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create_max_ndvi_mosaic
(dataset_in, clean_mask=None, no_data=-9999, dtype=None, intermediate_product=None, **kwargs)
Method for calculating the pixel value for the max ndvi value. Parameters ---------- dataset_in: xarray.Dataset A dataset retrieved from the Data Cube; should contain: coordinates: time, latitude, longitude variables: variables to be mosaicked (e.g. red, green, and blue bands) clean_mask: np.ndarray An ndarray of the same shape as `dataset_in` - specifying which values to mask out. If no clean mask is specified, then all values are kept during compositing. no_data: int or float The no data value. dtype: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. Returns ------- dataset_out: xarray.Dataset Compositited data with the format: coordinates: latitude, longitude variables: same as dataset_in
Method for calculating the pixel value for the max ndvi value.
def create_max_ndvi_mosaic(dataset_in, clean_mask=None, no_data=-9999, dtype=None, intermediate_product=None, **kwargs): """ Method for calculating the pixel value for the max ndvi value. Parameters ---------- dataset_in: xarray.Dataset A dataset retrieved from the Data Cube; should contain: coordinates: time, latitude, longitude variables: variables to be mosaicked (e.g. red, green, and blue bands) clean_mask: np.ndarray An ndarray of the same shape as `dataset_in` - specifying which values to mask out. If no clean mask is specified, then all values are kept during compositing. no_data: int or float The no data value. dtype: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. Returns ------- dataset_out: xarray.Dataset Compositited data with the format: coordinates: latitude, longitude variables: same as dataset_in """ dataset_in = dataset_in.copy(deep=True) # Default to masking nothing. if clean_mask is None: clean_mask = create_default_clean_mask(dataset_in) dataset_in_dtypes = None if dtype is None: # Save dtypes because masking with Dataset.where() converts to float64. band_list = list(dataset_in.data_vars) dataset_in_dtypes = {} for band in band_list: dataset_in_dtypes[band] = dataset_in[band].dtype # Mask out clouds and scan lines. dataset_in = dataset_in.where((dataset_in != -9999) & clean_mask) if intermediate_product is not None: dataset_out = intermediate_product.copy(deep=True) else: dataset_out = None time_slices = range(len(dataset_in.time)) for timeslice in time_slices: dataset_slice = dataset_in.isel(time=timeslice).drop('time') ndvi = (dataset_slice.nir - dataset_slice.red) / (dataset_slice.nir + dataset_slice.red) ndvi.values[np.invert(clean_mask)[timeslice, ::]] = -1000000000 dataset_slice['ndvi'] = ndvi if dataset_out is None: dataset_out = dataset_slice.copy(deep=True) utilities.clear_attrs(dataset_out) else: for key in list(dataset_slice.data_vars): dataset_out[key].values[dataset_slice.ndvi.values > dataset_out.ndvi.values] = \ dataset_slice[key].values[dataset_slice.ndvi.values > dataset_out.ndvi.values] # Handle datatype conversions. dataset_out = restore_or_convert_dtypes(dtype, band_list, dataset_in_dtypes, dataset_out, no_data) return dataset_out
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[ 213, 0 ]
[ 276, 22 ]
python
en
['en', 'error', 'th']
False
create_min_ndvi_mosaic
(dataset_in, clean_mask=None, no_data=-9999, dtype=None, intermediate_product=None, **kwargs)
Method for calculating the pixel value for the min ndvi value. Parameters ---------- dataset_in: xarray.Dataset A dataset retrieved from the Data Cube; should contain: coordinates: time, latitude, longitude variables: variables to be mosaicked (e.g. red, green, and blue bands) clean_mask: np.ndarray An ndarray of the same shape as `dataset_in` - specifying which values to mask out. If no clean mask is specified, then all values are kept during compositing. no_data: int or float The no data value. dtype: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. Returns ------- dataset_out: xarray.Dataset Compositited data with the format: coordinates: latitude, longitude variables: same as dataset_in
Method for calculating the pixel value for the min ndvi value.
def create_min_ndvi_mosaic(dataset_in, clean_mask=None, no_data=-9999, dtype=None, intermediate_product=None, **kwargs): """ Method for calculating the pixel value for the min ndvi value. Parameters ---------- dataset_in: xarray.Dataset A dataset retrieved from the Data Cube; should contain: coordinates: time, latitude, longitude variables: variables to be mosaicked (e.g. red, green, and blue bands) clean_mask: np.ndarray An ndarray of the same shape as `dataset_in` - specifying which values to mask out. If no clean mask is specified, then all values are kept during compositing. no_data: int or float The no data value. dtype: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. Returns ------- dataset_out: xarray.Dataset Compositited data with the format: coordinates: latitude, longitude variables: same as dataset_in """ dataset_in = dataset_in.copy(deep=True) # Default to masking nothing. if clean_mask is None: clean_mask = create_default_clean_mask(dataset_in) dataset_in_dtypes = None if dtype is None: # Save dtypes because masking with Dataset.where() converts to float64. band_list = list(dataset_in.data_vars) dataset_in_dtypes = {} for band in band_list: dataset_in_dtypes[band] = dataset_in[band].dtype # Mask out clouds and scan lines. dataset_in = dataset_in.where((dataset_in != -9999) & clean_mask) if intermediate_product is not None: dataset_out = intermediate_product.copy(deep=True) else: dataset_out = None time_slices = range(len(dataset_in.time)) for timeslice in time_slices: dataset_slice = dataset_in.isel(time=timeslice).drop('time') ndvi = (dataset_slice.nir - dataset_slice.red) / (dataset_slice.nir + dataset_slice.red) ndvi.values[np.invert(clean_mask)[timeslice, ::]] = 1000000000 dataset_slice['ndvi'] = ndvi if dataset_out is None: dataset_out = dataset_slice.copy(deep=True) utilities.clear_attrs(dataset_out) else: for key in list(dataset_slice.data_vars): dataset_out[key].values[dataset_slice.ndvi.values < dataset_out.ndvi.values] = dataset_slice[key].values[dataset_slice.ndvi.values < dataset_out.ndvi.values] # Handle datatype conversions. dataset_out = restore_or_convert_dtypes(dtype, None, dataset_in_dtypes, dataset_out, no_data) return dataset_out
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[ 279, 0 ]
[ 343, 22 ]
python
en
['en', 'error', 'th']
False
unpack_bits
(land_cover_endcoding, data_array, cover_type)
Description: Unpack bits for end of ls7 and ls8 functions ----- Input: land_cover_encoding(dict hash table) land cover endcoding provided by ls7 or ls8 data_array( xarray DataArray) cover_type(String) type of cover Output: unpacked DataArray
Description: Unpack bits for end of ls7 and ls8 functions ----- Input: land_cover_encoding(dict hash table) land cover endcoding provided by ls7 or ls8 data_array( xarray DataArray) cover_type(String) type of cover Output: unpacked DataArray
def unpack_bits(land_cover_endcoding, data_array, cover_type): """ Description: Unpack bits for end of ls7 and ls8 functions ----- Input: land_cover_encoding(dict hash table) land cover endcoding provided by ls7 or ls8 data_array( xarray DataArray) cover_type(String) type of cover Output: unpacked DataArray """ boolean_mask = np.isin(data_array.values, land_cover_endcoding[cover_type]) return xr.DataArray(boolean_mask.astype(bool), coords = data_array.coords, dims = data_array.dims, name = cover_type + "_mask", attrs = data_array.attrs)
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[ 345, 0 ]
[ 362, 49 ]
python
en
['en', 'error', 'th']
False
ls8_oli_unpack_qa
(data_array, cover_type)
Returns a boolean `xarray.DataArray` denoting which points in `data_array` are of the selected `cover_type` (True indicates presence and False indicates absence). For more information, see this: https://landsat.usgs.gov/collectionqualityband The most relevant section for this function is titled "Landsat 8 OLI/ OLI-TIRS Level-1 Possible Attributes, Pixel Values, and Pixel Value Interpretations". Parameters ---------- data_array: xarray.DataArray A DataArray of the QA band. cover_type: string A string in the set [fill, terrain_occ, clear, rad_sat_1_2, rad_sat_3_4, rad_sat_5_pls, cloud, low_conf_cl, med_conf_cl, high_conf_cl, high_cl_shdw, high_snow_ice, low_conf_cir, high_conf_cir]. 'fill' removes "no_data" values, which indicates an absence of data. This value is -9999 for Landsat platforms. Generally, don't use 'fill'. 'terrain_occ' allows only occluded terrain. 'clear' allows only clear terrain. 'water' allows only water. 'shadow' allows only cloud shadows. 'rad_sat_1_2' denotes radiometric saturation in 1 or 2 bands. 'rad_sat_3_4' denotes radiometric saturation in 3 or 4 bands. 'rad_sat_5_pls' denotes radiometric saturation in 5 or more bands. 'cloud' allows only clouds, but note that it often only selects cloud boundaries. 'low_conf_cl', 'med_conf_cl', and 'high_conf_cl' denote low, medium, and high confidence in cloud coverage. - 'low_conf_cl' is useful on its own for only removing clouds, however, 'clear' is usually better suited for this. - 'med_conf_cl' is useful in combination with 'low_conf_cl' to allow slightly heavier cloud coverage. - Note that 'med_conf_cl' and 'cloud' are very similar. - 'high_conf_cl' is useful in combination with both 'low_conf_cl' and 'med_conf_cl'. 'high_cl_shdw' denotes high confidence in cloud shadow. 'high_snow_ice' denotes high confidence in snow or ice. 'low_conf_cir' and 'high_conf_cir' denote low and high confidence in cirrus clouds. Returns ------- mask: xarray.DataArray The boolean `xarray.DataArray` denoting which points in `data_array` are of the selected `cover_type` (True indicates presence and False indicates absence). This will have the same dimensions and coordinates as `data_array`.
Returns a boolean `xarray.DataArray` denoting which points in `data_array` are of the selected `cover_type` (True indicates presence and False indicates absence).
def ls8_oli_unpack_qa(data_array, cover_type): """ Returns a boolean `xarray.DataArray` denoting which points in `data_array` are of the selected `cover_type` (True indicates presence and False indicates absence). For more information, see this: https://landsat.usgs.gov/collectionqualityband The most relevant section for this function is titled "Landsat 8 OLI/ OLI-TIRS Level-1 Possible Attributes, Pixel Values, and Pixel Value Interpretations". Parameters ---------- data_array: xarray.DataArray A DataArray of the QA band. cover_type: string A string in the set [fill, terrain_occ, clear, rad_sat_1_2, rad_sat_3_4, rad_sat_5_pls, cloud, low_conf_cl, med_conf_cl, high_conf_cl, high_cl_shdw, high_snow_ice, low_conf_cir, high_conf_cir]. 'fill' removes "no_data" values, which indicates an absence of data. This value is -9999 for Landsat platforms. Generally, don't use 'fill'. 'terrain_occ' allows only occluded terrain. 'clear' allows only clear terrain. 'water' allows only water. 'shadow' allows only cloud shadows. 'rad_sat_1_2' denotes radiometric saturation in 1 or 2 bands. 'rad_sat_3_4' denotes radiometric saturation in 3 or 4 bands. 'rad_sat_5_pls' denotes radiometric saturation in 5 or more bands. 'cloud' allows only clouds, but note that it often only selects cloud boundaries. 'low_conf_cl', 'med_conf_cl', and 'high_conf_cl' denote low, medium, and high confidence in cloud coverage. - 'low_conf_cl' is useful on its own for only removing clouds, however, 'clear' is usually better suited for this. - 'med_conf_cl' is useful in combination with 'low_conf_cl' to allow slightly heavier cloud coverage. - Note that 'med_conf_cl' and 'cloud' are very similar. - 'high_conf_cl' is useful in combination with both 'low_conf_cl' and 'med_conf_cl'. 'high_cl_shdw' denotes high confidence in cloud shadow. 'high_snow_ice' denotes high confidence in snow or ice. 'low_conf_cir' and 'high_conf_cir' denote low and high confidence in cirrus clouds. Returns ------- mask: xarray.DataArray The boolean `xarray.DataArray` denoting which points in `data_array` are of the selected `cover_type` (True indicates presence and False indicates absence). This will have the same dimensions and coordinates as `data_array`. """ land_cover_encoding = dict(fill =[1], terrain_occ =[2, 2722], clear =[2720, 2724, 2728, 2732], rad_sat_1_2 =[2724, 2756, 2804, 2980, 3012, 3748, 3780, 6820, 6852, 6900, 7076, 7108, 7844, 7876], rad_sat_3_4 =[2728, 2760, 2808, 2984, 3016, 3752, 3784, 6824, 6856, 6904, 7080, 7112, 7848, 7880], rad_sat_5_pls=[2732, 2764, 2812, 2988, 3020, 3756, 3788, 6828, 6860, 6908, 7084, 7116, 7852, 7884], cloud =[2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908], low_conf_cl =[2752, 2722, 2724, 2728, 2732, 2976, 2980, 2984, 2988, 3744, 3748, 3752, 3756, 6816, 6820, 6824, 6828, 7072, 7076, 7080, 7084, 7840, 7844, 7848, 7852], med_conf_cl =[2752, 2756, 2760, 2764, 3008, 3012, 3016, 3020, 3776, 3780, 3784, 3788, 6848, 6852, 6856, 6860, 7104, 7108, 7112, 7116, 7872, 7876, 7880, 7884], high_conf_cl =[2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908], high_cl_shdw=[2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020, 7072, 7076, 7080, 7084, 7104, 7108, 7112, 7116], high_snow_ice=[3744, 3748, 3752, 3756, 3776, 3780, 3784, 3788, 7840, 7844, 7848, 7852, 7872, 7876, 7880, 7884], low_conf_cir =[2720, 2722, 2724, 2728, 2732, 2752, 2756, 2760, 2764, 2800, 2804, 2808, 2812, 2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020, 3744, 3748, 3752, 3756, 3780, 3784, 3788], high_conf_cir=[6816, 6820, 6824, 6828, 6848, 6852, 6856, 6860, 6896, 6900, 6904, 6908, 7072, 7076, 7080, 7084, 7104, 7108, 7112, 7116, 7840, 7844, 7848, 7852, 7872, 7876, 7880, 7884] ) return unpack_bits(land_cover_encoding, data_array, cover_type)
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[ 382, 0 ]
[ 442, 67 ]
python
en
['en', 'error', 'th']
False
create_hdmedians_multiple_band_mosaic
(dataset_in, clean_mask=None, no_data=-9999, dtype=None, intermediate_product=None, operation="median", **kwargs)
Calculates the geomedian or geomedoid using a multi-band processing method. Parameters ---------- dataset_in: xarray.Dataset A dataset retrieved from the Data Cube; should contain: coordinates: time, latitude, longitude (in that order) variables: variables to be mosaicked (e.g. red, green, and blue bands) clean_mask: np.ndarray An ndarray of the same shape as `dataset_in` - specifying which values to mask out. If no clean mask is specified, then all values are kept during compositing. no_data: int or float The no data value. dtype: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. operation: str in ['median', 'medoid'] Returns ------- dataset_out: xarray.Dataset Compositited data with the format: coordinates: latitude, longitude variables: same as dataset_in
Calculates the geomedian or geomedoid using a multi-band processing method.
def create_hdmedians_multiple_band_mosaic(dataset_in, clean_mask=None, no_data=-9999, dtype=None, intermediate_product=None, operation="median", **kwargs): """ Calculates the geomedian or geomedoid using a multi-band processing method. Parameters ---------- dataset_in: xarray.Dataset A dataset retrieved from the Data Cube; should contain: coordinates: time, latitude, longitude (in that order) variables: variables to be mosaicked (e.g. red, green, and blue bands) clean_mask: np.ndarray An ndarray of the same shape as `dataset_in` - specifying which values to mask out. If no clean mask is specified, then all values are kept during compositing. no_data: int or float The no data value. dtype: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. operation: str in ['median', 'medoid'] Returns ------- dataset_out: xarray.Dataset Compositited data with the format: coordinates: latitude, longitude variables: same as dataset_in """ # Default to masking nothing. if clean_mask is None: clean_mask = create_default_clean_mask(dataset_in) assert operation in ['median', 'medoid'], "Only median and medoid operations are supported." band_list = list(dataset_in.data_vars) dataset_in_dtypes = None if dtype is None: # Save dtypes because masking with Dataset.where() converts to float64. dataset_in_dtypes = {} for band in band_list: dataset_in_dtypes[band] = dataset_in[band].dtype # Mask out clouds and scan lines. dataset_in = dataset_in.where((dataset_in != no_data) & clean_mask) arrays = [dataset_in[band] for band in band_list] stacked_data = np.stack(arrays) bands_shape, time_slices_shape, lat_shape, lon_shape = stacked_data.shape[0], \ stacked_data.shape[1], stacked_data.shape[2], \ stacked_data.shape[3] # Reshape to remove lat/lon reshaped_stack = stacked_data.reshape(bands_shape, time_slices_shape, lat_shape * lon_shape) # Build zeroes array across time slices. hdmedians_result = np.zeros((bands_shape, lat_shape * lon_shape)) # For each pixel (lat/lon combination), find the geomedian or geomedoid across time. for x in range(reshaped_stack.shape[2]): try: hdmedians_result[:, x] = hd.nangeomedian( reshaped_stack[:, :, x], axis=1) if operation == "median" else hd.nanmedoid( reshaped_stack[:, :, x], axis=1) except ValueError as e: # If all bands have nan values across time, the geomedians are nans. hdmedians_result[:, x] = np.full((bands_shape), np.nan) output_dict = { value: (('latitude', 'longitude'), hdmedians_result[index, :].reshape(lat_shape, lon_shape)) for index, value in enumerate(band_list) } dataset_out = xr.Dataset(output_dict, coords={'latitude': dataset_in['latitude'], 'longitude': dataset_in['longitude']}, attrs=dataset_in.attrs) dataset_out = restore_or_convert_dtypes(dtype, band_list, dataset_in_dtypes, dataset_out, no_data) return dataset_out
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[ 473, 0 ]
[ 551, 22 ]
python
en
['en', 'error', 'th']
False
restore_or_convert_dtypes
(dtype_for_all=None, band_list=None, dataset_in_dtypes=None, dataset_out=None, no_data=-9999)
Converts datatypes of data variables in a copy of an xarray Dataset. Parameters ---------- dtype_for_all: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. band_list: list-like !! UNUSED, DEPRECATED !! dataset_in_dtypes: dict A dictionary mapping band names to datatypes. One of `dtype_for_all` or `dataset_in_dtypes` must be `None`. no_data: int or float The no data value. Returns ------- dataset_out: xarray.Dataset The output Dataset.
Converts datatypes of data variables in a copy of an xarray Dataset.
def restore_or_convert_dtypes(dtype_for_all=None, band_list=None, dataset_in_dtypes=None, dataset_out=None, no_data=-9999): """ Converts datatypes of data variables in a copy of an xarray Dataset. Parameters ---------- dtype_for_all: str or numpy.dtype A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g. np.int16, np.float32) to convert the data to. band_list: list-like !! UNUSED, DEPRECATED !! dataset_in_dtypes: dict A dictionary mapping band names to datatypes. One of `dtype_for_all` or `dataset_in_dtypes` must be `None`. no_data: int or float The no data value. Returns ------- dataset_out: xarray.Dataset The output Dataset. """ assert dtype_for_all is None or dataset_in_dtypes is None, \ "One of `dtype_for_all` or `dataset_in_dtypes` must be `None`." if dtype_for_all is not None: # Integer types can't represent nan. if np.issubdtype(dtype_for_all, np.integer): # This also works for Python int type. utilities.nan_to_num(dataset_out, no_data) convert_to_dtype(dataset_out, dtype_for_all) else: # Restore dtypes to state before masking. for band in dataset_in_dtypes: band_dtype = dataset_in_dtypes[band] if np.issubdtype(band_dtype, np.integer): utilities.nan_to_num(dataset_out[band], no_data) dataset_out[band] = dataset_out[band].astype(band_dtype) return dataset_out
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[ 553, 0 ]
[ 588, 22 ]
python
en
['en', 'error', 'th']
False
EvictionModel.run
(self)
def run(self): settings = self.config.experiment_settings(self.name()) invocations = settings["invocations"] sleep = settings["sleep"] repetitions = settings["repetitions"] invocation_idx = settings["function_copy_idx"] port = settings["client-port"] from requests import get ip = get("http://checkip.amazonaws.com/").text.rstrip() """ """ function_names = self.functions_names[invocation_idx :: self.function_copies_per_time] functions = self.functions[invocation_idx :: self.function_copies_per_time] results = {} # Disable logging - otherwise we have RLock that can't get be pickled for func in functions: # func.disable_logging() for tr in func.triggers_all(): del tr._logging_handlers # self.disable_logging() # del self.logging for t in self.times: results[t] = [] fname = f"results_{invocations}_{repetitions}_{sleep}.json" """ Allocate one process for each invocation => process N invocations in parallel. Each process uses M threads to execute in parallel invocations with a different time sleep between executions. The result: repeated N invocations for M different imes. """ threads = len(self.times) with multiprocessing.Pool(processes=(invocations + threads)) as pool: for i in range(0, repetitions): """ Attempt to kill all existing containers. """ # for func in functions: # self._deployment_client.enforce_cold_start(func) # time.sleep(5) for _, t in enumerate(self.times): results[t].append([]) local_results = [] servers_results = [] """ Start M server intances. Each one handles one set of invocations. """ for j in range(0, threads): servers_results.append( pool.apply_async(EvictionModel.accept_replies, args=(port + j, invocations)) ) """ Start N parallel invocations """ for j in range(0, invocations): payload = {"ip-address": ip, "port": port} print(payload) local_results.append( pool.apply_async( EvictionModel.process_function, args=(i, j, invocations, functions, self.times, payload), ) ) time.sleep(10) import sys sys.stdout.flush() """ Rethrow exceptions if appear """ for result in servers_results: ret = result.get() for result in local_results: ret = result.get() for i, val in enumerate(ret): results[self.times[i]][-1].append(val) """ Make sure that parallel invocations are truly parallel, i.e. no execution happens after another one finished. """ # verify_results(results) with open(os.path.join(self._out_dir, fname), "w") as out_f: # print(results) print(f"Write results to {os.path.join(self._out_dir, fname)}") out_f.write(serialize(results))
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[ 206, 4 ]
[ 304, 47 ]
python
en
['en', 'error', 'th']
False
CommandTests.test_wait_for_db_ready
(self)
Test waiting for db when db is available
Test waiting for db when db is available
def test_wait_for_db_ready(self): """Test waiting for db when db is available""" with patch('django.db.utils.ConnectionHandler.__getitem__') as gi: gi.return_value = True call_command('wait_for_db') self.assertEqual(gi.call_count, 1)
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[ 9, 4 ]
[ 14, 46 ]
python
en
['en', 'en', 'en']
True
CommandTests.test_wait_for_db
(self, ts)
Test waiting for db
Test waiting for db
def test_wait_for_db(self, ts): """Test waiting for db""" with patch('django.db.utils.ConnectionHandler.__getitem__') as gi: gi.side_effect = [OperationalError] * 5 + [True] call_command('wait_for_db') self.assertEqual(gi.call_count, 6)
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[ 17, 4 ]
[ 22, 46 ]
python
en
['en', 'en', 'en']
True
ConfiguredAssetSqlDataConnector.add_data_asset
( self, name: str, config: dict, )
Add data_asset to DataConnector using data_asset name as key, and data_asset configuration as value.
Add data_asset to DataConnector using data_asset name as key, and data_asset configuration as value.
def add_data_asset( self, name: str, config: dict, ): """ Add data_asset to DataConnector using data_asset name as key, and data_asset configuration as value. """ self._assets[name] = config
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[ 57, 4 ]
[ 65, 35 ]
python
en
['en', 'error', 'th']
False
ConfiguredAssetSqlDataConnector.get_available_data_asset_names
(self)
Return the list of asset names known by this DataConnector. Returns: A list of available names
Return the list of asset names known by this DataConnector.
def get_available_data_asset_names(self) -> List[str]: """ Return the list of asset names known by this DataConnector. Returns: A list of available names """ return list(self.assets.keys())
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[ 122, 4 ]
[ 129, 39 ]
python
en
['en', 'error', 'th']
False
ConfiguredAssetSqlDataConnector.get_unmatched_data_references
(self)
Returns the list of data_references unmatched by configuration by looping through items in _data_references_cache and returning data_reference that do not have an associated data_asset. Returns: list of data_references that are not matched by configuration.
Returns the list of data_references unmatched by configuration by looping through items in _data_references_cache and returning data_reference that do not have an associated data_asset.
def get_unmatched_data_references(self) -> List[str]: """ Returns the list of data_references unmatched by configuration by looping through items in _data_references_cache and returning data_reference that do not have an associated data_asset. Returns: list of data_references that are not matched by configuration. """ return []
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[ 131, 4 ]
[ 139, 17 ]
python
en
['en', 'error', 'th']
False
ConfiguredAssetSqlDataConnector.build_batch_spec
( self, batch_definition: BatchDefinition )
Build BatchSpec from batch_definition by calling DataConnector's build_batch_spec function. Args: batch_definition (BatchDefinition): to be used to build batch_spec Returns: BatchSpec built from batch_definition
Build BatchSpec from batch_definition by calling DataConnector's build_batch_spec function.
def build_batch_spec( self, batch_definition: BatchDefinition ) -> SqlAlchemyDatasourceBatchSpec: """ Build BatchSpec from batch_definition by calling DataConnector's build_batch_spec function. Args: batch_definition (BatchDefinition): to be used to build batch_spec Returns: BatchSpec built from batch_definition """ data_asset_name: str = batch_definition.data_asset_name if ( data_asset_name in self.assets and self.assets[data_asset_name].get("batch_spec_passthrough") and isinstance( self.assets[data_asset_name].get("batch_spec_passthrough"), dict ) ): # batch_spec_passthrough from data_asset batch_spec_passthrough = deepcopy( self.assets[data_asset_name]["batch_spec_passthrough"] ) batch_definition_batch_spec_passthrough = ( deepcopy(batch_definition.batch_spec_passthrough) or {} ) # batch_spec_passthrough from Batch Definition supersedes batch_spec_passthrough from data_asset batch_spec_passthrough.update(batch_definition_batch_spec_passthrough) batch_definition.batch_spec_passthrough = batch_spec_passthrough batch_spec: BatchSpec = super().build_batch_spec( batch_definition=batch_definition ) return SqlAlchemyDatasourceBatchSpec(batch_spec)
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[ 187, 4 ]
[ 223, 56 ]
python
en
['en', 'error', 'th']
False
ConfiguredAssetSqlDataConnector._generate_batch_spec_parameters_from_batch_definition
( self, batch_definition: BatchDefinition )
Build BatchSpec parameters from batch_definition with the following components: 1. data_asset_name from batch_definition 2. batch_identifiers from batch_definition 3. data_asset from data_connector Args: batch_definition (BatchDefinition): to be used to build batch_spec Returns: dict built from batch_definition
Build BatchSpec parameters from batch_definition with the following components: 1. data_asset_name from batch_definition 2. batch_identifiers from batch_definition 3. data_asset from data_connector
def _generate_batch_spec_parameters_from_batch_definition( self, batch_definition: BatchDefinition ) -> dict: """ Build BatchSpec parameters from batch_definition with the following components: 1. data_asset_name from batch_definition 2. batch_identifiers from batch_definition 3. data_asset from data_connector Args: batch_definition (BatchDefinition): to be used to build batch_spec Returns: dict built from batch_definition """ data_asset_name: str = batch_definition.data_asset_name return { "data_asset_name": data_asset_name, "table_name": data_asset_name, "batch_identifiers": batch_definition.batch_identifiers, **self.assets[data_asset_name], }
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[ 225, 4 ]
[ 246, 9 ]
python
en
['en', 'error', 'th']
False
ConfiguredAssetSqlDataConnector._split_on_whole_table
( self, table_name: str, )
'Split' by returning the whole table Note: the table_name parameter is a required to keep the signature of this method consistent with other methods.
'Split' by returning the whole table
def _split_on_whole_table( self, table_name: str, ): """ 'Split' by returning the whole table Note: the table_name parameter is a required to keep the signature of this method consistent with other methods. """ return sa.select([sa.true()])
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[ 250, 4 ]
[ 260, 37 ]
python
en
['en', 'error', 'th']
False
ConfiguredAssetSqlDataConnector._split_on_column_value
( self, table_name: str, column_name: str, )
Split using the values in the named column
Split using the values in the named column
def _split_on_column_value( self, table_name: str, column_name: str, ): """Split using the values in the named column""" # query = f"SELECT DISTINCT(\"{self.column_name}\") FROM {self.table_name}" return sa.select([sa.func.distinct(sa.column(column_name))]).select_from( sa.text(table_name) )
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[ 262, 4 ]
[ 272, 9 ]
python
en
['en', 'no', 'en']
True
ConfiguredAssetSqlDataConnector._split_on_converted_datetime
( self, table_name: str, column_name: str, date_format_string: str = "%Y-%m-%d", )
Convert the values in the named column to the given date_format, and split on that
Convert the values in the named column to the given date_format, and split on that
def _split_on_converted_datetime( self, table_name: str, column_name: str, date_format_string: str = "%Y-%m-%d", ): """Convert the values in the named column to the given date_format, and split on that""" # query = f"SELECT DISTINCT( strftime(\"{date_format_string}\", \"{self.column_name}\")) as my_var FROM {self.table_name}" return sa.select( [ sa.func.distinct( sa.func.strftime( date_format_string, sa.column(column_name), ) ) ] ).select_from(sa.text(table_name))
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[ 274, 4 ]
[ 292, 42 ]
python
en
['en', 'en', 'en']
True
ConfiguredAssetSqlDataConnector._split_on_divided_integer
( self, table_name: str, column_name: str, divisor: int )
Divide the values in the named column by `divisor`, and split on that
Divide the values in the named column by `divisor`, and split on that
def _split_on_divided_integer( self, table_name: str, column_name: str, divisor: int ): """Divide the values in the named column by `divisor`, and split on that""" # query = f"SELECT DISTINCT(\"{self.column_name}\" / {divisor}) AS my_var FROM {self.table_name}" return sa.select( [sa.func.distinct(sa.cast(sa.column(column_name) / divisor, sa.Integer))] ).select_from(sa.text(table_name))
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[ 294, 4 ]
[ 302, 42 ]
python
en
['en', 'en', 'en']
True
ConfiguredAssetSqlDataConnector._split_on_mod_integer
(self, table_name: str, column_name: str, mod: int)
Divide the values in the named column by `divisor`, and split on that
Divide the values in the named column by `divisor`, and split on that
def _split_on_mod_integer(self, table_name: str, column_name: str, mod: int): """Divide the values in the named column by `divisor`, and split on that""" # query = f"SELECT DISTINCT(\"{self.column_name}\" / {divisor}) AS my_var FROM {self.table_name}" return sa.select( [sa.func.distinct(sa.cast(sa.column(column_name) % mod, sa.Integer))] ).select_from(sa.text(table_name))
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[ 304, 4 ]
[ 310, 42 ]
python
en
['en', 'en', 'en']
True
ConfiguredAssetSqlDataConnector._split_on_multi_column_values
( self, table_name: str, column_names: List[str], )
Split on the joint values in the named columns
Split on the joint values in the named columns
def _split_on_multi_column_values( self, table_name: str, column_names: List[str], ): """Split on the joint values in the named columns""" # query = f"SELECT DISTINCT(\"{self.column_name}\") FROM {self.table_name}" return ( sa.select([sa.column(column_name) for column_name in column_names]) .distinct() .select_from(sa.text(table_name)) )
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[ 312, 4 ]
[ 324, 9 ]
python
en
['en', 'en', 'en']
True
ConfiguredAssetSqlDataConnector._split_on_hashed_column
( self, table_name: str, column_name: str, hash_digits: int, )
Note: this method is experimental. It does not work with all SQL dialects.
Note: this method is experimental. It does not work with all SQL dialects.
def _split_on_hashed_column( self, table_name: str, column_name: str, hash_digits: int, ): """Note: this method is experimental. It does not work with all SQL dialects.""" # query = f"SELECT MD5(\"{self.column_name}\") = {matching_hash}) AS hashed_var FROM {self.table_name}" return sa.select([sa.func.md5(sa.column(column_name))]).select_from( sa.text(table_name) )
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[ 326, 4 ]
[ 337, 9 ]
python
en
['en', 'en', 'en']
True
download
(name, test_size=None, data_path=DATA_PATH)
Load the CSV with the given name from S3. If the CSV has never been loaded before, it will be downloaded from the [d3-ai-orion bucket](https://d3-ai-orion.s3.amazonaws.com) or the S3 bucket specified following the `s3://{bucket}/path/to/the.csv` format, and then cached inside the `data` folder, within the `orion` package directory, and then returned. Otherwise, if it has been downloaded and cached before, it will be directly loaded from the `orion/data` folder without contacting S3. If a `test_size` value is given, the data will be split in two parts without altering its order, making the second one proportionally as big as the given value. Args: name (str): Name of the CSV to load. test_size (float): Value between 0 and 1 indicating the proportional size of the test split. If 0 or None (default), the data is not split. Returns: If no test_size is given, a single pandas.DataFrame is returned containing all the data. If test_size is given, a tuple containing one pandas.DataFrame for the train split and another one for the test split is returned.
Load the CSV with the given name from S3.
def download(name, test_size=None, data_path=DATA_PATH): """Load the CSV with the given name from S3. If the CSV has never been loaded before, it will be downloaded from the [d3-ai-orion bucket](https://d3-ai-orion.s3.amazonaws.com) or the S3 bucket specified following the `s3://{bucket}/path/to/the.csv` format, and then cached inside the `data` folder, within the `orion` package directory, and then returned. Otherwise, if it has been downloaded and cached before, it will be directly loaded from the `orion/data` folder without contacting S3. If a `test_size` value is given, the data will be split in two parts without altering its order, making the second one proportionally as big as the given value. Args: name (str): Name of the CSV to load. test_size (float): Value between 0 and 1 indicating the proportional size of the test split. If 0 or None (default), the data is not split. Returns: If no test_size is given, a single pandas.DataFrame is returned containing all the data. If test_size is given, a tuple containing one pandas.DataFrame for the train split and another one for the test split is returned. """ url = None if name.startswith('s3://'): parts = name[5:].split('/', 1) bucket = parts[0] path = parts[1] url = S3_URL.format(bucket, path) filename = os.path.join(data_path, path.split('/')[-1]) else: filename = os.path.join(data_path, name + '.csv') if os.path.exists(filename): data = pd.read_csv(filename) else: url = url or S3_URL.format(BUCKET, '{}.csv'.format(name)) LOGGER.info('Downloading CSV %s from %s', name, url) os.makedirs(data_path, exist_ok=True) data = pd.read_csv(url) data.to_csv(filename, index=False) return data
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[ 42, 0 ]
[ 90, 15 ]
python
en
['en', 'en', 'en']
True
SubdirReaderBatchKwargsGenerator._build_batch_kwargs
(self, batch_parameters)
Args: batch_parameters: Returns: batch_kwargs
def _build_batch_kwargs(self, batch_parameters): """ Args: batch_parameters: Returns: batch_kwargs """ try: data_asset_name = batch_parameters.pop("data_asset_name") except KeyError: raise BatchKwargsError( "Unable to build BatchKwargs: no name provided in batch_parameters.", batch_kwargs=batch_parameters, ) if "partition_id" in batch_parameters: partition_id = batch_parameters.pop("partition_id") # Find the path path = None for extension in self.known_extensions: if os.path.isfile( os.path.join( self.base_directory, data_asset_name, partition_id + extension ) ): path = os.path.join( self.base_directory, data_asset_name, partition_id + extension ) if path is None: logger.warning( "Unable to find path with the provided partition; searching for asset-name partitions." ) # Fall through to this case in the event that there is not a subdir available, or if partition_id was # not provided if os.path.isfile(os.path.join(self.base_directory, data_asset_name)): path = os.path.join(self.base_directory, data_asset_name) for extension in self.known_extensions: if os.path.isfile( os.path.join(self.base_directory, data_asset_name + extension) ): path = os.path.join( self.base_directory, data_asset_name + extension ) if path is None: raise BatchKwargsError( "Unable to build batch kwargs from for asset '%s'" % data_asset_name, batch_parameters, ) return self._build_batch_kwargs_from_path(path, **batch_parameters) else: return self.yield_batch_kwargs( data_asset_name=data_asset_name, **batch_parameters )
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[ 123, 4 ]
[ 183, 13 ]
python
en
['en', 'error', 'th']
False
IRTLSContext.from_legacy
(cls, ir: 'IR', name: str, rkey: str, location: str, cert: 'IRAmbassadorTLS', termination: bool, validation_ca: Optional['IRAmbassadorTLS'])
Create an IRTLSContext from a legacy TLS-module style definition. 'cert' is the TLS certificate that we'll offer to our peer -- for a termination context, this is our server cert, and for an origination context, it's our client cert. For termination contexts, 'validation_ca' may also be provided. It's the TLS certificate that we'll use to validate the certificates our clients offer. Note that no private key is needed or supported. :param ir: IR in play :param name: name for the newly-created context :param rkey: rkey for the newly-created context :param location: location for the newly-created context :param cert: information about the cert to present to the peer :param termination: is this a termination context? :param validation_ca: information about how we'll validate the peer's cert :return: newly-created IRTLSContext
Create an IRTLSContext from a legacy TLS-module style definition.
def from_legacy(cls, ir: 'IR', name: str, rkey: str, location: str, cert: 'IRAmbassadorTLS', termination: bool, validation_ca: Optional['IRAmbassadorTLS']) -> 'IRTLSContext': """ Create an IRTLSContext from a legacy TLS-module style definition. 'cert' is the TLS certificate that we'll offer to our peer -- for a termination context, this is our server cert, and for an origination context, it's our client cert. For termination contexts, 'validation_ca' may also be provided. It's the TLS certificate that we'll use to validate the certificates our clients offer. Note that no private key is needed or supported. :param ir: IR in play :param name: name for the newly-created context :param rkey: rkey for the newly-created context :param location: location for the newly-created context :param cert: information about the cert to present to the peer :param termination: is this a termination context? :param validation_ca: information about how we'll validate the peer's cert :return: newly-created IRTLSContext """ new_args = {} for key in [ 'secret', 'cert_chain_file', 'private_key_file', 'alpn_protocols', 'redirect_cleartext_from' ]: value = cert.get(key, None) if value: new_args[key] = value if (('secret' not in new_args) and ('cert_chain_file' not in new_args) and ('private_key_file' not in new_args)): # Assume they want the 'ambassador-certs' secret. new_args['secret'] = 'ambassador-certs' if termination: new_args['hosts'] = [ '*' ] if validation_ca and validation_ca.get('enabled', True): for key in [ 'secret', 'cacert_chain_file', 'cert_required' ]: value = validation_ca.get(key, None) if value: if key == 'secret': new_args['ca_secret'] = value else: new_args[key] = value if (('ca_secret' not in new_args) and ('cacert_chain_file' not in new_args)): # Assume they want the 'ambassador-cacert' secret. new_args['secret'] = 'ambassador-cacert' ctx = IRTLSContext(ir, ir.aconf, rkey=rkey, name=name, location=location, kind="synthesized-TLS-context", _legacy=True, **new_args) return ctx
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[ 332, 4 ]
[ 396, 18 ]
python
en
['en', 'error', 'th']
False
Rule.__init__
( self, name: str, domain_builder: Optional[DomainBuilder] = None, parameter_builders: Optional[List[ParameterBuilder]] = None, expectation_configuration_builders: Optional[ List[ExpectationConfigurationBuilder] ] = None, variables: Optional[ParameterContainer] = None, )
Sets Profiler rule name, domain builders, parameters builders, configuration builders, and other necessary instance data (variables) :param name: A string representing the name of the ProfilerRule :param domain_builder: A Domain Builder object used to build rule data domain :param parameter_builders: A Parameter Builder list used to configure necessary rule evaluation parameters for every configuration :param expectation_configuration_builders: A list of Expectation Configuration Builders :param variables: Any instance data required to verify a rule
Sets Profiler rule name, domain builders, parameters builders, configuration builders, and other necessary instance data (variables) :param name: A string representing the name of the ProfilerRule :param domain_builder: A Domain Builder object used to build rule data domain :param parameter_builders: A Parameter Builder list used to configure necessary rule evaluation parameters for every configuration :param expectation_configuration_builders: A list of Expectation Configuration Builders :param variables: Any instance data required to verify a rule
def __init__( self, name: str, domain_builder: Optional[DomainBuilder] = None, parameter_builders: Optional[List[ParameterBuilder]] = None, expectation_configuration_builders: Optional[ List[ExpectationConfigurationBuilder] ] = None, variables: Optional[ParameterContainer] = None, ): """ Sets Profiler rule name, domain builders, parameters builders, configuration builders, and other necessary instance data (variables) :param name: A string representing the name of the ProfilerRule :param domain_builder: A Domain Builder object used to build rule data domain :param parameter_builders: A Parameter Builder list used to configure necessary rule evaluation parameters for every configuration :param expectation_configuration_builders: A list of Expectation Configuration Builders :param variables: Any instance data required to verify a rule """ self._name = name self._domain_builder = domain_builder self._parameter_builders = parameter_builders self._expectation_configuration_builders = expectation_configuration_builders self._variables = variables self._parameters = {}
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[ 15, 4 ]
[ 41, 29 ]
python
en
['en', 'error', 'th']
False
Rule.generate
( self, )
Builds a list of Expectation Configurations, returning a single Expectation Configuration entry for every ConfigurationBuilder available based on the instantiation. :return: List of Corresponding Expectation Configurations representing every configured rule
Builds a list of Expectation Configurations, returning a single Expectation Configuration entry for every ConfigurationBuilder available based on the instantiation.
def generate( self, ) -> List[ExpectationConfiguration]: """ Builds a list of Expectation Configurations, returning a single Expectation Configuration entry for every ConfigurationBuilder available based on the instantiation. :return: List of Corresponding Expectation Configurations representing every configured rule """ expectation_configurations: List[ExpectationConfiguration] = [] domains: List[Domain] = self._domain_builder.get_domains( variables=self.variables ) domain: Domain for domain in domains: parameter_container: ParameterContainer = ParameterContainer( parameter_nodes=None ) self._parameters[domain.id] = parameter_container parameter_builder: ParameterBuilder for parameter_builder in self._parameter_builders: parameter_builder.build_parameters( parameter_container=parameter_container, domain=domain, variables=self.variables, parameters=self.parameters, ) expectation_configuration_builder: ExpectationConfigurationBuilder for ( expectation_configuration_builder ) in self._expectation_configuration_builders: expectation_configurations.append( expectation_configuration_builder.build_expectation_configuration( domain=domain, variables=self.variables, parameters=self.parameters, ) ) return expectation_configurations
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[ 43, 4 ]
[ 85, 41 ]
python
en
['en', 'error', 'th']
False
venv
(request)
Prepares a virtual environment for nose. :rtype : virtual_environments.VirtualEnvDescription
Prepares a virtual environment for nose. :rtype : virtual_environments.VirtualEnvDescription
def venv(request): """ Prepares a virtual environment for nose. :rtype : virtual_environments.VirtualEnvDescription """ return virtual_environments.prepare_virtualenv([request.param])
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[ 13, 0 ]
[ 18, 67 ]
python
en
['en', 'error', 'th']
False
RunVar.get
(self, default=_NO_DEFAULT)
Gets the value of this :class:`RunVar` for the current run call.
Gets the value of this :class:`RunVar` for the current run call.
def get(self, default=_NO_DEFAULT): """Gets the value of this :class:`RunVar` for the current run call.""" try: return _run.GLOBAL_RUN_CONTEXT.runner._locals[self] except AttributeError: raise RuntimeError("Cannot be used outside of a run context") from None except KeyError: # contextvars consistency if default is not self._NO_DEFAULT: return default if self._default is not self._NO_DEFAULT: return self._default raise LookupError(self) from None
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[ 37, 4 ]
[ 51, 45 ]
python
en
['en', 'en', 'en']
True
RunVar.set
(self, value)
Sets the value of this :class:`RunVar` for this current run call.
Sets the value of this :class:`RunVar` for this current run call.
def set(self, value): """Sets the value of this :class:`RunVar` for this current run call. """ try: old_value = self.get() except LookupError: token = _RunVarToken.empty(self) else: token = _RunVarToken(self, old_value) # This can't fail, because if we weren't in Trio context then the # get() above would have failed. _run.GLOBAL_RUN_CONTEXT.runner._locals[self] = value return token
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[ 53, 4 ]
[ 68, 20 ]
python
en
['en', 'en', 'en']
True
RunVar.reset
(self, token)
Resets the value of this :class:`RunVar` to what it was previously specified by the token.
Resets the value of this :class:`RunVar` to what it was previously specified by the token.
def reset(self, token): """Resets the value of this :class:`RunVar` to what it was previously specified by the token. """ if token is None: raise TypeError("token must not be none") if token.redeemed: raise ValueError("token has already been used") if token._var is not self: raise ValueError("token is not for us") previous = token.previous_value try: if previous is _RunVarToken._no_value: _run.GLOBAL_RUN_CONTEXT.runner._locals.pop(self) else: _run.GLOBAL_RUN_CONTEXT.runner._locals[self] = previous except AttributeError: raise RuntimeError("Cannot be used outside of a run context") token.redeemed = True
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[ 70, 4 ]
[ 93, 29 ]
python
en
['en', 'en', 'en']
True
parse_result_format
(result_format)
This is a simple helper utility that can be used to parse a string result_format into the dict format used internally by great_expectations. It is not necessary but allows shorthand for result_format in cases where there is no need to specify a custom partial_unexpected_count.
This is a simple helper utility that can be used to parse a string result_format into the dict format used internally by great_expectations. It is not necessary but allows shorthand for result_format in cases where there is no need to specify a custom partial_unexpected_count.
def parse_result_format(result_format): """This is a simple helper utility that can be used to parse a string result_format into the dict format used internally by great_expectations. It is not necessary but allows shorthand for result_format in cases where there is no need to specify a custom partial_unexpected_count.""" if isinstance(result_format, str): result_format = {"result_format": result_format, "partial_unexpected_count": 20} else: if "partial_unexpected_count" not in result_format: result_format["partial_unexpected_count"] = 20 return result_format
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[ 18, 0 ]
[ 28, 24 ]
python
en
['en', 'en', 'en']
True
recursively_convert_to_json_serializable
(test_obj)
Helper function to convert a dict object to one that is serializable Args: test_obj: an object to attempt to convert a corresponding json-serializable object Returns: (dict) A converted test_object Warning: test_obj may also be converted in place.
Helper function to convert a dict object to one that is serializable
def recursively_convert_to_json_serializable(test_obj): """ Helper function to convert a dict object to one that is serializable Args: test_obj: an object to attempt to convert a corresponding json-serializable object Returns: (dict) A converted test_object Warning: test_obj may also be converted in place. """ # If it's one of our types, we pass if isinstance( test_obj, ( ExpectationConfiguration, ExpectationSuite, ExpectationValidationResult, ExpectationSuiteValidationResult, ), ): return test_obj # Validate that all aruguments are of approved types, coerce if it's easy, else exception # print(type(test_obj), test_obj) # Note: Not 100% sure I've resolved this correctly... try: if not isinstance(test_obj, list) and np.isnan(test_obj): # np.isnan is functionally vectorized, but we only want to apply this to single objects # Hence, why we test for `not isinstance(list))` return None except (TypeError, ValueError): pass if isinstance(test_obj, (str, int, float, bool)): # No problem to encode json return test_obj elif isinstance(test_obj, dict): new_dict = {} for key in test_obj: # A pandas index can be numeric, and a dict key can be numeric, but a json key must be a string new_dict[str(key)] = recursively_convert_to_json_serializable(test_obj[key]) return new_dict elif isinstance(test_obj, (list, tuple, set)): new_list = [] for val in test_obj: new_list.append(recursively_convert_to_json_serializable(val)) return new_list elif isinstance(test_obj, (np.ndarray, pd.Index)): # test_obj[key] = test_obj[key].tolist() # If we have an array or index, convert it first to a list--causing coercion to float--and then round # to the number of digits for which the string representation will equal the float representation return [recursively_convert_to_json_serializable(x) for x in test_obj.tolist()] # Note: This clause has to come after checking for np.ndarray or we get: # `ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()` elif test_obj is None: # No problem to encode json return test_obj elif isinstance(test_obj, (datetime.datetime, datetime.date)): return str(test_obj) # Use built in base type from numpy, https://docs.scipy.org/doc/numpy-1.13.0/user/basics.types.html # https://github.com/numpy/numpy/pull/9505 elif np.issubdtype(type(test_obj), np.bool_): return bool(test_obj) elif np.issubdtype(type(test_obj), np.integer) or np.issubdtype( type(test_obj), np.uint ): return int(test_obj) elif np.issubdtype(type(test_obj), np.floating): # Note: Use np.floating to avoid FutureWarning from numpy return float(round(test_obj, sys.float_info.dig)) elif isinstance(test_obj, pd.Series): # Converting a series is tricky since the index may not be a string, but all json # keys must be strings. So, we use a very ugly serialization strategy index_name = test_obj.index.name or "index" value_name = test_obj.name or "value" return [ { index_name: recursively_convert_to_json_serializable(idx), value_name: recursively_convert_to_json_serializable(val), } for idx, val in test_obj.iteritems() ] elif isinstance(test_obj, pd.DataFrame): return recursively_convert_to_json_serializable( test_obj.to_dict(orient="records") ) # elif np.issubdtype(type(test_obj), np.complexfloating): # Note: Use np.complexfloating to avoid Future Warning from numpy # Complex numbers consist of two floating point numbers # return complex( # float(round(test_obj.real, sys.float_info.dig)), # float(round(test_obj.imag, sys.float_info.dig))) elif isinstance(test_obj, decimal.Decimal): return float(test_obj) else: raise TypeError( "%s is of type %s which cannot be serialized." % (str(test_obj), type(test_obj).__name__) )
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[ 85, 0 ]
[ 202, 9 ]
python
en
['en', 'error', 'th']
False
TeamcityFormatter._report_suite_started
(self, suite, suite_name)
:param suite: behave suite :param suite_name: suite name that must be reported, be sure to use it instead of suite.name
:param suite: behave suite :param suite_name: suite name that must be reported, be sure to use it instead of suite.name
def _report_suite_started(self, suite, suite_name): """ :param suite: behave suite :param suite_name: suite name that must be reported, be sure to use it instead of suite.name """ self._messages.testSuiteStarted(suite_name)
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[ 78, 4 ]
[ 84, 51 ]
python
en
['en', 'error', 'th']
False
TeamcityFormatter._report_test_started
(self, test, test_name)
Suite name is always stripped, be sure to strip() it too :param test: behave test :param test_name: test name that must be reported, be sure to use it instead of test.name
Suite name is always stripped, be sure to strip() it too :param test: behave test :param test_name: test name that must be reported, be sure to use it instead of test.name
def _report_test_started(self, test, test_name): """ Suite name is always stripped, be sure to strip() it too :param test: behave test :param test_name: test name that must be reported, be sure to use it instead of test.name """ self._messages.testStarted(test_name)
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[ 86, 4 ]
[ 92, 45 ]
python
en
['en', 'error', 'th']
False
Template.load
(self)
Load this Template as an MLPipeline. Returns: MLPipeline
Load this Template as an MLPipeline.
def load(self): """Load this Template as an MLPipeline. Returns: MLPipeline """ return MLPipeline(self.json)
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[ 91, 4 ]
[ 97, 36 ]
python
en
['en', 'en', 'en']
True
Pipeline.load
(self)
Load this Pipeline as an MLPipeline. Returns: MLPipeline
Load this Pipeline as an MLPipeline.
def load(self): """Load this Pipeline as an MLPipeline. Returns: MLPipeline """ return MLPipeline(self.json)
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[ 118, 4 ]
[ 124, 36 ]
python
en
['en', 'en', 'en']
True
Datarun.start
(self)
Mark this Datarun as started on DB. The ``start_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to RUNNING and the software versions will be captured.
Mark this Datarun as started on DB.
def start(self): """Mark this Datarun as started on DB. The ``start_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to RUNNING and the software versions will be captured. """ self.start_time = datetime.utcnow() self.status = self.STATUS_RUNNING self.software_versions = self._software_versions self.save()
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[ 176, 4 ]
[ 186, 19 ]
python
en
['en', 'en', 'en']
True
Datarun.end
(self, status)
Mark this Datarun as ended on DB. The ``end_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to the given value, and the ``num_events`` field will be populated with the sum of the events detected by the children Signalruns.
Mark this Datarun as ended on DB.
def end(self, status): """Mark this Datarun as ended on DB. The ``end_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to the given value, and the ``num_events`` field will be populated with the sum of the events detected by the children Signalruns. """ self.end_time = datetime.utcnow() self.status = status self.num_events = Event.find(signalrun__in=self.signalruns).count() self.save()
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[ 188, 4 ]
[ 198, 19 ]
python
en
['en', 'en', 'en']
True
Signalrun.start
(self)
Mark this Signalrun as started on DB. The ``start_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to RUNNING.
Mark this Signalrun as started on DB.
def start(self): """Mark this Signalrun as started on DB. The ``start_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to RUNNING. """ self.start_time = datetime.utcnow() self.status = self.STATUS_RUNNING self.save()
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[ 223, 4 ]
[ 231, 19 ]
python
en
['en', 'en', 'en']
True
Signalrun.end
(self, status, events)
Mark this Signalrun as ended on DB. The ``end_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to the given value, and the given events will be inserted into the Database.
Mark this Signalrun as ended on DB.
def end(self, status, events): """Mark this Signalrun as ended on DB. The ``end_time`` will be set to ``datetime.utcnow()``, the ``status`` will be set to the given value, and the given events will be inserted into the Database. """ try: if events is None: events = [] for start_time, stop_time, severity in events: Event.insert( signalrun=self, signal=self.signal, start_time=start_time, stop_time=stop_time, severity=severity, source=Event.SOURCE_ORION, ) except Exception: LOGGER.exception('Error storing signalrun %s events', self.id) status = self.STATUS_ERRORED self.end_time = datetime.utcnow() self.status = status self.num_events = len(events) self.save()
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[ 233, 4 ]
[ 259, 19 ]
python
en
['en', 'en', 'en']
True
docs
(ctx)
Data Docs operations
Data Docs operations
def docs(ctx): """Data Docs operations""" directory: str = toolkit.parse_cli_config_file_location( config_file_location=ctx.obj.config_file_location ).get("directory") context: DataContext = toolkit.load_data_context_with_error_handling( directory=directory, from_cli_upgrade_command=False, ) # TODO consider moving this all the way up in to the CLIState constructor ctx.obj.data_context = context usage_stats_prefix = f"cli.docs.{ctx.invoked_subcommand}" toolkit.send_usage_message( data_context=context, event=f"{usage_stats_prefix}.begin", success=True, ) ctx.obj.usage_event_end = f"{usage_stats_prefix}.end"
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[ 11, 0 ]
[ 29, 57 ]
python
en
['en', 'bg', 'en']
True
docs_build
(ctx, site_name=None, no_view=False)
Build Data Docs for a project.
Build Data Docs for a project.
def docs_build(ctx, site_name=None, no_view=False): """Build Data Docs for a project.""" context: DataContext = ctx.obj.data_context usage_event_end: str = ctx.obj.usage_event_end if site_name is not None and site_name not in context.get_site_names(): toolkit.exit_with_failure_message_and_stats( data_context=context, usage_event=usage_event_end, message=f"<red>The specified site name `{site_name}` does not exist in this project.</red>", ) if site_name is None: sites_to_build = context.get_site_names() else: sites_to_build = [site_name] build_docs( context, usage_stats_event=usage_event_end, site_names=sites_to_build, view=not no_view, assume_yes=ctx.obj.assume_yes, ) toolkit.send_usage_message( data_context=context, event=usage_event_end, success=True )
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[ 47, 0 ]
[ 72, 5 ]
python
en
['en', 'en', 'en']
True
docs_list
(ctx)
List known Data Docs sites.
List known Data Docs sites.
def docs_list(ctx): """List known Data Docs sites.""" context = ctx.obj.data_context usage_event_end: str = ctx.obj.usage_event_end docs_sites_url_dicts = context.get_docs_sites_urls() try: if len(docs_sites_url_dicts) == 0: cli_message("No Data Docs sites found") else: docs_sites_strings = [ " - <cyan>{}</cyan>: {}".format( docs_site_dict["site_name"], docs_site_dict.get("site_url") or f"site configured but does not exist. Run the following command to build site: great_expectations " f'docs build --site-name {docs_site_dict["site_name"]}', ) for docs_site_dict in docs_sites_url_dicts ] list_intro_string = _build_intro_string(docs_sites_strings) cli_message_list(docs_sites_strings, list_intro_string) toolkit.send_usage_message( data_context=context, event=usage_event_end, success=True ) except Exception as e: toolkit.exit_with_failure_message_and_stats( context=context, usage_event=usage_event_end, message=f"<red>{e}</red>", ) return
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[ 77, 0 ]
[ 109, 14 ]
python
en
['en', 'fr', 'en']
True
docs_clean
(ctx, site_name=None, all_sites=False)
Remove all files from a Data Docs site. This is a useful first step if you wish to completely re-build a site from scratch.
Remove all files from a Data Docs site.
def docs_clean(ctx, site_name=None, all_sites=False): """ Remove all files from a Data Docs site. This is a useful first step if you wish to completely re-build a site from scratch. """ context = ctx.obj.data_context usage_event_end: str = ctx.obj.usage_event_end if (site_name is None and all_sites is False) or (site_name and all_sites): toolkit.exit_with_failure_message_and_stats( data_context=context, usage_event=usage_event_end, message="<red>Please specify either --all to clean all sites or a specific site using --site-name</red>", ) try: # if site_name is None, context.clean_data_docs(site_name=site_name) # will clean all sites. context.clean_data_docs(site_name=site_name) toolkit.send_usage_message( data_context=context, event=usage_event_end, success=True ) cli_message("<green>{}</green>".format("Cleaned data docs")) except DataContextError as de: toolkit.exit_with_failure_message_and_stats( data_context=context, usage_event=usage_event_end, message=f"<red>{de}</red>", )
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[ 126, 0 ]
[ 154, 9 ]
python
en
['en', 'error', 'th']
False
visual_baseline_folder_setup
()
Handle Logging
Handle Logging
def visual_baseline_folder_setup(): """ Handle Logging """ if not os.path.exists(visual_baseline_path): try: os.makedirs(visual_baseline_path) except Exception: pass
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[ 12, 0 ]
[ 18, 16 ]
python
en
['it', 'ja', 'en']
False
get_filetype_from_buffer
(buf, max_lines=5)
Scan the buffer for modelines and return filetype if one is found.
Scan the buffer for modelines and return filetype if one is found.
def get_filetype_from_buffer(buf, max_lines=5): """ Scan the buffer for modelines and return filetype if one is found. """ lines = buf.splitlines() for l in lines[-1:-max_lines-1:-1]: ret = get_filetype_from_line(l) if ret: return ret for l in lines[max_lines:-1:-1]: ret = get_filetype_from_line(l) if ret: return ret return None
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[ 28, 0 ]
[ 42, 15 ]
python
en
['en', 'error', 'th']
False
cli
(verbose)
Welcome to the great_expectations CLI! Most commands follow this format: great_expectations <NOUN> <VERB> The nouns are: datasource, docs, project, suite, validation-operator Most nouns accept the following verbs: new, list, edit In particular, the CLI supports the following special commands: - great_expectations init : create a new great_expectations project - great_expectations datasource profile : profile a datasource - great_expectations docs build : compile documentation from expectations
Welcome to the great_expectations CLI!
def cli(verbose): """ Welcome to the great_expectations CLI! Most commands follow this format: great_expectations <NOUN> <VERB> The nouns are: datasource, docs, project, suite, validation-operator Most nouns accept the following verbs: new, list, edit In particular, the CLI supports the following special commands: - great_expectations init : create a new great_expectations project - great_expectations datasource profile : profile a datasource - great_expectations docs build : compile documentation from expectations""" logger = _set_up_logger() if verbose: # Note we are explicitly not using a logger in all CLI output to have # more control over console UI. logger.setLevel(logging.DEBUG)
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[ 35, 0 ]
[ 56, 38 ]
python
en
['en', 'error', 'th']
False
Skeleton.remove_joints
(self, joints_to_remove)
Remove the joints specified in 'joints_to_remove'.
Remove the joints specified in 'joints_to_remove'.
def remove_joints(self, joints_to_remove): """ Remove the joints specified in 'joints_to_remove'. """ valid_joints = [] for joint in range(len(self._parents)): if joint not in joints_to_remove: valid_joints.append(joint) for i in range(len(self._parents)): while self._parents[i] in joints_to_remove: self._parents[i] = self._parents[self._parents[i]] index_offsets = np.zeros(len(self._parents), dtype=int) new_parents = [] for i, parent in enumerate(self._parents): if i not in joints_to_remove: new_parents.append(parent - index_offsets[parent]) else: index_offsets[i:] += 1 self._parents = np.array(new_parents) if self._joints_left is not None: new_joints_left = [] for joint in self._joints_left: if joint in valid_joints: new_joints_left.append(joint - index_offsets[joint]) self._joints_left = new_joints_left if self._joints_right is not None: new_joints_right = [] for joint in self._joints_right: if joint in valid_joints: new_joints_right.append(joint - index_offsets[joint]) self._joints_right = new_joints_right self._compute_metadata() return valid_joints
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[ 29, 4 ]
[ 66, 27 ]
python
en
['en', 'error', 'th']
False
test_password_masker_mask_db_url
(monkeypatch, tmp_path)
What does this test and why? The PasswordMasker.mask_db_url() should mask passwords consistently in database urls. The output of mask_db_url should be the same whether user_urlparse is set to True or False. This test uses database url examples from https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls
What does this test and why? The PasswordMasker.mask_db_url() should mask passwords consistently in database urls. The output of mask_db_url should be the same whether user_urlparse is set to True or False. This test uses database url examples from https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls
def test_password_masker_mask_db_url(monkeypatch, tmp_path): """ What does this test and why? The PasswordMasker.mask_db_url() should mask passwords consistently in database urls. The output of mask_db_url should be the same whether user_urlparse is set to True or False. This test uses database url examples from https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls """ # PostgreSQL # default db_hostname = os.getenv("GE_TEST_LOCAL_DB_HOSTNAME", "localhost") assert ( PasswordMasker.mask_db_url( f"postgresql://scott:tiger@{db_hostname}:65432/mydatabase" ) == f"postgresql://scott:***@{db_hostname}:65432/mydatabase" ) assert ( PasswordMasker.mask_db_url( f"postgresql://scott:tiger@{db_hostname}:65432/mydatabase", use_urlparse=True, ) == f"postgresql://scott:***@{db_hostname}:65432/mydatabase" ) # missing port number, using urlparse assert ( PasswordMasker.mask_db_url( f"postgresql://scott:tiger@{db_hostname}/mydatabase", use_urlparse=True ) == f"postgresql://scott:***@{db_hostname}/mydatabase" ) # psycopg2 assert ( PasswordMasker.mask_db_url( f"postgresql+psycopg2://scott:tiger@{db_hostname}:65432/mydatabase" ) == f"postgresql+psycopg2://scott:***@{db_hostname}:65432/mydatabase" ) assert ( PasswordMasker.mask_db_url( f"postgresql+psycopg2://scott:tiger@{db_hostname}:65432/mydatabase", use_urlparse=True, ) == f"postgresql+psycopg2://scott:***@{db_hostname}:65432/mydatabase" ) # pg8000 (if installed in test environment) try: assert ( PasswordMasker.mask_db_url( f"postgresql+pg8000://scott:tiger@{db_hostname}:65432/mydatabase" ) == f"postgresql+pg8000://scott:***@{db_hostname}:65432/mydatabase" ) except ModuleNotFoundError: pass assert ( PasswordMasker.mask_db_url( f"postgresql+pg8000://scott:tiger@{db_hostname}:65432/mydatabase", use_urlparse=True, ) == f"postgresql+pg8000://scott:***@{db_hostname}:65432/mydatabase" ) # MySQL # default (if installed in test environment) try: assert ( PasswordMasker.mask_db_url(f"mysql://scott:tiger@{db_hostname}:65432/foo") == f"mysql://scott:***@{db_hostname}:65432/foo" ) except ModuleNotFoundError: pass assert ( PasswordMasker.mask_db_url( f"mysql://scott:tiger@{db_hostname}:65432/foo", use_urlparse=True ) == f"mysql://scott:***@{db_hostname}:65432/foo" ) # mysqlclient (a maintained fork of MySQL-Python) (if installed in test environment) try: assert ( PasswordMasker.mask_db_url( f"mysql+mysqldb://scott:tiger@{db_hostname}:65432/foo" ) == f"mysql+mysqldb://scott:***@{db_hostname}:65432/foo" ) except ModuleNotFoundError: pass assert ( PasswordMasker.mask_db_url( f"mysql+mysqldb://scott:tiger@{db_hostname}:65432/foo", use_urlparse=True ) == f"mysql+mysqldb://scott:***@{db_hostname}:65432/foo" ) # PyMySQL assert ( PasswordMasker.mask_db_url( f"mysql+pymysql://scott:tiger@{db_hostname}:65432/foo" ) == f"mysql+pymysql://scott:***@{db_hostname}:65432/foo" ) assert ( PasswordMasker.mask_db_url( f"mysql+pymysql://scott:tiger@{db_hostname}:65432/foo", use_urlparse=True ) == f"mysql+pymysql://scott:***@{db_hostname}:65432/foo" ) # Oracle (if installed in test environment) url_host = os.getenv("GE_TEST_LOCALHOST_URL", "127.0.0.1") try: assert ( PasswordMasker.mask_db_url(f"oracle://scott:tiger@{url_host}:1521/sidname") == f"oracle://scott:***@{url_host}:1521/sidname" ) except ModuleNotFoundError: pass assert ( PasswordMasker.mask_db_url( f"oracle://scott:tiger@{url_host}:1521/sidname", use_urlparse=True ) == f"oracle://scott:***@{url_host}:1521/sidname" ) try: assert ( PasswordMasker.mask_db_url("oracle+cx_oracle://scott:tiger@tnsname") == "oracle+cx_oracle://scott:***@tnsname" ) except ModuleNotFoundError: pass assert ( PasswordMasker.mask_db_url( "oracle+cx_oracle://scott:tiger@tnsname", use_urlparse=True ) == "oracle+cx_oracle://scott:***@tnsname" ) # Microsoft SQL Server # pyodbc assert ( PasswordMasker.mask_db_url("mssql+pyodbc://scott:tiger@mydsn") == "mssql+pyodbc://scott:***@mydsn" ) assert ( PasswordMasker.mask_db_url( "mssql+pyodbc://scott:tiger@mydsn", use_urlparse=True ) == "mssql+pyodbc://scott:***@mydsn" ) # pymssql (if installed in test environment) try: assert ( PasswordMasker.mask_db_url( f"mssql+pymssql://scott:tiger@{db_hostname}:12345/dbname" ) == f"mssql+pymssql://scott:***@{db_hostname}:12345/dbname" ) except ModuleNotFoundError: pass assert ( PasswordMasker.mask_db_url( f"mssql+pymssql://scott:tiger@{db_hostname}:12345/dbname", use_urlparse=True ) == f"mssql+pymssql://scott:***@{db_hostname}:12345/dbname" ) # SQLite # relative path temp_dir = tmp_path / "sqllite_tests" temp_dir.mkdir() monkeypatch.chdir(temp_dir) assert ( PasswordMasker.mask_db_url(f"sqlite:///something/foo.db") == f"sqlite:///something/foo.db" ) assert ( PasswordMasker.mask_db_url(f"sqlite:///something/foo.db", use_urlparse=True) == f"sqlite:///something/foo.db" ) # absolute path # Unix/Mac - 4 initial slashes in total assert ( PasswordMasker.mask_db_url("sqlite:////absolute/path/to/foo.db") == "sqlite:////absolute/path/to/foo.db" ) assert ( PasswordMasker.mask_db_url( "sqlite:////absolute/path/to/foo.db", use_urlparse=True ) == "sqlite:////absolute/path/to/foo.db" ) # Windows assert ( PasswordMasker.mask_db_url("sqlite:///C:\\path\\to\\foo.db") == "sqlite:///C:\\path\\to\\foo.db" ) assert ( PasswordMasker.mask_db_url("sqlite:///C:\\path\\to\\foo.db", use_urlparse=True) == "sqlite:///C:\\path\\to\\foo.db" ) # Windows alternative using raw string assert ( PasswordMasker.mask_db_url(r"sqlite:///C:\path\to\foo.db") == r"sqlite:///C:\path\to\foo.db" ) assert ( PasswordMasker.mask_db_url(r"sqlite:///C:\path\to\foo.db", use_urlparse=True) == r"sqlite:///C:\path\to\foo.db" ) # in-memory assert PasswordMasker.mask_db_url("sqlite://") == "sqlite://" assert PasswordMasker.mask_db_url("sqlite://", use_urlparse=True) == "sqlite://"
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"f\"postgresql+psycopg2://scott:tiger@{db_hostname}:65432/mydatabase\"", ")", "==", "f\"postgresql+psycopg2://scott:***@{db_hostname}:65432/mydatabase\"", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"postgresql+psycopg2://scott:tiger@{db_hostname}:65432/mydatabase\"", ",", "use_urlparse", "=", "True", ",", ")", "==", "f\"postgresql+psycopg2://scott:***@{db_hostname}:65432/mydatabase\"", ")", "# pg8000 (if installed in test environment)", "try", ":", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"postgresql+pg8000://scott:tiger@{db_hostname}:65432/mydatabase\"", ")", "==", "f\"postgresql+pg8000://scott:***@{db_hostname}:65432/mydatabase\"", ")", "except", "ModuleNotFoundError", ":", "pass", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"postgresql+pg8000://scott:tiger@{db_hostname}:65432/mydatabase\"", ",", "use_urlparse", "=", "True", ",", ")", "==", "f\"postgresql+pg8000://scott:***@{db_hostname}:65432/mydatabase\"", ")", "# MySQL", 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"f\"mysql+mysqldb://scott:***@{db_hostname}:65432/foo\"", ")", "# PyMySQL", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"mysql+pymysql://scott:tiger@{db_hostname}:65432/foo\"", ")", "==", "f\"mysql+pymysql://scott:***@{db_hostname}:65432/foo\"", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"mysql+pymysql://scott:tiger@{db_hostname}:65432/foo\"", ",", "use_urlparse", "=", "True", ")", "==", "f\"mysql+pymysql://scott:***@{db_hostname}:65432/foo\"", ")", "# Oracle (if installed in test environment)", "url_host", "=", "os", ".", "getenv", "(", "\"GE_TEST_LOCALHOST_URL\"", ",", "\"127.0.0.1\"", ")", "try", ":", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"oracle://scott:tiger@{url_host}:1521/sidname\"", ")", "==", "f\"oracle://scott:***@{url_host}:1521/sidname\"", ")", "except", "ModuleNotFoundError", ":", "pass", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"oracle://scott:tiger@{url_host}:1521/sidname\"", ",", 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"mask_db_url", "(", "f\"mssql+pymssql://scott:tiger@{db_hostname}:12345/dbname\"", ")", "==", "f\"mssql+pymssql://scott:***@{db_hostname}:12345/dbname\"", ")", "except", "ModuleNotFoundError", ":", "pass", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"mssql+pymssql://scott:tiger@{db_hostname}:12345/dbname\"", ",", "use_urlparse", "=", "True", ")", "==", "f\"mssql+pymssql://scott:***@{db_hostname}:12345/dbname\"", ")", "# SQLite", "# relative path", "temp_dir", "=", "tmp_path", "/", "\"sqllite_tests\"", "temp_dir", ".", "mkdir", "(", ")", "monkeypatch", ".", "chdir", "(", "temp_dir", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"sqlite:///something/foo.db\"", ")", "==", "f\"sqlite:///something/foo.db\"", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "f\"sqlite:///something/foo.db\"", ",", "use_urlparse", "=", "True", ")", "==", "f\"sqlite:///something/foo.db\"", ")", "# absolute path", "# Unix/Mac - 4 initial slashes in total", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "\"sqlite:////absolute/path/to/foo.db\"", ")", "==", "\"sqlite:////absolute/path/to/foo.db\"", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "\"sqlite:////absolute/path/to/foo.db\"", ",", "use_urlparse", "=", "True", ")", "==", "\"sqlite:////absolute/path/to/foo.db\"", ")", "# Windows", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "\"sqlite:///C:\\\\path\\\\to\\\\foo.db\"", ")", "==", "\"sqlite:///C:\\\\path\\\\to\\\\foo.db\"", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "\"sqlite:///C:\\\\path\\\\to\\\\foo.db\"", ",", "use_urlparse", "=", "True", ")", "==", "\"sqlite:///C:\\\\path\\\\to\\\\foo.db\"", ")", "# Windows alternative using raw string", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "r\"sqlite:///C:\\path\\to\\foo.db\"", ")", "==", "r\"sqlite:///C:\\path\\to\\foo.db\"", ")", "assert", "(", "PasswordMasker", ".", "mask_db_url", "(", "r\"sqlite:///C:\\path\\to\\foo.db\"", ",", "use_urlparse", "=", "True", ")", "==", "r\"sqlite:///C:\\path\\to\\foo.db\"", ")", "# in-memory", "assert", "PasswordMasker", ".", "mask_db_url", "(", "\"sqlite://\"", ")", "==", "\"sqlite://\"", "assert", "PasswordMasker", ".", "mask_db_url", "(", "\"sqlite://\"", ",", "use_urlparse", "=", "True", ")", "==", "\"sqlite://\"" ]
[ 50, 0 ]
[ 272, 84 ]
python
en
['en', 'error', 'th']
False
test_parse_substitution_variable
()
What does this test and why? Ensure parse_substitution_variable works as expected. Returns:
What does this test and why? Ensure parse_substitution_variable works as expected. Returns:
def test_parse_substitution_variable(): """ What does this test and why? Ensure parse_substitution_variable works as expected. Returns: """ assert parse_substitution_variable("${SOME_VAR}") == "SOME_VAR" assert parse_substitution_variable("$SOME_VAR") == "SOME_VAR" assert parse_substitution_variable("SOME_STRING") is None assert parse_substitution_variable("SOME_$TRING") is None assert parse_substitution_variable("${some_var}") == "some_var" assert parse_substitution_variable("$some_var") == "some_var" assert parse_substitution_variable("some_string") is None assert parse_substitution_variable("some_$tring") is None assert parse_substitution_variable("${SOME_$TRING}") is None assert parse_substitution_variable("$SOME_$TRING") == "SOME_"
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[ 275, 0 ]
[ 291, 65 ]
python
en
['en', 'error', 'th']
False
BaseDRLearner.__init__
( self, learner=None, control_outcome_learner=None, treatment_outcome_learner=None, treatment_effect_learner=None, ate_alpha=0.05, control_name=0, )
Initialize a DR-learner. Args: learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment groups control_outcome_learner (optional): a model to estimate outcomes in the control group treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group
Initialize a DR-learner.
def __init__( self, learner=None, control_outcome_learner=None, treatment_outcome_learner=None, treatment_effect_learner=None, ate_alpha=0.05, control_name=0, ): """Initialize a DR-learner. Args: learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment groups control_outcome_learner (optional): a model to estimate outcomes in the control group treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group """ assert (learner is not None) or ( (control_outcome_learner is not None) and (treatment_outcome_learner is not None) and (treatment_effect_learner is not None) ) if control_outcome_learner is None: self.model_mu_c = deepcopy(learner) else: self.model_mu_c = control_outcome_learner if treatment_outcome_learner is None: self.model_mu_t = deepcopy(learner) else: self.model_mu_t = treatment_outcome_learner if treatment_effect_learner is None: self.model_tau = deepcopy(learner) else: self.model_tau = treatment_effect_learner self.ate_alpha = ate_alpha self.control_name = control_name self.propensity = None
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[ 30, 4 ]
[ 74, 30 ]
python
en
['en', 'en', 'it']
True
BaseDRLearner.fit
(self, X, treatment, y, p=None, seed=None)
Fit the inference model. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. seed (int): random seed for cross-fitting
Fit the inference model.
def fit(self, X, treatment, y, p=None, seed=None): """Fit the inference model. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. seed (int): random seed for cross-fitting """ X, treatment, y = convert_pd_to_np(X, treatment, y) check_treatment_vector(treatment, self.control_name) self.t_groups = np.unique(treatment[treatment != self.control_name]) self.t_groups.sort() self._classes = {group: i for i, group in enumerate(self.t_groups)} # The estimator splits the data into 3 partitions for cross-fit on the propensity score estimation, # the outcome regression, and the treatment regression on the doubly robust estimates. The use of # the partitions is rotated so we do not lose on the sample size. cv = KFold(n_splits=3, shuffle=True, random_state=seed) split_indices = [index for _, index in cv.split(y)] self.models_mu_c = [ deepcopy(self.model_mu_c), deepcopy(self.model_mu_c), deepcopy(self.model_mu_c), ] self.models_mu_t = { group: [ deepcopy(self.model_mu_t), deepcopy(self.model_mu_t), deepcopy(self.model_mu_t), ] for group in self.t_groups } self.models_tau = { group: [ deepcopy(self.model_tau), deepcopy(self.model_tau), deepcopy(self.model_tau), ] for group in self.t_groups } if p is None: self.propensity = {group: np.zeros(y.shape[0]) for group in self.t_groups} for ifold in range(3): treatment_idx = split_indices[ifold] outcome_idx = split_indices[(ifold + 1) % 3] tau_idx = split_indices[(ifold + 2) % 3] treatment_treat, treatment_out, treatment_tau = ( treatment[treatment_idx], treatment[outcome_idx], treatment[tau_idx], ) y_out, y_tau = y[outcome_idx], y[tau_idx] X_treat, X_out, X_tau = X[treatment_idx], X[outcome_idx], X[tau_idx] if p is None: logger.info("Generating propensity score") cur_p = dict() for group in self.t_groups: mask = (treatment_treat == group) | ( treatment_treat == self.control_name ) treatment_filt = treatment_treat[mask] X_filt = X_treat[mask] w_filt = (treatment_filt == group).astype(int) w = (treatment_tau == group).astype(int) cur_p[group], _ = compute_propensity_score( X=X_filt, treatment=w_filt, X_pred=X_tau, treatment_pred=w ) self.propensity[group][tau_idx] = cur_p[group] else: cur_p = dict() if isinstance(p, (np.ndarray, pd.Series)): cur_p = {self.t_groups[0]: convert_pd_to_np(p[tau_idx])} else: cur_p = {g: prop[tau_idx] for g, prop in p.items()} check_p_conditions(cur_p, self.t_groups) logger.info("Generate outcome regressions") self.models_mu_c[ifold].fit( X_out[treatment_out == self.control_name], y_out[treatment_out == self.control_name], ) for group in self.t_groups: self.models_mu_t[group][ifold].fit( X_out[treatment_out == group], y_out[treatment_out == group] ) logger.info("Fit pseudo outcomes from the DR formula") for group in self.t_groups: mask = (treatment_tau == group) | (treatment_tau == self.control_name) treatment_filt = treatment_tau[mask] X_filt = X_tau[mask] y_filt = y_tau[mask] w_filt = (treatment_filt == group).astype(int) p_filt = cur_p[group][mask] mu_t = self.models_mu_t[group][ifold].predict(X_filt) mu_c = self.models_mu_c[ifold].predict(X_filt) dr = ( (w_filt - p_filt) / p_filt / (1 - p_filt) * (y_filt - mu_t * w_filt - mu_c * (1 - w_filt)) + mu_t - mu_c ) self.models_tau[group][ifold].fit(X_filt, dr)
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[ 88, 4 ]
[ 202, 61 ]
python
en
['en', 'en', 'en']
True
BaseDRLearner.predict
(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True)
Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects.
Predict treatment effects.
def predict(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True): """Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. """ X, treatment, y = convert_pd_to_np(X, treatment, y) te = np.zeros((X.shape[0], self.t_groups.shape[0])) yhat_cs = {} yhat_ts = {} for i, group in enumerate(self.t_groups): models_tau = self.models_tau[group] _te = np.r_[[model.predict(X) for model in models_tau]].mean(axis=0) te[:, i] = np.ravel(_te) yhat_cs[group] = np.r_[[model.predict(X) for model in self.models_mu_c]].mean(axis=0) yhat_ts[group] = np.r_[[model.predict(X) for model in self.models_mu_t[group]]].mean(axis=0) if (y is not None) and (treatment is not None) and verbose: mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] X_filt = X[mask] y_filt = y[mask] w = (treatment_filt == group).astype(int) yhat = np.zeros_like(y_filt, dtype=float) yhat[w == 0] = yhat_cs[group][mask][w == 0] yhat[w == 1] = yhat_ts[group][mask][w == 1] logger.info("Error metrics for group {}".format(group)) regression_metrics(y_filt, yhat, w) if not return_components: return te else: return te, yhat_cs, yhat_ts
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[ 204, 4 ]
[ 245, 39 ]
python
en
['fr', 'en', 'en']
True
BaseDRLearner.fit_predict
( self, X, treatment, y, p=None, return_ci=False, n_bootstraps=1000, bootstrap_size=10000, return_components=False, verbose=True, seed=None, )
Fit the treatment effect and outcome models of the R learner and predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. return_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (str): whether to output progress logs seed (int): random seed for cross-fitting Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment] If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment], UB [n_samples, n_treatment]
Fit the treatment effect and outcome models of the R learner and predict treatment effects.
def fit_predict( self, X, treatment, y, p=None, return_ci=False, n_bootstraps=1000, bootstrap_size=10000, return_components=False, verbose=True, seed=None, ): """Fit the treatment effect and outcome models of the R learner and predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. return_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (str): whether to output progress logs seed (int): random seed for cross-fitting Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment] If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment], UB [n_samples, n_treatment] """ X, treatment, y = convert_pd_to_np(X, treatment, y) self.fit(X, treatment, y, p, seed) if p is None: p = self.propensity check_p_conditions(p, self.t_groups) if isinstance(p, (np.ndarray, pd.Series)): treatment_name = self.t_groups[0] p = {treatment_name: convert_pd_to_np(p)} elif isinstance(p, dict): p = { treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items() } te = self.predict( X, treatment=treatment, y=y, return_components=return_components ) if not return_ci: return te else: t_groups_global = self.t_groups _classes_global = self._classes models_mu_c_global = deepcopy(self.models_mu_c) models_mu_t_global = deepcopy(self.models_mu_t) models_tau_global = deepcopy(self.models_tau) te_bootstraps = np.zeros( shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps) ) logger.info("Bootstrap Confidence Intervals") for i in tqdm(range(n_bootstraps)): te_b = self.bootstrap(X, treatment, y, p, size=bootstrap_size) te_bootstraps[:, :, i] = te_b te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2) te_upper = np.percentile( te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2 ) # set member variables back to global (currently last bootstrapped outcome) self.t_groups = t_groups_global self._classes = _classes_global self.models_mu_c = deepcopy(models_mu_c_global) self.models_mu_t = deepcopy(models_mu_t_global) self.models_tau = deepcopy(models_tau_global) return (te, te_lower, te_upper)
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[ 247, 4 ]
[ 328, 43 ]
python
en
['en', 'en', 'en']
True
BaseDRLearner.estimate_ate
( self, X, treatment, y, p=None, bootstrap_ci=False, n_bootstraps=1000, bootstrap_size=10000, seed=None, )
Estimate the Average Treatment Effect (ATE). Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. bootstrap_ci (bool): whether run bootstrap for confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap seed (int): random seed for cross-fitting Returns: The mean and confidence interval (LB, UB) of the ATE estimate.
Estimate the Average Treatment Effect (ATE).
def estimate_ate( self, X, treatment, y, p=None, bootstrap_ci=False, n_bootstraps=1000, bootstrap_size=10000, seed=None, ): """Estimate the Average Treatment Effect (ATE). Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector p (np.ndarray or pd.Series or dict, optional): an array of propensity scores of float (0,1) in the single-treatment case; or, a dictionary of treatment groups that map to propensity vectors of float (0,1); if None will run ElasticNetPropensityModel() to generate the propensity scores. bootstrap_ci (bool): whether run bootstrap for confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap seed (int): random seed for cross-fitting Returns: The mean and confidence interval (LB, UB) of the ATE estimate. """ te, yhat_cs, yhat_ts = self.fit_predict(X, treatment, y, p, return_components=True, seed=seed) X, treatment, y = convert_pd_to_np(X, treatment, y) if p is None: p = self.propensity else: check_p_conditions(p, self.t_groups) if isinstance(p, (np.ndarray, pd.Series)): treatment_name = self.t_groups[0] p = {treatment_name: convert_pd_to_np(p)} elif isinstance(p, dict): p = { treatment_name: convert_pd_to_np(_p) for treatment_name, _p in p.items() } ate = np.zeros(self.t_groups.shape[0]) ate_lb = np.zeros(self.t_groups.shape[0]) ate_ub = np.zeros(self.t_groups.shape[0]) for i, group in enumerate(self.t_groups): _ate = te[:, i].mean() mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] w = (treatment_filt == group).astype(int) prob_treatment = float(sum(w)) / w.shape[0] yhat_c = yhat_cs[group][mask] yhat_t = yhat_ts[group][mask] y_filt = y[mask] # SE formula is based on the lower bound formula (7) from Imbens, Guido W., and Jeffrey M. Wooldridge. 2009. # "Recent Developments in the Econometrics of Program Evaluation." Journal of Economic Literature se = np.sqrt(( (y_filt[w == 0] - yhat_c[w == 0]).var() / (1 - prob_treatment) + (y_filt[w == 1] - yhat_t[w == 1]).var() / prob_treatment + (yhat_t - yhat_c).var() ) / y_filt.shape[0]) _ate_lb = _ate - se * norm.ppf(1 - self.ate_alpha / 2) _ate_ub = _ate + se * norm.ppf(1 - self.ate_alpha / 2) ate[i] = _ate ate_lb[i] = _ate_lb ate_ub[i] = _ate_ub if not bootstrap_ci: return ate, ate_lb, ate_ub else: t_groups_global = self.t_groups _classes_global = self._classes models_mu_c_global = deepcopy(self.models_mu_c) models_mu_t_global = deepcopy(self.models_mu_t) models_tau_global = deepcopy(self.models_tau) logger.info("Bootstrap Confidence Intervals for ATE") ate_bootstraps = np.zeros(shape=(self.t_groups.shape[0], n_bootstraps)) for n in tqdm(range(n_bootstraps)): cate_b = self.bootstrap(X, treatment, y, p, size=bootstrap_size, seed=seed) ate_bootstraps[:, n] = cate_b.mean() ate_lower = np.percentile( ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1 ) ate_upper = np.percentile( ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1 ) # set member variables back to global (currently last bootstrapped outcome) self.t_groups = t_groups_global self._classes = _classes_global self.models_mu_c = deepcopy(models_mu_c_global) self.models_mu_t = deepcopy(models_mu_t_global) self.models_tau = deepcopy(models_tau_global) return ate, ate_lower, ate_upper
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[ 330, 4 ]
[ 434, 44 ]
python
en
['en', 'it', 'en']
True
BaseDRRegressor.__init__
(self, learner=None, control_outcome_learner=None, treatment_outcome_learner=None, treatment_effect_learner=None, ate_alpha=.05, control_name=0)
Initialize an DR-learner regressor. Args: learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment groups control_outcome_learner (optional): a model to estimate outcomes in the control group treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group control_effect_learner (optional): a model to estimate treatment effects in the control group treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group
Initialize an DR-learner regressor.
def __init__(self, learner=None, control_outcome_learner=None, treatment_outcome_learner=None, treatment_effect_learner=None, ate_alpha=.05, control_name=0): """Initialize an DR-learner regressor. Args: learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment groups control_outcome_learner (optional): a model to estimate outcomes in the control group treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group control_effect_learner (optional): a model to estimate treatment effects in the control group treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group """ super().__init__( learner=learner, control_outcome_learner=control_outcome_learner, treatment_outcome_learner=treatment_outcome_learner, treatment_effect_learner=treatment_effect_learner, ate_alpha=ate_alpha, control_name=control_name)
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[ 442, 4 ]
[ 467, 38 ]
python
en
['en', 'en', 'nl']
True
XGBDRRegressor.__init__
(self, ate_alpha=.05, control_name=0, *args, **kwargs)
Initialize a DR-learner with two XGBoost models.
Initialize a DR-learner with two XGBoost models.
def __init__(self, ate_alpha=.05, control_name=0, *args, **kwargs): """Initialize a DR-learner with two XGBoost models.""" super().__init__(learner=XGBRegressor(*args, **kwargs), ate_alpha=ate_alpha, control_name=control_name)
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[ 470, 4 ]
[ 474, 51 ]
python
en
['en', 'en', 'en']
True
gaussian_fit
(x, y, x_smooth=None, n_pts=n_pts_smooth)
Fits a Gaussian to some data - x and y. Returns predicted interpolation values. Parameters ---------- x: list-like The x values of the data to fit to. Must have range [0,1]. y: list-like The y values of the data to fit to. x_smooth: list-like The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int The number of evenly spaced points spanning the range of `x` to interpolate for. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit.
Fits a Gaussian to some data - x and y. Returns predicted interpolation values.
def gaussian_fit(x, y, x_smooth=None, n_pts=n_pts_smooth): """ Fits a Gaussian to some data - x and y. Returns predicted interpolation values. Parameters ---------- x: list-like The x values of the data to fit to. Must have range [0,1]. y: list-like The y values of the data to fit to. x_smooth: list-like The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int The number of evenly spaced points spanning the range of `x` to interpolate for. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit. """ if x_smooth is None: x_smooth_inds = np.linspace(0, len(x), n_pts) x_smooth = np.interp(x_smooth_inds, np.arange(len(x)), x) mean, sigma = np.nanmean(y), np.nanstd(y) popt, pcov = curve_fit(gauss, np_scale(x), y, p0=[1, mean, sigma], maxfev=np.iinfo(np.int32).max) y_smooth = gauss(np_scale(x_smooth), *popt) return x_smooth, y_smooth
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[ 13, 0 ]
[ 40, 29 ]
python
en
['en', 'error', 'th']
False
gaussian_filter_fit
(x, y, x_smooth=None, n_pts=n_pts_smooth, sigma=None)
Fits a Gaussian filter to some data - x and y. Returns predicted interpolation values. Currently, smoothing is achieved by fitting a cubic spline to the gaussian filter fit of `x` and `y`. Parameters ---------- x: list-like The x values of the data to fit to. y: list-like The y values of the data to fit to. x_smooth: list-like, optional The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int, optional The number of evenly spaced points spanning the range of `x` to interpolate for. sigma: numeric, optional The standard deviation of the Gaussian kernel. A larger value yields a smoother curve, but also reduced the closeness of the fit. By default, it is `4 * np.std(y)`. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit.
Fits a Gaussian filter to some data - x and y. Returns predicted interpolation values. Currently, smoothing is achieved by fitting a cubic spline to the gaussian filter fit of `x` and `y`.
def gaussian_filter_fit(x, y, x_smooth=None, n_pts=n_pts_smooth, sigma=None): """ Fits a Gaussian filter to some data - x and y. Returns predicted interpolation values. Currently, smoothing is achieved by fitting a cubic spline to the gaussian filter fit of `x` and `y`. Parameters ---------- x: list-like The x values of the data to fit to. y: list-like The y values of the data to fit to. x_smooth: list-like, optional The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int, optional The number of evenly spaced points spanning the range of `x` to interpolate for. sigma: numeric, optional The standard deviation of the Gaussian kernel. A larger value yields a smoother curve, but also reduced the closeness of the fit. By default, it is `4 * np.std(y)`. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit. """ if x_smooth is None: x_smooth_inds = np.linspace(0, len(x)-1, n_pts) x_smooth = np.interp(x_smooth_inds, np.arange(len(x)), x) sigma = sigma if sigma is not None else 4 * np.std(y) gauss_filter_y = gaussian_filter1d(y, sigma) cs = CubicSpline(x, gauss_filter_y) y_smooth = cs(x_smooth) return x_smooth, y_smooth
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[ 43, 0 ]
[ 75, 29 ]
python
en
['en', 'error', 'th']
False
poly_fit
(x, y, degree, x_smooth=None, n_pts=n_pts_smooth)
Fits a polynomial of any positive integer degree to some data - x and y. Returns predicted interpolation values. Parameters ---------- x: list-like The x values of the data to fit to. y: list-like The y values of the data to fit to. x_smooth: list-like The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int The number of evenly spaced points spanning the range of `x` to interpolate for. degree: int The degree of the polynomial to fit. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit.
Fits a polynomial of any positive integer degree to some data - x and y. Returns predicted interpolation values.
def poly_fit(x, y, degree, x_smooth=None, n_pts=n_pts_smooth): """ Fits a polynomial of any positive integer degree to some data - x and y. Returns predicted interpolation values. Parameters ---------- x: list-like The x values of the data to fit to. y: list-like The y values of the data to fit to. x_smooth: list-like The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int The number of evenly spaced points spanning the range of `x` to interpolate for. degree: int The degree of the polynomial to fit. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit. """ if x_smooth is None: x_smooth_inds = np.linspace(0, len(x), n_pts) x_smooth = np.interp(x_smooth_inds, np.arange(len(x)), x) y_smooth = np.array([np.array([coef * (x_val ** current_degree) for coef, current_degree in zip(np.polyfit(x, y, degree), range(degree, -1, -1))]).sum() for x_val in x_smooth]) return x_smooth, y_smooth
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[ 78, 0 ]
[ 106, 29 ]
python
en
['en', 'error', 'th']
False
fourier_fit
(x, y, n_predict=0, x_smooth=None, n_pts=n_pts_smooth, n_harm=default_fourier_n_harm)
Creates a Fourier fit of a NumPy array. Also supports extrapolation. Credit goes to https://gist.github.com/tartakynov/83f3cd8f44208a1856ce. Parameters ---------- x, y: numpy.ndarray 1D NumPy arrays of the x and y values to fit to. Must not contain NaNs. n_predict: int The number of points to extrapolate. The points will be spaced evenly by the mean spacing of values in `x`. x_smooth: list-like, optional The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int, optional The number of evenly spaced points spanning the range of `x` to interpolate for. n_harm: int The number of harmonics to use. A higher value yields a closer fit. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit.
Creates a Fourier fit of a NumPy array. Also supports extrapolation. Credit goes to https://gist.github.com/tartakynov/83f3cd8f44208a1856ce.
def fourier_fit(x, y, n_predict=0, x_smooth=None, n_pts=n_pts_smooth, n_harm=default_fourier_n_harm): """ Creates a Fourier fit of a NumPy array. Also supports extrapolation. Credit goes to https://gist.github.com/tartakynov/83f3cd8f44208a1856ce. Parameters ---------- x, y: numpy.ndarray 1D NumPy arrays of the x and y values to fit to. Must not contain NaNs. n_predict: int The number of points to extrapolate. The points will be spaced evenly by the mean spacing of values in `x`. x_smooth: list-like, optional The exact x values to interpolate for. Supercedes `n_pts`. n_pts: int, optional The number of evenly spaced points spanning the range of `x` to interpolate for. n_harm: int The number of harmonics to use. A higher value yields a closer fit. Returns ------- x_smooth, y_smooth: numpy.ndarray The smoothed x and y values of the curve fit. """ if x_smooth is None: x_smooth_inds = np.linspace(0, len(x), n_pts) x_smooth = np.interp(x_smooth_inds, np.arange(len(x)), x) n_predict_smooth = int((len(x_smooth) / len(x)) * n_predict) # These points are evenly spaced for the fourier fit implementation we use. # More points are selected than are in `x_smooth` so we can interpolate accurately. fourier_mult_pts = 2 x_smooth_fourier = np.linspace(x_smooth.min(), x_smooth.max(), fourier_mult_pts * len(x_smooth)) y_smooth_fourier = np.interp(x_smooth_fourier, x, y) n_predict_smooth_fourier = int((len(x_smooth_fourier) / len(x)) * n_predict) # Perform the Fourier fit and extrapolation. n = y_smooth_fourier.size t = np.arange(0, n) p = np.polyfit(t, y_smooth_fourier, 1) # find linear trend in arr x_notrend = y_smooth_fourier - p[0] * t # detrended arr x_freqdom = fft.fft(x_notrend) # detrended arr in frequency domain f = fft.fftfreq(n) # frequencies # sort indexes by frequency, lower -> higher indexes = list(range(n)) indexes.sort(key=lambda i: np.absolute(x_freqdom[i])) indexes.reverse() t = np.arange(0, n + n_predict_smooth_fourier) restored_sig = np.zeros(t.size) for i in indexes[:1 + n_harm * 2]: ampli = np.absolute(x_freqdom[i]) / n # amplitude phase = np.angle(x_freqdom[i]) # phase restored_sig += ampli * np.cos(2 * np.pi * f[i] * t + phase) y_smooth_fourier = restored_sig + p[0] * t # Find the points in `x_smooth_fourier` that are near to points in `x_smooth` # and then interpolate the y values to match the new x values. x_smooth = x_smooth_fourier[np.searchsorted(x_smooth_fourier, x_smooth)] # Ensure `x_smooth` includes the extrapolations. mean_x_smooth_space = np.diff(x_smooth).mean() x_predict_smooth = np.linspace(x_smooth[-1] + mean_x_smooth_space, x_smooth[-1] + mean_x_smooth_space * n_predict_smooth, n_predict_smooth) x_smooth = np.concatenate((x_smooth, x_predict_smooth)) # Ensure `x_smooth_fourier` includes the extrapolations. mean_x_smooth_fourier_space = np.diff(x_smooth).mean() x_predict_smooth_fourier = \ np.linspace( x_smooth_fourier[-1] + mean_x_smooth_fourier_space, x_smooth_fourier[-1] + mean_x_smooth_fourier_space * n_predict_smooth_fourier, n_predict_smooth_fourier) x_smooth_fourier = np.concatenate((x_smooth_fourier, x_predict_smooth_fourier)) y_smooth = np.interp(x_smooth, x_smooth_fourier, y_smooth_fourier) return x_smooth, y_smooth
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[ 109, 0 ]
[ 184, 29 ]
python
en
['en', 'error', 'th']
False
RslLexer.analyse_text
(text)
Check for the most common text in the beginning of a RSL file.
Check for the most common text in the beginning of a RSL file.
def analyse_text(text): """ Check for the most common text in the beginning of a RSL file. """ if re.search(r'scheme\s*.*?=\s*class\s*type', text, re.I) is not None: return 1.0
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[ 407, 4 ]
[ 412, 22 ]
python
en
['en', 'error', 'th']
False
RunawayRegexTest.assert_single_token
(self, s, token)
Show that a given string generates only one token.
Show that a given string generates only one token.
def assert_single_token(self, s, token): """Show that a given string generates only one token.""" tokens = list(self.lexer.get_tokens_unprocessed(s)) self.assertEqual(len(tokens), 1, tokens) self.assertEqual(s, tokens[0][2]) self.assertEqual(token, tokens[0][1])
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[ 26, 4 ]
[ 31, 45 ]
python
en
['en', 'en', 'en']
True
RunawayRegexTest.assert_tokens
(self, strings, expected_tokens)
Show that a given string generates the expected tokens.
Show that a given string generates the expected tokens.
def assert_tokens(self, strings, expected_tokens): """Show that a given string generates the expected tokens.""" tokens = list(self.lexer.get_tokens_unprocessed(''.join(strings))) self.assertEqual(len(tokens), len(expected_tokens), tokens) for index, s in enumerate(strings): self.assertEqual(s, tokens[index][2]) self.assertEqual(expected_tokens[index], tokens[index][1])
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[ 33, 4 ]
[ 39, 70 ]
python
en
['en', 'en', 'en']
True
RunawayRegexTest.assert_fast_tokenization
(self, s)
Show that a given string is tokenized quickly.
Show that a given string is tokenized quickly.
def assert_fast_tokenization(self, s): """Show that a given string is tokenized quickly.""" start = time.time() tokens = list(self.lexer.get_tokens_unprocessed(s)) end = time.time() # Isn't 10 seconds kind of a long time? Yes, but we don't want false # positives when the tests are starved for CPU time. if end-start > 10: self.fail('tokenization took too long') return tokens
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[ 41, 4 ]
[ 50, 21 ]
python
en
['en', 'en', 'en']
True
CALLING_CONVENTION_TYPES.extract
(text, default=UNKNOWN)
extracts calling convention from the text. If the calling convention could not be found, the "default"is used
extracts calling convention from the text. If the calling convention could not be found, the "default"is used
def extract(text, default=UNKNOWN): """extracts calling convention from the text. If the calling convention could not be found, the "default"is used""" if not text: return default found = CALLING_CONVENTION_TYPES.pattern.match(text) if found: return found.group('cc') else: return default
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[ 37, 4 ]
[ 46, 26 ]
python
en
['en', 'en', 'en']
True
S3LoggingBucket.get_key
(self, _name)
Create a new Key instance with the given name.
Create a new Key instance with the given name.
def get_key(self, _name): """ Create a new Key instance with the given name. """ return Key(bucket=self.bucket, name=_name)
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[ 27, 4 ]
[ 29, 50 ]
python
en
['en', 'en', 'en']
True
S3LoggingBucket.get_bucket
(self)
Return the bucket being used.
Return the bucket being used.
def get_bucket(self): """ Return the bucket being used. """ return self.bucket
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[ 31, 4 ]
[ 33, 26 ]
python
en
['en', 'en', 'en']
True
S3LoggingBucket.upload_file
(self, file_name, file_path)
Upload a given file from the file_path to the bucket with the new name/path file_name.
Upload a given file from the file_path to the bucket with the new name/path file_name.
def upload_file(self, file_name, file_path): """ Upload a given file from the file_path to the bucket with the new name/path file_name. """ upload_key = Key(bucket=self.bucket, name=file_name) content_type = "text/plain" if file_name.endswith(".html"): content_type = "text/html" elif file_name.endswith(".jpg"): content_type = "image/jpeg" elif file_name.endswith(".png"): content_type = "image/png" upload_key.set_contents_from_filename( file_path, headers={"Content-Type": content_type}) upload_key.url = \ upload_key.generate_url(expires_in=3600).split("?")[0] try: upload_key.make_public() except Exception: pass
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[ 35, 4 ]
[ 54, 16 ]
python
en
['en', 'en', 'en']
True
S3LoggingBucket.upload_index_file
(self, test_address, timestamp)
Create an index.html file with links to all the log files that were just uploaded.
Create an index.html file with links to all the log files that were just uploaded.
def upload_index_file(self, test_address, timestamp): """ Create an index.html file with links to all the log files that were just uploaded. """ global already_uploaded_files already_uploaded_files = list(set(already_uploaded_files)) already_uploaded_files.sort() file_name = "%s/%s/index.html" % (test_address, timestamp) index = self.get_key(file_name) index_str = [] for completed_file in already_uploaded_files: index_str.append("<a href='" + self.bucket_url + "" "%s'>%s</a>" % (completed_file, completed_file)) index.set_contents_from_string( "<br>".join(index_str), headers={"Content-Type": "text/html"}) index.make_public() return "%s%s" % (self.bucket_url, file_name)
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[ 56, 4 ]
[ 72, 52 ]
python
en
['en', 'en', 'en']
True
S3LoggingBucket.save_uploaded_file_names
(self, files)
Keep a record of all file names that've been uploaded. Upload log files related to each test after its execution. Once done, use already_uploaded_files to create an index file.
Keep a record of all file names that've been uploaded. Upload log files related to each test after its execution. Once done, use already_uploaded_files to create an index file.
def save_uploaded_file_names(self, files): """ Keep a record of all file names that've been uploaded. Upload log files related to each test after its execution. Once done, use already_uploaded_files to create an index file. """ global already_uploaded_files already_uploaded_files.extend(files)
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[ 74, 4 ]
[ 79, 44 ]
python
en
['en', 'en', 'en']
True
System.get_function
(self, code_package: Benchmark, func_name: Optional[str] = None)
There's no function with that name? a) yes -> create new function. Implementation might check if a function with that name already exists in the cloud and update its code. b) no -> retrieve function from the cache. Function code in cloud will be updated if the local version is different.
There's no function with that name? a) yes -> create new function. Implementation might check if a function with that name already exists in the cloud and update its code. b) no -> retrieve function from the cache. Function code in cloud will be updated if the local version is different.
def get_function(self, code_package: Benchmark, func_name: Optional[str] = None) -> Function: if code_package.language_version not in self.system_config.supported_language_versions( self.name(), code_package.language_name ): raise Exception( "Unsupported {language} version {version} in {system}!".format( language=code_package.language_name, version=code_package.language_version, system=self.name(), ) ) if not func_name: func_name = self.default_function_name(code_package) rebuilt, _ = code_package.build(self.package_code) """ There's no function with that name? a) yes -> create new function. Implementation might check if a function with that name already exists in the cloud and update its code. b) no -> retrieve function from the cache. Function code in cloud will be updated if the local version is different. """ functions = code_package.functions if not functions or func_name not in functions: msg = ( "function name not provided." if not func_name else "function {} not found in cache.".format(func_name) ) self.logging.info("Creating new function! Reason: " + msg) function = self.create_function(code_package, func_name) self.cache_client.add_function( deployment_name=self.name(), language_name=code_package.language_name, code_package=code_package, function=function, ) code_package.query_cache() return function else: # retrieve function cached_function = functions[func_name] code_location = code_package.code_location function = self.function_type().deserialize(cached_function) self.cached_function(function) self.logging.info( "Using cached function {fname} in {loc}".format(fname=func_name, loc=code_location) ) # is the function up-to-date? if function.code_package_hash != code_package.hash or rebuilt: self.logging.info( f"Cached function {func_name} with hash " f"{function.code_package_hash} is not up to date with " f"current build {code_package.hash} in " f"{code_location}, updating cloud version!" ) self.update_function(function, code_package) function.code_package_hash = code_package.hash function.updated_code = True self.cache_client.add_function( deployment_name=self.name(), language_name=code_package.language_name, code_package=code_package, function=function, ) code_package.query_cache() return function
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[ 121, 4 ]
[ 189, 27 ]
python
en
['en', 'error', 'th']
False
eval_data_from_json
(filename: str, max_docs: Union[int, bool] = None, preprocessor: PreProcessor = None)
Read Documents + Labels from a SQuAD-style file. Document and Labels can then be indexed to the DocumentStore and be used for evaluation. :param filename: Path to file in SQuAD format :param max_docs: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents. :return: (List of Documents, List of Labels)
Read Documents + Labels from a SQuAD-style file. Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
def eval_data_from_json(filename: str, max_docs: Union[int, bool] = None, preprocessor: PreProcessor = None) -> Tuple[List[Document], List[Label]]: """ Read Documents + Labels from a SQuAD-style file. Document and Labels can then be indexed to the DocumentStore and be used for evaluation. :param filename: Path to file in SQuAD format :param max_docs: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents. :return: (List of Documents, List of Labels) """ docs: List[Document] = [] labels = [] problematic_ids = [] with open(filename, "r", encoding='utf-8') as file: data = json.load(file) if "title" not in data["data"][0]: logger.warning(f"No title information found for documents in QA file: {filename}") for document in data["data"]: if max_docs: if len(docs) > max_docs: break # Extracting paragraphs and their labels from a SQuAD document dict cur_docs, cur_labels, cur_problematic_ids = _extract_docs_and_labels_from_dict(document, preprocessor) docs.extend(cur_docs) labels.extend(cur_labels) problematic_ids.extend(cur_problematic_ids) if len(problematic_ids) > 0: logger.warning(f"Could not convert an answer for {len(problematic_ids)} questions.\n" f"There were conversion errors for question ids: {problematic_ids}") return docs, labels
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[ 24, 0 ]
[ 55, 23 ]
python
en
['en', 'error', 'th']
False
eval_data_from_jsonl
(filename: str, batch_size: Optional[int] = None, max_docs: Union[int, bool] = None, preprocessor: PreProcessor = None)
Read Documents + Labels from a SQuAD-style file in jsonl format, i.e. one document per line. Document and Labels can then be indexed to the DocumentStore and be used for evaluation. This is a generator which will yield one tuple per iteration containing a list of batch_size documents and a list with the documents' labels. If batch_size is set to None, this method will yield all documents and labels. :param filename: Path to file in SQuAD format :param max_docs: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents. :return: (List of Documents, List of Labels)
Read Documents + Labels from a SQuAD-style file in jsonl format, i.e. one document per line. Document and Labels can then be indexed to the DocumentStore and be used for evaluation.
def eval_data_from_jsonl(filename: str, batch_size: Optional[int] = None, max_docs: Union[int, bool] = None, preprocessor: PreProcessor = None) -> Generator[Tuple[List[Document], List[Label]], None, None]: """ Read Documents + Labels from a SQuAD-style file in jsonl format, i.e. one document per line. Document and Labels can then be indexed to the DocumentStore and be used for evaluation. This is a generator which will yield one tuple per iteration containing a list of batch_size documents and a list with the documents' labels. If batch_size is set to None, this method will yield all documents and labels. :param filename: Path to file in SQuAD format :param max_docs: This sets the number of documents that will be loaded. By default, this is set to None, thus reading in all available eval documents. :return: (List of Documents, List of Labels) """ docs: List[Document] = [] labels = [] problematic_ids = [] with open(filename, "r", encoding='utf-8') as file: for document in file: if max_docs: if len(docs) > max_docs: break # Extracting paragraphs and their labels from a SQuAD document dict document_dict = json.loads(document) cur_docs, cur_labels, cur_problematic_ids = _extract_docs_and_labels_from_dict(document_dict, preprocessor) docs.extend(cur_docs) labels.extend(cur_labels) problematic_ids.extend(cur_problematic_ids) if batch_size is not None: if len(docs) >= batch_size: if len(problematic_ids) > 0: logger.warning(f"Could not convert an answer for {len(problematic_ids)} questions.\n" f"There were conversion errors for question ids: {problematic_ids}") yield docs, labels docs = [] labels = [] problematic_ids = [] yield docs, labels
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[ 58, 0 ]
[ 99, 22 ]
python
en
['en', 'error', 'th']
False
convert_files_to_dicts
(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False)
Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param dir_path: path for the documents to be written to the DocumentStore :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str) :param split_paragraphs: split text in paragraphs. :return: None
Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a Document Store.
def convert_files_to_dicts(dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False) -> \ List[dict]: """ Convert all files(.txt, .pdf, .docx) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param dir_path: path for the documents to be written to the DocumentStore :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str) :param split_paragraphs: split text in paragraphs. :return: None """ file_paths = [p for p in Path(dir_path).glob("**/*")] allowed_suffixes = [".pdf", ".txt", ".docx"] suffix2converter: Dict[str, BaseConverter] = {} suffix2paths: Dict[str, List[Path]] = {} for path in file_paths: file_suffix = path.suffix.lower() if file_suffix in allowed_suffixes: if file_suffix not in suffix2paths: suffix2paths[file_suffix] = [] suffix2paths[file_suffix].append(path) elif not path.is_dir(): logger.warning('Skipped file {0} as type {1} is not supported here. ' 'See haystack.file_converter for support of more file types'.format(path, file_suffix)) # No need to initialize converter if file type not present for file_suffix in suffix2paths.keys(): if file_suffix == ".pdf": suffix2converter[file_suffix] = PDFToTextConverter() if file_suffix == ".txt": suffix2converter[file_suffix] = TextConverter() if file_suffix == ".docx": suffix2converter[file_suffix] = DocxToTextConverter() documents = [] for suffix, paths in suffix2paths.items(): for path in paths: logger.info('Converting {}'.format(path)) document = suffix2converter[suffix].convert(file_path=path, meta=None) text = document["text"] if clean_func: text = clean_func(text) if split_paragraphs: for para in text.split("\n\n"): if not para.strip(): # skip empty paragraphs continue documents.append({"text": para, "meta": {"name": path.name}}) else: documents.append({"text": text, "meta": {"name": path.name}}) return documents
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[ 198, 0 ]
[ 253, 20 ]
python
en
['en', 'error', 'th']
False
tika_convert_files_to_dicts
( dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True )
Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param merge_lowercase: allow conversion of merged paragraph to lowercase :param merge_short: allow merging of short paragraphs :param dir_path: path for the documents to be written to the DocumentStore :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str) :param split_paragraphs: split text in paragraphs. :return: None
Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a Document Store.
def tika_convert_files_to_dicts( dir_path: str, clean_func: Optional[Callable] = None, split_paragraphs: bool = False, merge_short: bool = True, merge_lowercase: bool = True ) -> List[dict]: """ Convert all files(.txt, .pdf) in the sub-directories of the given path to Python dicts that can be written to a Document Store. :param merge_lowercase: allow conversion of merged paragraph to lowercase :param merge_short: allow merging of short paragraphs :param dir_path: path for the documents to be written to the DocumentStore :param clean_func: a custom cleaning function that gets applied to each doc (input: str, output:str) :param split_paragraphs: split text in paragraphs. :return: None """ converter = TikaConverter() paths = [p for p in Path(dir_path).glob("**/*")] allowed_suffixes = [".pdf", ".txt"] file_paths: List[Path] = [] for path in paths: file_suffix = path.suffix.lower() if file_suffix in allowed_suffixes: file_paths.append(path) elif not path.is_dir(): logger.warning('Skipped file {0} as type {1} is not supported here. ' 'See haystack.file_converter for support of more file types'.format(path, file_suffix)) documents = [] for path in file_paths: logger.info('Converting {}'.format(path)) document = converter.convert(path) meta = document["meta"] or {} meta["name"] = path.name text = document["text"] pages = text.split("\f") if split_paragraphs: if pages: paras = pages[0].split("\n\n") # pop the last paragraph from the first page last_para = paras.pop(-1) if paras else '' for page in pages[1:]: page_paras = page.split("\n\n") # merge the last paragraph in previous page to the first paragraph in this page if page_paras: page_paras[0] = last_para + ' ' + page_paras[0] last_para = page_paras.pop(-1) paras += page_paras if last_para: paras.append(last_para) if paras: last_para = '' for para in paras: para = para.strip() if not para: continue # merge paragraphs to improve qa # merge this paragraph if less than 10 characters or 2 words # or this paragraph starts with a lower case and last paragraph does not end with a punctuation if merge_short and len(para) < 10 or len(re.findall(r'\s+', para)) < 2 \ or merge_lowercase and para and para[0].islower() and last_para \ and last_para[-1] not in r'.?!"\'\]\)': last_para += ' ' + para else: if last_para: documents.append({"text": last_para, "meta": meta}) last_para = para # don't forget the last one if last_para: documents.append({"text": last_para, "meta": meta}) else: if clean_func: text = clean_func(text) documents.append({"text": text, "meta": meta}) return documents
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[ 256, 0 ]
[ 336, 20 ]
python
en
['en', 'error', 'th']
False
fetch_archive_from_http
(url: str, output_dir: str, proxies: Optional[dict] = None)
Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory. :param url: http address :type url: str :param output_dir: local path :type output_dir: str :param proxies: proxies details as required by requests library :type proxies: dict :return: bool if anything got fetched
Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory.
def fetch_archive_from_http(url: str, output_dir: str, proxies: Optional[dict] = None): """ Fetch an archive (zip or tar.gz) from a url via http and extract content to an output directory. :param url: http address :type url: str :param output_dir: local path :type output_dir: str :param proxies: proxies details as required by requests library :type proxies: dict :return: bool if anything got fetched """ # verify & prepare local directory path = Path(output_dir) if not path.exists(): path.mkdir(parents=True) is_not_empty = len(list(Path(path).rglob("*"))) > 0 if is_not_empty: logger.info( f"Found data stored in `{output_dir}`. Delete this first if you really want to fetch new data." ) return False else: logger.info(f"Fetching from {url} to `{output_dir}`") # download & extract with tempfile.NamedTemporaryFile() as temp_file: http_get(url, temp_file, proxies=proxies) temp_file.flush() temp_file.seek(0) # making tempfile accessible # extract if url[-4:] == ".zip": zip_archive = zipfile.ZipFile(temp_file.name) zip_archive.extractall(output_dir) elif url[-7:] == ".tar.gz": tar_archive = tarfile.open(temp_file.name) tar_archive.extractall(output_dir) elif url[-3:] == ".gz": filename = url.split("/")[-1].replace(".gz", "") output_filename = Path(output_dir) / filename with gzip.open(temp_file.name) as f, open(output_filename, "wb") as output: for line in f: output.write(line) else: logger.warning('Skipped url {0} as file type is not supported here. ' 'See haystack documentation for support of more file types'.format(url)) # temp_file gets deleted here return True
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[ 339, 0 ]
[ 387, 19 ]
python
en
['en', 'error', 'th']
False
squad_json_to_jsonl
(squad_file: str, output_file: str)
Converts a SQuAD-json-file into jsonl format with one document per line. :param squad_file: SQuAD-file in json format. :type squad_file: str :param output_file: Name of output file (SQuAD in jsonl format) :type output_file: str
Converts a SQuAD-json-file into jsonl format with one document per line.
def squad_json_to_jsonl(squad_file: str, output_file: str): """ Converts a SQuAD-json-file into jsonl format with one document per line. :param squad_file: SQuAD-file in json format. :type squad_file: str :param output_file: Name of output file (SQuAD in jsonl format) :type output_file: str """ with open(squad_file, encoding='utf-8') as json_file, open(output_file, "w", encoding='utf-8') as jsonl_file: squad_json = json.load(json_file) for doc in squad_json["data"]: json.dump(doc, jsonl_file) jsonl_file.write("\n")
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[ 390, 0 ]
[ 404, 34 ]
python
en
['en', 'error', 'th']
False
RtfFormatter.__init__
(self, **options)
r""" Additional options accepted: ``fontface`` Name of the font used. Could for example be ``'Courier New'`` to further specify the default which is ``'\fmodern'``. The RTF specification claims that ``\fmodern`` are "Fixed-pitch serif and sans serif fonts". Hope every RTF implementation thinks the same about modern...
r""" Additional options accepted:
def __init__(self, **options): r""" Additional options accepted: ``fontface`` Name of the font used. Could for example be ``'Courier New'`` to further specify the default which is ``'\fmodern'``. The RTF specification claims that ``\fmodern`` are "Fixed-pitch serif and sans serif fonts". Hope every RTF implementation thinks the same about modern... """ Formatter.__init__(self, **options) self.fontface = options.get('fontface') or '' self.fontsize = get_int_opt(options, 'fontsize', 0)
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[ 50, 4 ]
[ 64, 59 ]
python
cy
['en', 'cy', 'hi']
False
critical_suite_with_citations
()
This hand made fixture has a wide range of expectations, and has a mix of metadata including an BasicSuiteBuilderProfiler entry, and citations.
This hand made fixture has a wide range of expectations, and has a mix of metadata including an BasicSuiteBuilderProfiler entry, and citations.
def critical_suite_with_citations(): """ This hand made fixture has a wide range of expectations, and has a mix of metadata including an BasicSuiteBuilderProfiler entry, and citations. """ schema = ExpectationSuiteSchema() critical_suite = { "expectation_suite_name": "critical", "meta": { "great_expectations_version": "0.9.1+9.gf17eff1f.dirty", "columns": { "npi": {"description": ""}, "nppes_provider_last_org_name": {"description": ""}, "nppes_provider_first_name": {"description": ""}, "nppes_provider_mi": {"description": ""}, "nppes_credentials": {"description": ""}, "nppes_provider_gender": {"description": ""}, "nppes_entity_code": {"description": ""}, "nppes_provider_street1": {"description": ""}, "nppes_provider_street2": {"description": ""}, "nppes_provider_city": {"description": ""}, }, "citations": [ { "citation_date": "2020-02-28T17:34:31.307271", "batch_kwargs": { "path": "/home/foo/data/10k.csv", "datasource": "files_datasource", }, "batch_markers": { "ge_load_time": "20200229T013430.655026Z", "pandas_data_fingerprint": "f6037d92eb4c01f976513bc0aec2420d", }, "batch_parameters": None, "comment": "BasicSuiteBuilderProfiler added a citation based on the current batch.", } ], "notes": { "format": "markdown", "content": [ "#### This is an _example_ suite\n\n- This suite was made by quickly glancing at 1000 rows of your data.\n- This is **not a production suite**. It is meant to show examples of expectations.\n- Because this suite was auto-generated using a very basic profiler that does not know your data like you do, many of the expectations may not be meaningful.\n" ], }, "BasicSuiteBuilderProfiler": { "created_by": "BasicSuiteBuilderProfiler", "created_at": 1582838223.843476, "batch_kwargs": { "path": "/Users/foo/data/10k.csv", "datasource": "files_datasource", }, }, }, "expectations": [ { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "npi"}, "meta": { "question": True, "Notes": "There are empty strings that should probably be nulls", "BasicSuiteBuilderProfiler": {"confidence": "very low"}, }, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "provider_type"}, }, ], "data_asset_type": "Dataset", } return schema.loads(json.dumps(critical_suite))
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[ 22, 0 ]
[ 91, 51 ]
python
en
['en', 'error', 'th']
False
suite_with_multiple_citations
()
A handmade suite with multiple citations each with different batch_kwargs. The most recent citation does not have batch_kwargs
A handmade suite with multiple citations each with different batch_kwargs.
def suite_with_multiple_citations(): """ A handmade suite with multiple citations each with different batch_kwargs. The most recent citation does not have batch_kwargs """ schema = ExpectationSuiteSchema() critical_suite = { "expectation_suite_name": "critical", "meta": { "great_expectations_version": "0.9.1+9.gf17eff1f.dirty", "citations": [ { "citation_date": "2001-01-01T00:00:01.000001", "batch_kwargs": { "path": "3.csv", "datasource": "3", }, }, { "citation_date": "2000-01-01T00:00:01.000001", "batch_kwargs": { "path": "2.csv", "datasource": "2", }, }, # This citation is the most recent and has no batch_kwargs { "citation_date": "2020-01-01T00:00:01.000001", }, { "citation_date": "1999-01-01T00:00:01.000001", "batch_kwargs": { "path": "1.csv", "datasource": "1", }, }, ], }, "expectations": [ { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "npi"}, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "provider_type"}, }, ], "data_asset_type": "Dataset", } return schema.loads(json.dumps(critical_suite))
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[ 95, 0 ]
[ 146, 51 ]
python
en
['en', 'error', 'th']
False
warning_suite
()
This hand made fixture has a wide range of expectations, and has a mix of metadata including BasicSuiteBuilderProfiler entries.
This hand made fixture has a wide range of expectations, and has a mix of metadata including BasicSuiteBuilderProfiler entries.
def warning_suite(): """ This hand made fixture has a wide range of expectations, and has a mix of metadata including BasicSuiteBuilderProfiler entries. """ schema = ExpectationSuiteSchema() warning_suite = { "expectation_suite_name": "warning", "meta": { "great_expectations_version": "0.8.4.post0", "citations": [ { "citation_date": "2020-02-28T17:34:31.307271", "batch_kwargs": { "path": "/home/foo/data/10k.csv", "datasource": "files_datasource", }, "batch_markers": { "ge_load_time": "20200229T013430.655026Z", "pandas_data_fingerprint": "f6037d92eb4c01f976513bc0aec2420d", }, "batch_parameters": None, "comment": "BasicSuiteBuilderProfiler added a citation based on the current batch.", } ], }, "expectations": [ { "expectation_type": "expect_table_row_count_to_be_between", "kwargs": {"min_value": 800000, "max_value": 1200000}, }, { "expectation_type": "expect_table_column_count_to_equal", "kwargs": {"value": 71}, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "npi"}, "meta": {"BasicSuiteBuilderProfiler": {"confidence": "very low"}}, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "provider_type"}, "meta": {"BasicSuiteBuilderProfiler": {"confidence": "very low"}}, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "nppes_provider_last_org_name"}, }, { "expectation_type": "expect_column_values_to_be_in_set", "kwargs": { "column": "nppes_provider_gender", "value_set": ["M", "F", ""], }, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "nppes_entity_code"}, }, { "expectation_type": "expect_column_values_to_be_in_set", "kwargs": {"column": "nppes_entity_code", "value_set": ["I", "O"]}, }, { "expectation_type": "expect_column_kl_divergence_to_be_less_than", "kwargs": { "column": "nppes_entity_code", "partition_object": { "values": ["I", "O"], "weights": [0.9431769750233306, 0.056823024976669335], }, "threshold": 0.1, }, }, { "expectation_type": "expect_column_values_to_be_in_set", "kwargs": { "column": "nppes_provider_state", "value_set": [ "AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "FL", "GA", "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC", "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY", "DC", "PR", "AE", "VI", ], "mostly": 0.999, }, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "medicare_participation_indicator"}, }, { "expectation_type": "expect_column_values_to_be_in_set", "kwargs": { "column": "medicare_participation_indicator", "value_set": ["Y", "N"], }, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "number_of_hcpcs"}, }, { "expectation_type": "expect_column_values_to_be_between", "kwargs": { "column": "number_of_hcpcs", "min_value": 0, "max_value": 500, "mostly": 0.999, }, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "total_unique_benes"}, }, { "expectation_type": "expect_column_values_to_be_between", "kwargs": { "column": "total_unique_benes", "min_value": 0, "max_value": 2000, "mostly": 0.95, }, }, { "expectation_type": "expect_column_values_to_be_null", "kwargs": {"column": "med_suppress_indicator", "mostly": 0.85}, }, { "expectation_type": "expect_column_values_to_be_in_set", "kwargs": {"column": "med_suppress_indicator", "value_set": ["#", "*"]}, }, { "expectation_type": "expect_column_values_to_be_between", "kwargs": { "column": "beneficiary_average_age", "min_value": 40, "max_value": 90, "mostly": 0.995, }, }, { "expectation_type": "expect_column_kl_divergence_to_be_less_than", "kwargs": { "column": "beneficiary_average_age", "partition_object": { "bins": [8, 16.5, 25, 33.5, 42, 50.5, 59, 67.5, 76, 84.5, 93], "weights": [ 0.00025259576594384474, 0.00013318685840675451, 0.0009653750909344757, 0.0012363414580378728, 0.01081660996274442, 0.030813927854975127, 0.13495227317818748, 0.6919590041664524, 0.1244213260634741, 0.004449359600843578, ], }, "threshold": 0.9, }, }, { "expectation_type": "expect_column_values_to_be_between", "kwargs": { "column": "total_submitted_chrg_amt", "min_value": 2000, "max_value": 5000000, "mostly": 0.98, }, }, { "expectation_type": "expect_column_values_to_not_be_null", "kwargs": {"column": "nppes_provider_first_name", "mostly": 0.9}, }, { "expectation_type": "expect_column_values_to_match_regex", "kwargs": { "column": "nppes_provider_zip", "regex": "^\\d*$", "mostly": 0.999, }, }, ], "data_asset_type": "Dataset", } return schema.loads(json.dumps(warning_suite))
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[ 150, 0 ]
[ 388, 50 ]
python
en
['en', 'error', 'th']
False
test_notebook_execution_with_pandas_backend
(titanic_data_context_no_data_docs)
To set this test up we: - create a suite - add a few expectations (both table and column level) - verify that no validations have happened - create the suite edit notebook by hijacking the private cli method We then: - execute that notebook (Note this will raise various errors like CellExecutionError if any cell in the notebook fails - create a new context from disk - verify that a validation has been run with our expectation suite
To set this test up we:
def test_notebook_execution_with_pandas_backend(titanic_data_context_no_data_docs): """ To set this test up we: - create a suite - add a few expectations (both table and column level) - verify that no validations have happened - create the suite edit notebook by hijacking the private cli method We then: - execute that notebook (Note this will raise various errors like CellExecutionError if any cell in the notebook fails - create a new context from disk - verify that a validation has been run with our expectation suite """ # Since we'll run the notebook, we use a context with no data docs to avoid # the renderer's default behavior of building and opening docs, which is not # part of this test. context = titanic_data_context_no_data_docs root_dir = context.root_directory uncommitted_dir = os.path.join(root_dir, "uncommitted") suite_name = "warning" context.create_expectation_suite(suite_name) csv_path = os.path.join(root_dir, "..", "data", "Titanic.csv") batch_kwargs = {"datasource": "mydatasource", "path": csv_path} batch = context.get_batch(batch_kwargs, suite_name) batch.expect_table_column_count_to_equal(1) batch.expect_table_row_count_to_equal(1313) batch.expect_column_values_to_be_in_set("Sex", ["female", "male"]) batch.save_expectation_suite(discard_failed_expectations=False) # Sanity check test setup suite = context.get_expectation_suite(suite_name) original_suite = suite assert len(suite.expectations) == 3 assert context.list_expectation_suite_names() == [suite_name] assert context.list_datasources() == [ { "module_name": "great_expectations.datasource", "class_name": "PandasDatasource", "data_asset_type": { "module_name": "great_expectations.dataset", "class_name": "PandasDataset", }, "batch_kwargs_generators": { "mygenerator": { "class_name": "SubdirReaderBatchKwargsGenerator", "base_directory": "../data", } }, "name": "mydatasource", } ] assert context.get_validation_result("warning") == {} # Create notebook json_batch_kwargs = json.dumps(batch_kwargs) _suite_edit( suite_name, "mydatasource", directory=root_dir, jupyter=False, batch_kwargs=json_batch_kwargs, usage_event="test_notebook_execution", ) edit_notebook_path = os.path.join(uncommitted_dir, "edit_warning.ipynb") assert os.path.isfile(edit_notebook_path) with open(edit_notebook_path) as f: nb = nbformat.read(f, as_version=4) # Run notebook ep = ExecutePreprocessor(timeout=600, kernel_name="python3") ep.preprocess(nb, {"metadata": {"path": uncommitted_dir}}) # Assertions about output context = DataContext(root_dir) obs_validation_result = context.get_validation_result("warning") assert obs_validation_result.statistics == { "evaluated_expectations": 3, "successful_expectations": 2, "unsuccessful_expectations": 1, "success_percent": 66.66666666666666, } suite = context.get_expectation_suite(suite_name) assert suite == original_suite
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[ 1319, 0 ]
[ 1406, 34 ]
python
en
['en', 'error', 'th']
False
test_notebook_execution_with_custom_notebooks_wrong_module
( suite_with_multiple_citations, data_context_with_bad_notebooks )
Test the error message in case of "bad" custom module is clear
Test the error message in case of "bad" custom module is clear
def test_notebook_execution_with_custom_notebooks_wrong_module( suite_with_multiple_citations, data_context_with_bad_notebooks ): """ Test the error message in case of "bad" custom module is clear """ with pytest.raises( SuiteEditNotebookCustomTemplateModuleNotFoundError, match=r"invalid\.module" ): SuiteEditNotebookRenderer.from_data_context( data_context_with_bad_notebooks ).render(suite_with_multiple_citations)
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[ 1409, 0 ]
[ 1420, 47 ]
python
en
['en', 'error', 'th']
False
test_notebook_execution_with_custom_notebooks
( suite_with_multiple_citations, data_context_custom_notebooks )
Test the different parts of the notebooks can be modified
Test the different parts of the notebooks can be modified
def test_notebook_execution_with_custom_notebooks( suite_with_multiple_citations, data_context_custom_notebooks ): """ Test the different parts of the notebooks can be modified """ obs = SuiteEditNotebookRenderer.from_data_context( data_context_custom_notebooks ).render(suite_with_multiple_citations) assert isinstance(obs, dict) expected = { "nbformat": 4, "nbformat_minor": 4, "metadata": {}, "cells": [ { "cell_type": "markdown", "source": "# Custom header for MyCompany", "metadata": {}, }, { "cell_type": "code", "metadata": {}, "execution_count": None, "source": 'import datetime\nimport great_expectations as ge\nimport great_expectations.jupyter_ux\nfrom great_expectations.checkpoint import LegacyCheckpoint\nfrom great_expectations.data_context.types.resource_identifiers import (\n ValidationResultIdentifier,\n)\n\ncontext = ge.data_context.DataContext()\n\n# Feel free to change the name of your suite here. Renaming this will not\n# remove the other one.\nexpectation_suite_name = "critical"\nsuite = context.get_expectation_suite(expectation_suite_name)\nsuite.expectations = []\n\nbatch_kwargs = {"path": "../../3.csv", "datasource": "3"}\nbatch = context.get_batch(batch_kwargs, suite)\nbatch.head()', "outputs": [], }, { "cell_type": "markdown", "source": "## Create & Edit Expectations\n\nAdd expectations by calling specific expectation methods on the `batch` object. They all begin with `.expect_` which makes autocompleting easy using tab.\n\nYou can see all the available expectations in the **[expectation glossary](https://docs.greatexpectations.io/en/latest/reference/glossary_of_expectations.html?utm_source=notebook&utm_medium=create_expectations)**.", "metadata": {}, }, { "cell_type": "markdown", "source": "### Table Expectation(s)", "metadata": {}, }, { "cell_type": "markdown", "source": "No table level expectations are in this suite. Feel free to add some here. They all begin with `batch.expect_table_...`.", "metadata": {}, }, { "cell_type": "markdown", "source": "### Column Expectation(s)\nwrite your column expectations here", "metadata": {}, }, {"cell_type": "markdown", "source": "#### `npi`", "metadata": {}}, { "cell_type": "code", "metadata": {}, "execution_count": None, "source": 'batch.expect_column_values_to_not_be_null(column="npi")', "outputs": [], }, {"cell_type": "markdown", "source": "#### `provider_type`", "metadata": {}}, { "cell_type": "code", "metadata": {}, "execution_count": None, "source": 'batch.expect_column_values_to_not_be_null(column="provider_type")', "outputs": [], }, { "cell_type": "markdown", "source": "## Save & Review Your Expectations\n\nLet's save the expectation suite as a JSON file in the `great_expectations/expectations` directory of your project.\nIf you decide not to save some expectations that you created, use [remove_expectation method](https://docs.greatexpectations.io/en/latest/autoapi/great_expectations/data_asset/index.html?highlight=remove_expectation&utm_source=notebook&utm_medium=edit_expectations#great_expectations.data_asset.DataAsset.remove_expectation).\n\nLet's now rebuild your Data Docs, which helps you communicate about your data with both machines and humans.", "metadata": {}, }, { "cell_type": "code", "metadata": {}, "execution_count": None, "source": 'batch.save_expectation_suite(discard_failed_expectations=False)\nrun_id = {\n "run_name": "some_string_that_uniquely_identifies_this_run", # insert your own run_name here\n "run_time": datetime.datetime.now(datetime.timezone.utc),\n}\nresults = context.run_validation_operator(\n "local", assets_to_validate=[batch], run_id=run_id\n)\nvalidation_result_identifier = results.list_validation_result_identifiers()[0]\ncontext.build_data_docs(site_names=["site_local"])\ncontext.open_data_docs(validation_result_identifier, site_name="site_local")', "outputs": [], }, ], } del expected["nbformat_minor"] del obs["nbformat_minor"] for obs_cell, expected_cell in zip(obs["cells"], expected["cells"]): obs_cell.pop("id", None) assert obs_cell == expected_cell assert obs == expected
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Renaming this will not\\n# remove the other one.\\nexpectation_suite_name = \"critical\"\\nsuite = context.get_expectation_suite(expectation_suite_name)\\nsuite.expectations = []\\n\\nbatch_kwargs = {\"path\": \"../../3.csv\", \"datasource\": \"3\"}\\nbatch = context.get_batch(batch_kwargs, suite)\\nbatch.head()'", ",", "\"outputs\"", ":", "[", "]", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"## Create & Edit Expectations\\n\\nAdd expectations by calling specific expectation methods on the `batch` object. They all begin with `.expect_` which makes autocompleting easy using tab.\\n\\nYou can see all the available expectations in the **[expectation glossary](https://docs.greatexpectations.io/en/latest/reference/glossary_of_expectations.html?utm_source=notebook&utm_medium=create_expectations)**.\"", ",", "\"metadata\"", ":", "{", "}", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"### Table Expectation(s)\"", ",", "\"metadata\"", ":", "{", "}", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"No table level expectations are in this suite. Feel free to add some here. They all begin with `batch.expect_table_...`.\"", ",", "\"metadata\"", ":", "{", "}", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"### Column Expectation(s)\\nwrite your column expectations here\"", ",", "\"metadata\"", ":", "{", "}", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"#### `npi`\"", ",", "\"metadata\"", ":", "{", "}", "}", ",", "{", "\"cell_type\"", ":", "\"code\"", ",", "\"metadata\"", ":", "{", "}", ",", "\"execution_count\"", ":", "None", ",", "\"source\"", ":", "'batch.expect_column_values_to_not_be_null(column=\"npi\")'", ",", "\"outputs\"", ":", "[", "]", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"#### `provider_type`\"", ",", "\"metadata\"", ":", "{", "}", "}", ",", "{", "\"cell_type\"", ":", "\"code\"", ",", "\"metadata\"", ":", "{", "}", ",", "\"execution_count\"", ":", "None", ",", "\"source\"", ":", "'batch.expect_column_values_to_not_be_null(column=\"provider_type\")'", ",", "\"outputs\"", ":", "[", "]", ",", "}", ",", "{", "\"cell_type\"", ":", "\"markdown\"", ",", "\"source\"", ":", "\"## Save & Review Your Expectations\\n\\nLet's save the expectation suite as a JSON file in the `great_expectations/expectations` directory of your project.\\nIf you decide not to save some expectations that you created, use [remove_expectation method](https://docs.greatexpectations.io/en/latest/autoapi/great_expectations/data_asset/index.html?highlight=remove_expectation&utm_source=notebook&utm_medium=edit_expectations#great_expectations.data_asset.DataAsset.remove_expectation).\\n\\nLet's now rebuild your Data Docs, which helps you communicate about your data with both machines and humans.\"", ",", "\"metadata\"", ":", "{", "}", ",", "}", ",", "{", "\"cell_type\"", ":", "\"code\"", ",", "\"metadata\"", ":", "{", "}", ",", "\"execution_count\"", ":", "None", ",", "\"source\"", ":", "'batch.save_expectation_suite(discard_failed_expectations=False)\\nrun_id = {\\n \"run_name\": \"some_string_that_uniquely_identifies_this_run\", # insert your own run_name here\\n \"run_time\": datetime.datetime.now(datetime.timezone.utc),\\n}\\nresults = context.run_validation_operator(\\n \"local\", assets_to_validate=[batch], run_id=run_id\\n)\\nvalidation_result_identifier = results.list_validation_result_identifiers()[0]\\ncontext.build_data_docs(site_names=[\"site_local\"])\\ncontext.open_data_docs(validation_result_identifier, site_name=\"site_local\")'", ",", "\"outputs\"", ":", "[", "]", ",", "}", ",", "]", ",", "}", "del", "expected", "[", "\"nbformat_minor\"", "]", "del", "obs", "[", "\"nbformat_minor\"", "]", "for", "obs_cell", ",", "expected_cell", "in", "zip", "(", "obs", "[", "\"cells\"", "]", ",", "expected", "[", "\"cells\"", "]", ")", ":", "obs_cell", ".", "pop", "(", "\"id\"", ",", "None", ")", "assert", "obs_cell", "==", "expected_cell", "assert", "obs", "==", "expected" ]
[ 1423, 0 ]
[ 1505, 26 ]
python
en
['en', 'error', 'th']
False
plot
(dfs, anomalies=[])
Line plot for time series. This function plots time series and highlights anomalous regions. The first anomaly in anomalies is considered the ground truth. Args: dfs (list or `pd.DataFrame`): List of time series in `pd.DataFrame`. Or a single dataframe. All dataframes must have the same shape. anomalies (list): List of anomalies in tuple format.
Line plot for time series. This function plots time series and highlights anomalous regions. The first anomaly in anomalies is considered the ground truth. Args: dfs (list or `pd.DataFrame`): List of time series in `pd.DataFrame`. Or a single dataframe. All dataframes must have the same shape. anomalies (list): List of anomalies in tuple format.
def plot(dfs, anomalies=[]): """ Line plot for time series. This function plots time series and highlights anomalous regions. The first anomaly in anomalies is considered the ground truth. Args: dfs (list or `pd.DataFrame`): List of time series in `pd.DataFrame`. Or a single dataframe. All dataframes must have the same shape. anomalies (list): List of anomalies in tuple format. """ if isinstance(dfs, pd.DataFrame): dfs = [dfs] if not isinstance(anomalies, list): anomalies = [anomalies] df = dfs[0] time = convert_date(df['timestamp']) months = mdates.MonthLocator() # every month days = mdates.DayLocator() # every day month_fmt = mdates.DateFormatter('%b') fig = plt.figure(figsize=(30, 6)) ax = fig.add_subplot(111) for df in dfs: plt.plot(time, df['value']) colors = ['red'] + ['green'] * (len(anomalies) - 1) for i, anomaly in enumerate(anomalies): if not isinstance(anomaly, list): anomaly = list(anomaly[['start', 'end']].itertuples(index=False)) for _, anom in enumerate(anomaly): t1 = convert_date_single(anom[0]) t2 = convert_date_single(anom[1]) plt.axvspan(t1, t2, color=colors[i], alpha=0.2) plt.title('NYC Taxi Demand', size=34) plt.ylabel('# passengers', size=30) plt.xlabel('Time', size=30) plt.xticks(size=26) plt.yticks(size=26) plt.xlim([time[0], time[-1]]) # format xticks ax.xaxis.set_major_locator(months) ax.xaxis.set_major_formatter(month_fmt) ax.xaxis.set_minor_locator(days) # format yticks ylabels = ['{:,.0f}'.format(x) + 'K' for x in ax.get_yticks()/1000] ax.set_yticklabels(ylabels) plt.show()
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[ 90, 0 ]
[ 146, 14 ]
python
en
['en', 'en', 'en']
True
StatsmodelsOLS.__init__
(self, cov_type='HC1', alpha=.05)
Initialize a statsmodels' OLS wrapper class object. Args: cov_type (str, optional): covariance estimator type. alpha (float, optional): the confidence level alpha.
Initialize a statsmodels' OLS wrapper class object. Args: cov_type (str, optional): covariance estimator type. alpha (float, optional): the confidence level alpha.
def __init__(self, cov_type='HC1', alpha=.05): """Initialize a statsmodels' OLS wrapper class object. Args: cov_type (str, optional): covariance estimator type. alpha (float, optional): the confidence level alpha. """ self.cov_type = cov_type self.alpha = alpha
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[ 20, 4 ]
[ 27, 26 ]
python
en
['ca', 'en', 'en']
True
StatsmodelsOLS.fit
(self, X, y)
Fit OLS. Args: X (np.matrix): a feature matrix y (np.array): a label vector
Fit OLS. Args: X (np.matrix): a feature matrix y (np.array): a label vector
def fit(self, X, y): """Fit OLS. Args: X (np.matrix): a feature matrix y (np.array): a label vector """ # Append ones. The first column is for the treatment indicator. X = sm.add_constant(X, prepend=False, has_constant='add') self.model = sm.OLS(y, X).fit(cov_type=self.cov_type) self.coefficients = self.model.params self.conf_ints = self.model.conf_int(alpha=self.alpha)
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[ 29, 4 ]
[ 39, 62 ]
python
en
['en', 'id', 'en']
False
BaseSLearner.__init__
(self, learner=None, ate_alpha=0.05, control_name=0)
Initialize an S-learner. Args: learner (optional): a model to estimate the treatment effect control_name (str or int, optional): name of control group
Initialize an S-learner. Args: learner (optional): a model to estimate the treatment effect control_name (str or int, optional): name of control group
def __init__(self, learner=None, ate_alpha=0.05, control_name=0): """Initialize an S-learner. Args: learner (optional): a model to estimate the treatment effect control_name (str or int, optional): name of control group """ if learner is not None: self.model = learner else: self.model = DummyRegressor() self.ate_alpha = ate_alpha self.control_name = control_name
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[ 53, 4 ]
[ 64, 40 ]
python
en
['en', 'en', 'nl']
True
BaseSLearner.fit
(self, X, treatment, y, p=None)
Fit the inference model Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector
Fit the inference model Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector
def fit(self, X, treatment, y, p=None): """Fit the inference model Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector """ X, treatment, y = convert_pd_to_np(X, treatment, y) check_treatment_vector(treatment, self.control_name) self.t_groups = np.unique(treatment[treatment != self.control_name]) self.t_groups.sort() self._classes = {group: i for i, group in enumerate(self.t_groups)} self.models = {group: deepcopy(self.model) for group in self.t_groups} for group in self.t_groups: mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] X_filt = X[mask] y_filt = y[mask] w = (treatment_filt == group).astype(int) X_new = np.hstack((w.reshape((-1, 1)), X_filt)) self.models[group].fit(X_new, y_filt)
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[ 70, 4 ]
[ 92, 49 ]
python
en
['en', 'en', 'en']
True
BaseSLearner.predict
(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True)
Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects.
Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects.
def predict(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True): """Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. """ X, treatment, y = convert_pd_to_np(X, treatment, y) yhat_cs = {} yhat_ts = {} for group in self.t_groups: model = self.models[group] # set the treatment column to zero (the control group) X_new = np.hstack((np.zeros((X.shape[0], 1)), X)) yhat_cs[group] = model.predict(X_new) # set the treatment column to one (the treatment group) X_new[:, 0] = 1 yhat_ts[group] = model.predict(X_new) if (y is not None) and (treatment is not None) and verbose: mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] w = (treatment_filt == group).astype(int) y_filt = y[mask] yhat = np.zeros_like(y_filt, dtype=float) yhat[w == 0] = yhat_cs[group][mask][w == 0] yhat[w == 1] = yhat_ts[group][mask][w == 1] logger.info('Error metrics for group {}'.format(group)) regression_metrics(y_filt, yhat, w) te = np.zeros((X.shape[0], self.t_groups.shape[0])) for i, group in enumerate(self.t_groups): te[:, i] = yhat_ts[group] - yhat_cs[group] if not return_components: return te else: return te, yhat_cs, yhat_ts
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[ 94, 4 ]
[ 140, 39 ]
python
en
['fr', 'en', 'en']
True
BaseSLearner.fit_predict
(self, X, treatment, y, p=None, return_ci=False, n_bootstraps=1000, bootstrap_size=10000, return_components=False, verbose=True)
Fit the inference model of the S learner and predict treatment effects. Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector return_ci (bool, optional): whether to return confidence intervals n_bootstraps (int, optional): number of bootstrap iterations bootstrap_size (int, optional): number of samples per bootstrap return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment]. If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment], UB [n_samples, n_treatment]
Fit the inference model of the S learner and predict treatment effects. Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector return_ci (bool, optional): whether to return confidence intervals n_bootstraps (int, optional): number of bootstrap iterations bootstrap_size (int, optional): number of samples per bootstrap return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment]. If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment], UB [n_samples, n_treatment]
def fit_predict(self, X, treatment, y, p=None, return_ci=False, n_bootstraps=1000, bootstrap_size=10000, return_components=False, verbose=True): """Fit the inference model of the S learner and predict treatment effects. Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector return_ci (bool, optional): whether to return confidence intervals n_bootstraps (int, optional): number of bootstrap iterations bootstrap_size (int, optional): number of samples per bootstrap return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. Output dim: [n_samples, n_treatment]. If return_ci, returns CATE [n_samples, n_treatment], LB [n_samples, n_treatment], UB [n_samples, n_treatment] """ self.fit(X, treatment, y) te = self.predict(X, treatment, y, return_components=return_components) if not return_ci: return te else: t_groups_global = self.t_groups _classes_global = self._classes models_global = deepcopy(self.models) te_bootstraps = np.zeros(shape=(X.shape[0], self.t_groups.shape[0], n_bootstraps)) logger.info('Bootstrap Confidence Intervals') for i in tqdm(range(n_bootstraps)): te_b = self.bootstrap(X, treatment, y, size=bootstrap_size) te_bootstraps[:, :, i] = te_b te_lower = np.percentile(te_bootstraps, (self.ate_alpha/2)*100, axis=2) te_upper = np.percentile(te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2) # set member variables back to global (currently last bootstrapped outcome) self.t_groups = t_groups_global self._classes = _classes_global self.models = deepcopy(models_global) return (te, te_lower, te_upper)
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[ 142, 4 ]
[ 183, 43 ]
python
en
['en', 'en', 'en']
True
BaseSLearner.estimate_ate
(self, X, treatment, y, p=None, return_ci=False, bootstrap_ci=False, n_bootstraps=1000, bootstrap_size=10000)
Estimate the Average Treatment Effect (ATE). Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector return_ci (bool, optional): whether to return confidence intervals bootstrap_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap Returns: The mean and confidence interval (LB, UB) of the ATE estimate.
Estimate the Average Treatment Effect (ATE).
def estimate_ate(self, X, treatment, y, p=None, return_ci=False, bootstrap_ci=False, n_bootstraps=1000, bootstrap_size=10000): """Estimate the Average Treatment Effect (ATE). Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector return_ci (bool, optional): whether to return confidence intervals bootstrap_ci (bool): whether to return confidence intervals n_bootstraps (int): number of bootstrap iterations bootstrap_size (int): number of samples per bootstrap Returns: The mean and confidence interval (LB, UB) of the ATE estimate. """ X, treatment, y = convert_pd_to_np(X, treatment, y) te, yhat_cs, yhat_ts = self.fit_predict(X, treatment, y, return_components=True) ate = np.zeros(self.t_groups.shape[0]) ate_lb = np.zeros(self.t_groups.shape[0]) ate_ub = np.zeros(self.t_groups.shape[0]) for i, group in enumerate(self.t_groups): _ate = te[:, i].mean() mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] y_filt = y[mask] w = (treatment_filt == group).astype(int) prob_treatment = float(sum(w)) / w.shape[0] yhat_c = yhat_cs[group][mask] yhat_t = yhat_ts[group][mask] se = np.sqrt(( (y_filt[w == 0] - yhat_c[w == 0]).var() / (1 - prob_treatment) + (y_filt[w == 1] - yhat_t[w == 1]).var() / prob_treatment + (yhat_t - yhat_c).var() ) / y_filt.shape[0]) _ate_lb = _ate - se * norm.ppf(1 - self.ate_alpha / 2) _ate_ub = _ate + se * norm.ppf(1 - self.ate_alpha / 2) ate[i] = _ate ate_lb[i] = _ate_lb ate_ub[i] = _ate_ub if not return_ci: return ate elif return_ci and not bootstrap_ci: return ate, ate_lb, ate_ub else: t_groups_global = self.t_groups _classes_global = self._classes models_global = deepcopy(self.models) logger.info('Bootstrap Confidence Intervals for ATE') ate_bootstraps = np.zeros(shape=(self.t_groups.shape[0], n_bootstraps)) for n in tqdm(range(n_bootstraps)): ate_b = self.bootstrap(X, treatment, y, size=bootstrap_size) ate_bootstraps[:, n] = ate_b.mean() ate_lower = np.percentile(ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1) ate_upper = np.percentile(ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1) # set member variables back to global (currently last bootstrapped outcome) self.t_groups = t_groups_global self._classes = _classes_global self.models = deepcopy(models_global) return ate, ate_lower, ate_upper
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[ 185, 4 ]
[ 258, 44 ]
python
en
['en', 'it', 'en']
True
BaseSRegressor.__init__
(self, learner=None, ate_alpha=0.05, control_name=0)
Initialize an S-learner regressor. Args: learner (optional): a model to estimate the treatment effect control_name (str or int, optional): name of control group
Initialize an S-learner regressor. Args: learner (optional): a model to estimate the treatment effect control_name (str or int, optional): name of control group
def __init__(self, learner=None, ate_alpha=0.05, control_name=0): """Initialize an S-learner regressor. Args: learner (optional): a model to estimate the treatment effect control_name (str or int, optional): name of control group """ super().__init__( learner=learner, ate_alpha=ate_alpha, control_name=control_name)
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[ 266, 4 ]
[ 275, 38 ]
python
en
['en', 'fy', 'nl']
False
BaseSClassifier.__init__
(self, learner=None, ate_alpha=0.05, control_name=0)
Initialize an S-learner classifier. Args: learner (optional): a model to estimate the treatment effect. Should have a predict_proba() method. control_name (str or int, optional): name of control group
Initialize an S-learner classifier. Args: learner (optional): a model to estimate the treatment effect. Should have a predict_proba() method. control_name (str or int, optional): name of control group
def __init__(self, learner=None, ate_alpha=0.05, control_name=0): """Initialize an S-learner classifier. Args: learner (optional): a model to estimate the treatment effect. Should have a predict_proba() method. control_name (str or int, optional): name of control group """ super().__init__( learner=learner, ate_alpha=ate_alpha, control_name=control_name)
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[ 283, 4 ]
[ 293, 38 ]
python
en
['en', 'fy', 'nl']
False
BaseSClassifier.predict
(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True)
Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects.
Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects.
def predict(self, X, treatment=None, y=None, p=None, return_components=False, verbose=True): """Predict treatment effects. Args: X (np.matrix or np.array or pd.Dataframe): a feature matrix treatment (np.array or pd.Series, optional): a treatment vector y (np.array or pd.Series, optional): an outcome vector return_components (bool, optional): whether to return outcome for treatment and control seperately verbose (bool, optional): whether to output progress logs Returns: (numpy.ndarray): Predictions of treatment effects. """ X, treatment, y = convert_pd_to_np(X, treatment, y) yhat_cs = {} yhat_ts = {} for group in self.t_groups: model = self.models[group] # set the treatment column to zero (the control group) X_new = np.hstack((np.zeros((X.shape[0], 1)), X)) yhat_cs[group] = model.predict_proba(X_new)[:, 1] # set the treatment column to one (the treatment group) X_new[:, 0] = 1 yhat_ts[group] = model.predict_proba(X_new)[:, 1] if y is not None and (treatment is not None) and verbose: mask = (treatment == group) | (treatment == self.control_name) treatment_filt = treatment[mask] w = (treatment_filt == group).astype(int) y_filt = y[mask] yhat = np.zeros_like(y_filt, dtype=float) yhat[w == 0] = yhat_cs[group][mask][w == 0] yhat[w == 1] = yhat_ts[group][mask][w == 1] logger.info('Error metrics for group {}'.format(group)) classification_metrics(y_filt, yhat, w) te = np.zeros((X.shape[0], self.t_groups.shape[0])) for i, group in enumerate(self.t_groups): te[:, i] = yhat_ts[group] - yhat_cs[group] if not return_components: return te else: return te, yhat_cs, yhat_ts
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[ 295, 4 ]
[ 341, 39 ]
python
en
['fr', 'en', 'en']
True
LRSRegressor.__init__
(self, ate_alpha=.05, control_name=0)
Initialize an S-learner with a linear regression model. Args: ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group
Initialize an S-learner with a linear regression model. Args: ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group
def __init__(self, ate_alpha=.05, control_name=0): """Initialize an S-learner with a linear regression model. Args: ate_alpha (float, optional): the confidence level alpha of the ATE estimate control_name (str or int, optional): name of control group """ super().__init__(StatsmodelsOLS(alpha=ate_alpha), ate_alpha, control_name)
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[ 345, 4 ]
[ 351, 82 ]
python
en
['en', 'en', 'en']
True
LRSRegressor.estimate_ate
(self, X, treatment, y, p=None)
Estimate the Average Treatment Effect (ATE). Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector Returns: The mean and confidence interval (LB, UB) of the ATE estimate.
Estimate the Average Treatment Effect (ATE). Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector Returns: The mean and confidence interval (LB, UB) of the ATE estimate.
def estimate_ate(self, X, treatment, y, p=None): """Estimate the Average Treatment Effect (ATE). Args: X (np.matrix, np.array, or pd.Dataframe): a feature matrix treatment (np.array or pd.Series): a treatment vector y (np.array or pd.Series): an outcome vector Returns: The mean and confidence interval (LB, UB) of the ATE estimate. """ X, treatment, y = convert_pd_to_np(X, treatment, y) self.fit(X, treatment, y) ate = np.zeros(self.t_groups.shape[0]) ate_lb = np.zeros(self.t_groups.shape[0]) ate_ub = np.zeros(self.t_groups.shape[0]) for i, group in enumerate(self.t_groups): ate[i] = self.models[group].coefficients[0] ate_lb[i] = self.models[group].conf_ints[0, 0] ate_ub[i] = self.models[group].conf_ints[0, 1] return ate, ate_lb, ate_ub
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[ 353, 4 ]
[ 374, 34 ]
python
en
['en', 'it', 'en']
True
S3Logging.configure
(self, options, conf)
Get the options.
Get the options.
def configure(self, options, conf): """ Get the options. """ super(S3Logging, self).configure(options, conf) self.options = options
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[ 17, 4 ]
[ 20, 30 ]
python
en
['en', 'en', 'en']
True
S3Logging.afterTest
(self, test)
After each testcase, upload logs to the S3 bucket.
After each testcase, upload logs to the S3 bucket.
def afterTest(self, test): """ After each testcase, upload logs to the S3 bucket. """ s3_bucket = S3LoggingBucket() guid = str(uuid.uuid4().hex) path = "%s/%s" % (self.options.log_path, test.test.id()) uploaded_files = [] for logfile in os.listdir(path): logfile_name = "%s/%s/%s" % (guid, test.test.id(), logfile.split(path)[-1]) s3_bucket.upload_file(logfile_name, "%s/%s" % (path, logfile)) uploaded_files.append(logfile_name) s3_bucket.save_uploaded_file_names(uploaded_files) index_file = s3_bucket.upload_index_file(test.id(), guid) print("\n\n*** Log files uploaded: ***\n%s\n" % index_file) logging.error("\n\n*** Log files uploaded: ***\n%s\n" % index_file) # If the database plugin is running, attach a link # to the logs index database row if hasattr(test.test, "testcase_guid"): from seleniumbase.core.testcase_manager \ import TestcaseDataPayload, TestcaseManager self.testcase_manager = TestcaseManager(self.options.database_env) data_payload = TestcaseDataPayload() data_payload.guid = test.test.testcase_guid data_payload.log_url = index_file self.testcase_manager.update_testcase_log_url(data_payload)
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[ 22, 4 ]
[ 50, 71 ]
python
en
['en', 'en', 'en']
True