],
[-2, +2, -2, -2],
[-2, -2, +2, -2],
[+2, -2, -2, -2]])
got = lb.fit_transform(inp)
assert_array_equal(expected, got)
assert_array_equal(lb.inverse_transform(got), inp)
@ignore_warnings
def test_label_binarizer_errors():
# Check that invalid arguments yield ValueError
one_class = np.array([0, 0, 0, 0])
lb = LabelBinarizer().fit(one_class)
multi_label = [(2, 3), (0,), (0, 2)]
assert_raises(ValueError, lb.transform, multi_label)
lb = LabelBinarizer()
assert_raises(ValueError, lb.transform, [])
assert_raises(ValueError, lb.inverse_transform, [])
assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=1)
assert_raises(ValueError, LabelBinarizer, neg_label=2, pos_label=2)
assert_raises(ValueError, LabelBinarizer, neg_label=1, pos_label=2,
sparse_output=True)
# Fail on y_type
assert_raises(ValueError, _inverse_binarize_thresholding,
y=csr_matrix([[1, 2], [2, 1]]), output_type="foo",
classes=[1, 2], threshold=0)
# Fail on the number of classes
assert_raises(ValueError, _inverse_binarize_thresholding,
y=csr_matrix([[1, 2], [2, 1]]), output_type="foo",
classes=[1, 2, 3], threshold=0)
# Fail on the dimension of 'binary'
assert_raises(ValueError, _inverse_binarize_thresholding,
y=np.array([[1, 2, 3], [2, 1, 3]]), output_type="binary",
classes=[1, 2, 3], threshold=0)
# Fail on multioutput data
assert_raises(ValueError, LabelBinarizer().fit, np.array([[1, 3], [2, 1]]))
assert_raises(ValueError, label_binarize, np.array([[1, 3], [2, 1]]),
[1, 2, 3])
def test_label_encoder():
# Test LabelEncoder's transform and inverse_transform methods
le = LabelEncoder()
le.fit([1, 1, 4, 5, -1, 0])
assert_array_equal(le.classes_, [-1, 0, 1, 4, 5])
assert_array_equal(le.transform([0, 1, 4, 4, 5, -1, -1]),
[1, 2, 3, 3, 4, 0, 0])
assert_array_equal(le.inverse_transform([1, 2, 3, 3, 4, 0, 0]),
[0, 1, 4, 4, 5, -1, -1])
assert_raises(ValueError, le.transform, [0, 6])
def test_label_encoder_fit_transform():
# Test fit_transform
le = LabelEncoder()
ret = le.fit_transform([1, 1, 4, 5, -1, 0])
assert_array_equal(ret, [2, 2, 3, 4, 0, 1])
le = LabelEncoder()
ret = le.fit_transform(["paris", "paris", "tokyo", "amsterdam"])
assert_array_equal(ret, [1, 1, 2, 0])
def test_label_encoder_errors():
# Check that invalid arguments yield ValueError
le = LabelEncoder()
assert_raises(ValueError, le.transform, [])
assert_raises(ValueError, le.inverse_transform, [])
# Fail on unseen labels
le = LabelEncoder()
le.fit([1, 2, 3, 1, -1])
assert_raises(ValueError, le.inverse_transform, [-1])
def test_sparse_output_multilabel_binarizer():
# test input as iterable of iterables
inputs = [
lambda: [(2, 3), (1,), (1, 2)],
lambda: (set([2, 3]), set([1]), set([1, 2])),
lambda: iter([iter((2, 3)), iter((1,)), set([1, 2])]),
]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 1, 0]])
inverse = inputs[0]()
for sparse_output in [True, False]:
for inp in inputs:
# With fit_tranform
mlb = MultiLabelBinarizer(sparse_output=sparse_output)
got = mlb.fit_transform(inp())
assert_equal(issparse(got), sparse_output)
if sparse_output:
got = got.toarray()
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert_equal(mlb.inverse_transform(got), inverse)
# With fit
mlb = MultiLabelBinarizer(sparse_output=sparse_output)
got = mlb.fit(inp()).transform(inp())
assert_equal(issparse(got), sparse_output)
if sparse_output:
got = got.toarray()
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert_equal(mlb.inverse_transform(got), inverse)
assert_raises(ValueError, mlb.inverse_transform,
csr_matrix(np.array([[0, 1, 1],
[2, 0, 0],
[1, 1, 0]])))
def test_multilabel_binarizer():
# test input as iterable of iterables
inputs = [
lambda: [(2, 3), (1,), (1, 2)],
lambda: (set([2, 3]), set([1]), set([1, 2])),
lambda: iter([iter((2, 3)), iter((1,)), set([1, 2])]),
]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 1, 0]])
inverse = inputs[0]()
for inp in inputs:
# With fit_tranform
mlb = MultiLabelBinarizer()
got = mlb.fit_transform(inp())
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert_equal(mlb.inverse_transform(got), inverse)
# With fit
mlb = MultiLabelBinarizer()
got = mlb.fit(inp()).transform(inp())
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert_equal(mlb.inverse_transform(got), inverse)
def test_multilabel_binarizer_empty_sample():
mlb = MultiLabelBinarizer()
y = [[1, 2], [1], []]
Y = np.array([[1, 1],
[1, 0],
[0, 0]])
assert_array_equal(mlb.fit_transform(y), Y)
def test_multilabel_binarizer_unknown_class():
mlb = MultiLabelBinarizer()
y = [[1, 2]]
assert_raises(KeyError, mlb.fit(y).transform, [[0]])
mlb = MultiLabelBinarizer(classes=[1, 2])
assert_raises(KeyError, mlb.fit_transform, [[0]])
def test_multilabel_binarizer_given_classes():
inp = [(2, 3), (1,), (1, 2)]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 0, 1]])
# fit_transform()
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, [1, 3, 2])
# fit().transform()
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, [1, 3, 2])
# ensure works with extra class
mlb = MultiLabelBinarizer(classes=[4, 1, 3, 2])
assert_array_equal(mlb.fit_transform(inp),
np.hstack(([[0], [0], [0]], indicator_mat)))
assert_array_equal(mlb.classes_, [4, 1, 3, 2])
# ensure fit is no-op as iterable is not consumed
inp = iter(inp)
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
def test_multilabel_binarizer_same_length_sequence():
# Ensure sequences of the same length are not interpreted as a 2-d array
inp = [[1], [0], [2]]
indicator_mat = np.array([[0, 1, 0],
[1, 0, 0],
[0, 0, 1]])
# fit_transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
# fit().transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
def test_multilabel_binarizer_non_integer_labels():
tuple_classes = np.empty(3, dtype=object)
tuple_classes[:] = [(1,), (2,), (3,)]
inputs = [
([('2', '3'), ('1',), ('1', '2')], ['1', '2', '3']),
([('b', 'c'), ('a',), ('a', 'b')], ['a', 'b', 'c']),
([((2,), (3,)), ((1,),), ((1,), (2,))], tuple_classes),
]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 1, 0]])
for inp, classes in inputs:
# fit_transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, classes)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
# fit().transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, classes)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
mlb = MultiLabelBinarizer()
assert_raises(TypeError, mlb.fit_transform, [({}), ({}, {'a': 'b'})])
def test_multilabel_binarizer_non_unique():
inp = [(1, 1, 1, 0)]
indicator_mat = np.array([[1, 1]])
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
def test_multilabel_binarizer_inverse_validation():
inp = [(1, 1, 1, 0)]
mlb = MultiLabelBinarizer()
mlb.fit_transform(inp)
# Not binary
assert_raises(ValueError, mlb.inverse_transform, np.array([[1, 3]]))
# The following binary cases are fine, however
mlb.inverse_transform(np.array([[0, 0]]))
mlb.inverse_transform(np.array([[1, 1]]))
mlb.inverse_transform(np.array([[1, 0]]))
# Wrong shape
assert_raises(ValueError, mlb.inverse_transform, np.array([[1]]))
assert_raises(ValueError, mlb.inverse_transform, np.array([[1, 1, 1]]))
def test_label_binarize_with_class_order():
out = label_binarize([1, 6], classes=[1, 2, 4, 6])
expected = np.array([[1, 0, 0, 0], [0, 0, 0, 1]])
assert_array_equal(out, expected)
# Modified class order
out = label_binarize([1, 6], classes=[1, 6, 4, 2])
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])
assert_array_equal(out, expected)
out = label_binarize([0, 1, 2, 3], classes=[3, 2, 0, 1])
expected = np.array([[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 1, 0, 0],
[1, 0, 0, 0]])
assert_array_equal(out, expected)
def check_binarized_results(y, classes, pos_label, neg_label, expected):
for sparse_output in [True, False]:
if ((pos_label == 0 or neg_label != 0) and sparse_output):
assert_raises(ValueError, label_binarize, y, classes,
neg_label=neg_label, pos_label=pos_label,
sparse_output=sparse_output)
continue
# check label_binarize
binarized = label_binarize(y, classes, neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output)
assert_array_equal(toarray(binarized), expected)
assert_equal(issparse(binarized), sparse_output)
# check inverse
y_type = type_of_target(y)
if y_type == "multiclass":
inversed = _inverse_binarize_multiclass(binarized, classes=classes)
else:
inversed = _inverse_binarize_thresholding(binarized,
output_type=y_type,
classes=classes,
threshold=((neg_label +
pos_label) /
2.))
assert_array_equal(toarray(inversed), toarray(y))
# Check label binarizer
lb = LabelBinarizer(neg_label=neg_label, pos_label=pos_label,
sparse_output=sparse_output)
binarized = lb.fit_transform(y)
assert_array_equal(toarray(binarized), expected)
assert_equal(issparse(binarized), sparse_output)
inverse_output = lb.inverse_transform(binarized)
assert_array_equal(toarray(inverse_output), toarray(y))
assert_equal(issparse(inverse_output), issparse(y))
def test_label_binarize_binary():
y = [0, 1, 0]
classes = [0, 1]
pos_label = 2
neg_label = -1
expected = np.array([[2, -1], [-1, 2], [2, -1]])[:, 1].reshape((-1, 1))
yield check_binarized_results, y, classes, pos_label, neg_label, expected
# Binary case where sparse_output = True will not result in a ValueError
y = [0, 1, 0]
classes = [0, 1]
pos_label = 3
neg_label = 0
expected = np.array([[3, 0], [0, 3], [3, 0]])[:, 1].reshape((-1, 1))
yield check_binarized_results, y, classes, pos_label, neg_label, expected
def test_label_binarize_multiclass():
y = [0, 1, 2]
classes = [0, 1, 2]
pos_label = 2
neg_label = 0
expected = 2 * np.eye(3)
yield check_binarized_results, y, classes, pos_label, neg_label, expected
assert_raises(ValueError, label_binarize, y, classes, neg_label=-1,
pos_label=pos_label, sparse_output=True)
def test_label_binarize_multilabel():
y_ind = np.array([[0, 1, 0], [1, 1, 1], [0, 0, 0]])
classes = [0, 1, 2]
pos_label = 2
neg_label = 0
expected = pos_label * y_ind
y_sparse = [sparse_matrix(y_ind)
for sparse_matrix in [coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix]]
for y in [y_ind] + y_sparse:
yield (check_binarized_results, y, classes, pos_label, neg_label,
expected)
assert_raises(ValueError, label_binarize, y, classes, neg_label=-1,
pos_label=pos_label, sparse_output=True)
def test_invalid_input_label_binarize():
assert_raises(ValueError, label_binarize, [0, 2], classes=[0, 2],
pos_label=0, neg_label=1)
def test_inverse_binarize_multiclass():
got = _inverse_binarize_multiclass(csr_matrix([[0, 1, 0],
[-1, 0, -1],
[0, 0, 0]]),
np.arange(3))
assert_array_equal(got, np.array([1, 1, 0]))
| bsd-3-clause |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/util/_test_decorators.py | 2 | 6300 | """
This module provides decorator functions which can be applied to test objects
in order to skip those objects when certain conditions occur. A sample use case
is to detect if the platform is missing ``matplotlib``. If so, any test objects
which require ``matplotlib`` and decorated with ``@td.skip_if_no_mpl`` will be
skipped by ``pytest`` during the execution of the test suite.
To illustrate, after importing this module:
import pandas.util._test_decorators as td
The decorators can be applied to classes:
@td.skip_if_some_reason
class Foo:
...
Or individual functions:
@td.skip_if_some_reason
def test_foo():
...
For more information, refer to the ``pytest`` documentation on ``skipif``.
"""
from distutils.version import LooseVersion
import locale
from typing import Optional
from _pytest.mark.structures import MarkDecorator
import pytest
from pandas.compat import is_platform_32bit, is_platform_windows
from pandas.compat.numpy import _np_version
from pandas.core.computation.expressions import _NUMEXPR_INSTALLED, _USE_NUMEXPR
def safe_import(mod_name, min_version=None):
"""
Parameters:
-----------
mod_name : str
Name of the module to be imported
min_version : str, default None
Minimum required version of the specified mod_name
Returns:
--------
object
The imported module if successful, or False
"""
try:
mod = __import__(mod_name)
except ImportError:
return False
if not min_version:
return mod
else:
import sys
try:
version = getattr(sys.modules[mod_name], "__version__")
except AttributeError:
# xlrd uses a capitalized attribute name
version = getattr(sys.modules[mod_name], "__VERSION__")
if version:
from distutils.version import LooseVersion
if LooseVersion(version) >= LooseVersion(min_version):
return mod
return False
def _skip_if_no_mpl():
mod = safe_import("matplotlib")
if mod:
mod.use("Agg", warn=True)
else:
return True
def _skip_if_has_locale():
lang, _ = locale.getlocale()
if lang is not None:
return True
def _skip_if_not_us_locale():
lang, _ = locale.getlocale()
if lang != "en_US":
return True
def _skip_if_no_scipy():
return not (
safe_import("scipy.stats")
and safe_import("scipy.sparse")
and safe_import("scipy.interpolate")
and safe_import("scipy.signal")
)
def skip_if_installed(package: str,) -> MarkDecorator:
"""
Skip a test if a package is installed.
Parameters
----------
package : str
The name of the package.
"""
return pytest.mark.skipif(
safe_import(package), reason="Skipping because {} is installed.".format(package)
)
def skip_if_no(package: str, min_version: Optional[str] = None) -> MarkDecorator:
"""
Generic function to help skip tests when required packages are not
present on the testing system.
This function returns a pytest mark with a skip condition that will be
evaluated during test collection. An attempt will be made to import the
specified ``package`` and optionally ensure it meets the ``min_version``
The mark can be used as either a decorator for a test function or to be
applied to parameters in pytest.mark.parametrize calls or parametrized
fixtures.
If the import and version check are unsuccessful, then the test function
(or test case when used in conjunction with parametrization) will be
skipped.
Parameters
----------
package: str
The name of the required package.
min_version: str or None, default None
Optional minimum version of the package.
Returns
-------
_pytest.mark.structures.MarkDecorator
a pytest.mark.skipif to use as either a test decorator or a
parametrization mark.
"""
msg = "Could not import '{}'".format(package)
if min_version:
msg += " satisfying a min_version of {}".format(min_version)
return pytest.mark.skipif(
not safe_import(package, min_version=min_version), reason=msg
)
skip_if_no_mpl = pytest.mark.skipif(
_skip_if_no_mpl(), reason="Missing matplotlib dependency"
)
skip_if_mpl = pytest.mark.skipif(not _skip_if_no_mpl(), reason="matplotlib is present")
skip_if_32bit = pytest.mark.skipif(is_platform_32bit(), reason="skipping for 32 bit")
skip_if_windows = pytest.mark.skipif(is_platform_windows(), reason="Running on Windows")
skip_if_windows_python_3 = pytest.mark.skipif(
is_platform_windows(), reason="not used on win32"
)
skip_if_has_locale = pytest.mark.skipif(
_skip_if_has_locale(),
reason="Specific locale is set {lang}".format(lang=locale.getlocale()[0]),
)
skip_if_not_us_locale = pytest.mark.skipif(
_skip_if_not_us_locale(),
reason="Specific locale is set " "{lang}".format(lang=locale.getlocale()[0]),
)
skip_if_no_scipy = pytest.mark.skipif(
_skip_if_no_scipy(), reason="Missing SciPy requirement"
)
skip_if_no_ne = pytest.mark.skipif(
not _USE_NUMEXPR,
reason="numexpr enabled->{enabled}, "
"installed->{installed}".format(enabled=_USE_NUMEXPR, installed=_NUMEXPR_INSTALLED),
)
def skip_if_np_lt(ver_str, reason=None, *args, **kwds):
if reason is None:
reason = "NumPy %s or greater required" % ver_str
return pytest.mark.skipif(
_np_version < LooseVersion(ver_str), reason=reason, *args, **kwds
)
def parametrize_fixture_doc(*args):
"""
Intended for use as a decorator for parametrized fixture,
this function will wrap the decorated function with a pytest
``parametrize_fixture_doc`` mark. That mark will format
initial fixture docstring by replacing placeholders {0}, {1} etc
with parameters passed as arguments.
Parameters:
----------
args: iterable
Positional arguments for docstring.
Returns:
-------
documented_fixture: function
The decorated function wrapped within a pytest
``parametrize_fixture_doc`` mark
"""
def documented_fixture(fixture):
fixture.__doc__ = fixture.__doc__.format(*args)
return fixture
return documented_fixture
| apache-2.0 |
gfyoung/pandas | pandas/io/formats/latex.py | 2 | 25201 | """
Module for formatting output data in Latex.
"""
from abc import ABC, abstractmethod
from typing import Iterator, List, Optional, Sequence, Tuple, Type, Union
import numpy as np
from pandas.core.dtypes.generic import ABCMultiIndex
from pandas.io.formats.format import DataFrameFormatter
def _split_into_full_short_caption(
caption: Optional[Union[str, Tuple[str, str]]]
) -> Tuple[str, str]:
"""Extract full and short captions from caption string/tuple.
Parameters
----------
caption : str or tuple, optional
Either table caption string or tuple (full_caption, short_caption).
If string is provided, then it is treated as table full caption,
while short_caption is considered an empty string.
Returns
-------
full_caption, short_caption : tuple
Tuple of full_caption, short_caption strings.
"""
if caption:
if isinstance(caption, str):
full_caption = caption
short_caption = ""
else:
try:
full_caption, short_caption = caption
except ValueError as err:
msg = "caption must be either a string or a tuple of two strings"
raise ValueError(msg) from err
else:
full_caption = ""
short_caption = ""
return full_caption, short_caption
class RowStringConverter(ABC):
r"""Converter for dataframe rows into LaTeX strings.
Parameters
----------
formatter : `DataFrameFormatter`
Instance of `DataFrameFormatter`.
multicolumn: bool, optional
Whether to use \multicolumn macro.
multicolumn_format: str, optional
Multicolumn format.
multirow: bool, optional
Whether to use \multirow macro.
"""
def __init__(
self,
formatter: DataFrameFormatter,
multicolumn: bool = False,
multicolumn_format: Optional[str] = None,
multirow: bool = False,
):
self.fmt = formatter
self.frame = self.fmt.frame
self.multicolumn = multicolumn
self.multicolumn_format = multicolumn_format
self.multirow = multirow
self.clinebuf: List[List[int]] = []
self.strcols = self._get_strcols()
self.strrows = list(zip(*self.strcols))
def get_strrow(self, row_num: int) -> str:
"""Get string representation of the row."""
row = self.strrows[row_num]
is_multicol = (
row_num < self.column_levels and self.fmt.header and self.multicolumn
)
is_multirow = (
row_num >= self.header_levels
and self.fmt.index
and self.multirow
and self.index_levels > 1
)
is_cline_maybe_required = is_multirow and row_num < len(self.strrows) - 1
crow = self._preprocess_row(row)
if is_multicol:
crow = self._format_multicolumn(crow)
if is_multirow:
crow = self._format_multirow(crow, row_num)
lst = []
lst.append(" & ".join(crow))
lst.append(" \\\\")
if is_cline_maybe_required:
cline = self._compose_cline(row_num, len(self.strcols))
lst.append(cline)
return "".join(lst)
@property
def _header_row_num(self) -> int:
"""Number of rows in header."""
return self.header_levels if self.fmt.header else 0
@property
def index_levels(self) -> int:
"""Integer number of levels in index."""
return self.frame.index.nlevels
@property
def column_levels(self) -> int:
return self.frame.columns.nlevels
@property
def header_levels(self) -> int:
nlevels = self.column_levels
if self.fmt.has_index_names and self.fmt.show_index_names:
nlevels += 1
return nlevels
def _get_strcols(self) -> List[List[str]]:
"""String representation of the columns."""
if self.fmt.frame.empty:
strcols = [[self._empty_info_line]]
else:
strcols = self.fmt.get_strcols()
# reestablish the MultiIndex that has been joined by get_strcols()
if self.fmt.index and isinstance(self.frame.index, ABCMultiIndex):
out = self.frame.index.format(
adjoin=False,
sparsify=self.fmt.sparsify,
names=self.fmt.has_index_names,
na_rep=self.fmt.na_rep,
)
# index.format will sparsify repeated entries with empty strings
# so pad these with some empty space
def pad_empties(x):
for pad in reversed(x):
if pad:
break
return [x[0]] + [i if i else " " * len(pad) for i in x[1:]]
gen = (pad_empties(i) for i in out)
# Add empty spaces for each column level
clevels = self.frame.columns.nlevels
out = [[" " * len(i[-1])] * clevels + i for i in gen]
# Add the column names to the last index column
cnames = self.frame.columns.names
if any(cnames):
new_names = [i if i else "{}" for i in cnames]
out[self.frame.index.nlevels - 1][:clevels] = new_names
# Get rid of old multiindex column and add new ones
strcols = out + strcols[1:]
return strcols
@property
def _empty_info_line(self):
return (
f"Empty {type(self.frame).__name__}\n"
f"Columns: {self.frame.columns}\n"
f"Index: {self.frame.index}"
)
def _preprocess_row(self, row: Sequence[str]) -> List[str]:
"""Preprocess elements of the row."""
if self.fmt.escape:
crow = _escape_symbols(row)
else:
crow = [x if x else "{}" for x in row]
if self.fmt.bold_rows and self.fmt.index:
crow = _convert_to_bold(crow, self.index_levels)
return crow
def _format_multicolumn(self, row: List[str]) -> List[str]:
r"""
Combine columns belonging to a group to a single multicolumn entry
according to self.multicolumn_format
e.g.:
a & & & b & c &
will become
\multicolumn{3}{l}{a} & b & \multicolumn{2}{l}{c}
"""
row2 = row[: self.index_levels]
ncol = 1
coltext = ""
def append_col():
# write multicolumn if needed
if ncol > 1:
row2.append(
f"\\multicolumn{{{ncol:d}}}{{{self.multicolumn_format}}}"
f"{{{coltext.strip()}}}"
)
# don't modify where not needed
else:
row2.append(coltext)
for c in row[self.index_levels :]:
# if next col has text, write the previous
if c.strip():
if coltext:
append_col()
coltext = c
ncol = 1
# if not, add it to the previous multicolumn
else:
ncol += 1
# write last column name
if coltext:
append_col()
return row2
def _format_multirow(self, row: List[str], i: int) -> List[str]:
r"""
Check following rows, whether row should be a multirow
e.g.: becomes:
a & 0 & \multirow{2}{*}{a} & 0 &
& 1 & & 1 &
b & 0 & \cline{1-2}
b & 0 &
"""
for j in range(self.index_levels):
if row[j].strip():
nrow = 1
for r in self.strrows[i + 1 :]:
if not r[j].strip():
nrow += 1
else:
break
if nrow > 1:
# overwrite non-multirow entry
row[j] = f"\\multirow{{{nrow:d}}}{{*}}{{{row[j].strip()}}}"
# save when to end the current block with \cline
self.clinebuf.append([i + nrow - 1, j + 1])
return row
def _compose_cline(self, i: int, icol: int) -> str:
"""
Create clines after multirow-blocks are finished.
"""
lst = []
for cl in self.clinebuf:
if cl[0] == i:
lst.append(f"\n\\cline{{{cl[1]:d}-{icol:d}}}")
# remove entries that have been written to buffer
self.clinebuf = [x for x in self.clinebuf if x[0] != i]
return "".join(lst)
class RowStringIterator(RowStringConverter):
"""Iterator over rows of the header or the body of the table."""
@abstractmethod
def __iter__(self) -> Iterator[str]:
"""Iterate over LaTeX string representations of rows."""
class RowHeaderIterator(RowStringIterator):
"""Iterator for the table header rows."""
def __iter__(self) -> Iterator[str]:
for row_num in range(len(self.strrows)):
if row_num < self._header_row_num:
yield self.get_strrow(row_num)
class RowBodyIterator(RowStringIterator):
"""Iterator for the table body rows."""
def __iter__(self) -> Iterator[str]:
for row_num in range(len(self.strrows)):
if row_num >= self._header_row_num:
yield self.get_strrow(row_num)
class TableBuilderAbstract(ABC):
"""
Abstract table builder producing string representation of LaTeX table.
Parameters
----------
formatter : `DataFrameFormatter`
Instance of `DataFrameFormatter`.
column_format: str, optional
Column format, for example, 'rcl' for three columns.
multicolumn: bool, optional
Use multicolumn to enhance MultiIndex columns.
multicolumn_format: str, optional
The alignment for multicolumns, similar to column_format.
multirow: bool, optional
Use multirow to enhance MultiIndex rows.
caption: str, optional
Table caption.
short_caption: str, optional
Table short caption.
label: str, optional
LaTeX label.
position: str, optional
Float placement specifier, for example, 'htb'.
"""
def __init__(
self,
formatter: DataFrameFormatter,
column_format: Optional[str] = None,
multicolumn: bool = False,
multicolumn_format: Optional[str] = None,
multirow: bool = False,
caption: Optional[str] = None,
short_caption: Optional[str] = None,
label: Optional[str] = None,
position: Optional[str] = None,
):
self.fmt = formatter
self.column_format = column_format
self.multicolumn = multicolumn
self.multicolumn_format = multicolumn_format
self.multirow = multirow
self.caption = caption
self.short_caption = short_caption
self.label = label
self.position = position
def get_result(self) -> str:
"""String representation of LaTeX table."""
elements = [
self.env_begin,
self.top_separator,
self.header,
self.middle_separator,
self.env_body,
self.bottom_separator,
self.env_end,
]
result = "\n".join([item for item in elements if item])
trailing_newline = "\n"
result += trailing_newline
return result
@property
@abstractmethod
def env_begin(self) -> str:
"""Beginning of the environment."""
@property
@abstractmethod
def top_separator(self) -> str:
"""Top level separator."""
@property
@abstractmethod
def header(self) -> str:
"""Header lines."""
@property
@abstractmethod
def middle_separator(self) -> str:
"""Middle level separator."""
@property
@abstractmethod
def env_body(self) -> str:
"""Environment body."""
@property
@abstractmethod
def bottom_separator(self) -> str:
"""Bottom level separator."""
@property
@abstractmethod
def env_end(self) -> str:
"""End of the environment."""
class GenericTableBuilder(TableBuilderAbstract):
"""Table builder producing string representation of LaTeX table."""
@property
def header(self) -> str:
iterator = self._create_row_iterator(over="header")
return "\n".join(list(iterator))
@property
def top_separator(self) -> str:
return "\\toprule"
@property
def middle_separator(self) -> str:
return "\\midrule" if self._is_separator_required() else ""
@property
def env_body(self) -> str:
iterator = self._create_row_iterator(over="body")
return "\n".join(list(iterator))
def _is_separator_required(self) -> bool:
return bool(self.header and self.env_body)
@property
def _position_macro(self) -> str:
r"""Position macro, extracted from self.position, like [h]."""
return f"[{self.position}]" if self.position else ""
@property
def _caption_macro(self) -> str:
r"""Caption macro, extracted from self.caption.
With short caption:
\caption[short_caption]{caption_string}.
Without short caption:
\caption{caption_string}.
"""
if self.caption:
return "".join(
[
r"\caption",
f"[{self.short_caption}]" if self.short_caption else "",
f"{{{self.caption}}}",
]
)
return ""
@property
def _label_macro(self) -> str:
r"""Label macro, extracted from self.label, like \label{ref}."""
return f"\\label{{{self.label}}}" if self.label else ""
def _create_row_iterator(self, over: str) -> RowStringIterator:
"""Create iterator over header or body of the table.
Parameters
----------
over : {'body', 'header'}
Over what to iterate.
Returns
-------
RowStringIterator
Iterator over body or header.
"""
iterator_kind = self._select_iterator(over)
return iterator_kind(
formatter=self.fmt,
multicolumn=self.multicolumn,
multicolumn_format=self.multicolumn_format,
multirow=self.multirow,
)
def _select_iterator(self, over: str) -> Type[RowStringIterator]:
"""Select proper iterator over table rows."""
if over == "header":
return RowHeaderIterator
elif over == "body":
return RowBodyIterator
else:
msg = f"'over' must be either 'header' or 'body', but {over} was provided"
raise ValueError(msg)
class LongTableBuilder(GenericTableBuilder):
"""Concrete table builder for longtable.
>>> from pandas import DataFrame
>>> from pandas.io.formats import format as fmt
>>> df = DataFrame({"a": [1, 2], "b": ["b1", "b2"]})
>>> formatter = fmt.DataFrameFormatter(df)
>>> builder = LongTableBuilder(formatter, caption='a long table',
... label='tab:long', column_format='lrl')
>>> table = builder.get_result()
>>> print(table)
\\begin{longtable}{lrl}
\\caption{a long table}
\\label{tab:long}\\\\
\\toprule
{} & a & b \\\\
\\midrule
\\endfirsthead
\\caption[]{a long table} \\\\
\\toprule
{} & a & b \\\\
\\midrule
\\endhead
\\midrule
\\multicolumn{3}{r}{{Continued on next page}} \\\\
\\midrule
\\endfoot
<BLANKLINE>
\\bottomrule
\\endlastfoot
0 & 1 & b1 \\\\
1 & 2 & b2 \\\\
\\end{longtable}
<BLANKLINE>
"""
@property
def env_begin(self) -> str:
first_row = (
f"\\begin{{longtable}}{self._position_macro}{{{self.column_format}}}"
)
elements = [first_row, f"{self._caption_and_label()}"]
return "\n".join([item for item in elements if item])
def _caption_and_label(self) -> str:
if self.caption or self.label:
double_backslash = "\\\\"
elements = [f"{self._caption_macro}", f"{self._label_macro}"]
caption_and_label = "\n".join([item for item in elements if item])
caption_and_label += double_backslash
return caption_and_label
else:
return ""
@property
def middle_separator(self) -> str:
iterator = self._create_row_iterator(over="header")
# the content between \endfirsthead and \endhead commands
# mitigates repeated List of Tables entries in the final LaTeX
# document when dealing with longtable environments; GH #34360
elements = [
"\\midrule",
"\\endfirsthead",
f"\\caption[]{{{self.caption}}} \\\\" if self.caption else "",
self.top_separator,
self.header,
"\\midrule",
"\\endhead",
"\\midrule",
f"\\multicolumn{{{len(iterator.strcols)}}}{{r}}"
"{{Continued on next page}} \\\\",
"\\midrule",
"\\endfoot\n",
"\\bottomrule",
"\\endlastfoot",
]
if self._is_separator_required():
return "\n".join(elements)
return ""
@property
def bottom_separator(self) -> str:
return ""
@property
def env_end(self) -> str:
return "\\end{longtable}"
class RegularTableBuilder(GenericTableBuilder):
"""Concrete table builder for regular table.
>>> from pandas import DataFrame
>>> from pandas.io.formats import format as fmt
>>> df = DataFrame({"a": [1, 2], "b": ["b1", "b2"]})
>>> formatter = fmt.DataFrameFormatter(df)
>>> builder = RegularTableBuilder(formatter, caption='caption', label='lab',
... column_format='lrc')
>>> table = builder.get_result()
>>> print(table)
\\begin{table}
\\centering
\\caption{caption}
\\label{lab}
\\begin{tabular}{lrc}
\\toprule
{} & a & b \\\\
\\midrule
0 & 1 & b1 \\\\
1 & 2 & b2 \\\\
\\bottomrule
\\end{tabular}
\\end{table}
<BLANKLINE>
"""
@property
def env_begin(self) -> str:
elements = [
f"\\begin{{table}}{self._position_macro}",
"\\centering",
f"{self._caption_macro}",
f"{self._label_macro}",
f"\\begin{{tabular}}{{{self.column_format}}}",
]
return "\n".join([item for item in elements if item])
@property
def bottom_separator(self) -> str:
return "\\bottomrule"
@property
def env_end(self) -> str:
return "\n".join(["\\end{tabular}", "\\end{table}"])
class TabularBuilder(GenericTableBuilder):
"""Concrete table builder for tabular environment.
>>> from pandas import DataFrame
>>> from pandas.io.formats import format as fmt
>>> df = DataFrame({"a": [1, 2], "b": ["b1", "b2"]})
>>> formatter = fmt.DataFrameFormatter(df)
>>> builder = TabularBuilder(formatter, column_format='lrc')
>>> table = builder.get_result()
>>> print(table)
\\begin{tabular}{lrc}
\\toprule
{} & a & b \\\\
\\midrule
0 & 1 & b1 \\\\
1 & 2 & b2 \\\\
\\bottomrule
\\end{tabular}
<BLANKLINE>
"""
@property
def env_begin(self) -> str:
return f"\\begin{{tabular}}{{{self.column_format}}}"
@property
def bottom_separator(self) -> str:
return "\\bottomrule"
@property
def env_end(self) -> str:
return "\\end{tabular}"
class LatexFormatter:
r"""
Used to render a DataFrame to a LaTeX tabular/longtable environment output.
Parameters
----------
formatter : `DataFrameFormatter`
longtable : bool, default False
Use longtable environment.
column_format : str, default None
The columns format as specified in `LaTeX table format
<https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g 'rcl' for 3 columns
multicolumn : bool, default False
Use \multicolumn to enhance MultiIndex columns.
multicolumn_format : str, default 'l'
The alignment for multicolumns, similar to `column_format`
multirow : bool, default False
Use \multirow to enhance MultiIndex rows.
caption : str or tuple, optional
Tuple (full_caption, short_caption),
which results in \caption[short_caption]{full_caption};
if a single string is passed, no short caption will be set.
label : str, optional
The LaTeX label to be placed inside ``\label{}`` in the output.
position : str, optional
The LaTeX positional argument for tables, to be placed after
``\begin{}`` in the output.
See Also
--------
HTMLFormatter
"""
def __init__(
self,
formatter: DataFrameFormatter,
longtable: bool = False,
column_format: Optional[str] = None,
multicolumn: bool = False,
multicolumn_format: Optional[str] = None,
multirow: bool = False,
caption: Optional[Union[str, Tuple[str, str]]] = None,
label: Optional[str] = None,
position: Optional[str] = None,
):
self.fmt = formatter
self.frame = self.fmt.frame
self.longtable = longtable
self.column_format = column_format
self.multicolumn = multicolumn
self.multicolumn_format = multicolumn_format
self.multirow = multirow
self.caption, self.short_caption = _split_into_full_short_caption(caption)
self.label = label
self.position = position
def to_string(self) -> str:
"""
Render a DataFrame to a LaTeX tabular, longtable, or table/tabular
environment output.
"""
return self.builder.get_result()
@property
def builder(self) -> TableBuilderAbstract:
"""Concrete table builder.
Returns
-------
TableBuilder
"""
builder = self._select_builder()
return builder(
formatter=self.fmt,
column_format=self.column_format,
multicolumn=self.multicolumn,
multicolumn_format=self.multicolumn_format,
multirow=self.multirow,
caption=self.caption,
short_caption=self.short_caption,
label=self.label,
position=self.position,
)
def _select_builder(self) -> Type[TableBuilderAbstract]:
"""Select proper table builder."""
if self.longtable:
return LongTableBuilder
if any([self.caption, self.label, self.position]):
return RegularTableBuilder
return TabularBuilder
@property
def column_format(self) -> Optional[str]:
"""Column format."""
return self._column_format
@column_format.setter
def column_format(self, input_column_format: Optional[str]) -> None:
"""Setter for column format."""
if input_column_format is None:
self._column_format = (
self._get_index_format() + self._get_column_format_based_on_dtypes()
)
elif not isinstance(input_column_format, str):
raise ValueError(
f"column_format must be str or unicode, "
f"not {type(input_column_format)}"
)
else:
self._column_format = input_column_format
def _get_column_format_based_on_dtypes(self) -> str:
"""Get column format based on data type.
Right alignment for numbers and left - for strings.
"""
def get_col_type(dtype):
if issubclass(dtype.type, np.number):
return "r"
return "l"
dtypes = self.frame.dtypes._values
return "".join(map(get_col_type, dtypes))
def _get_index_format(self) -> str:
"""Get index column format."""
return "l" * self.frame.index.nlevels if self.fmt.index else ""
def _escape_symbols(row: Sequence[str]) -> List[str]:
"""Carry out string replacements for special symbols.
Parameters
----------
row : list
List of string, that may contain special symbols.
Returns
-------
list
list of strings with the special symbols replaced.
"""
return [
(
x.replace("\\", "\\textbackslash ")
.replace("_", "\\_")
.replace("%", "\\%")
.replace("$", "\\$")
.replace("#", "\\#")
.replace("{", "\\{")
.replace("}", "\\}")
.replace("~", "\\textasciitilde ")
.replace("^", "\\textasciicircum ")
.replace("&", "\\&")
if (x and x != "{}")
else "{}"
)
for x in row
]
def _convert_to_bold(crow: Sequence[str], ilevels: int) -> List[str]:
"""Convert elements in ``crow`` to bold."""
return [
f"\\textbf{{{x}}}" if j < ilevels and x.strip() not in ["", "{}"] else x
for j, x in enumerate(crow)
]
if __name__ == "__main__":
import doctest
doctest.testmod()
| bsd-3-clause |
glemaitre/UnbalancedDataset | examples/applications/plot_multi_class_under_sampling.py | 2 | 1478 | """
=============================================
Multiclass classification with under-sampling
=============================================
Some balancing methods allow for balancing dataset with multiples classes.
We provide an example to illustrate the use of those methods which do
not differ from the binary case.
"""
# Authors: Guillaume Lemaitre <[email protected]>
# License: MIT
from collections import Counter
from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split
from imblearn.datasets import make_imbalance
from imblearn.under_sampling import NearMiss
from imblearn.pipeline import make_pipeline
from imblearn.metrics import classification_report_imbalanced
print(__doc__)
RANDOM_STATE = 42
# Create a folder to fetch the dataset
iris = load_iris()
X, y = make_imbalance(iris.data, iris.target, ratio={0: 25, 1: 50, 2: 50},
random_state=0)
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=RANDOM_STATE)
print('Training target statistics: {}'.format(Counter(y_train)))
print('Testing target statistics: {}'.format(Counter(y_test)))
# Create a pipeline
pipeline = make_pipeline(NearMiss(version=2, random_state=RANDOM_STATE),
LinearSVC(random_state=RANDOM_STATE))
pipeline.fit(X_train, y_train)
# Classify and report the results
print(classification_report_imbalanced(y_test, pipeline.predict(X_test)))
| mit |
hsiaoyi0504/scikit-learn | sklearn/linear_model/ridge.py | 89 | 39360 | """
Ridge regression
"""
# Author: Mathieu Blondel <[email protected]>
# Reuben Fletcher-Costin <[email protected]>
# Fabian Pedregosa <[email protected]>
# Michael Eickenberg <[email protected]>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy import linalg
from scipy import sparse
from scipy.sparse import linalg as sp_linalg
from .base import LinearClassifierMixin, LinearModel
from ..base import RegressorMixin
from ..utils.extmath import safe_sparse_dot
from ..utils import check_X_y
from ..utils import compute_sample_weight
from ..utils import column_or_1d
from ..preprocessing import LabelBinarizer
from ..grid_search import GridSearchCV
from ..externals import six
from ..metrics.scorer import check_scoring
def _solve_sparse_cg(X, y, alpha, max_iter=None, tol=1e-3, verbose=0):
n_samples, n_features = X.shape
X1 = sp_linalg.aslinearoperator(X)
coefs = np.empty((y.shape[1], n_features))
if n_features > n_samples:
def create_mv(curr_alpha):
def _mv(x):
return X1.matvec(X1.rmatvec(x)) + curr_alpha * x
return _mv
else:
def create_mv(curr_alpha):
def _mv(x):
return X1.rmatvec(X1.matvec(x)) + curr_alpha * x
return _mv
for i in range(y.shape[1]):
y_column = y[:, i]
mv = create_mv(alpha[i])
if n_features > n_samples:
# kernel ridge
# w = X.T * inv(X X^t + alpha*Id) y
C = sp_linalg.LinearOperator(
(n_samples, n_samples), matvec=mv, dtype=X.dtype)
coef, info = sp_linalg.cg(C, y_column, tol=tol)
coefs[i] = X1.rmatvec(coef)
else:
# linear ridge
# w = inv(X^t X + alpha*Id) * X.T y
y_column = X1.rmatvec(y_column)
C = sp_linalg.LinearOperator(
(n_features, n_features), matvec=mv, dtype=X.dtype)
coefs[i], info = sp_linalg.cg(C, y_column, maxiter=max_iter,
tol=tol)
if info < 0:
raise ValueError("Failed with error code %d" % info)
if max_iter is None and info > 0 and verbose:
warnings.warn("sparse_cg did not converge after %d iterations." %
info)
return coefs
def _solve_lsqr(X, y, alpha, max_iter=None, tol=1e-3):
n_samples, n_features = X.shape
coefs = np.empty((y.shape[1], n_features))
# According to the lsqr documentation, alpha = damp^2.
sqrt_alpha = np.sqrt(alpha)
for i in range(y.shape[1]):
y_column = y[:, i]
coefs[i] = sp_linalg.lsqr(X, y_column, damp=sqrt_alpha[i],
atol=tol, btol=tol, iter_lim=max_iter)[0]
return coefs
def _solve_cholesky(X, y, alpha):
# w = inv(X^t X + alpha*Id) * X.T y
n_samples, n_features = X.shape
n_targets = y.shape[1]
A = safe_sparse_dot(X.T, X, dense_output=True)
Xy = safe_sparse_dot(X.T, y, dense_output=True)
one_alpha = np.array_equal(alpha, len(alpha) * [alpha[0]])
if one_alpha:
A.flat[::n_features + 1] += alpha[0]
return linalg.solve(A, Xy, sym_pos=True,
overwrite_a=True).T
else:
coefs = np.empty([n_targets, n_features])
for coef, target, current_alpha in zip(coefs, Xy.T, alpha):
A.flat[::n_features + 1] += current_alpha
coef[:] = linalg.solve(A, target, sym_pos=True,
overwrite_a=False).ravel()
A.flat[::n_features + 1] -= current_alpha
return coefs
def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False):
# dual_coef = inv(X X^t + alpha*Id) y
n_samples = K.shape[0]
n_targets = y.shape[1]
if copy:
K = K.copy()
alpha = np.atleast_1d(alpha)
one_alpha = (alpha == alpha[0]).all()
has_sw = isinstance(sample_weight, np.ndarray) \
or sample_weight not in [1.0, None]
if has_sw:
# Unlike other solvers, we need to support sample_weight directly
# because K might be a pre-computed kernel.
sw = np.sqrt(np.atleast_1d(sample_weight))
y = y * sw[:, np.newaxis]
K *= np.outer(sw, sw)
if one_alpha:
# Only one penalty, we can solve multi-target problems in one time.
K.flat[::n_samples + 1] += alpha[0]
try:
# Note: we must use overwrite_a=False in order to be able to
# use the fall-back solution below in case a LinAlgError
# is raised
dual_coef = linalg.solve(K, y, sym_pos=True,
overwrite_a=False)
except np.linalg.LinAlgError:
warnings.warn("Singular matrix in solving dual problem. Using "
"least-squares solution instead.")
dual_coef = linalg.lstsq(K, y)[0]
# K is expensive to compute and store in memory so change it back in
# case it was user-given.
K.flat[::n_samples + 1] -= alpha[0]
if has_sw:
dual_coef *= sw[:, np.newaxis]
return dual_coef
else:
# One penalty per target. We need to solve each target separately.
dual_coefs = np.empty([n_targets, n_samples])
for dual_coef, target, current_alpha in zip(dual_coefs, y.T, alpha):
K.flat[::n_samples + 1] += current_alpha
dual_coef[:] = linalg.solve(K, target, sym_pos=True,
overwrite_a=False).ravel()
K.flat[::n_samples + 1] -= current_alpha
if has_sw:
dual_coefs *= sw[np.newaxis, :]
return dual_coefs.T
def _solve_svd(X, y, alpha):
U, s, Vt = linalg.svd(X, full_matrices=False)
idx = s > 1e-15 # same default value as scipy.linalg.pinv
s_nnz = s[idx][:, np.newaxis]
UTy = np.dot(U.T, y)
d = np.zeros((s.size, alpha.size))
d[idx] = s_nnz / (s_nnz ** 2 + alpha)
d_UT_y = d * UTy
return np.dot(Vt.T, d_UT_y).T
def _rescale_data(X, y, sample_weight):
"""Rescale data so as to support sample_weight"""
n_samples = X.shape[0]
sample_weight = sample_weight * np.ones(n_samples)
sample_weight = np.sqrt(sample_weight)
sw_matrix = sparse.dia_matrix((sample_weight, 0),
shape=(n_samples, n_samples))
X = safe_sparse_dot(sw_matrix, X)
y = safe_sparse_dot(sw_matrix, y)
return X, y
def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
max_iter=None, tol=1e-3, verbose=0):
"""Solve the ridge equation by the method of normal equations.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
X : {array-like, sparse matrix, LinearOperator},
shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
alpha : {float, array-like},
shape = [n_targets] if array-like
The l_2 penalty to be used. If an array is passed, penalties are
assumed to be specific to targets
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample. If sample_weight is set, then
the solver will automatically be set to 'cholesky'
solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg'}
Solver to use in the computational routines:
- 'auto' chooses the solver automatically based on the type of data.
- 'svd' uses a Singular Value Decomposition of X to compute the Ridge
coefficients. More stable for singular matrices than
'cholesky'.
- 'cholesky' uses the standard scipy.linalg.solve function to
obtain a closed-form solution via a Cholesky decomposition of
dot(X.T, X)
- 'sparse_cg' uses the conjugate gradient solver as found in
scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
more appropriate than 'cholesky' for large-scale data
(possibility to set `tol` and `max_iter`).
- 'lsqr' uses the dedicated regularized least-squares routine
scipy.sparse.linalg.lsqr. It is the fatest but may not be available
in old scipy versions. It also uses an iterative procedure.
All three solvers support both dense and sparse data.
tol : float
Precision of the solution.
verbose : int
Verbosity level. Setting verbose > 0 will display additional information
depending on the solver used.
Returns
-------
coef : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
Notes
-----
This function won't compute the intercept.
"""
n_samples, n_features = X.shape
if y.ndim > 2:
raise ValueError("Target y has the wrong shape %s" % str(y.shape))
ravel = False
if y.ndim == 1:
y = y.reshape(-1, 1)
ravel = True
n_samples_, n_targets = y.shape
if n_samples != n_samples_:
raise ValueError("Number of samples in X and y does not correspond:"
" %d != %d" % (n_samples, n_samples_))
has_sw = sample_weight is not None
if solver == 'auto':
# cholesky if it's a dense array and cg in
# any other case
if not sparse.issparse(X) or has_sw:
solver = 'cholesky'
else:
solver = 'sparse_cg'
elif solver == 'lsqr' and not hasattr(sp_linalg, 'lsqr'):
warnings.warn("""lsqr not available on this machine, falling back
to sparse_cg.""")
solver = 'sparse_cg'
if has_sw:
if np.atleast_1d(sample_weight).ndim > 1:
raise ValueError("Sample weights must be 1D array or scalar")
# Sample weight can be implemented via a simple rescaling.
X, y = _rescale_data(X, y, sample_weight)
# There should be either 1 or n_targets penalties
alpha = np.asarray(alpha).ravel()
if alpha.size not in [1, n_targets]:
raise ValueError("Number of targets and number of penalties "
"do not correspond: %d != %d"
% (alpha.size, n_targets))
if alpha.size == 1 and n_targets > 1:
alpha = np.repeat(alpha, n_targets)
if solver not in ('sparse_cg', 'cholesky', 'svd', 'lsqr'):
raise ValueError('Solver %s not understood' % solver)
if solver == 'sparse_cg':
coef = _solve_sparse_cg(X, y, alpha, max_iter, tol, verbose)
elif solver == "lsqr":
coef = _solve_lsqr(X, y, alpha, max_iter, tol)
elif solver == 'cholesky':
if n_features > n_samples:
K = safe_sparse_dot(X, X.T, dense_output=True)
try:
dual_coef = _solve_cholesky_kernel(K, y, alpha)
coef = safe_sparse_dot(X.T, dual_coef, dense_output=True).T
except linalg.LinAlgError:
# use SVD solver if matrix is singular
solver = 'svd'
else:
try:
coef = _solve_cholesky(X, y, alpha)
except linalg.LinAlgError:
# use SVD solver if matrix is singular
solver = 'svd'
if solver == 'svd':
if sparse.issparse(X):
raise TypeError('SVD solver does not support sparse'
' inputs currently')
coef = _solve_svd(X, y, alpha)
if ravel:
# When y was passed as a 1d-array, we flatten the coefficients.
coef = coef.ravel()
return coef
class _BaseRidge(six.with_metaclass(ABCMeta, LinearModel)):
@abstractmethod
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=1e-3, solver="auto"):
self.alpha = alpha
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.max_iter = max_iter
self.tol = tol
self.solver = solver
def fit(self, X, y, sample_weight=None):
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float,
multi_output=True, y_numeric=True)
if ((sample_weight is not None) and
np.atleast_1d(sample_weight).ndim > 1):
raise ValueError("Sample weights must be 1D array or scalar")
X, y, X_mean, y_mean, X_std = self._center_data(
X, y, self.fit_intercept, self.normalize, self.copy_X,
sample_weight=sample_weight)
self.coef_ = ridge_regression(X, y,
alpha=self.alpha,
sample_weight=sample_weight,
max_iter=self.max_iter,
tol=self.tol,
solver=self.solver)
self._set_intercept(X_mean, y_mean, X_std)
return self
class Ridge(_BaseRidge, RegressorMixin):
"""Linear least squares with l2 regularization.
This model solves a regression model where the loss function is
the linear least squares function and regularization is given by
the l2-norm. Also known as Ridge Regression or Tikhonov regularization.
This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alpha : {float, array-like}
shape = [n_targets]
Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
``(2*C)^-1`` in other linear models such as LogisticRegression or
LinearSVC. If an array is passed, penalties are assumed to be specific
to the targets. Hence they must correspond in number.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg'}
Solver to use in the computational routines:
- 'auto' chooses the solver automatically based on the type of data.
- 'svd' uses a Singular Value Decomposition of X to compute the Ridge
coefficients. More stable for singular matrices than
'cholesky'.
- 'cholesky' uses the standard scipy.linalg.solve function to
obtain a closed-form solution.
- 'sparse_cg' uses the conjugate gradient solver as found in
scipy.sparse.linalg.cg. As an iterative algorithm, this solver is
more appropriate than 'cholesky' for large-scale data
(possibility to set `tol` and `max_iter`).
- 'lsqr' uses the dedicated regularized least-squares routine
scipy.sparse.linalg.lsqr. It is the fatest but may not be available
in old scipy versions. It also uses an iterative procedure.
All three solvers support both dense and sparse data.
tol : float
Precision of the solution.
Attributes
----------
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
See also
--------
RidgeClassifier, RidgeCV, KernelRidge
Examples
--------
>>> from sklearn.linear_model import Ridge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = Ridge(alpha=1.0)
>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, solver='auto', tol=0.001)
"""
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=1e-3, solver="auto"):
super(Ridge, self).__init__(alpha=alpha, fit_intercept=fit_intercept,
normalize=normalize, copy_X=copy_X,
max_iter=max_iter, tol=tol, solver=solver)
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or numpy array of shape [n_samples]
Individual weights for each sample
Returns
-------
self : returns an instance of self.
"""
return super(Ridge, self).fit(X, y, sample_weight=sample_weight)
class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
"""Classifier using Ridge regression.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alpha : float
Small positive values of alpha improve the conditioning of the problem
and reduce the variance of the estimates. Alpha corresponds to
``(2*C)^-1`` in other linear models such as LogisticRegression or
LinearSVC.
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set to false, no
intercept will be used in calculations (e.g. data is expected to be
already centered).
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver.
The default value is determined by scipy.sparse.linalg.
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg'}
Solver to use in the computational
routines. 'svd' will use a Singular value decomposition to obtain
the solution, 'cholesky' will use the standard
scipy.linalg.solve function, 'sparse_cg' will use the
conjugate gradient solver as found in
scipy.sparse.linalg.cg while 'auto' will chose the most
appropriate depending on the matrix X. 'lsqr' uses
a direct regularized least-squares routine provided by scipy.
tol : float
Precision of the solution.
Attributes
----------
coef_ : array, shape = [n_features] or [n_classes, n_features]
Weight vector(s).
See also
--------
Ridge, RidgeClassifierCV
Notes
-----
For multi-class classification, n_class classifiers are trained in
a one-versus-all approach. Concretely, this is implemented by taking
advantage of the multi-variate response support in Ridge.
"""
def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=1e-3, class_weight=None,
solver="auto"):
super(RidgeClassifier, self).__init__(
alpha=alpha, fit_intercept=fit_intercept, normalize=normalize,
copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver)
self.class_weight = class_weight
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples,n_features]
Training data
y : array-like, shape = [n_samples]
Target values
sample_weight : float or numpy array of shape (n_samples,)
Sample weight.
Returns
-------
self : returns an instance of self.
"""
self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
Y = self._label_binarizer.fit_transform(y)
if not self._label_binarizer.y_type_.startswith('multilabel'):
y = column_or_1d(y, warn=True)
if self.class_weight:
if sample_weight is None:
sample_weight = 1.
# modify the sample weights with the corresponding class weight
sample_weight = (sample_weight *
compute_sample_weight(self.class_weight, y))
super(RidgeClassifier, self).fit(X, Y, sample_weight=sample_weight)
return self
@property
def classes_(self):
return self._label_binarizer.classes_
class _RidgeGCV(LinearModel):
"""Ridge regression with built-in Generalized Cross-Validation
It allows efficient Leave-One-Out cross-validation.
This class is not intended to be used directly. Use RidgeCV instead.
Notes
-----
We want to solve (K + alpha*Id)c = y,
where K = X X^T is the kernel matrix.
Let G = (K + alpha*Id)^-1.
Dual solution: c = Gy
Primal solution: w = X^T c
Compute eigendecomposition K = Q V Q^T.
Then G = Q (V + alpha*Id)^-1 Q^T,
where (V + alpha*Id) is diagonal.
It is thus inexpensive to inverse for many alphas.
Let loov be the vector of prediction values for each example
when the model was fitted with all examples but this example.
loov = (KGY - diag(KG)Y) / diag(I-KG)
Let looe be the vector of prediction errors for each example
when the model was fitted with all examples but this example.
looe = y - loov = c / diag(G)
References
----------
http://cbcl.mit.edu/projects/cbcl/publications/ps/MIT-CSAIL-TR-2007-025.pdf
http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf
"""
def __init__(self, alphas=(0.1, 1.0, 10.0),
fit_intercept=True, normalize=False,
scoring=None, copy_X=True,
gcv_mode=None, store_cv_values=False):
self.alphas = np.asarray(alphas)
self.fit_intercept = fit_intercept
self.normalize = normalize
self.scoring = scoring
self.copy_X = copy_X
self.gcv_mode = gcv_mode
self.store_cv_values = store_cv_values
def _pre_compute(self, X, y):
# even if X is very sparse, K is usually very dense
K = safe_sparse_dot(X, X.T, dense_output=True)
v, Q = linalg.eigh(K)
QT_y = np.dot(Q.T, y)
return v, Q, QT_y
def _decomp_diag(self, v_prime, Q):
# compute diagonal of the matrix: dot(Q, dot(diag(v_prime), Q^T))
return (v_prime * Q ** 2).sum(axis=-1)
def _diag_dot(self, D, B):
# compute dot(diag(D), B)
if len(B.shape) > 1:
# handle case where B is > 1-d
D = D[(slice(None), ) + (np.newaxis, ) * (len(B.shape) - 1)]
return D * B
def _errors(self, alpha, y, v, Q, QT_y):
# don't construct matrix G, instead compute action on y & diagonal
w = 1.0 / (v + alpha)
c = np.dot(Q, self._diag_dot(w, QT_y))
G_diag = self._decomp_diag(w, Q)
# handle case where y is 2-d
if len(y.shape) != 1:
G_diag = G_diag[:, np.newaxis]
return (c / G_diag) ** 2, c
def _values(self, alpha, y, v, Q, QT_y):
# don't construct matrix G, instead compute action on y & diagonal
w = 1.0 / (v + alpha)
c = np.dot(Q, self._diag_dot(w, QT_y))
G_diag = self._decomp_diag(w, Q)
# handle case where y is 2-d
if len(y.shape) != 1:
G_diag = G_diag[:, np.newaxis]
return y - (c / G_diag), c
def _pre_compute_svd(self, X, y):
if sparse.issparse(X):
raise TypeError("SVD not supported for sparse matrices")
U, s, _ = linalg.svd(X, full_matrices=0)
v = s ** 2
UT_y = np.dot(U.T, y)
return v, U, UT_y
def _errors_svd(self, alpha, y, v, U, UT_y):
w = ((v + alpha) ** -1) - (alpha ** -1)
c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha ** -1) * y
G_diag = self._decomp_diag(w, U) + (alpha ** -1)
if len(y.shape) != 1:
# handle case where y is 2-d
G_diag = G_diag[:, np.newaxis]
return (c / G_diag) ** 2, c
def _values_svd(self, alpha, y, v, U, UT_y):
w = ((v + alpha) ** -1) - (alpha ** -1)
c = np.dot(U, self._diag_dot(w, UT_y)) + (alpha ** -1) * y
G_diag = self._decomp_diag(w, U) + (alpha ** -1)
if len(y.shape) != 1:
# handle case when y is 2-d
G_diag = G_diag[:, np.newaxis]
return y - (c / G_diag), c
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or array-like of shape [n_samples]
Sample weight
Returns
-------
self : Returns self.
"""
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float,
multi_output=True, y_numeric=True)
n_samples, n_features = X.shape
X, y, X_mean, y_mean, X_std = LinearModel._center_data(
X, y, self.fit_intercept, self.normalize, self.copy_X,
sample_weight=sample_weight)
gcv_mode = self.gcv_mode
with_sw = len(np.shape(sample_weight))
if gcv_mode is None or gcv_mode == 'auto':
if sparse.issparse(X) or n_features > n_samples or with_sw:
gcv_mode = 'eigen'
else:
gcv_mode = 'svd'
elif gcv_mode == "svd" and with_sw:
# FIXME non-uniform sample weights not yet supported
warnings.warn("non-uniform sample weights unsupported for svd, "
"forcing usage of eigen")
gcv_mode = 'eigen'
if gcv_mode == 'eigen':
_pre_compute = self._pre_compute
_errors = self._errors
_values = self._values
elif gcv_mode == 'svd':
# assert n_samples >= n_features
_pre_compute = self._pre_compute_svd
_errors = self._errors_svd
_values = self._values_svd
else:
raise ValueError('bad gcv_mode "%s"' % gcv_mode)
v, Q, QT_y = _pre_compute(X, y)
n_y = 1 if len(y.shape) == 1 else y.shape[1]
cv_values = np.zeros((n_samples * n_y, len(self.alphas)))
C = []
scorer = check_scoring(self, scoring=self.scoring, allow_none=True)
error = scorer is None
for i, alpha in enumerate(self.alphas):
weighted_alpha = (sample_weight * alpha
if sample_weight is not None
else alpha)
if error:
out, c = _errors(weighted_alpha, y, v, Q, QT_y)
else:
out, c = _values(weighted_alpha, y, v, Q, QT_y)
cv_values[:, i] = out.ravel()
C.append(c)
if error:
best = cv_values.mean(axis=0).argmin()
else:
# The scorer want an object that will make the predictions but
# they are already computed efficiently by _RidgeGCV. This
# identity_estimator will just return them
def identity_estimator():
pass
identity_estimator.decision_function = lambda y_predict: y_predict
identity_estimator.predict = lambda y_predict: y_predict
out = [scorer(identity_estimator, y.ravel(), cv_values[:, i])
for i in range(len(self.alphas))]
best = np.argmax(out)
self.alpha_ = self.alphas[best]
self.dual_coef_ = C[best]
self.coef_ = safe_sparse_dot(self.dual_coef_.T, X)
self._set_intercept(X_mean, y_mean, X_std)
if self.store_cv_values:
if len(y.shape) == 1:
cv_values_shape = n_samples, len(self.alphas)
else:
cv_values_shape = n_samples, n_y, len(self.alphas)
self.cv_values_ = cv_values.reshape(cv_values_shape)
return self
class _BaseRidgeCV(LinearModel):
def __init__(self, alphas=(0.1, 1.0, 10.0),
fit_intercept=True, normalize=False, scoring=None,
cv=None, gcv_mode=None,
store_cv_values=False):
self.alphas = alphas
self.fit_intercept = fit_intercept
self.normalize = normalize
self.scoring = scoring
self.cv = cv
self.gcv_mode = gcv_mode
self.store_cv_values = store_cv_values
def fit(self, X, y, sample_weight=None):
"""Fit Ridge regression model
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or array-like of shape [n_samples]
Sample weight
Returns
-------
self : Returns self.
"""
if self.cv is None:
estimator = _RidgeGCV(self.alphas,
fit_intercept=self.fit_intercept,
normalize=self.normalize,
scoring=self.scoring,
gcv_mode=self.gcv_mode,
store_cv_values=self.store_cv_values)
estimator.fit(X, y, sample_weight=sample_weight)
self.alpha_ = estimator.alpha_
if self.store_cv_values:
self.cv_values_ = estimator.cv_values_
else:
if self.store_cv_values:
raise ValueError("cv!=None and store_cv_values=True "
" are incompatible")
parameters = {'alpha': self.alphas}
fit_params = {'sample_weight' : sample_weight}
gs = GridSearchCV(Ridge(fit_intercept=self.fit_intercept),
parameters, fit_params=fit_params, cv=self.cv)
gs.fit(X, y)
estimator = gs.best_estimator_
self.alpha_ = gs.best_estimator_.alpha
self.coef_ = estimator.coef_
self.intercept_ = estimator.intercept_
return self
class RidgeCV(_BaseRidgeCV, RegressorMixin):
"""Ridge regression with built-in cross-validation.
By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alphas : numpy array of shape [n_alphas]
Array of alpha values to try.
Small positive values of alpha improve the conditioning of the
problem and reduce the variance of the estimates.
Alpha corresponds to ``(2*C)^-1`` in other linear models such as
LogisticRegression or LinearSVC.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
cv : integer or cross-validation generator, optional
If None, Generalized Cross-Validation (efficient Leave-One-Out)
will be used.
If an integer is passed, it is the number of folds for KFold cross
validation. Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects
gcv_mode : {None, 'auto', 'svd', eigen'}, optional
Flag indicating which strategy to use when performing
Generalized Cross-Validation. Options are::
'auto' : use svd if n_samples > n_features or when X is a sparse
matrix, otherwise use eigen
'svd' : force computation via singular value decomposition of X
(does not work for sparse matrices)
'eigen' : force computation via eigendecomposition of X^T X
The 'auto' mode is the default and is intended to pick the cheaper
option of the two depending upon the shape and format of the training
data.
store_cv_values : boolean, default=False
Flag indicating if the cross-validation values corresponding to
each alpha should be stored in the `cv_values_` attribute (see
below). This flag is only compatible with `cv=None` (i.e. using
Generalized Cross-Validation).
Attributes
----------
cv_values_ : array, shape = [n_samples, n_alphas] or \
shape = [n_samples, n_targets, n_alphas], optional
Cross-validation values for each alpha (if `store_cv_values=True` and \
`cv=None`). After `fit()` has been called, this attribute will \
contain the mean squared errors (by default) or the values of the \
`{loss,score}_func` function (if provided in the constructor).
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
alpha_ : float
Estimated regularization parameter.
intercept_ : float | array, shape = (n_targets,)
Independent term in decision function. Set to 0.0 if
``fit_intercept = False``.
See also
--------
Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeClassifierCV: Ridge classifier with built-in cross validation
"""
pass
class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
"""Ridge classifier with built-in cross-validation.
By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation. Currently, only the n_features >
n_samples case is handled efficiently.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters
----------
alphas : numpy array of shape [n_alphas]
Array of alpha values to try.
Small positive values of alpha improve the conditioning of the
problem and reduce the variance of the estimates.
Alpha corresponds to ``(2*C)^-1`` in other linear models such as
LogisticRegression or LinearSVC.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
cv : cross-validation generator, optional
If None, Generalized Cross-Validation (efficient Leave-One-Out)
will be used.
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
Attributes
----------
cv_values_ : array, shape = [n_samples, n_alphas] or \
shape = [n_samples, n_responses, n_alphas], optional
Cross-validation values for each alpha (if `store_cv_values=True` and
`cv=None`). After `fit()` has been called, this attribute will contain \
the mean squared errors (by default) or the values of the \
`{loss,score}_func` function (if provided in the constructor).
coef_ : array, shape = [n_features] or [n_targets, n_features]
Weight vector(s).
alpha_ : float
Estimated regularization parameter
See also
--------
Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeCV: Ridge regression with built-in cross validation
Notes
-----
For multi-class classification, n_class classifiers are trained in
a one-versus-all approach. Concretely, this is implemented by taking
advantage of the multi-variate response support in Ridge.
"""
def __init__(self, alphas=(0.1, 1.0, 10.0), fit_intercept=True,
normalize=False, scoring=None, cv=None, class_weight=None):
super(RidgeClassifierCV, self).__init__(
alphas=alphas, fit_intercept=fit_intercept, normalize=normalize,
scoring=scoring, cv=cv)
self.class_weight = class_weight
def fit(self, X, y, sample_weight=None):
"""Fit the ridge classifier.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
sample_weight : float or numpy array of shape (n_samples,)
Sample weight.
Returns
-------
self : object
Returns self.
"""
self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
Y = self._label_binarizer.fit_transform(y)
if not self._label_binarizer.y_type_.startswith('multilabel'):
y = column_or_1d(y, warn=True)
if self.class_weight:
if sample_weight is None:
sample_weight = 1.
# modify the sample weights with the corresponding class weight
sample_weight = (sample_weight *
compute_sample_weight(self.class_weight, y))
_BaseRidgeCV.fit(self, X, Y, sample_weight=sample_weight)
return self
@property
def classes_(self):
return self._label_binarizer.classes_
| bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/sklearn/tests/test_learning_curve.py | 45 | 11897 | # Author: Alexander Fabisch <[email protected]>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.datasets import make_classification
with warnings.catch_warnings():
warnings.simplefilter('ignore')
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.cross_validation import KFold
from sklearn.linear_model import PassiveAggressiveClassifier
class MockImprovingEstimator(BaseEstimator):
"""Dummy classifier to test the learning curve"""
def __init__(self, n_max_train_sizes):
self.n_max_train_sizes = n_max_train_sizes
self.train_sizes = 0
self.X_subset = None
def fit(self, X_subset, y_subset=None):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, Y=None):
# training score becomes worse (2 -> 1), test error better (0 -> 1)
if self._is_training_data(X):
return 2. - float(self.train_sizes) / self.n_max_train_sizes
else:
return float(self.train_sizes) / self.n_max_train_sizes
def _is_training_data(self, X):
return X is self.X_subset
class MockIncrementalImprovingEstimator(MockImprovingEstimator):
"""Dummy classifier that provides partial_fit"""
def __init__(self, n_max_train_sizes):
super(MockIncrementalImprovingEstimator,
self).__init__(n_max_train_sizes)
self.x = None
def _is_training_data(self, X):
return self.x in X
def partial_fit(self, X, y=None, **params):
self.train_sizes += X.shape[0]
self.x = X[0]
class MockEstimatorWithParameter(BaseEstimator):
"""Dummy classifier to test the validation curve"""
def __init__(self, param=0.5):
self.X_subset = None
self.param = param
def fit(self, X_subset, y_subset):
self.X_subset = X_subset
self.train_sizes = X_subset.shape[0]
return self
def predict(self, X):
raise NotImplementedError
def score(self, X=None, y=None):
return self.param if self._is_training_data(X) else 1 - self.param
def _is_training_data(self, X):
return X is self.X_subset
class MockEstimatorFailing(BaseEstimator):
"""Dummy classifier to test error_score in learning curve"""
def fit(self, X_subset, y_subset):
raise ValueError()
def score(self, X=None, y=None):
return None
def test_learning_curve():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
with warnings.catch_warnings(record=True) as w:
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert_equal(train_scores.shape, (10, 3))
assert_equal(test_scores.shape, (10, 3))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_unsupervised():
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y=None, cv=3, train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_verbose():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
train_sizes, train_scores, test_scores = \
learning_curve(estimator, X, y, cv=3, verbose=1)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = old_stdout
assert("[learning_curve]" in out)
def test_learning_curve_error_score():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockEstimatorFailing()
_, _, test_scores = learning_curve(estimator, X, y, cv=3, error_score=0)
all_zeros = not np.any(test_scores)
assert(all_zeros)
def test_learning_curve_error_score_default_raise():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockEstimatorFailing()
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3)
def test_learning_curve_incremental_learning_not_possible():
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
# The mockup does not have partial_fit()
estimator = MockImprovingEstimator(1)
assert_raises(ValueError, learning_curve, estimator, X, y,
exploit_incremental_learning=True)
def test_learning_curve_incremental_learning():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockIncrementalImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=3, exploit_incremental_learning=True,
train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_incremental_learning_unsupervised():
X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockIncrementalImprovingEstimator(20)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y=None, cv=3, exploit_incremental_learning=True,
train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_learning_curve_batch_and_incremental_learning_are_equal():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
train_sizes = np.linspace(0.2, 1.0, 5)
estimator = PassiveAggressiveClassifier(n_iter=1, shuffle=False)
train_sizes_inc, train_scores_inc, test_scores_inc = \
learning_curve(
estimator, X, y, train_sizes=train_sizes,
cv=3, exploit_incremental_learning=True)
train_sizes_batch, train_scores_batch, test_scores_batch = \
learning_curve(
estimator, X, y, cv=3, train_sizes=train_sizes,
exploit_incremental_learning=False)
assert_array_equal(train_sizes_inc, train_sizes_batch)
assert_array_almost_equal(train_scores_inc.mean(axis=1),
train_scores_batch.mean(axis=1))
assert_array_almost_equal(test_scores_inc.mean(axis=1),
test_scores_batch.mean(axis=1))
def test_learning_curve_n_sample_range_out_of_bounds():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0, 1])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0.0, 1.0])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0.1, 1.1])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[0, 20])
assert_raises(ValueError, learning_curve, estimator, X, y, cv=3,
train_sizes=[1, 21])
def test_learning_curve_remove_duplicate_sample_sizes():
X, y = make_classification(n_samples=3, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(2)
train_sizes, _, _ = assert_warns(
RuntimeWarning, learning_curve, estimator, X, y, cv=3,
train_sizes=np.linspace(0.33, 1.0, 3))
assert_array_equal(train_sizes, [1, 2])
def test_learning_curve_with_boolean_indices():
X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(20)
cv = KFold(n=30, n_folds=3)
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, train_sizes=np.linspace(0.1, 1.0, 10))
assert_array_equal(train_sizes, np.linspace(2, 20, 10))
assert_array_almost_equal(train_scores.mean(axis=1),
np.linspace(1.9, 1.0, 10))
assert_array_almost_equal(test_scores.mean(axis=1),
np.linspace(0.1, 1.0, 10))
def test_validation_curve():
X, y = make_classification(n_samples=2, n_features=1, n_informative=1,
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
param_range = np.linspace(0, 1, 10)
with warnings.catch_warnings(record=True) as w:
train_scores, test_scores = validation_curve(
MockEstimatorWithParameter(), X, y, param_name="param",
param_range=param_range, cv=2
)
if len(w) > 0:
raise RuntimeError("Unexpected warning: %r" % w[0].message)
assert_array_almost_equal(train_scores.mean(axis=1), param_range)
assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)
| mit |
Titan-C/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 6 | 1258 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDClassifier
from sklearn.datasets.samples_generator import make_blobs
# we create 50 separable points
X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)
# fit the model
clf = SGDClassifier(loss="hinge", alpha=0.01, n_iter=200, fit_intercept=True)
clf.fit(X, Y)
# plot the line, the points, and the nearest vectors to the plane
xx = np.linspace(-1, 5, 10)
yy = np.linspace(-1, 5, 10)
X1, X2 = np.meshgrid(xx, yy)
Z = np.empty(X1.shape)
for (i, j), val in np.ndenumerate(X1):
x1 = val
x2 = X2[i, j]
p = clf.decision_function([[x1, x2]])
Z[i, j] = p[0]
levels = [-1.0, 0.0, 1.0]
linestyles = ['dashed', 'solid', 'dashed']
colors = 'k'
plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired,
edgecolor='black', s=20)
plt.axis('tight')
plt.show()
| bsd-3-clause |
nicjhan/MOM6-examples | ice_ocean_SIS2/OM4_025/preprocessing/editTopo.py | 7 | 18852 | #!/usr/bin/env python
def error(msg,code=9):
print 'Error: ' + msg
exit(code)
# Imports
try: import argparse
except: error('This version of python is not new enough. python 2.7 or newer is required.')
try: from netCDF4 import Dataset
except: error('Unable to import netCDF4 module. Check your PYTHONPATH.\n'
+'Perhaps try:\n module load python_netcdf4')
try: import numpy as np
except: error('Unable to import numpy module. Check your PYTHONPATH.\n'
+'Perhaps try:\n module load python_numpy')
try: import matplotlib.pyplot as plt
except: error('Unable to import matplotlib.pyplot module. Check your PYTHONPATH.\n'
+'Perhaps try:\n module load python_matplotlib')
from matplotlib.widgets import Button, RadioButtons
from matplotlib.colors import LinearSegmentedColormap
import shutil as sh
def main():
# Command line arguments
parser = argparse.ArgumentParser(description=
'''Point-wise editting of topography.
Button 1 assigns the prescribed level to the cell at the mouse pointer.
Adjust the prescribed value with buttons on the bottom.
Double click button 1 assigns the highest of the nearest ocean points.
Right click on a cell resets to the original value.
Scroll wheel zooms in and out.
Move the "data window" around with the North, South, East and West buttons.
Closing the window writes the file to the output file if one is specified with --output.
''',
epilog='Written by A.Adcroft, 2013.')
parser.add_argument('filename', type=str,
help='netcdf file to read.')
parser.add_argument('variable', type=str,
nargs='?', default='depth',
help='Name of variable to edit. Defaults to "depth".')
parser.add_argument('--output', type=str,
nargs='?', default=' ',
help='Write an output file. If no output file is specified, creates the file with the "edit_" prepended to the name of the input file.')
optCmdLineArgs = parser.parse_args()
createGUI(optCmdLineArgs.filename, optCmdLineArgs.variable, optCmdLineArgs.output)
def createGUI(fileName, variable, outFile):
# Open netcdf file
try: rg=Dataset( fileName, 'r' );
except: error('There was a problem opening "'+fileName+'".')
rgVar = rg.variables[variable] # handle to the variable
dims = rgVar.dimensions # tuple of dimensions
depth = rgVar[:] # Read the data
#depth = depth[0:600,0:600]
(nj,ni) = depth.shape
print 'Range of input depths: min=',np.amin(depth),'max=',np.amax(depth)
try:
sg=Dataset( 'supergrid.nc', 'r' );
lon = sg.variables['x'][:]; lon = lon[0:2*nj+1:2,0:2*ni+1:2]
lat = sg.variables['y'][:]; lat = lat[0:2*nj+1:2,0:2*ni+1:2]
except:
lon, lat = np.meshgrid( np.arange(ni+1), np.arange(nj+1) )
fullData = Topography(lon, lat, depth)
class Container:
def __init__(self):
self.view = None
self.edits = None
self.data = None
self.quadMesh = None
self.ax = None
self.syms = None
cdict = {'red': ((0.0, 0.0, 0.0), (0.5, 0.7, 0.0), (1.0, 0.9, 0.0)),
'green': ((0.0, 0.0, 0.0), (0.5, 0.7, 0.2), (1.0, 1.0, 0.0)),
'blue': ((0.0, 0.0, 0.2), (0.5, 1.0, 0.0), (1.0, 0.9, 0.0))}
self.cmap = LinearSegmentedColormap('my_colormap',cdict,256)
self.clim = 6000
self.climLabel = None
All = Container()
All.view = View(ni,nj)
All.edits = Edits()
# Read edit data, if it exists
if 'iEdit' in rg.variables:
jEdit = rg.variables['iEdit'][:]; iEdit = rg.variables['jEdit'][:]
zEdit = rg.variables['zEdit'][:]
for l,i in enumerate(iEdit):
All.edits.setVal( fullData.height[iEdit[l],jEdit[l]] )
fullData.height[iEdit[l],jEdit[l]] = zEdit[l] # Restore data
All.edits.add( iEdit[l],jEdit[l] )
All.data = fullData.cloneWindow( (All.view.i0,All.view.j0), (All.view.iw,All.view.jw) )
if All.edits.ijz: All.data.applyEdits(fullData, All.edits.ijz)
# A mask based solely on value of depth
#notLand = np.where( depth<0, 1, 0)
#wet = ice9it(600,270,depth)
All.quadMesh = plt.pcolormesh(All.data.longitude,All.data.latitude,All.data.height,cmap=All.cmap,vmin=-All.clim,vmax=All.clim)
All.syms = All.edits.plot(fullData)
dir(All.syms)
All.ax=plt.gca(); All.ax.set_xlim( All.data.xlim ); All.ax.set_ylim( All.data.ylim )
All.climLabel = plt.figtext(.97,.97, 'XXXXX', ha='right', va='top')
All.climLabel.set_text('clim = $\pm$%i'%(All.clim))
All.edits.label = plt.figtext(.97,.03, 'XXXXX', ha='right', va='bottom')
All.edits.label.set_text('New depth = %i'%(All.edits.get()))
lowerButtons = Buttons()
def resetDto0(event): All.edits.setVal(0)
lowerButtons.add('Set 0', resetDto0)
def resetDto100(event): All.edits.addToVal(100)
lowerButtons.add('+100', resetDto100)
def resetDto100(event): All.edits.addToVal(30)
lowerButtons.add('+30', resetDto100)
def resetDto100(event): All.edits.addToVal(10)
lowerButtons.add('+10', resetDto100)
def resetDto100(event): All.edits.addToVal(3)
lowerButtons.add('+3', resetDto100)
def resetDto100(event): All.edits.addToVal(1)
lowerButtons.add('+1', resetDto100)
def resetDto100(event): All.edits.addToVal(-1)
lowerButtons.add('-1', resetDto100)
def resetDto100(event): All.edits.addToVal(-3)
lowerButtons.add('-3', resetDto100)
def resetDto100(event): All.edits.addToVal(-10)
lowerButtons.add('-10', resetDto100)
def resetDto100(event): All.edits.addToVal(-30)
lowerButtons.add('-30', resetDto100)
def resetDto100(event): All.edits.addToVal(-100)
lowerButtons.add('-100', resetDto100)
def resetDto100(event): All.edits.addToVal(-500)
lowerButtons.add('-500', resetDto100)
def undoLast(event):
All.edits.pop()
All.data = fullData.cloneWindow( (All.view.i0,All.view.j0), (All.view.iw,All.view.jw) )
All.data.applyEdits(fullData, All.edits.ijz)
All.quadMesh.set_array(All.data.height.ravel())
All.edits.updatePlot(fullData,All.syms)
plt.draw()
lowerButtons.add('Undo', undoLast)
upperButtons = Buttons(bottom=1-.0615)
def colorScale(event):
Levs = [50, 200, 1000, 6000]
i = Levs.index(All.clim)
if event=='+clim': i = min(i+1, len(Levs)-1)
elif event==' -clim': i = max(i-1, 0)
All.clim = Levs[i]
#All.quadMesh = plt.pcolormesh(All.data.longitude,All.data.latitude,All.data.height,cmap=All.cmap,vmin=-All.clim,vmax=All.clim)
#All.ax.set_xlim( All.data.xlim ); All.ax.set_ylim( All.data.ylim )
All.quadMesh.set_clim(vmin=-All.clim, vmax=All.clim)
All.climLabel.set_text('clim = $\pm$%i'%(All.clim))
plt.draw()
def moveVisData(di,dj):
All.view.move(di,dj)
All.data = fullData.cloneWindow( (All.view.i0,All.view.j0), (All.view.iw,All.view.jw) )
All.data.applyEdits(fullData, All.edits.ijz)
plt.sca(All.ax); plt.cla()
All.quadMesh = plt.pcolormesh(All.data.longitude,All.data.latitude,All.data.height,cmap=All.cmap,vmin=-All.clim,vmax=All.clim)
All.ax.set_xlim( All.data.xlim ); All.ax.set_ylim( All.data.ylim )
All.syms = All.edits.plot(fullData)
plt.draw()
def moveWindowLeft(event): moveVisData(-1,0)
upperButtons.add('West', moveWindowLeft)
def moveWindowRight(event): moveVisData(1,0)
upperButtons.add('East', moveWindowRight);
def moveWindowDown(event): moveVisData(0,-1)
upperButtons.add('South', moveWindowDown)
def moveWindowUp(event): moveVisData(0,1)
upperButtons.add('North', moveWindowUp)
climButtons = Buttons(bottom=1-.0615,left=0.65)
def incrCScale(event): colorScale('+clim')
climButtons.add('Incr', incrCScale)
def incrCScale(event): colorScale(' -clim')
climButtons.add('Decr', incrCScale)
plt.sca(All.ax)
def onClick(event): # Mouse button click
if event.inaxes==All.ax and event.button==1 and event.xdata:
(i,j) = findPointInMesh(fullData.longitude, fullData.latitude, event.xdata, event.ydata)
if not i==None:
(I,J) = findPointInMesh(All.data.longitude, All.data.latitude, event.xdata, event.ydata)
if event.dblclick:
nVal = -99999
if All.data.height[I+1,J]<0: nVal = max(nVal, All.data.height[I+1,J])
if All.data.height[I-1,J]<0: nVal = max(nVal, All.data.height[I-1,J])
if All.data.height[I,J+1]<0: nVal = max(nVal, All.data.height[I,J+1])
if All.data.height[I,J-1]<0: nVal = max(nVal, All.data.height[I,J-1])
if nVal==-99999: return
All.edits.add(i,j,nVal)
All.data.height[I,J] = nVal
else:
All.edits.add(i,j)
All.data.height[I,J] = All.edits.get()
All.quadMesh.set_array(All.data.height.ravel())
All.edits.updatePlot(fullData,All.syms)
plt.draw()
elif event.inaxes==All.ax and event.button==3 and event.xdata:
(i,j) = findPointInMesh(fullData.longitude, fullData.latitude, event.xdata, event.ydata)
if not i==None:
All.edits.delete(i,j)
All.data = fullData.cloneWindow( (All.view.i0,All.view.j0), (All.view.iw,All.view.jw) )
All.data.applyEdits(fullData, All.edits.ijz)
All.quadMesh.set_array(All.data.height.ravel())
All.edits.updatePlot(fullData,All.syms)
plt.draw()
elif event.inaxes==All.ax and event.button==2 and event.xdata: zoom(event) # Re-center
plt.gcf().canvas.mpl_connect('button_press_event', onClick)
def zoom(event): # Scroll wheel up/down
if event.button == 'up': scale_factor = 1/1.5 # deal with zoom in
elif event.button == 'down': scale_factor = 1.5 # deal with zoom out
else: scale_factor = 1.0
new_xlim, new_ylim = newLims( \
All.ax.get_xlim(), All.ax.get_ylim(), (event.xdata,event.ydata), \
All.data.xlim, All.data.ylim, scale_factor)
if not new_xlim: return # No changein limits
All.ax.set_xlim(new_xlim[0], new_xlim[1]); All.ax.set_ylim(new_ylim[0], new_ylim[1])
plt.draw() # force re-draw
plt.gcf().canvas.mpl_connect('scroll_event', zoom)
def statusMesg(x,y):
j,i = findPointInMesh(fullData.longitude, fullData.latitude, x, y)
if not i==None: return 'lon,lat=%.2f,%.2f depth(%i,%i)=%.2f'%(x,y,i,j,fullData.height[j,i])
else: return 'lon,lat=%.3f,%.3f'%(x,y)
All.ax.format_coord = statusMesg
plt.show()
All.edits.list()
if not outFile: outFile = 'edit_'+fileName
if not outFile==' ':
print 'Creating new file "'+outFile+'"'
# Create new netcdf file
if not fileName==outFile: sh.copyfile(fileName,outFile)
try: rg=Dataset( outFile, 'r+' );
except: error('There was a problem opening "'+outFile+'".')
rgVar = rg.variables[variable] # handle to the variable
dims = rgVar.dimensions # tuple of dimensions
rgVar[:] = fullData.height[:,:] # Write the data
if All.edits.ijz:
print 'Applying %i edits'%(len(All.edits.ijz))
if 'nEdits' in rg.dimensions:
numEdits = rg.dimensions['nEdits']
else: numEdits = rg.createDimension('nEdits', 0)#len(All.edits.ijz))
if 'iEdit' in rg.variables: iEd = rg.variables['iEdit']
else:
iEd = rg.createVariable('iEdit','i4',('nEdits',))
iEd.long_name = 'i-index of edited data'
if 'jEdit' in rg.variables: jEd = rg.variables['jEdit']
else:
jEd = rg.createVariable('jEdit','i4',('nEdits',))
jEd.long_name = 'j-index of edited data'
if 'zEdit' in rg.variables: zEd = rg.variables['zEdit']
else:
zEd = rg.createVariable('zEdit','f4',('nEdits',))
zEd.long_name = 'Original value of edited data'
zEd.units = rgVar.units
for l,(i,j,z) in enumerate(All.edits.ijz):
iEd[l] = j; jEd[l] = i; zEd[l] = rgVar[i,j]; rgVar[i,j] = z
rg.close()
def ice9it(i,j,depth):
# Iterative implementation of "ice 9"
wetMask = 0*depth
(ni,nj) = wetMask.shape
stack = set()
stack.add( (i,j) )
while stack:
(i,j) = stack.pop()
if wetMask[i,j] or depth[i,j] >= 0: continue
wetMask[i,j] = 1
if i>0: stack.add( (i-1,j) )
else: stack.add( (ni-1,j) )
if i<ni-1: stack.add( (i+1,j) )
else: stack.add( (0,j) )
if j>0: stack.add( (i,j-1) )
if j<nj-1: stack.add( (i,j+1) )
return wetMask
def findPointInMesh(meshX, meshY, pointX, pointY):
def sign(x):
if x>0: return 1.0
elif x<0: return -1.0
else: return 0.
def crossProd(u0,v0,u1,v1):
return sign( u0*v1 - u1*v0 )
def isPointInConvexPolygon(pX, pY, p):
u0 = pX[0]-pX[-1]; v0 = pY[0]-pY[-1]
u1 = pX[-1] - p[0]; v1 = pY[-1] - p[1]
firstSign = crossProd(u0,v0,u1,v1)
for n in range(len(pX)-1):
u0 = pX[n+1]-pX[n]; v0 = pY[n+1]-pY[n]
u1 = pX[n] - p[0]; v1 = pY[n] - p[1]
if crossProd(u0,v0,u1,v1)*firstSign<0: return False
return True
def recurIJ(mX, mY, p, ij00, ij22):
# Unpack indices
i0 = ij00[0]; i2 = ij22[0]; j0 = ij00[1]; j2 = ij22[1];
# Test bounding box first (bounding box is larger than polygon)
xmin=min( np.amin(mX[i0,j0:j2]), np.amin(mX[i2,j0:j2]), np.amin(mX[i0:i2,j0]), np.amin(mX[i0:i2,j2]) )
xmax=max( np.amax(mX[i0,j0:j2]), np.amax(mX[i2,j0:j2]), np.amax(mX[i0:i2,j0]), np.amax(mX[i0:i2,j2]) )
ymin=min( np.amin(mY[i0,j0:j2]), np.amin(mY[i2,j0:j2]), np.amin(mY[i0:i2,j0]), np.amin(mY[i0:i2,j2]) )
ymax=max( np.amax(mY[i0,j0:j2]), np.amax(mY[i2,j0:j2]), np.amax(mY[i0:i2,j0]), np.amax(mY[i0:i2,j2]) )
if p[0]<xmin or p[0]>xmax or p[1]<ymin or p[1]>ymax: return None, None
if i2>i0+1:
i1=int(0.5*(i0+i2))
if j2>j0+1: # Four quadrants to test
j1=int(0.5*(j0+j2))
iAns, jAns = recurIJ(mX, mY, p, (i0,j0), (i1,j1))
if iAns==None: iAns, jAns = recurIJ(mX, mY, p, (i1,j1), (i2,j2))
if iAns==None: iAns, jAns = recurIJ(mX, mY, p, (i0,j1), (i1,j2))
if iAns==None: iAns, jAns = recurIJ(mX, mY, p, (i1,j0), (i2,j1))
else: # Two halves, east/west, to test
j1=int(0.5*(j0+j2))
iAns, jAns = recurIJ(mX, mY, p, (i0,j0), (i1,j2))
if iAns==None: iAns, jAns = recurIJ(mX, mY, p, (i1,j0), (i2,j2))
else:
if j2>j0+1: # Two halves, north/south, to test
j1=int(0.5*(j0+j2))
iAns, jAns = recurIJ(mX, mY, p, (i0,j0), (i2,j1))
if iAns==None: iAns, jAns = recurIJ(mX, mY, p, (i0,j1), (i2,j2))
else: # Only one cell left (based on the bounding box)
if not isPointInConvexPolygon( \
[mX[i0,j0],mX[i0+1,j0],mX[i0+1,j0+1],mX[i0,j0+1]], \
[mY[i0,j0],mY[i0+1,j0],mY[i0+1,j0+1],mY[i0,j0+1]], \
p): return None, None
return i0,j0
return iAns, jAns
(ni,nj) = meshX.shape; ij00 = [0, 0]; ij22 = [ni-1, nj-1]
return recurIJ(meshX, meshY, (pointX, pointY), ij00, ij22)
# Calculate a new window by scaling the current window, centering
# on the cursor if possible.
def newLims(cur_xlim, cur_ylim, cursor, xlim, ylim, scale_factor):
cur_xrange = (cur_xlim[1] - cur_xlim[0])*.5
cur_yrange = (cur_ylim[1] - cur_ylim[0])*.5
xdata = cursor[0]; ydata = cursor[1]
new_xrange = cur_xrange*scale_factor; new_yrange = cur_yrange*scale_factor
xdata = min( max( xdata, xlim[0]+new_xrange ), xlim[1]-new_xrange )
ydata = min( max( ydata, ylim[0]+new_yrange ), ylim[1]-new_yrange )
xL = max( xlim[0], xdata - new_xrange ); xR = min( xlim[1], xdata + new_xrange )
yL = max( ylim[0], ydata - new_yrange ); yR = min( ylim[1], ydata + new_yrange )
if xL==cur_xlim[0] and xR==cur_xlim[1] and \
yL==cur_ylim[0] and yR==cur_ylim[1]: return None, None
return (xL, xR), (yL, yR)
# Class to handle adding buttons to GUI
class Buttons:
scale = 0.014; space = .01
def __init__(self, bottom=.015, left=.015):
self.leftEdge = left
self.bottomEdge = bottom
self.height = .05
self.list = []
def add(self,label,fn): # fn is callback
width = self.scale*len(label)
np = [ self.leftEdge, self.bottomEdge, width, self.height ]
self.leftEdge = self.leftEdge + width + self.space
button = Button(plt.axes(np),label); button.on_clicked( fn )
self.list.append( button )
# Class to contain edits
class Edits:
def __init__(self):
self.newDepth = 0
self.ijz = []
self.label = None # Handle to text box
def setVal(self, newVal):
self.newDepth = newVal
if self.label: self.label.set_text('New depth = %i'%(self.newDepth))
def addToVal(self, increment):
self.newDepth += increment
if self.label: self.label.set_text('New depth = %i'%(self.newDepth))
plt.draw()
def get(self): return self.newDepth
def delete(self,i,j):
for I,J,D in self.ijz:
if (i,j)==(I,J): self.ijz.remove((I,J,D))
def add(self,i,j,nVal=None):
self.delete(i,j)
if not nVal==None: self.ijz.append( (i,j,nVal) )
else: self.ijz.append( (i,j,self.newDepth) )
def pop(self):
if self.ijz: self.ijz.pop()
def list(self):
for a in self.ijz: print a
def plot(self,topo):
x = []; y= []
for i,j,z in self.ijz:
tx,ty = topo.cellCoord(j,i)
if tx:
x.append(tx); y.append(ty)
if x:
h, = plt.plot(x, y, 'ro', hold=True)
return h
else: return None
def updatePlot(self,topo,h):
x = []; y= []
for i,j,z in self.ijz:
tx,ty = topo.cellCoord(j,i)
if tx:
x.append(tx); y.append(ty)
if x:
h.set_xdata(x); h.set_ydata(y)
# Class to contain data
class Topography:
def __init__(self, lon, lat, height):
self.longitude = lon
self.latitude = lat
self.height = np.copy( height )
self.xlim = ( np.min(lon), np.max(lon) )
self.ylim = ( np.min(lat), np.max(lat) )
def cloneWindow(self, (i0,j0), (iw,jw)):
i1 = i0 + iw; j1 = j0 + jw
return Topography( self.longitude[j0:j1+1,i0:i1+1], \
self.latitude[j0:j1+1,i0:i1+1], \
self.height[j0:j1,i0:i1] )
def applyEdits(self, origData, ijz):
for i,j,z in ijz:
x = (origData.longitude[i,j] + origData.longitude[i+1,j+1])/2.
y = (origData.latitude[i,j] + origData.latitude[i+1,j+1])/2.
(I,J) = findPointInMesh(self.longitude, self.latitude, x, y)
if not I==None: self.height[I,J] = z
def cellCoord(self,j,i):
#ni, nj = self.longitude.shape
#if i<0 or j<0 or i>=ni-1 or j>=nj-1: return None, None
x = (self.longitude[i,j] + self.longitude[i+1,j+1])/2.
y = (self.latitude[i,j] + self.latitude[i+1,j+1])/2.
return x,y
# CLass to record the editing window
class View:
def __init__(self, ni, nj):
self.ni = ni
self.nj = nj
self.i0 = 0
self.j0 = 0
self.iw = min(512,ni)
self.jw = min(512,nj)
def move(self, di, dj):
self.i0 = min( max(0, self.i0+int(di*self.iw/2.)), self.ni-self.iw)
self.j0 = min( max(0, self.j0+int(dj*self.jw/2.)), self.nj-self.jw)
def geti(self): return (self.i0,self.i0+self.iw)
def getj(self): return (self.j0,self.j0+self.jw)
# Invoke main()
if __name__ == '__main__': main()
| gpl-3.0 |
xiaojngxu/cuda-convnet2 | convdata.py | 174 | 14675 | # Copyright 2014 Google Inc. All rights reserved.
#
# Licensed 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 python_util.data import *
import numpy.random as nr
import numpy as n
import random as r
from time import time
from threading import Thread
from math import sqrt
import sys
#from matplotlib import pylab as pl
from PIL import Image
from StringIO import StringIO
from time import time
import itertools as it
class JPEGBatchLoaderThread(Thread):
def __init__(self, dp, batch_num, label_offset, list_out):
Thread.__init__(self)
self.list_out = list_out
self.label_offset = label_offset
self.dp = dp
self.batch_num = batch_num
@staticmethod
def load_jpeg_batch(rawdics, dp, label_offset):
if type(rawdics) != list:
rawdics = [rawdics]
nc_total = sum(len(r['data']) for r in rawdics)
jpeg_strs = list(it.chain.from_iterable(rd['data'] for rd in rawdics))
labels = list(it.chain.from_iterable(rd['labels'] for rd in rawdics))
img_mat = n.empty((nc_total * dp.data_mult, dp.inner_pixels * dp.num_colors), dtype=n.float32)
lab_mat = n.zeros((nc_total, dp.get_num_classes()), dtype=n.float32)
dp.convnet.libmodel.decodeJpeg(jpeg_strs, img_mat, dp.img_size, dp.inner_size, dp.test, dp.multiview)
lab_vec = n.tile(n.asarray([(l[nr.randint(len(l))] if len(l) > 0 else -1) + label_offset for l in labels], dtype=n.single).reshape((nc_total, 1)), (dp.data_mult,1))
for c in xrange(nc_total):
lab_mat[c, [z + label_offset for z in labels[c]]] = 1
lab_mat = n.tile(lab_mat, (dp.data_mult, 1))
return {'data': img_mat[:nc_total * dp.data_mult,:],
'labvec': lab_vec[:nc_total * dp.data_mult,:],
'labmat': lab_mat[:nc_total * dp.data_mult,:]}
def run(self):
rawdics = self.dp.get_batch(self.batch_num)
p = JPEGBatchLoaderThread.load_jpeg_batch(rawdics,
self.dp,
self.label_offset)
self.list_out.append(p)
class ColorNoiseMakerThread(Thread):
def __init__(self, pca_stdevs, pca_vecs, num_noise, list_out):
Thread.__init__(self)
self.pca_stdevs, self.pca_vecs = pca_stdevs, pca_vecs
self.num_noise = num_noise
self.list_out = list_out
def run(self):
noise = n.dot(nr.randn(self.num_noise, 3).astype(n.single) * self.pca_stdevs.T, self.pca_vecs.T)
self.list_out.append(noise)
class ImageDataProvider(LabeledDataProvider):
def __init__(self, data_dir, batch_range=None, init_epoch=1, init_batchnum=None, dp_params=None, test=False):
LabeledDataProvider.__init__(self, data_dir, batch_range, init_epoch, init_batchnum, dp_params, test)
self.data_mean = self.batch_meta['data_mean'].astype(n.single)
self.color_eig = self.batch_meta['color_pca'][1].astype(n.single)
self.color_stdevs = n.c_[self.batch_meta['color_pca'][0].astype(n.single)]
self.color_noise_coeff = dp_params['color_noise']
self.num_colors = 3
self.img_size = int(sqrt(self.batch_meta['num_vis'] / self.num_colors))
self.mini = dp_params['minibatch_size']
self.inner_size = dp_params['inner_size'] if dp_params['inner_size'] > 0 else self.img_size
self.inner_pixels = self.inner_size **2
self.border_size = (self.img_size - self.inner_size) / 2
self.multiview = dp_params['multiview_test'] and test
self.num_views = 5*2
self.data_mult = self.num_views if self.multiview else 1
self.batch_size = self.batch_meta['batch_size']
self.label_offset = 0 if 'label_offset' not in self.batch_meta else self.batch_meta['label_offset']
self.scalar_mean = dp_params['scalar_mean']
# Maintain pointers to previously-returned data matrices so they don't get garbage collected.
self.data = [None, None] # These are pointers to previously-returned data matrices
self.loader_thread, self.color_noise_thread = None, None
self.convnet = dp_params['convnet']
self.num_noise = self.batch_size
self.batches_generated, self.loaders_started = 0, 0
self.data_mean_crop = self.data_mean.reshape((self.num_colors,self.img_size,self.img_size))[:,self.border_size:self.border_size+self.inner_size,self.border_size:self.border_size+self.inner_size].reshape((1,3*self.inner_size**2))
if self.scalar_mean >= 0:
self.data_mean_crop = self.scalar_mean
def showimg(self, img):
from matplotlib import pylab as pl
pixels = img.shape[0] / 3
size = int(sqrt(pixels))
img = img.reshape((3,size,size)).swapaxes(0,2).swapaxes(0,1)
pl.imshow(img, interpolation='nearest')
pl.show()
def get_data_dims(self, idx=0):
if idx == 0:
return self.inner_size**2 * 3
if idx == 2:
return self.get_num_classes()
return 1
def start_loader(self, batch_idx):
self.load_data = []
self.loader_thread = JPEGBatchLoaderThread(self,
self.batch_range[batch_idx],
self.label_offset,
self.load_data)
self.loader_thread.start()
def start_color_noise_maker(self):
color_noise_list = []
self.color_noise_thread = ColorNoiseMakerThread(self.color_stdevs, self.color_eig, self.num_noise, color_noise_list)
self.color_noise_thread.start()
return color_noise_list
def set_labels(self, datadic):
pass
def get_data_from_loader(self):
if self.loader_thread is None:
self.start_loader(self.batch_idx)
self.loader_thread.join()
self.data[self.d_idx] = self.load_data[0]
self.start_loader(self.get_next_batch_idx())
else:
# Set the argument to join to 0 to re-enable batch reuse
self.loader_thread.join()
if not self.loader_thread.is_alive():
self.data[self.d_idx] = self.load_data[0]
self.start_loader(self.get_next_batch_idx())
#else:
# print "Re-using batch"
self.advance_batch()
def add_color_noise(self):
# At this point the data already has 0 mean.
# So I'm going to add noise to it, but I'm also going to scale down
# the original data. This is so that the overall scale of the training
# data doesn't become too different from the test data.
s = self.data[self.d_idx]['data'].shape
cropped_size = self.get_data_dims(0) / 3
ncases = s[0]
if self.color_noise_thread is None:
self.color_noise_list = self.start_color_noise_maker()
self.color_noise_thread.join()
self.color_noise = self.color_noise_list[0]
self.color_noise_list = self.start_color_noise_maker()
else:
self.color_noise_thread.join(0)
if not self.color_noise_thread.is_alive():
self.color_noise = self.color_noise_list[0]
self.color_noise_list = self.start_color_noise_maker()
self.data[self.d_idx]['data'] = self.data[self.d_idx]['data'].reshape((ncases*3, cropped_size))
self.color_noise = self.color_noise[:ncases,:].reshape((3*ncases, 1))
self.data[self.d_idx]['data'] += self.color_noise * self.color_noise_coeff
self.data[self.d_idx]['data'] = self.data[self.d_idx]['data'].reshape((ncases, 3* cropped_size))
self.data[self.d_idx]['data'] *= 1.0 / (1.0 + self.color_noise_coeff) # <--- NOTE: This is the slow line, 0.25sec. Down from 0.75sec when I used division.
def get_next_batch(self):
self.d_idx = self.batches_generated % 2
epoch, batchnum = self.curr_epoch, self.curr_batchnum
self.get_data_from_loader()
# Subtract mean
self.data[self.d_idx]['data'] -= self.data_mean_crop
if self.color_noise_coeff > 0 and not self.test:
self.add_color_noise()
self.batches_generated += 1
return epoch, batchnum, [self.data[self.d_idx]['data'].T, self.data[self.d_idx]['labvec'].T, self.data[self.d_idx]['labmat'].T]
# Takes as input an array returned by get_next_batch
# Returns a (numCases, imgSize, imgSize, 3) array which can be
# fed to pylab for plotting.
# This is used by shownet.py to plot test case predictions.
def get_plottable_data(self, data, add_mean=True):
mean = self.data_mean_crop.reshape((data.shape[0],1)) if data.flags.f_contiguous or self.scalar_mean else self.data_mean_crop.reshape((data.shape[0],1))
return n.require((data + (mean if add_mean else 0)).T.reshape(data.shape[1], 3, self.inner_size, self.inner_size).swapaxes(1,3).swapaxes(1,2) / 255.0, dtype=n.single)
class CIFARDataProvider(LabeledDataProvider):
def __init__(self, data_dir, batch_range=None, init_epoch=1, init_batchnum=None, dp_params=None, test=False):
LabeledDataProvider.__init__(self, data_dir, batch_range, init_epoch, init_batchnum, dp_params, test)
self.img_size = 32
self.num_colors = 3
self.inner_size = dp_params['inner_size'] if dp_params['inner_size'] > 0 else self.batch_meta['img_size']
self.border_size = (self.img_size - self.inner_size) / 2
self.multiview = dp_params['multiview_test'] and test
self.num_views = 9
self.scalar_mean = dp_params['scalar_mean']
self.data_mult = self.num_views if self.multiview else 1
self.data_dic = []
for i in batch_range:
self.data_dic += [unpickle(self.get_data_file_name(i))]
self.data_dic[-1]["labels"] = n.require(self.data_dic[-1]['labels'], dtype=n.single)
self.data_dic[-1]["labels"] = n.require(n.tile(self.data_dic[-1]["labels"].reshape((1, n.prod(self.data_dic[-1]["labels"].shape))), (1, self.data_mult)), requirements='C')
self.data_dic[-1]['data'] = n.require(self.data_dic[-1]['data'] - self.scalar_mean, dtype=n.single, requirements='C')
self.cropped_data = [n.zeros((self.get_data_dims(), self.data_dic[0]['data'].shape[1]*self.data_mult), dtype=n.single) for x in xrange(2)]
self.batches_generated = 0
self.data_mean = self.batch_meta['data_mean'].reshape((self.num_colors,self.img_size,self.img_size))[:,self.border_size:self.border_size+self.inner_size,self.border_size:self.border_size+self.inner_size].reshape((self.get_data_dims(), 1))
def get_next_batch(self):
epoch, batchnum = self.curr_epoch, self.curr_batchnum
self.advance_batch()
bidx = batchnum - self.batch_range[0]
cropped = self.cropped_data[self.batches_generated % 2]
self.__trim_borders(self.data_dic[bidx]['data'], cropped)
cropped -= self.data_mean
self.batches_generated += 1
return epoch, batchnum, [cropped, self.data_dic[bidx]['labels']]
def get_data_dims(self, idx=0):
return self.inner_size**2 * self.num_colors if idx == 0 else 1
# Takes as input an array returned by get_next_batch
# Returns a (numCases, imgSize, imgSize, 3) array which can be
# fed to pylab for plotting.
# This is used by shownet.py to plot test case predictions.
def get_plottable_data(self, data):
return n.require((data + self.data_mean).T.reshape(data.shape[1], 3, self.inner_size, self.inner_size).swapaxes(1,3).swapaxes(1,2) / 255.0, dtype=n.single)
def __trim_borders(self, x, target):
y = x.reshape(self.num_colors, self.img_size, self.img_size, x.shape[1])
if self.test: # don't need to loop over cases
if self.multiview:
start_positions = [(0,0), (0, self.border_size), (0, self.border_size*2),
(self.border_size, 0), (self.border_size, self.border_size), (self.border_size, self.border_size*2),
(self.border_size*2, 0), (self.border_size*2, self.border_size), (self.border_size*2, self.border_size*2)]
end_positions = [(sy+self.inner_size, sx+self.inner_size) for (sy,sx) in start_positions]
for i in xrange(self.num_views):
target[:,i * x.shape[1]:(i+1)* x.shape[1]] = y[:,start_positions[i][0]:end_positions[i][0],start_positions[i][1]:end_positions[i][1],:].reshape((self.get_data_dims(),x.shape[1]))
else:
pic = y[:,self.border_size:self.border_size+self.inner_size,self.border_size:self.border_size+self.inner_size, :] # just take the center for now
target[:,:] = pic.reshape((self.get_data_dims(), x.shape[1]))
else:
for c in xrange(x.shape[1]): # loop over cases
startY, startX = nr.randint(0,self.border_size*2 + 1), nr.randint(0,self.border_size*2 + 1)
endY, endX = startY + self.inner_size, startX + self.inner_size
pic = y[:,startY:endY,startX:endX, c]
if nr.randint(2) == 0: # also flip the image with 50% probability
pic = pic[:,:,::-1]
target[:,c] = pic.reshape((self.get_data_dims(),))
class DummyConvNetLogRegDataProvider(LabeledDummyDataProvider):
def __init__(self, data_dim):
LabeledDummyDataProvider.__init__(self, data_dim)
self.img_size = int(sqrt(data_dim/3))
def get_next_batch(self):
epoch, batchnum, dic = LabeledDummyDataProvider.get_next_batch(self)
dic = {'data': dic[0], 'labels': dic[1]}
print dic['data'].shape, dic['labels'].shape
return epoch, batchnum, [dic['data'], dic['labels']]
# Returns the dimensionality of the two data matrices returned by get_next_batch
def get_data_dims(self, idx=0):
return self.batch_meta['num_vis'] if idx == 0 else 1
| apache-2.0 |
mark-hoffmann/icd | icd/long_to_short_transformation.py | 1 | 3026 | import numpy as np
import pandas as pd
def long_to_short_transformation(df, idx, icds):
"""
Summary:
Used for processing a dataframe from a long format where you want to roll up many claims into an episode for example,
but aggregate all of the possible icd codes. This is a usual preprocessing step to then use function icd_to_comorbidities.
REQUIRED: RESET THE INDEX BEFORE USING ON DF
REQUIRED: DATAFRAME MUST ALREADY BE SORTED IN IDX AND SECONDAR_IDX THAT YOU ARE ROLLING UP
Args:
df (Pandas DataFrame): Dataframe containing the data witht he valid ids and columns holding the icd codes on a LONG dataset
idx (str): id column name for reference on the output
icds (list(strs)): list of columns that contain the icd codes
Returns:
New pandas DataFrame containing the ids specified with the rolled up icd codes appended wide wise with format icd_1, icd_2, ... icd_n
"""
info_dict = {}
current_icd_set = set()
id_list = []
unique_icd_count = 0
current_id = ""
last_id = df.loc[0,idx] #Initializing last_id to get over first iteration
#Step 1
#Populate the appropriate info and find the largest unique_icd_count to allocate column space
for row in range(0,len(df)):
#Initialize the new row id
current_id = df.loc[row,idx]
#Know when to switch to a new set and save the temp info
if current_id != last_id:
#update new dataframe column counter
if len(current_icd_set) > unique_icd_count:
unique_icd_count = len(current_icd_set)
#save the icd set casted to a list for faster iteration in the next step
info_dict[last_id] = list(current_icd_set)
#clear the current set
current_icd_set = set()
#Loop over columns, adding to set
for col in icds:
icd = df.loc[row,col]
current_icd_set.add(icd)
#Remember the last id for next loop
last_id = current_id
#Loop is done save out one last time for last record
if len(current_icd_set) > unique_icd_count:
unique_icd_count = len(current_icd_set)
#save the icd set casted to a list for faster iteration in the next step
info_dict[last_id] = list(current_icd_set)
#Step 2
#Create and populate output df
#Need equal length lists if mapping a df from dict, so we pad the lists in the current dict
for key in info_dict.keys():
info_dict[key] += [''] * (unique_icd_count - len(info_dict[key]))
#Populating the out_df
out_df = pd.DataFrame.from_dict(info_dict, orient="index")
#Creating columns list
columns = ["icd_" + str(i) for i in range(0,unique_icd_count)]
out_df.columns = columns #rename to out columns
out_df[idx] = out_df.index.tolist() #Give us a column 'id' in addition in case we want to throw into icd_to_comorbidities next
return(out_df)
| mit |
hitszxp/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.linear_model import SGDClassifier
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
colors = "bry"
# shuffle
idx = np.arange(X.shape[0])
np.random.seed(13)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
h = .02 # step size in the mesh
clf = SGDClassifier(alpha=0.001, n_iter=100).fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
plt.axis('tight')
# Plot also the training points
for i, color in zip(clf.classes_, colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
cmap=plt.cm.Paired)
plt.title("Decision surface of multi-class SGD")
plt.axis('tight')
# Plot the three one-against-all classifiers
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
coef = clf.coef_
intercept = clf.intercept_
def plot_hyperplane(c, color):
def line(x0):
return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
plt.plot([xmin, xmax], [line(xmin), line(xmax)],
ls="--", color=color)
for i, color in zip(clf.classes_, colors):
plot_hyperplane(i, color)
plt.legend()
plt.show()
| bsd-3-clause |
sorgerlab/indra | models/braf_model/run_model.py | 6 | 6718 | from __future__ import absolute_import, print_function, unicode_literals
from builtins import dict, str
import os
import pickle
import matplotlib
import numpy as np
# To suppress plots from popping up
import matplotlib.pyplot as plt
from pysb.integrate import Solver
from pysb.bng import generate_equations
from pysb.export import export
from indra.util.plot_formatting import *
import assemble_model
def simulate_untreated(model, ts, egf_dose=1000):
model.parameters['EGF_0'].value = egf_dose
model.parameters['VEMURAFENIB_0'].value = 0
solver = Solver(model, ts)
solver.run()
return (solver.yobs, solver.y)
def simulate_vemurafenib_treatment(model, ts, y0):
# New initial conditions for simulation post event
y_init = y0
y_init[model.observables['Vem_free'].species[0]] = 2e5
# Continue model simulation with y_init as new initial condition
solver = Solver(model, ts)
solver.run(y0=y_init)
# Concatenate the two simulations
return solver.yobs, solver.y
def is_steady_state(y):
for k in y.dtype.fields.keys():
d = np.diff(y[k])
if not all(abs(d) < max(abs(y[k]))*0.01):
return False
return True
def get_steady_state(model, y0):
sim_hours = 1
t = np.linspace(0, sim_hours*3600, sim_hours*60)
solver = Solver(model, t)
ss = False
y_init = y0
while not ss:
solver.run(y0=y_init)
ss = is_steady_state(solver.yobs)
y_init = solver.y[-1]
steady_state = solver.yobs[-1]
timecourse = solver.yobs
return steady_state, timecourse
def plot_fold_change_time(ts, yobs, yobs_ref, save_plot):
erk_foldchange = yobs['ERK_p'] / yobs_ref['ERK_p']
ras_foldchange = yobs['RAS_active'] / yobs_ref['RAS_active']
plt.figure(figsize=(2, 2), dpi=300)
set_fig_params()
plt.plot(ts, erk_foldchange, 'b')
plt.plot(ts, ras_foldchange, 'g')
plt.xticks([])
plt.xlabel('Time (a.u)', fontsize=7)
plt.xlim([0, 30000])
plt.ylabel('Fold-change after Vemurafenib treatment', fontsize=7)
plt.legend(['Phosphorylated ERK', 'Active RAS'], fontsize=7)
ax = plt.gca()
format_axis(ax)
plt.savefig(save_plot)
plt.clf()
def get_egf_vem_doseresp(egf_doses, vem_doses, readout='absolute'):
erk = np.zeros((len(egf_doses), len(vem_doses)))
ras = np.zeros((len(egf_doses), len(vem_doses)))
for i, ed in enumerate(egf_doses):
yobs0, y0 = simulate_untreated(model, ts, ed)
for j, vd in enumerate(vem_doses):
y0_last = y0[-1]
y0_last[model.observables['Vem_free'].species[0]] = vd
yobs_ss, yobs_tc = get_steady_state(model, y0_last)
if readout == 'absolute':
erk[i,j] = yobs_ss['ERK_p']
ras[i,j] = yobs_ss['RAS_active']
elif readout == 'foldchange':
erk[i,j] = yobs_ss['ERK_p'] / yobs0['ERK_p'][-1]
ras[i,j] = yobs_ss['RAS_active'] / yobs0['RAS_active'][-1]
elif readout == 'ss_min_diff':
erk[i,j] = yobs_ss['ERK_p'] - yobs_tc['ERK_p'].min()
ras[i,j] = yobs_ss['RAS_active']
return erk, ras
def plot_egf_vem_dose(egf_doses, vem_doses, erk, ras, save_plot):
cmap = plt.get_cmap('jet')
plt.figure(figsize=(3, 3), dpi=300)
set_fig_params()
f, (ax1, ax2) = plt.subplots(1, 2)
max_erk = np.amax(erk) + 1
max_ras = np.amax(ras) + 1
f1 = ax1.imshow(np.flipud(ras), cmap=cmap, vmin=0, vmax=max_ras,
interpolation='hermite')
ax1.set_title('Active RAS')
ax1.set_xlabel('[Vemurafenib]', fontsize=7)
ax1.set_ylabel('[EGF]', fontsize=7)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_aspect('equal')
cb = f.colorbar(f1, ax=ax1, fraction=0.05, pad=0.05)
cb.set_ticks([0, max_ras])
f2 = ax2.imshow(np.flipud(erk), cmap=cmap, vmin=0, vmax=max_erk,
interpolation='hermite')
ax2.set_title('pERK rebound')
ax2.set_xlabel('[Vemurafenib]', fontsize=7)
ax2.set_ylabel('[EGF]', fontsize=7)
ax2.set_aspect('equal')
ax2.set_xticks([])
ax2.set_yticks([])
cb = f.colorbar(f2, ax=ax2, fraction=0.05, pad=0.05)
cb.set_ticks([0, max_erk])
format_axis(ax1)
format_axis(ax2)
f.savefig(save_plot)
def load_model(model_id):
model = pickle.load(open('model%d.pkl' % model_id, 'rb'))
return model
def save_model(model):
with open('model%d.pkl' % model_id, 'wb') as fh:
pickle.dump(model, fh)
def print_statistics(model):
print('# monomers: %d' % len(model.monomers))
print('# rules: %d' % len(model.rules))
p_rate = [p for p in model.parameters if p.name.startswith('k')]
print('# rate constants: %d' % len(p_rate))
print('# initial conditions: %d' % len(model.initial_conditions))
print('# ODEs: %d' % len(model.odes))
def export_memi(model, formats, version):
for f in formats:
model_export = export(model, f)
extension = (f if f != 'pysb_flat' else 'py')
fname = 'MEMI%s.%s' % (version, extension)
with open(fname, 'wb') as fh:
fh.write(model_export)
if __name__ == '__main__':
sim_hours = 10
ts = np.linspace(0, sim_hours*3600, sim_hours*60)
egf_doses = np.logspace(1, 4, 9)
vem_doses = np.logspace(4, 6, 9)
for model_id in (1, 2, 3):
print('Running model %d' % model_id)
print('----------------')
if os.path.exists('model%d.pkl' % model_id):
model = load_model(model_id)
else:
model = assemble_model.assemble_model(model_id)
generate_equations(model)
save_model(model)
print_statistics(model)
# Run time-course simulation
yobs1, y1 = simulate_untreated(model, ts)
yobs2, y2 = simulate_vemurafenib_treatment(model, ts, y1[-1])
plot_fold_change_time(ts, yobs2, yobs1[-1],
'model%s_vem_time.pdf' % model_id)
# Run dose response experiments
erk_foldchange, ras_foldchange = \
get_egf_vem_doseresp(egf_doses, vem_doses, 'ss_min_diff')
plot_egf_vem_dose(egf_doses, vem_doses,
erk_foldchange, ras_foldchange,
'model%s_egf_vem_dose.pdf' % model_id)
# Save results of dose response runs
with open('doseresp_result_model%d.pkl' % model_id, 'wb') as fh:
pickle.dump([egf_doses, vem_doses, erk_foldchange, ras_foldchange],
fh)
# Export models in various formats
version = '1.%d' % (model_id-1)
formats = ['sbml', 'bngl', 'kappa', 'pysb_flat']
export_memi(model, formats, version)
| bsd-2-clause |
IntroDS2017/TrafficAccidents | 9_mapping.py | 1 | 3745 | import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
from shapely.geometry import Point
from geopandas import GeoDataFrame
def read_em():
city = gpd.read_file('./stadi/kaupunginosat.dxf')
moottori = gpd.read_file('./stadi/klinj_moottorivayla.dxf')
alu_kok = gpd.read_file('./stadi/klinj_alu_kokoojakatu.dxf')
kokooja = gpd.read_file('./stadi/klinj_paik_kokoojakatu.dxf')
paa = gpd.read_file('./stadi/klinj_paakadut.dxf')
asunto = gpd.read_file('./stadi/klinj_asuntokatu.dxf')
return {'city': city, 'moottori': moottori, 'alu_kok': alu_kok, 'kokooja': kokooja, 'paa': paa, 'asunto': asunto}
added = []
def plot_em(gpd_dict, show_names=False):
base = gpd_dict['city'].plot(color='gray', edgecolor='white', linewidth=1.0, zorder=0)
gpd_dict['moottori'].plot(ax=base, color='black', linewidth=1.0, markersize=1, zorder=1)
gpd_dict['alu_kok'].plot(ax=base, color='cyan', linewidth=0.5, markersize=0.5, zorder=1)
gpd_dict['kokooja'].plot(ax=base, color='orange', linewidth=0.9, markersize=0.9, zorder=1)
gpd_dict['paa'].plot(ax=base, color='blue', linewidth=0.6, markersize=0.6, zorder=1)
gpd_dict['asunto'].plot(ax=base, color='yellow', linewidth=0.2, markersize=0.2, zorder=1)
points = gpd_dict['points']
points.plot(ax=base, color='red', marker='*', zorder=3, markersize=1.5)
points.apply(lambda x: draw_point(x, base), axis=1)
x1, x2, y1, y2 = plt.axis()
# whole map
base.set_xlim([x1, x2])
base.set_ylim([6670000, 6687500])
# central
# base.set_xlim([5000+2.549e7, 8000+2.549e7])
# base.set_ylim([6671000, 6674000])
base.set_axis_off()
if show_names:
points.apply(lambda x: annotate_point(x, base), axis=1)
added.clear()
def draw_point(row, ax):
marker_size = row['Accidents per Traffic sum'] * 1350000
ax.plot(row.geometry.x, row.geometry.y, 'o', markersize=marker_size, markerfacecolor=(1, 1, 0, 0.5))
def annotate_point(row, ax):
if row.nimi not in added:
ax.annotate(row.nimi, xy=(row.geometry.x, row.geometry.y))
added.append(row.nimi)
def usage_df_to_gpd(path):
df = pd.read_csv(path)
coord = df[['piste', 'x_gk25', 'y_gk25', 'nimi']].drop_duplicates().reset_index()
geometry = [Point(xy) for xy in zip(coord.x_gk25, coord.y_gk25)]
coord.drop(['x_gk25', 'y_gk25'], axis=1)
crs = {'init': 'epsg:3879'}
return GeoDataFrame(coord, crs=crs, geometry=geometry)
def get_accident_ratio(path):
df = pd.read_csv(path)
return df[['piste', 'Accidents per Traffic']].drop_duplicates()
def combine_ratio_and_usage(usage_path, accident_ratio_path):
usage_gdp = usage_df_to_gpd(usage_path)
accident_ratio_df = get_accident_ratio(accident_ratio_path)
accident_ratio_sum_df = accident_ratio_df.groupby('piste')\
.agg({'Accidents per Traffic': 'sum'})\
.reset_index()\
.rename(columns={'Accidents per Traffic': 'Accidents per Traffic sum'})\
combined_gdp = usage_gdp.merge(accident_ratio_sum_df, on='piste')
return combined_gdp.sort_values('Accidents per Traffic sum', ascending=False)
def main():
usage_load_path = './data/4_road_usages.csv'
accident_ratio_path = './data/8_traffic_accidents_ratio.csv'
points_and_accident_ratio_sum = combine_ratio_and_usage(usage_load_path, accident_ratio_path)
gpd_dict = read_em()
gpd_dict['points'] = points_and_accident_ratio_sum
plot_em(gpd_dict, show_names=True)
# plt.savefig('./data/figures/5_measurement_points.png'
# , format='png', bbox_inches='tight', dpi=500)
plt.show()
return 0
if __name__ == '__main__':
import sys
sys.exit(main())
| mit |
boland1992/SeisSuite | build/lib/ambient/spectrum/S_spectrum.py | 2 | 14076 | # -*- coding: utf-8 -*-
"""
Created on Fri July 6 11:04:03 2015
@author: boland
"""
import os
import glob
import scipy
import datetime
import numpy as np
import datetime as dt
import multiprocessing as mp
import matplotlib.pyplot as plt
from numpy.lib.stride_tricks import as_strided
from numpy.fft import rfft, irfft
from obspy import read_inventory
from scipy import signal
from obspy import read
from scipy.interpolate import interp1d
from pysismo import pscrosscorr
from pysismo.psconfig import (CROSSCORR_TMAX)
#PICKLE_PATH = '/home/boland/Desktop/XCORR-STACK_01.08.1999-10.06.2000\
#_datalesspaz.part.pickle'
#xc = pscrosscorr.load_pickled_xcorr(PICKLE_PATH)
# optimizing time-scale: max time = max distance / vmin (vmin = 2.5 km/s)
#maxdist = max([xc[s1][s2].dist() for s1, s2 in xc.pairs()])
#maxt = min(CROSSCORR_TMAX, maxdist / 2.5)
#plot distance plot of cross-correlations
#xc.plot(plot_type='distance', xlim=(-maxt, maxt),
# outfile="/home/boland/Desktop/something1342.png", showplot=False)
#------------------------------------------------------------------------------
# IMPORT PATHS TO MSEED FILES
#------------------------------------------------------------------------------
def spectrum(tr):
wave = tr.data #this is how to extract a data array from a mseed file
fs = tr.stats.sampling_rate
#hour = str(hour).zfill(2) #create correct format for eqstring
f, Pxx_spec = signal.welch(wave, fs, 'flattop', nperseg=1024, scaling='spectrum')
#plt.semilogy(f, np.sqrt(Pxx_spec))
if len(f) >= 256:
column = np.column_stack((f[:255], np.abs(np.sqrt(Pxx_spec)[:255])))
return column
else:
return 0.
# x = np.linspace(0, 10, 1000)
# f_interp = interp1d(np.sqrt(Pxx_spec),f, kind='cubic')
#x.reverse()
#y.reverse()
# print f_interp(x)
#f,np.sqrt(Pxx_spec),'o',
# plt.figure()
# plt.plot(x,f_interp(x),'-' )
# plt.show()
def paths_sort(path):
"""
Function defined for customised sorting of the abs_paths list
and will be used in conjunction with the sorted() built in python
function in order to produce file paths in chronological order.
"""
base_name = os.path.basename(path)
stat_name = base_name.split('.')[0]
date = base_name.split('.')[1]
try:
date = datetime.datetime.strptime(date, '%Y-%m-%d')
return date, stat_name
except Exception as e:
a=4
def paths(folder_path, extension):
"""
Function that returns a list of desired absolute paths called abs_paths
of files that contains a given extension e.g. .txt should be entered as
folder_path, txt. This function will run recursively through and find
any and all files within this folder with that extension!
"""
abs_paths = []
for root, dirs, files in os.walk(folder_path):
for f in files:
fullpath = os.path.join(root, f)
if os.path.splitext(fullpath)[1] == '.{}'.format(extension):
abs_paths.append(fullpath)
abs_paths = sorted(abs_paths, key=paths_sort)
return abs_paths
folder_path = '/storage/ANT/INPUT/DATA/AU-2014'
extension = 'mseed'
paths_list = paths(folder_path, extension)
t0_total = datetime.datetime.now()
figs_counter = 0
fig1 = plt.figure(figsize=(15,10))
ax1 = fig1.add_subplot(111)
ax1.set_title("Seismic Waveform Power Density Spectrum\n{}".format('S | 2014'))
ax1.set_xlabel('Frequency (Hz)')
ax1.set_ylabel('Power Density Spectrum (V RMS)')
ax1.set_xlim([0,4])
ax1.grid(True, axis='both', color='gray')
ax1.set_autoscaley_on(True)
ax1.set_yscale('log')
for s in paths_list:
try:
split_path = s.split('/')
stat_info = split_path[-1][:-6]
net = stat_info.split('.')[0]
year = split_path[-2].split('-')[0]
t0 = datetime.datetime.now()
st = read(s)
t1 = datetime.datetime.now()
if net == 'S':
print "time taken to import one month mseed was: ", t1-t0
# set up loop for all traces within each imported stream.
t0 = datetime.datetime.now()
pool = mp.Pool()
spectra = pool.map(spectrum, st[:])
pool.close()
pool.join()
t1 = datetime.datetime.now()
print "time taken to calculate monthly spectra: ", t1-t0
# Caclulate weighted average spectrum for this station for this month
spectra = np.asarray(spectra)
search = np.where(spectra==0.)
spectra = np.delete(spectra, search)
spectra = np.average(spectra, axis=0)
plt.plot(spectra[:,0], spectra[:,1], c='k', alpha=0.1)
except:
a=5
fig1.savefig('network_spectrum/PDS_S_2014.svg', format='svg', dpi=300)
plt.clf()
quit()
#plt.plot(f, np.sqrt(Pxx_spec), alpha=alpha, c='k')
#plt.xlim([0,2])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_all.svg', format='svg', dpi=1000)
plt.xlim([0,1])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-1Hz.svg', format='svg', dpi=1000)
plt.xlim([0,2])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-2Hz.svg', format='svg', dpi=1000)
plt.xlim([0,3])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-3Hz.svg', format='svg', dpi=1000)
plt.xlim([0,4])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-4Hz.svg', format='svg', dpi=1000)
plt.xlim([0,5])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-5Hz.svg', format='svg', dpi=1000)
plt.xlim([0,6])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-6Hz.svg', format='svg', dpi=1000)
plt.xlim([0,7])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-7Hz.svg', format='svg', dpi=1000)
plt.xlim([0,8])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-8Hz.svg', format='svg', dpi=1000)
plt.xlim([0,9])
#plt.ylim([0,25000])
fig.savefig('Power Density Spectrum_0-9Hz.svg', format='svg', dpi=1000)
t1_total = datetime.datetime.now()
print "total time taken to process and plot all PDS: ", t1_total-t0_total
quit()
def get_stationxml_inventories(stationxml_dir, verbose=False):
"""
Reads inventories in all StationXML (*.xml) files
of specified dir
@type stationxml_dir: unicode or str
@type verbose: bool
@rtype: list of L{obspy.station.inventory.Inventory}
"""
inventories = []
# list of *.xml files
flist = glob.glob(pathname=os.path.join(stationxml_dir, "*.xml"))
if verbose:
if flist:
print "Reading inventory in StationXML file:",
else:
s = u"Could not find any StationXML file (*.xml) in dir: {}!"
print s.format(stationxml_dir)
for f in flist:
if verbose:
print os.path.basename(f),
inv = read_inventory(f, format='stationxml')
inventories.append(inv)
if flist and verbose:
print
return inventories
def spectrum(tr):
wave = tr.data #this is how to extract a data array from a mseed file
fs = tr.stats.sampling_rate
#hour = str(hour).zfill(2) #create correct format for eqstring
f, Pxx_spec = signal.welch(wave, fs, 'flattop', 1024, scaling='spectrum')
#plt.semilogy(f, np.sqrt(Pxx_spec))
plt.title("Frequency Density Plot of PNG Earthquake from station PMG.IU")
plt.plot(f, np.sqrt(Pxx_spec))
plt.xlim([0, 5])
plt.xlabel('frequency [Hz]')
plt.ylabel('Linear spectrum [V RMS]')
def resample(trace, dt_resample):
"""
Subroutine to resample trace
@type trace: L{obspy.core.trace.Trace}
@type dt_resample: float
@rtype: L{obspy.core.trace.Trace}
"""
dt = 1.0 / trace.stats.sampling_rate
factor = dt_resample / dt
if int(factor) == factor:
# simple decimation (no filt because it shifts the data)
trace.decimate(int(factor), no_filter=True)
else:
# linear interpolation
tp = np.arange(0, trace.stats.npts) * trace.stats.delta
zp = trace.data
ninterp = int(max(tp) / dt_resample) + 1
tinterp = np.arange(0, ninterp) * dt_resample
trace.data = np.interp(tinterp, tp, zp)
trace.stats.npts = ninterp
trace.stats.delta = dt_resample
trace.stats.sampling_rate = 1.0 / dt_resample
#trace.stats.endtime = trace.stats.endtime + max(tinterp)-max(tp)
def moving_avg(a, halfwindow, mask=None):
"""
Performs a fast n-point moving average of (the last
dimension of) array *a*, by using stride tricks to roll
a window on *a*.
Note that *halfwindow* gives the nb of points on each side,
so that n = 2*halfwindow + 1.
If *mask* is provided, values of *a* where mask = False are
skipped.
Returns an array of same size as *a* (which means that near
the edges, the averaging window is actually < *npt*).
"""
# padding array with zeros on the left and on the right:
# e.g., if halfwindow = 2:
# a_padded = [0 0 a0 a1 ... aN 0 0]
# mask_padded = [F F ? ? ? F F]
if mask is None:
mask = np.ones_like(a, dtype='bool')
zeros = np.zeros(a.shape[:-1] + (halfwindow,))
falses = zeros.astype('bool')
a_padded = np.concatenate((zeros, np.where(mask, a, 0), zeros), axis=-1)
mask_padded = np.concatenate((falses, mask, falses), axis=-1)
# rolling window on padded array using stride trick
#
# E.g., if halfwindow=2:
# rolling_a[:, 0] = [0 0 a0 a1 ... aN]
# rolling_a[:, 1] = [0 a0 a1 a2 ... aN 0 ]
# ...
# rolling_a[:, 4] = [a2 a3 ... aN 0 0]
npt = 2 * halfwindow + 1 # total size of the averaging window
rolling_a = as_strided(a_padded,
shape=a.shape + (npt,),
strides=a_padded.strides + (a.strides[-1],))
rolling_mask = as_strided(mask_padded,
shape=mask.shape + (npt,),
strides=mask_padded.strides + (mask.strides[-1],))
# moving average
n = rolling_mask.sum(axis=-1)
return np.where(n > 0, rolling_a.sum(axis=-1).astype('float') / n, np.nan)
def butterworth(trace):
#filter
#print("first filter")
trace.filter(type="bandpass",
freqmin =freqmin,
freqmax = freqmax,
corners=corners,
zerophase=zerophase)
return trace
def normal(trace,
freqmin_earthquake,
freqmax_earthquake):
# normalization of the signal by the running mean
# in the earthquake frequency band
trcopy = trace
#print("normalising filter")
trcopy.filter(type="bandpass",
freqmin=freqmin_earthquake,
freqmax=freqmax_earthquake,
corners=corners,
zerophase=zerophase)
# decimating trace
resample(trcopy, period_resample)
# Time-normalization weights from smoothed abs(data)
# Note that trace's data can be a masked array
halfwindow = int(round(window_time * trcopy.stats.sampling_rate / 2))
mask = ~trcopy.data.mask if np.ma.isMA(trcopy.data) else None
tnorm_w = moving_avg(np.abs(trcopy.data),
halfwindow=halfwindow,
mask=mask)
if np.ma.isMA(trcopy.data):
# turning time-normalization weights into a masked array
s = "[warning: {}.{} trace's data is a masked array]"
print s.format(trace.stats.network, trace.stats.station),
tnorm_w = np.ma.masked_array(tnorm_w, trcopy.data.mask)
# time-normalization
trace.data /= tnorm_w
return trace
def whiten(trace, window_freq, freqmin, freqmax, corners, zerophase):
"""
function that produces a whitened spectrum
"""
fft = rfft(trace.data) # real FFT
deltaf = trace.stats.sampling_rate / trace.stats.npts # frequency step
# smoothing amplitude spectrum
halfwindow = int(round(window_freq / deltaf / 2.0))
weight = moving_avg(abs(fft), halfwindow=halfwindow)
# normalizing spectrum and back to time domain
trace.data = irfft(fft / weight, n=len(trace.data))
# re bandpass to avoid low/high freq noise
#print("Whiten filter")
trace.filter(type="bandpass",
freqmin =freqmin,
freqmax = freqmax,
corners=corners,
zerophase=zerophase)
return trace
def preprocess(trace):
#trace.attach_response(inventories=xml_inventories)
trace = butterworth(trace)
#trace.remove_response(output="VEL", zero_mean=True)
#plt.figure()
#spectrum(trace)
#trace = normal(trace, freqmin_earthquake, freqmax_earthquake)
#plt.figure()
#spectrum(trace)
#print(trace.stats.sampling_rate)
trace = whiten(trace, window_freq, freqmin, freqmax, corners, zerophase)
#plt.figure()
#spectrum(trace)
return trace
xcorr = 0
freqmin = 1.0/25.0
freqmax = 1.0/1
corners = 1
zerophase = True
freqmin_earthquake = 1/50.0
freqmax_earthquake = 1/25.0
window_time = 0.5 * freqmax_earthquake
window_freq = 0.02
period_resample = 0.45
STATIONXML_DIR = '/storage/ANT/PROGRAMS/ANT_OUTPUT/INPUT/XML'
xml_inventories = []
sample_rate = 250
counts = 0
for time in times:
st0 = read(dir1, starttime=time, endtime=time + dt.timedelta(minutes=XCORR_INTERVAL))
st1 = read(dir2, starttime=time, endtime=time + dt.timedelta(minutes=XCORR_INTERVAL))
tr0 = st0[0]
tr1 = st1[0]
tr0.stats.sampling_rate = sample_rate
tr1.stats.sampling_rate = sample_rate
tr0 = preprocess(tr0)
tr1 = preprocess(tr1)
xcorr = scipy.signal.correlate(tr0, tr1, mode='same')
xcorr += xcorr
plt.figure(1)
plt.plot(xcorr)
plt.show()
print(counts)
counts +=1
import matplotlib.pyplot as plt
| gpl-3.0 |
ueshin/apache-spark | python/pyspark/pandas/plot/plotly.py | 14 | 7646 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF 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 TYPE_CHECKING, Union
import pandas as pd
from pyspark.pandas.plot import (
HistogramPlotBase,
name_like_string,
PandasOnSparkPlotAccessor,
BoxPlotBase,
KdePlotBase,
)
if TYPE_CHECKING:
import pyspark.pandas as ps # noqa: F401 (SPARK-34943)
def plot_pandas_on_spark(data: Union["ps.DataFrame", "ps.Series"], kind: str, **kwargs):
import plotly
# pandas-on-Spark specific plots
if kind == "pie":
return plot_pie(data, **kwargs)
if kind == "hist":
return plot_histogram(data, **kwargs)
if kind == "box":
return plot_box(data, **kwargs)
if kind == "kde" or kind == "density":
return plot_kde(data, **kwargs)
# Other plots.
return plotly.plot(PandasOnSparkPlotAccessor.pandas_plot_data_map[kind](data), kind, **kwargs)
def plot_pie(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
from plotly import express
data = PandasOnSparkPlotAccessor.pandas_plot_data_map["pie"](data)
if isinstance(data, pd.Series):
pdf = data.to_frame()
return express.pie(pdf, values=pdf.columns[0], names=pdf.index, **kwargs)
elif isinstance(data, pd.DataFrame):
values = kwargs.pop("y", None)
default_names = None
if values is not None:
default_names = data.index
return express.pie(
data,
values=kwargs.pop("values", values),
names=kwargs.pop("names", default_names),
**kwargs,
)
else:
raise RuntimeError("Unexpected type: [%s]" % type(data))
def plot_histogram(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
import plotly.graph_objs as go
import pyspark.pandas as ps
bins = kwargs.get("bins", 10)
y = kwargs.get("y")
if y and isinstance(data, ps.DataFrame):
# Note that the results here are matched with matplotlib. x and y
# handling is different from pandas' plotly output.
data = data[y]
psdf, bins = HistogramPlotBase.prepare_hist_data(data, bins)
assert len(bins) > 2, "the number of buckets must be higher than 2."
output_series = HistogramPlotBase.compute_hist(psdf, bins)
prev = float("%.9f" % bins[0]) # to make it prettier, truncate.
text_bins = []
for b in bins[1:]:
norm_b = float("%.9f" % b)
text_bins.append("[%s, %s)" % (prev, norm_b))
prev = norm_b
text_bins[-1] = text_bins[-1][:-1] + "]" # replace ) to ] for the last bucket.
bins = 0.5 * (bins[:-1] + bins[1:])
output_series = list(output_series)
bars = []
for series in output_series:
bars.append(
go.Bar(
x=bins,
y=series,
name=name_like_string(series.name),
text=text_bins,
hovertemplate=(
"variable=" + name_like_string(series.name) + "<br>value=%{text}<br>count=%{y}"
),
)
)
fig = go.Figure(data=bars, layout=go.Layout(barmode="stack"))
fig["layout"]["xaxis"]["title"] = "value"
fig["layout"]["yaxis"]["title"] = "count"
return fig
def plot_box(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
import plotly.graph_objs as go
import pyspark.pandas as ps
if isinstance(data, ps.DataFrame):
raise RuntimeError(
"plotly does not support a box plot with pandas-on-Spark DataFrame. Use Series instead."
)
# 'whis' isn't actually an argument in plotly (but in matplotlib). But seems like
# plotly doesn't expose the reach of the whiskers to the beyond the first and
# third quartiles (?). Looks they use default 1.5.
whis = kwargs.pop("whis", 1.5)
# 'precision' is pandas-on-Spark specific to control precision for approx_percentile
precision = kwargs.pop("precision", 0.01)
# Plotly options
boxpoints = kwargs.pop("boxpoints", "suspectedoutliers")
notched = kwargs.pop("notched", False)
if boxpoints not in ["suspectedoutliers", False]:
raise ValueError(
"plotly plotting backend does not support 'boxpoints' set to '%s'. "
"Set to 'suspectedoutliers' or False." % boxpoints
)
if notched:
raise ValueError(
"plotly plotting backend does not support 'notched' set to '%s'. "
"Set to False." % notched
)
colname = name_like_string(data.name)
spark_column_name = data._internal.spark_column_name_for(data._column_label)
# Computes mean, median, Q1 and Q3 with approx_percentile and precision
col_stats, col_fences = BoxPlotBase.compute_stats(data, spark_column_name, whis, precision)
# Creates a column to flag rows as outliers or not
outliers = BoxPlotBase.outliers(data, spark_column_name, *col_fences)
# Computes min and max values of non-outliers - the whiskers
whiskers = BoxPlotBase.calc_whiskers(spark_column_name, outliers)
fliers = None
if boxpoints:
fliers = BoxPlotBase.get_fliers(spark_column_name, outliers, whiskers[0])
fliers = [fliers] if len(fliers) > 0 else None
fig = go.Figure()
fig.add_trace(
go.Box(
name=colname,
q1=[col_stats["q1"]],
median=[col_stats["med"]],
q3=[col_stats["q3"]],
mean=[col_stats["mean"]],
lowerfence=[whiskers[0]],
upperfence=[whiskers[1]],
y=fliers,
boxpoints=boxpoints,
notched=notched,
**kwargs, # this is for workarounds. Box takes different options from express.box.
)
)
fig["layout"]["xaxis"]["title"] = colname
fig["layout"]["yaxis"]["title"] = "value"
return fig
def plot_kde(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
from plotly import express
import pyspark.pandas as ps
if isinstance(data, ps.DataFrame) and "color" not in kwargs:
kwargs["color"] = "names"
psdf = KdePlotBase.prepare_kde_data(data)
sdf = psdf._internal.spark_frame
data_columns = psdf._internal.data_spark_columns
ind = KdePlotBase.get_ind(sdf.select(*data_columns), kwargs.pop("ind", None))
bw_method = kwargs.pop("bw_method", None)
pdfs = []
for label in psdf._internal.column_labels:
pdfs.append(
pd.DataFrame(
{
"Density": KdePlotBase.compute_kde(
sdf.select(psdf._internal.spark_column_for(label)),
ind=ind,
bw_method=bw_method,
),
"names": name_like_string(label),
"index": ind,
}
)
)
pdf = pd.concat(pdfs)
fig = express.line(pdf, x="index", y="Density", **kwargs)
fig["layout"]["xaxis"]["title"] = None
return fig
| apache-2.0 |
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