feat: add LogisticRegression.predict_explain() to generate `ML.EXPL…

…AIN_PREDICT` columns (#1222)

* feat: add LogisticRegression.predict_explain() to generate ML.EXPLAIN_PREDICT columns

* update tests

bigframes/ml/linear_model.py

@@ -353,6 +353,34 @@ def predict(
 
         return self._bqml_model.predict(X)
 
+    def predict_explain(
+        self,
+        X: utils.ArrayType,
+    ) -> bpd.DataFrame:
+        """
+        Explain predictions for a logistic regression model.
+
+        .. note::
+            Output matches that of the BigQuery ML.EXPLAIN_PREDICT function.
+            See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-explain-predict
+
+        Args:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series or
+            pandas.core.frame.DataFrame or pandas.core.series.Series):
+                Series or a DataFrame to explain its predictions.
+
+        Returns:
+            bigframes.pandas.DataFrame:
+                The predicted DataFrames with explanation columns.
+        """
+        # TODO(b/377366612): Add support for `top_k_features` parameter
+        if not self._bqml_model:
+            raise RuntimeError("A model must be fitted before predict")
+
+        (X,) = utils.batch_convert_to_dataframe(X, session=self._bqml_model.session)
+
+        return self._bqml_model.explain_predict(X)
+
     def score(
         self,
         X: utils.ArrayType,

tests/system/small/ml/test_linear_model.py

@@ -307,6 +307,50 @@ def test_logistic_model_predict(penguins_logistic_model, new_penguins_df):
     )
 
 
+def test_logistic_model_predict_params(
+    penguins_logistic_model: linear_model.LogisticRegression, new_penguins_df
+):
+    predictions = penguins_logistic_model.predict(new_penguins_df).to_pandas()
+    assert predictions.shape[0] >= 1
+    prediction_columns = set(predictions.columns)
+    expected_columns = {
+        "predicted_sex",
+        "predicted_sex_probs",
+        "species",
+        "island",
+        "culmen_length_mm",
+        "culmen_depth_mm",
+        "flipper_length_mm",
+        "body_mass_g",
+        "sex",
+    }
+    assert expected_columns <= prediction_columns
+
+
+def test_logistic_model_predict_explain_params(
+    penguins_logistic_model: linear_model.LogisticRegression, new_penguins_df
+):
+    predictions = penguins_logistic_model.predict_explain(new_penguins_df).to_pandas()
+    assert predictions.shape[0] >= 1
+    prediction_columns = set(predictions.columns)
+    expected_columns = {
+        "predicted_sex",
+        "probability",
+        "top_feature_attributions",
+        "baseline_prediction_value",
+        "prediction_value",
+        "approximation_error",
+        "species",
+        "island",
+        "culmen_length_mm",
+        "culmen_depth_mm",
+        "flipper_length_mm",
+        "body_mass_g",
+        "sex",
+    }
+    assert expected_columns <= prediction_columns
+
+
 def test_logistic_model_to_gbq_saved_score(
     penguins_logistic_model, table_id_unique, penguins_df_default_index
 ):