chore: import upstream snapshot with attribution

examples/shap/binary_classification.py

@@ -0,0 +1,38 @@
+import os
+
+import numpy as np
+import shap
+from sklearn.datasets import load_breast_cancer
+from sklearn.ensemble import RandomForestClassifier
+
+import mlflow
+from mlflow.artifacts import download_artifacts
+from mlflow.tracking import MlflowClient
+
+# prepare training data
+X, y = load_breast_cancer(return_X_y=True, as_frame=True)
+X = X.iloc[:50, :8]
+y = y.iloc[:50]
+
+# train a model
+model = RandomForestClassifier()
+model.fit(X, y)
+
+# log an explanation
+with mlflow.start_run() as run:
+    mlflow.shap.log_explanation(lambda X: model.predict_proba(X)[:, 1], X)
+
+# list artifacts
+client = MlflowClient()
+artifact_path = "model_explanations_shap"
+artifacts = [x.path for x in client.list_artifacts(run.info.run_id, artifact_path)]
+print("# artifacts:")
+print(artifacts)
+
+# load back the logged explanation
+dst_path = download_artifacts(run_id=run.info.run_id, artifact_path=artifact_path)
+base_values = np.load(os.path.join(dst_path, "base_values.npy"))
+shap_values = np.load(os.path.join(dst_path, "shap_values.npy"))
+
+# show a force plot
+shap.force_plot(float(base_values), shap_values[0, :], X.iloc[0, :], matplotlib=True)