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60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license.
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from azureml.core.run import Run
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from azureml.interpret import ExplanationClient
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from interpret_community.adapter import ExplanationAdapter
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import joblib
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import os
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import shap
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import xgboost
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OUTPUT_DIR = './outputs/'
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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run = Run.get_context()
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client = ExplanationClient.from_run(run)
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# get a dataset on income prediction
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X, y = shap.datasets.adult()
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features = X.columns.values
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# train an XGBoost model (but any other tree model type should work)
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model = xgboost.XGBClassifier()
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model.fit(X, y)
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explainer = shap.explainers.GPUTree(model, X)
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X_shap = X[:100]
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shap_values = explainer(X_shap)
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print("computed shap values:")
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print(shap_values)
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# Use the explanation adapter to convert the importances into an interpret-community
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# style explanation which can be uploaded to AzureML or visualized in the
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# ExplanationDashboard widget
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adapter = ExplanationAdapter(features, classification=True)
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global_explanation = adapter.create_global(shap_values.values, X_shap, expected_values=shap_values.base_values)
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# write X_shap out as a pickle file for later visualization
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x_shap_pkl = 'x_shap.pkl'
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with open(x_shap_pkl, 'wb') as file:
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joblib.dump(value=X_shap, filename=os.path.join(OUTPUT_DIR, x_shap_pkl))
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run.upload_file('x_shap_adult_census.pkl', os.path.join(OUTPUT_DIR, x_shap_pkl))
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model_file_name = 'xgboost_.pkl'
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# save model in the outputs folder so it automatically gets uploaded
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with open(model_file_name, 'wb') as file:
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joblib.dump(value=model, filename=os.path.join(OUTPUT_DIR,
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model_file_name))
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# register the model
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run.upload_file('xgboost_model.pkl', os.path.join('./outputs/', model_file_name))
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original_model = run.register_model(model_name='xgboost_with_gpu_tree_explainer',
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model_path='xgboost_model.pkl')
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# Uploading model explanation data for storage or visualization in webUX
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# The explanation can then be downloaded on any compute
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comment = 'Global explanation on classification model trained on adult census income dataset'
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client.upload_model_explanation(global_explanation, comment=comment, model_id=original_model.id)
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