MAINT: align daal4py, onedal and sklearnex modules with isort a…

…nd black (#1375)

* apply isort and black fixes for daal4py, onedal and sklearnex modules

daal4py/__init__.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,44 +13,47 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 import platform
+
 if "Windows" in platform.system():
     import os
-    import sys
     import site
+    import sys
+
     current_path = os.path.dirname(__file__)
     path_to_env = site.getsitepackages()[0]
     path_to_libs = os.path.join(path_to_env, "Library", "bin")
     path_to_oneapi_backend = os.path.join(current_path, "oneapi")
     if sys.version_info.minor >= 8:
-        if 'DALROOT' in os.environ:
-            dal_root_redist = os.path.join(os.environ['DALROOT'], "redist", "intel64")
+        if "DALROOT" in os.environ:
+            dal_root_redist = os.path.join(os.environ["DALROOT"], "redist", "intel64")
             if os.path.exists(dal_root_redist):
                 os.add_dll_directory(dal_root_redist)
-                os.environ['PATH'] = dal_root_redist + os.pathsep + os.environ['PATH']
+                os.environ["PATH"] = dal_root_redist + os.pathsep + os.environ["PATH"]
         os.add_dll_directory(path_to_libs)
         os.add_dll_directory(path_to_oneapi_backend)
-    os.environ['PATH'] = path_to_libs + os.pathsep + os.environ['PATH']
+    os.environ["PATH"] = path_to_libs + os.pathsep + os.environ["PATH"]
 
 try:
     from daal4py._daal4py import *
     from daal4py._daal4py import (
-        _get__version__,
+        __has_dist__,
         _get__daal_link_version__,
         _get__daal_run_version__,
-        __has_dist__)
+        _get__version__,
+    )
 except ImportError as e:
     s = str(e)
-    if 'libfabric' in s:
+    if "libfabric" in s:
         raise ImportError(
-            s + '\n\nActivating your conda environment or sourcing mpivars.'
-            '[c]sh/psxevars.[c]sh may solve the issue.\n')
+            s + "\n\nActivating your conda environment or sourcing mpivars."
+            "[c]sh/psxevars.[c]sh may solve the issue.\n"
+        )
 
     raise
 
-from . import mb
-from . import sklearn
+from . import mb, sklearn
 
-__all__ = ['mb', 'sklearn']
+__all__ = ["mb", "sklearn"]

daal4py/__main__.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-#===============================================================================
+# ===============================================================================
 # Copyright 2014 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,9 +13,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#===============================================================================
+# ===============================================================================
 
 import sys
+
 from .sklearn import patch_sklearn
 
 
@@ -29,27 +30,30 @@ def _main():
             Python* patches of scikit-learn, optimizing solvers of
             scikit-learn with Intel(R) oneAPI Data Analytics Library.
             """,
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    parser.add_argument('-m', action='store_true', dest='module',
-                        help="Executes following as a module")
-    parser.add_argument('name', help="Script or module name")
-    parser.add_argument('args', nargs=argparse.REMAINDER,
-                        help="Command line arguments")
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "-m", action="store_true", dest="module", help="Executes following as a module"
+    )
+    parser.add_argument("name", help="Script or module name")
+    parser.add_argument("args", nargs=argparse.REMAINDER, help="Command line arguments")
     args = parser.parse_args()
 
     try:
         import sklearn
+
         patch_sklearn()
     except ImportError:
         print("Scikit-learn could not be imported. Nothing to patch")
 
     sys.argv = [args.name] + args.args
-    if '_' + args.name in globals():
-        return globals()['_' + args.name](*args.args)
+    if "_" + args.name in globals():
+        return globals()["_" + args.name](*args.args)
     import runpy
+
     runf = runpy.run_module if args.module else runpy.run_path
-    runf(args.name, run_name='__main__')
+    runf(args.name, run_name="__main__")
 
 
 sys.exit(_main())

daal4py/mb/__init__.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-#===============================================================================
+# ===============================================================================
 # Copyright 2023 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,8 +13,8 @@
 # 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 .model_builders import GBTDAALBaseModel, convert_model
 
-__all__ = ['GBTDAALBaseModel', 'convert_model']
+__all__ = ["GBTDAALBaseModel", "convert_model"]

daal4py/mb/model_builders.py

@@ -1,4 +1,4 @@
-#===============================================================================
+# ===============================================================================
 # Copyright 2023 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,16 +12,18 @@
 # 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.
-#===============================================================================
+# ===============================================================================
 
 # daal4py Model builders API
 
 import numpy as np
+
 import daal4py as d4p
 
 try:
     from pandas import DataFrame
     from pandas.core.dtypes.cast import find_common_type
+
     pandas_is_imported = True
 except (ImportError, ModuleNotFoundError):
     pandas_is_imported = False
@@ -41,7 +43,7 @@ def getFPType(X):
             dt = find_common_type(X.dtypes.tolist())
             return parse_dtype(dt)
 
-    dt = getattr(X, 'dtype', None)
+    dt = getattr(X, "dtype", None)
     return parse_dtype(dt)
 
 
@@ -65,9 +67,9 @@ def _get_params_from_xgboost(self, params):
         self.n_features_in_ = int(params["learner"]["learner_model_param"]["num_feature"])
 
     def _get_params_from_catboost(self, params):
-        if 'class_params' in params['model_info']:
-            self.n_classes_ = len(params['model_info']['class_params']['class_to_label'])
-        self.n_features_in_ = len(params['features_info']['float_features'])
+        if "class_params" in params["model_info"]:
+            self.n_classes_ = len(params["model_info"]["class_params"]["class_to_label"])
+        self.n_features_in_ = len(params["features_info"]["float_features"])
 
     def _convert_model_from_lightgbm(self, booster):
         lgbm_params = d4p.get_lightgbm_params(booster)
@@ -85,91 +87,112 @@ def _convert_model_from_catboost(self, booster):
         self._get_params_from_catboost(catboost_params)
 
     def _convert_model(self, model):
-        (submodule_name, class_name) = (model.__class__.__module__,
-                                        model.__class__.__name__)
+        (submodule_name, class_name) = (
+            model.__class__.__module__,
+            model.__class__.__name__,
+        )
         self_class_name = self.__class__.__name__
 
         # Build GBTDAALClassifier from LightGBM
         if (submodule_name, class_name) == ("lightgbm.sklearn", "LGBMClassifier"):
             if self_class_name == "GBTDAALClassifier":
                 self._convert_model_from_lightgbm(model.booster_)
             else:
-                raise TypeError(f"Only GBTDAALClassifier can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALClassifier can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALClassifier from XGBoost
         elif (submodule_name, class_name) == ("xgboost.sklearn", "XGBClassifier"):
             if self_class_name == "GBTDAALClassifier":
                 self._convert_model_from_xgboost(model.get_booster())
             else:
-                raise TypeError(f"Only GBTDAALClassifier can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALClassifier can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALClassifier from CatBoost
         elif (submodule_name, class_name) == ("catboost.core", "CatBoostClassifier"):
             if self_class_name == "GBTDAALClassifier":
                 self._convert_model_from_catboost(model)
             else:
-                raise TypeError(f"Only GBTDAALClassifier can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALClassifier can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALRegressor from LightGBM
         elif (submodule_name, class_name) == ("lightgbm.sklearn", "LGBMRegressor"):
             if self_class_name == "GBTDAALRegressor":
                 self._convert_model_from_lightgbm(model.booster_)
             else:
-                raise TypeError(f"Only GBTDAALRegressor can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALRegressor can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALRegressor from XGBoost
         elif (submodule_name, class_name) == ("xgboost.sklearn", "XGBRegressor"):
             if self_class_name == "GBTDAALRegressor":
                 self._convert_model_from_xgboost(model.get_booster())
             else:
-                raise TypeError(f"Only GBTDAALRegressor can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALRegressor can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALRegressor from CatBoost
         elif (submodule_name, class_name) == ("catboost.core", "CatBoostRegressor"):
             if self_class_name == "GBTDAALRegressor":
                 self._convert_model_from_catboost(model)
             else:
-                raise TypeError(f"Only GBTDAALRegressor can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALRegressor can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALModel from LightGBM
         elif (submodule_name, class_name) == ("lightgbm.basic", "Booster"):
             if self_class_name == "GBTDAALModel":
                 self._convert_model_from_lightgbm(model)
             else:
-                raise TypeError(f"Only GBTDAALModel can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALModel can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALModel from XGBoost
         elif (submodule_name, class_name) == ("xgboost.core", "Booster"):
             if self_class_name == "GBTDAALModel":
                 self._convert_model_from_xgboost(model)
             else:
-                raise TypeError(f"Only GBTDAALModel can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALModel can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         # Build GBTDAALModel from CatBoost
         elif (submodule_name, class_name) == ("catboost.core", "CatBoost"):
             if self_class_name == "GBTDAALModel":
                 self._convert_model_from_catboost(model)
             else:
-                raise TypeError(f"Only GBTDAALModel can be created from\
-                                 {submodule_name}.{class_name} (got {self_class_name})")
+                raise TypeError(
+                    f"Only GBTDAALModel can be created from\
+                                 {submodule_name}.{class_name} (got {self_class_name})"
+                )
         else:
             raise TypeError(f"Unknown model format {submodule_name}.{class_name}")
 
     def _predict_classification(self, X, fptype, resultsToEvaluate):
         if X.shape[1] != self.n_features_in_:
-            raise ValueError('Shape of input is different from what was seen in `fit`')
+            raise ValueError("Shape of input is different from what was seen in `fit`")
 
-        if not hasattr(self, 'daal_model_'):
-            raise ValueError((
-                "The class {} instance does not have 'daal_model_' attribute set. "
-                "Call 'fit' with appropriate arguments before using this method.")
-                .format(type(self).__name__))
+        if not hasattr(self, "daal_model_"):
+            raise ValueError(
+                (
+                    "The class {} instance does not have 'daal_model_' attribute set. "
+                    "Call 'fit' with appropriate arguments before using this method."
+                ).format(type(self).__name__)
+            )
 
         # Prediction
         predict_algo = d4p.gbt_classification_prediction(
-            fptype=fptype,
-            nClasses=self.n_classes_,
-            resultsToEvaluate=resultsToEvaluate)
+            fptype=fptype, nClasses=self.n_classes_, resultsToEvaluate=resultsToEvaluate
+        )
         predict_result = predict_algo.compute(X, self.daal_model_)
 
         if resultsToEvaluate == "computeClassLabels":
@@ -179,13 +202,15 @@ def _predict_classification(self, X, fptype, resultsToEvaluate):
 
     def _predict_regression(self, X, fptype):
         if X.shape[1] != self.n_features_in_:
-            raise ValueError('Shape of input is different from what was seen in `fit`')
-
-        if not hasattr(self, 'daal_model_'):
-            raise ValueError((
-                "The class {} instance does not have 'daal_model_' attribute set. "
-                "Call 'fit' with appropriate arguments before using this method.").format(
-                    type(self).__name__))
+            raise ValueError("Shape of input is different from what was seen in `fit`")
+
+        if not hasattr(self, "daal_model_"):
+            raise ValueError(
+                (
+                    "The class {} instance does not have 'daal_model_' attribute set. "
+                    "Call 'fit' with appropriate arguments before using this method."
+                ).format(type(self).__name__)
+            )
 
         # Prediction
         predict_algo = d4p.gbt_regression_prediction(fptype=fptype)