Add support for feature importance.

Previously feature importance is added with a params parameter, the user has to set params = {"feature_importance": "true"}. This PR simplifies the logic, the user just have to pass feature_importance = True.

Author: Helin Wang

automl/google/cloud/automl_v1beta1/tables/tables_client.py

@@ -2596,7 +2596,7 @@ def predict(
  model=None,
  model_name=None,
  model_display_name=None,
- params=None,
+ feature_importance=False,
  project=None,
  region=None,
  **kwargs
@@ -2643,9 +2643,9 @@ def predict(
  The `model` instance you want to predict with . This must be
  supplied if `model_display_name` or `model_name` are not
  supplied.
- params (dict[str, str]):
- `feature_importance` can be set as True to enable local
- explainability. The default is false.
+ feature_importance (bool):
+ True if enable feature importance explainability. The default is
+ False.
 
  Returns:
  A :class:`~google.cloud.automl_v1beta1.types.PredictResponse`
@@ -2687,6 +2687,10 @@ def predict(
 
  request = {"row": {"values": values}}
 
+ params = None
+ if feature_importance:
+ params = {"feature_importance": "true"}
+
  return self.prediction_client.predict(model.name, request, params, **kwargs)
 
  def batch_predict(

automl/tests/unit/gapic/v1beta1/test_tables_client_v1beta1.py

@@ -1137,6 +1137,25 @@ def test_predict_from_dict(self):
  None,
  )
 
+ def test_predict_from_dict_with_feature_importance(self):
+ data_type = mock.Mock(type_code=data_types_pb2.CATEGORY)
+ column_spec_a = mock.Mock(display_name="a", data_type=data_type)
+ column_spec_b = mock.Mock(display_name="b", data_type=data_type)
+ model_metadata = mock.Mock(
+ input_feature_column_specs=[column_spec_a, column_spec_b]
+ )
+ model = mock.Mock()
+ model.configure_mock(tables_model_metadata=model_metadata, name="my_model")
+ client = self.tables_client({"get_model.return_value": model}, {})
+ client.predict(
+ {"a": "1", "b": "2"}, model_name="my_model", feature_importance=True
+ )
+ client.prediction_client.predict.assert_called_with(
+ "my_model",
+ {"row": {"values": [{"string_value": "1"}, {"string_value": "2"}]}},
+ {"feature_importance": "true"},
+ )
+
  def test_predict_from_dict_missing(self):
  data_type = mock.Mock(type_code=data_types_pb2.CATEGORY)
  column_spec_a = mock.Mock(display_name="a", data_type=data_type)