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101 lines
3.6 KiB
Python
101 lines
3.6 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 (
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automl_setup_model_explanations,
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automl_check_model_if_explainable,
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)
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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(
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"Model explanations are currently not supported for "
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+ automl_run.get_properties().get("run_algorithm")
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)
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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 labeled 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 test 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 labeled 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 AutoML models
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automl_explainer_setup_obj = automl_setup_model_explanations(
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fitted_model, "<<task>>", X=X_train, X_test=X_test, y=y_train, automl_run=automl_run
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)
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# Initialize the Mimic Explainer
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explainer = MimicWrapper(
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ws,
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automl_explainer_setup_obj.automl_estimator,
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LGBMExplainableModel,
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init_dataset=automl_explainer_setup_obj.X_transform,
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run=automl_explainer_setup_obj.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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)
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# Compute the engineered explanations
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engineered_explanations = explainer.explain(
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["local", "global"],
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tag="engineered explanations",
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eval_dataset=automl_explainer_setup_obj.X_test_transform,
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)
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# Compute the raw explanations
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raw_explanations = explainer.explain(
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["local", "global"],
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get_raw=True,
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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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)
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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(
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explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map]
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)
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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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