chore: import upstream snapshot with attribution

examples/mlflow-3/evaluate_example.py

@@ -0,0 +1,42 @@
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+
+import mlflow
+from mlflow.models import infer_signature
+
+X, y = load_iris(return_X_y=True, as_frame=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
+X_test_1, X_test_2, y_test_1, y_test_2 = train_test_split(
+    X_test, y_test, test_size=0.5, random_state=42
+)
+model = LogisticRegression().fit(X_train, y_train)
+
+predictions = model.predict(X_train)
+signature = infer_signature(X_train, predictions)
+
+with mlflow.start_run() as run:
+    model_info = mlflow.sklearn.log_model(model, name="model", signature=signature)
+    print(model_info.name)
+
+    # Evaluate the model URI
+    mlflow.evaluate(
+        model_info.model_uri,
+        X_test_1.assign(label=y_test_1),
+        targets="label",
+        model_type="classifier",
+        evaluators=["default"],
+    )
+    print(mlflow.get_logged_model(model_info.model_id))
+
+    # Evaluate the pyfunc model object
+    model = mlflow.pyfunc.load_model(model_info.model_uri)
+    assert model.model_id is not None
+    mlflow.evaluate(
+        model,
+        X_test_2.assign(label=y_test_2),
+        targets="label",
+        model_type="classifier",
+        evaluators=["default"],
+    )
+    print(mlflow.get_logged_model(model_info.model_id))