text: # Copyright (c) 2020-2021, 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 cupy
  
  from dask_ml.wrappers import ParallelPostFit
  
  from cuml.dask.cluster import KMeans
  from cuml.dask.naive_bayes.naive_bayes import MultinomialNB
  from cuml.test.dask.utils import load_text_corpus
  
  from cuml.dask.datasets import make_blobs
  
  import cuml
  import numpy as np
  import pytest
  
  from numpy.testing import assert_equal
  
  from cuml.dask.linear_model import LinearRegression
  
  from cuml.dask.datasets import make_regression
  from sklearn.model_selection import train_test_split
  
  
  def make_dataset(datatype, nrows, ncols, n_info):
      X, y = make_regression(n_samples=nrows, n_features=ncols,
                             n_informative=n_info, random_state=0)
      X = X.astype(datatype)
      y = y.astype(datatype)
      X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)
      return X_train, y_train, X_test
  
  
  @pytest.mark.parametrize('datatype', [np.float32, np.float64])
  @pytest.mark.parametrize('keys', [cuml.dask.linear_model.LinearRegression])
  @pytest.mark.parametrize('data_size', [[500, 20, 10]])
  @pytest.mark.parametrize('fit_intercept', [True, False])
  def test_get_combined_model(datatype, keys, data_size, fit_intercept,
                              client):
  
      nrows, ncols, n_info = data_size
      X_train, y_train, X_test = make_dataset(datatype, nrows,
                                              ncols, n_info)
      model = LinearRegression(fit_intercept=fit_intercept,
                               client=client)
      model.fit(X_train, y_train)
  
      combined_model = model.get_combined_model()
      assert combined_model.coef_ is not None
      assert combined_model.intercept_ is not None
  
      y_hat = combined_model.predict(X_train.compute())
  
      np.testing.assert_allclose(y_hat.get(), y_train.compute().get(),
                                 atol=1e-3, rtol=1e-3)
  
  
  def test_check_internal_model_failures(client):
  
      # Test model not trained yet
      model = LinearRegression(client=client)
      assert model.get_combined_model() is None
  
      # Test single Int future fails
      int_future = client.submit(lambda: 1)
      with pytest.raises(ValueError):
          model._set_internal_model(int_future)
  
      # Test list Int future fails
      with pytest.raises(ValueError):
          model._set_internal_model([int_future])
  
      # Test directly setting Int fails
      with pytest.raises(ValueError):
          model._set_internal_model(1)
  
  
  @pytest.mark.parametrize('datatype', [np.float32, np.float64])
  @pytest.mark.parametrize('keys', [cuml.dask.linear_model.LinearRegression])
  @pytest.mark.parametrize('data_size', [[500, 20, 10]])
  @pytest.mark.parametrize('fit_intercept', [True, False])
  def test_regressor_mg_train_sg_predict(datatype, keys, data_size,
                                         fit_intercept, client):
  
      nrows, ncols, n_info = data_size
      X_train, y_train, X_test = make_dataset(datatype, nrows, ncols, n_info)
  
      X_test_local = X_test.compute()
  
      dist_model = LinearRegression(fit_intercept=fit_intercept, client=client)
      dist_model.fit(X_train, y_train)
  
      expected = dist_model.predict(X_test).compute()
  
      local_model = dist_model.get_combined_model()
      actual = local_model.predict(X_test_local)
  
      assert_equal(expected.get(), actual.get())
  
  
  @pytest.mark.parametrize('datatype', [np.float32, np.float64])
  @pytest.mark.parametrize('keys', [cuml.linear_model.LinearRegression])
  @pytest.mark.parametrize('data_size', [[500, 20, 10]])
  @pytest.mark.parametrize('fit_intercept', [True, False])
  def test_regressor_sg_train_mg_predict(datatype, keys, data_size,
                                         fit_intercept, client):
  
      # Just testing for basic compatibility w/ dask-ml's ParallelPostFit.
      # Refer to test_pickle.py for more extensive testing of single-GPU
      # model serialization.
  
      nrows, ncols, n_info = data_size
      X_train, y_train, _ = make_dataset(datatype, nrows, ncols, n_info)
  
      X_train_local = X_train.compute()
      y_train_local = y_train.compute()
  
      local_model = cuml.linear_model.LinearRegression(
          fit_intercept=fit_intercept)
      local_model.fit(X_train_local, y_train_local)
  
      dist_model = ParallelPostFit(estimator=local_model)
  
      predictions = dist_model.predict(X_train).compute()
  
      assert isinstance(predictions, cupy.ndarray)
  
      # Dataset should be fairly linear already so the predictions should
      # be very close to the training data.
      np.testing.assert_allclose(predictions.get(), y_train.compute().get(),
                                 atol=1e-3, rtol=1e-3)
  
  
  def test_getattr(client):
  
      # Test getattr on local param
      kmeans_model = KMeans(client=client)
  
      # Test AttributeError
      with pytest.raises(AttributeError):
          kmeans_model.cluster_centers_
  
      assert kmeans_model.client is not None
  
      # Test getattr on local_model param with a non-distributed model
  
      X, y = make_blobs(n_samples=5,
                        n_features=5,
                        centers=2,
                        n_parts=2,
                        cluster_std=0.01,
                        random_state=10)
  
      kmeans_model.fit(X)
  
      assert kmeans_model.cluster_centers_ is not None
      assert isinstance(kmeans_model.cluster_centers_, cupy.ndarray)
  
      # Test getattr on trained distributed model
  
      X, y = load_text_corpus(client)
  
      nb_model = MultinomialNB(client=client)
      nb_model.fit(X, y)
  
      assert nb_model.feature_count_ is not None
      assert isinstance(nb_model.feature_count_, cupy.ndarray)
  