Sklearn meta-estimators into namespace (#3493)

Closes #3484

Imports sklearn's Pipeline and GridSearch meta-estimators into cuML namespace for ease-of-use.

Authors:
  - Victor Lafargue (@viclafargue)

Approvers:
  - William Hicks (@wphicks)
  - John Zedlewski (@JohnZed)

URL: #3493

python/cuml/model_selection/__init__.py

@@ -1,4 +1,28 @@
+#
+# Copyright (c) 2021, NVIDIA 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 cuml.model_selection._split import train_test_split
+from sklearn.model_selection import GridSearchCV
+
 
+GridSearchCV.__doc__ = """
+This code is developed and maintained by scikit-learn and imported
+by cuML to maintain the familiar sklearn namespace structure.
+cuML includes tests to ensure full compatibility of these wrappers
+with CUDA-based data and cuML estimators, but all of the underlying code
+is due to the scikit-learn developers.\n\n""" + GridSearchCV.__doc__
 
-__all__ = ['train_test_split']
+__all__ = ['train_test_split', 'GridSearchCV']

python/cuml/pipeline/__init__.py

@@ -0,0 +1,27 @@
+#
+# Copyright (c) 2021, NVIDIA 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 sklearn.pipeline import Pipeline
+
+
+Pipeline.__doc__ = """
+This code is developed and maintained by scikit-learn and imported
+by cuML to maintain the familiar sklearn namespace structure.
+cuML includes tests to ensure full compatibility of these wrappers
+with CUDA-based data and cuML estimators, but all of the underlying code
+is due to the scikit-learn developers.\n\n""" + Pipeline.__doc__
+
+__all__ = ['Pipeline']

python/cuml/test/test_meta_estimators.py

@@ -0,0 +1,102 @@
+#
+# Copyright (c) 2021, NVIDIA 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.
+#
+
+import pytest
+import cuml
+import cupy
+
+from cuml.pipeline import Pipeline
+from cuml.model_selection import GridSearchCV
+
+from cuml.test.utils import ClassEnumerator
+
+from cuml.datasets import make_regression, make_classification
+from cuml.model_selection import train_test_split
+from sklearn.datasets import load_iris
+
+from cuml.experimental.preprocessing import StandardScaler
+from cuml.svm import SVC
+
+
+def test_pipeline():
+    X, y = make_classification(random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    pipe = Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
+    pipe.fit(X_train, y_train)
+    score = pipe.score(X_test, y_test)
+    assert score > 0.8
+
+
+def test_gridsearchCV():
+    iris = load_iris()
+    parameters = {'kernel': ('linear', 'rbf'), 'C': [1, 10]}
+    clf = GridSearchCV(SVC(), parameters)
+    clf.fit(iris.data, iris.target)
+    assert clf.best_params_['kernel'] == 'rbf'
+    assert clf.best_params_['C'] == 10
+
+
+@pytest.fixture(scope="session")
+def regression_dataset(request):
+    X, y = make_regression(n_samples=10, n_features=5, random_state=0)
+    return train_test_split(X, y, random_state=0)
+
+
+@pytest.fixture(scope="session")
+def classification_dataset(request):
+    X, y = make_classification(n_samples=10, n_features=5, random_state=0)
+    return train_test_split(X, y, random_state=0)
+
+
+models_config = ClassEnumerator(module=cuml)
+models = models_config.get_models()
+
+
+@pytest.mark.parametrize('model_key', ['ElasticNet',
+                                       'Lasso',
+                                       'Ridge',
+                                       'LinearRegression',
+                                       'LogisticRegression',
+                                       'MBSGDRegressor',
+                                       'RandomForestRegressor',
+                                       'KNeighborsRegressor'])
+def test_pipeline_with_regression(regression_dataset, model_key):
+    X_train, X_test, y_train, y_test = regression_dataset
+    model_const = models[model_key]
+    if model_key == 'RandomForestRegressor':
+        model = model_const(n_bins=2)
+    else:
+        model = model_const()
+    pipe = Pipeline(steps=[('scaler', StandardScaler()), ('model', model)])
+    pipe.fit(X_train, y_train)
+    prediction = pipe.predict(X_test)
+    assert isinstance(prediction, cupy.ndarray)
+
+
+@pytest.mark.parametrize('model_key', ['MBSGDClassifier',
+                                       'RandomForestClassifier',
+                                       'KNeighborsClassifier'])
+def test_pipeline_with_classification(classification_dataset, model_key):
+    X_train, X_test, y_train, y_test = classification_dataset
+    model_const = models[model_key]
+    if model_key == 'RandomForestClassifier':
+        model = model_const(n_bins=2)
+    else:
+        model = model_const()
+    pipe = Pipeline(steps=[('scaler', StandardScaler()), ('model', model)])
+    pipe.fit(X_train, y_train)
+    prediction = pipe.predict(X_test)
+    assert isinstance(prediction, cupy.ndarray)