feat: add ARIMA_EVAULATE options in forecasting models

bigframes/ml/core.py

@@ -136,6 +136,13 @@ def evaluate(self, input_data: Optional[bpd.DataFrame] = None):
 
         return self._session.read_gbq(sql)
 
+    def arima_evaluate(self, show_all_candidate_models: bool = False):
+        sql = self._model_manipulation_sql_generator.ml_arima_evaluate(
+            show_all_candidate_models
+        )
+
+        return self._session.read_gbq(sql)
+
     def centroids(self) -> bpd.DataFrame:
         assert self._model.model_type == "KMEANS"
 

bigframes/ml/forecasting.py

@@ -151,6 +151,31 @@ def score(
         input_data = X.join(y, how="outer")
         return self._bqml_model.evaluate(input_data)
 
+    def summary(
+        self,
+        show_all_candidate_models: bool = False,
+    ) -> bpd.DataFrame:
+        """Summary of the evaluation metrics of the time series model.
+
+        .. note::
+
+            Output matches that of the BigQuery ML.ARIMA_EVALUATE function.
+            See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-arima-evaluate
+            for the outputs relevant to this model type.
+
+        Args:
+            show_all_candidate_models (bool, default to False):
+                Whether to show evaluation metrics or an error message for either
+                all candidate models or for only the best model with the lowest
+                AIC. Default to False.
+
+        Returns:
+            bigframes.dataframe.DataFrame: A DataFrame as evaluation result.
+        """
+        if not self._bqml_model:
+            raise RuntimeError("A model must be fitted before score")
+        return self._bqml_model.arima_evaluate(show_all_candidate_models)
+
     def to_gbq(self, model_name: str, replace: bool = False) -> ARIMAPlus:
         """Save the model to BigQuery.
 

bigframes/ml/sql.py

@@ -260,6 +260,12 @@ def ml_evaluate(self, source_df: Optional[bpd.DataFrame] = None) -> str:
             return f"""SELECT * FROM ML.EVALUATE(MODEL `{self._model_name}`,
   ({source_sql}))"""
 
+    # ML evaluation TVFs
+    def ml_arima_evaluate(self, show_all_candidate_models: bool = False) -> str:
+        """Encode ML.ARMIA_EVALUATE for BQML"""
+        return f"""SELECT * FROM ML.ARIMA_EVALUATE(MODEL `{self._model_name}`,
+            STRUCT({show_all_candidate_models} AS show_all_candidate_models))"""
+
     def ml_centroids(self) -> str:
         """Encode ML.CENTROIDS for BQML"""
         return f"""SELECT * FROM ML.CENTROIDS(MODEL `{self._model_name}`)"""

tests/system/large/ml/test_forecasting.py

@@ -16,6 +16,20 @@
 
 from bigframes.ml import forecasting
 
+ARIMA_EVALUATE_OUTPUT_COL = [
+    "non_seasonal_p",
+    "non_seasonal_d",
+    "non_seasonal_q",
+    "log_likelihood",
+    "AIC",
+    "variance",
+    "seasonal_periods",
+    "has_holiday_effect",
+    "has_spikes_and_dips",
+    "has_step_changes",
+    "error_message",
+]
+
 
 def test_arima_plus_model_fit_score(
     time_series_df_default_index, dataset_id, new_time_series_df
@@ -42,7 +56,24 @@ def test_arima_plus_model_fit_score(
     pd.testing.assert_frame_equal(result, expected, check_exact=False, rtol=0.1)
 
     # save, load to ensure configuration was kept
-    reloaded_model = model.to_gbq(f"{dataset_id}.temp_configured_model", replace=True)
+    reloaded_model = model.to_gbq(f"{dataset_id}.temp_arima_plus_model", replace=True)
+    assert (
+        f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name
+    )
+
+
+def test_arima_plus_model_fit_summary(time_series_df_default_index, dataset_id):
+    model = forecasting.ARIMAPlus()
+    X_train = time_series_df_default_index[["parsed_date"]]
+    y_train = time_series_df_default_index[["total_visits"]]
+    model.fit(X_train, y_train)
+
+    result = model.summary()
+    assert result.shape == (1, 12)
+    assert all(column in result.columns for column in ARIMA_EVALUATE_OUTPUT_COL)
+
+    # save, load to ensure configuration was kept
+    reloaded_model = model.to_gbq(f"{dataset_id}.temp_arima_plus_model", replace=True)
     assert (
-        f"{dataset_id}.temp_configured_model" in reloaded_model._bqml_model.model_name
+        f"{dataset_id}.temp_arima_plus_model" in reloaded_model._bqml_model.model_name
     )

tests/system/small/ml/test_forecasting.py

@@ -20,6 +20,20 @@
 
 from bigframes.ml import forecasting
 
+ARIMA_EVALUATE_OUTPUT_COL = [
+    "non_seasonal_p",
+    "non_seasonal_d",
+    "non_seasonal_q",
+    "log_likelihood",
+    "AIC",
+    "variance",
+    "seasonal_periods",
+    "has_holiday_effect",
+    "has_spikes_and_dips",
+    "has_step_changes",
+    "error_message",
+]
+
 
 def test_model_predict_default(time_series_arima_plus_model: forecasting.ARIMAPlus):
     utc = pytz.utc
@@ -104,6 +118,24 @@ def test_model_score(
     )
 
 
+def test_model_summary(
+    time_series_arima_plus_model: forecasting.ARIMAPlus, new_time_series_df
+):
+    result = time_series_arima_plus_model.summary()
+    assert result.shape == (1, 12)
+    assert all(column in result.columns for column in ARIMA_EVALUATE_OUTPUT_COL)
+
+
+def test_model_summary_show_all_candidates(
+    time_series_arima_plus_model: forecasting.ARIMAPlus, new_time_series_df
+):
+    result = time_series_arima_plus_model.summary(
+        show_all_candidate_models=True,
+    )
+    assert result.shape[0] > 1
+    assert all(column in result.columns for column in ARIMA_EVALUATE_OUTPUT_COL)
+
+
 def test_model_score_series(
     time_series_arima_plus_model: forecasting.ARIMAPlus, new_time_series_df
 ):
@@ -126,3 +158,11 @@ def test_model_score_series(
         rtol=0.1,
         check_index_type=False,
     )
+
+
+def test_model_summary_series(
+    time_series_arima_plus_model: forecasting.ARIMAPlus, new_time_series_df
+):
+    result = time_series_arima_plus_model.summary()
+    assert result.shape == (1, 12)
+    assert all(column in result.columns for column in ARIMA_EVALUATE_OUTPUT_COL)

tests/unit/ml/test_sql.py

@@ -273,6 +273,19 @@ def test_ml_evaluate_produces_correct_sql(
     )
 
 
+def test_ml_arima_evaluate_produces_correct_sql(
+    model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
+):
+    sql = model_manipulation_sql_generator.ml_arima_evaluate(
+        show_all_candidate_models=True
+    )
+    assert (
+        sql
+        == """SELECT * FROM ML.ARIMA_EVALUATE(MODEL `my_project_id.my_dataset_id.my_model_id`,
+            STRUCT(True AS show_all_candidate_models))"""
+    )
+
+
 def test_ml_evaluate_no_source_produces_correct_sql(
     model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
 ):