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83 lines
3.8 KiB
Python
83 lines
3.8 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license.
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import os
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import joblib
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from interpret.ext.glassbox import LGBMExplainableModel
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from azureml.automl.core.shared.constants import MODEL_PATH
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from azureml.core.experiment import Experiment
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from azureml.core.dataset import Dataset
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from azureml.core.run import Run
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from azureml.interpret.mimic_wrapper import MimicWrapper
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from azureml.interpret.scoring.scoring_explainer import TreeScoringExplainer
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from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations, \
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automl_check_model_if_explainable
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OUTPUT_DIR = './outputs/'
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# Get workspace from the run context
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run = Run.get_context()
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ws = run.experiment.workspace
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# Get the AutoML run object from the experiment name and the workspace
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experiment = Experiment(ws, '<<experiment_name>>')
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automl_run = Run(experiment=experiment, run_id='<<run_id>>')
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# Check if this AutoML model is explainable
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if not automl_check_model_if_explainable(automl_run):
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raise Exception("Model explanations is currently not supported for " + automl_run.get_properties().get(
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'run_algorithm'))
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# Download the best model from the artifact store
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automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
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# Load the AutoML model into memory
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fitted_model = joblib.load('model.pkl')
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# Get the train dataset from the workspace
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train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
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# Drop the lablled column to get the training set.
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X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
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y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
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# Get the train dataset from the workspace
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test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
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# Drop the lablled column to get the testing set.
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X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
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# Setup the class for explaining the AtuoML models
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automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
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X=X_train, X_test=X_test,
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y=y_train)
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# Initialize the Mimic Explainer
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explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
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init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,
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features=automl_explainer_setup_obj.engineered_feature_names,
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feature_maps=[automl_explainer_setup_obj.feature_map],
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classes=automl_explainer_setup_obj.classes)
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# Compute the engineered explanations
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engineered_explanations = explainer.explain(['local', 'global'], tag='engineered explanations',
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eval_dataset=automl_explainer_setup_obj.X_test_transform)
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# Compute the raw explanations
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raw_explanations = explainer.explain(['local', 'global'], get_raw=True, tag='raw explanations',
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raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
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eval_dataset=automl_explainer_setup_obj.X_test_transform,
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raw_eval_dataset=automl_explainer_setup_obj.X_test_raw)
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print("Engineered and raw explanations computed successfully")
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# Initialize the ScoringExplainer
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scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])
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# Pickle scoring explainer locally
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with open('scoring_explainer.pkl', 'wb') as stream:
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joblib.dump(scoring_explainer, stream)
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# Upload the scoring explainer to the automl run
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automl_run.upload_file('outputs/scoring_explainer.pkl', 'scoring_explainer.pkl')
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