wehub-resource-sync
39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import os
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import numpy as np
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import shap
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from sklearn.datasets import load_breast_cancer
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from sklearn.ensemble import RandomForestClassifier
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import mlflow
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from mlflow.artifacts import download_artifacts
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from mlflow.tracking import MlflowClient
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# prepare training data
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X, y = load_breast_cancer(return_X_y=True, as_frame=True)
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X = X.iloc[:50, :8]
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y = y.iloc[:50]
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# train a model
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model = RandomForestClassifier()
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model.fit(X, y)
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# log an explanation
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with mlflow.start_run() as run:
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mlflow.shap.log_explanation(lambda X: model.predict_proba(X)[:, 1], X)
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# list artifacts
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client = MlflowClient()
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artifact_path = "model_explanations_shap"
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artifacts = [x.path for x in client.list_artifacts(run.info.run_id, artifact_path)]
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print("# artifacts:")
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print(artifacts)
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# load back the logged explanation
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dst_path = download_artifacts(run_id=run.info.run_id, artifact_path=artifact_path)
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base_values = np.load(os.path.join(dst_path, "base_values.npy"))
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shap_values = np.load(os.path.join(dst_path, "shap_values.npy"))
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# show a force plot
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shap.force_plot(float(base_values), shap_values[0, :], X.iloc[0, :], matplotlib=True)
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