#=============================================================================== # Copyright 2014 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #=============================================================================== from daal4py.sklearn._utils import set_idp_sklearn_verbose from ..neighbors import KNeighborsRegressor as KNeighborsRegressor_daal4py from ..neighbors import NearestNeighbors as NearestNeighbors_daal4py from ..neighbors import KNeighborsClassifier as KNeighborsClassifier_daal4py from ..model_selection import _daal_train_test_split from ..utils.validation import _daal_assert_all_finite from ..svm.svm import SVC as SVC_daal4py from ..ensemble._forest import RandomForestClassifier as RandomForestClassifier_daal4py from ..ensemble._forest import RandomForestRegressor as RandomForestRegressor_daal4py from ..metrics import _daal_roc_auc_score from ..metrics import daal_pairwise_distances from ..cluster.k_means import KMeans as KMeans_daal4py from ..cluster.dbscan import DBSCAN as DBSCAN_daal4py from ..linear_model.coordinate_descent import Lasso as Lasso_daal4py from ..linear_model.coordinate_descent import ElasticNet as ElasticNet_daal4py from ..linear_model.linear import LinearRegression as LinearRegression_daal4py from ..linear_model.ridge import Ridge as Ridge_daal4py from ..linear_model.logistic_path import LogisticRegression as LogisticRegression_daal4py from ..linear_model.logistic_path import logistic_regression_path as \ daal_optimized_logistic_path from ..decomposition._pca import PCA as PCA_daal4py from ..manifold import TSNE as TSNE_daal4py from sklearn import model_selection from sklearn import metrics from sklearn.utils import validation import sys from functools import lru_cache import sklearn.cluster as cluster_module import sklearn.ensemble as ensemble_module import sklearn.svm as svm_module import sklearn.linear_model._logistic as logistic_module import sklearn.neighbors as neighbors_module import sklearn.decomposition as decomposition_module import sklearn.linear_model as linear_model_module import sklearn.manifold as manifold_module import warnings @lru_cache(maxsize=None) def _get_map_of_algorithms(): mapping = { 'pca': [[(decomposition_module, 'PCA', PCA_daal4py), None]], 'kmeans': [[(cluster_module, 'KMeans', KMeans_daal4py), None]], 'dbscan': [[(cluster_module, 'DBSCAN', DBSCAN_daal4py), None]], 'distances': [[(metrics, 'pairwise_distances', daal_pairwise_distances), None]], 'linear': [[(linear_model_module, 'LinearRegression', LinearRegression_daal4py), None]], 'ridge': [[(linear_model_module, 'Ridge', Ridge_daal4py), None]], 'elasticnet': [[(linear_model_module, 'ElasticNet', ElasticNet_daal4py), None]], 'lasso': [[(linear_model_module, 'Lasso', Lasso_daal4py), None]], 'svm': [[(svm_module, 'SVC', SVC_daal4py), None]], 'logistic': [[(logistic_module, '_logistic_regression_path', daal_optimized_logistic_path), None]], 'log_reg': [[(linear_model_module, 'LogisticRegression', LogisticRegression_daal4py), None]], 'knn_classifier': [[(neighbors_module, 'KNeighborsClassifier', KNeighborsClassifier_daal4py), None]], 'nearest_neighbors': [[(neighbors_module, 'NearestNeighbors', NearestNeighbors_daal4py), None]], 'knn_regressor': [[(neighbors_module, 'KNeighborsRegressor', KNeighborsRegressor_daal4py), None]], 'random_forest_classifier': [[(ensemble_module, 'RandomForestClassifier', RandomForestClassifier_daal4py), None]], 'random_forest_regressor': [[(ensemble_module, 'RandomForestRegressor', RandomForestRegressor_daal4py), None]], 'train_test_split': [[(model_selection, 'train_test_split', _daal_train_test_split), None]], 'fin_check': [[(validation, '_assert_all_finite', _daal_assert_all_finite), None]], 'roc_auc_score': [[(metrics, 'roc_auc_score', _daal_roc_auc_score), None]], 'tsne': [[(manifold_module, 'TSNE', TSNE_daal4py), None]], } mapping['svc'] = mapping['svm'] mapping['logisticregression'] = mapping['log_reg'] mapping['kneighborsclassifier'] = mapping['knn_classifier'] mapping['nearestneighbors'] = mapping['nearest_neighbors'] mapping['kneighborsregressor'] = mapping['knn_regressor'] mapping['randomrorestclassifier'] = mapping['random_forest_classifier'] mapping['randomforestregressor'] = mapping['random_forest_regressor'] return mapping def do_patch(name, get_map=_get_map_of_algorithms): lname = name.lower() if lname in get_map(): for descriptor in get_map()[lname]: which, what, replacer = descriptor[0] if descriptor[1] is None: descriptor[1] = getattr(which, what, None) setattr(which, what, replacer) else: raise ValueError("Has no patch for: " + name) def do_unpatch(name, get_map=_get_map_of_algorithms): lname = name.lower() if lname in get_map(): for descriptor in get_map()[lname]: if descriptor[1] is not None: which, what, _ = descriptor[0] setattr(which, what, descriptor[1]) else: raise ValueError("Has no patch for: " + name) def enable(name=None, verbose=True, deprecation=True, get_map=_get_map_of_algorithms): if name is not None: do_patch(name, get_map) else: for key in get_map(): do_patch(key, get_map) if deprecation: set_idp_sklearn_verbose() warnings.warn_explicit("\nScikit-learn patching with daal4py is deprecated " "and will be removed in the future.\n" "Use Intel(R) Extension " "for Scikit-learn* module instead " "(pip install scikit-learn-intelex).\n" "To enable patching, please use one of the " "following options:\n" "1) From the command line:\n" " python -m sklearnex \n" "2) From your script:\n" " from sklearnex import patch_sklearn\n" " patch_sklearn()", FutureWarning, "dispatcher.py", 151) if verbose and deprecation and sys.stderr is not None: sys.stderr.write( "Intel(R) oneAPI Data Analytics Library solvers for sklearn enabled: " "https://intelpython.github.io/daal4py/sklearn.html\n") def disable(name=None, get_map=_get_map_of_algorithms): if name is not None: do_unpatch(name, get_map) else: for key in get_map(): do_unpatch(key, get_map) get_map.cache_clear() def _is_enabled(name, get_map=_get_map_of_algorithms): lname = name.lower() if lname in get_map(): enabled = True for descriptor in get_map()[lname]: which, what, replacer = descriptor[0] enabled = enabled and getattr(which, what) == replacer return enabled else: raise ValueError("Has no patch for: " + name) def patch_is_enabled(name=None, get_map=_get_map_of_algorithms, return_map=False): if name is not None: return _is_enabled(name, get_map) else: if return_map: enabled = {} for key in get_map(): enabled[key] = _is_enabled(key, get_map) else: enabled = True for key in get_map(): enabled = enabled and _is_enabled(key, get_map) return enabled def _patch_names(): return list(_get_map_of_algorithms().keys())