update samples from Release-141 as a part of 1.0.57 SDK release

how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py

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+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+from sklearn import datasets
+from sklearn.linear_model import Ridge
+from azureml.explain.model.tabular_explainer import TabularExplainer
+from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient
+from sklearn.model_selection import train_test_split
+from azureml.core.run import Run
+from sklearn.externals import joblib
+import os
+import numpy as np
+
+OUTPUT_DIR = './outputs/'
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+boston_data = datasets.load_boston()
+
+run = Run.get_context()
+client = ExplanationClient.from_run(run)
+
+X_train, X_test, y_train, y_test = train_test_split(boston_data.data,
+                                                    boston_data.target,
+                                                    test_size=0.2,
+                                                    random_state=0)
+# write x_test out as a pickle file for later visualization
+x_test_pkl = 'x_test.pkl'
+with open(x_test_pkl, 'wb') as file:
+    joblib.dump(value=X_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
+run.upload_file('x_test_boston_housing.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
+
+
+alpha = 0.5
+# Use Ridge algorithm to create a regression model
+reg = Ridge(alpha)
+model = reg.fit(X_train, y_train)
+
+preds = reg.predict(X_test)
+run.log('alpha', alpha)
+
+model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
+# save model in the outputs folder so it automatically get uploaded
+with open(model_file_name, 'wb') as file:
+    joblib.dump(value=reg, filename=os.path.join(OUTPUT_DIR,
+                                                 model_file_name))
+
+# register the model
+run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))
+original_model = run.register_model(model_name='original_model', model_path='original_model.pkl')
+
+# Explain predictions on your local machine
+tabular_explainer = TabularExplainer(model, X_train, features=boston_data.feature_names)
+
+# Explain overall model predictions (global explanation)
+# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data
+# x_train can be passed as well, but with more examples explanations it will
+# take longer although they may be more accurate
+global_explanation = tabular_explainer.explain_global(X_test)
+
+# Uploading model explanation data for storage or visualization in webUX
+# The explanation can then be downloaded on any compute
+comment = 'Global explanation on regression model trained on boston dataset'
+client.upload_model_explanation(global_explanation, comment=comment)