docs: add snippet to evaluate ARIMA plus model in the Forecast a single time series with a univariate model tutorial

samples/snippets/create_single_timeseries_forecasting_model_test.py

@@ -72,9 +72,27 @@ def test_create_single_timeseries() -> None:
  # Expected output:
  # ar_coefficients ma_coefficients intercept_or_drift
  # 0 [0.40944762] [-0.81168198] 0.0
-
  # [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_coef]
+
+ # [START bigquery_dataframes_single_timeseries_forecasting_model_tutorial_evaluate]
+ # Evaluate the time series models by using the summary() function. The summary()
+ # function shows you the evaluation metrics of all the candidate models evaluated
+ # during the process of automatic hyperparameter tuning.
+ summary = model.summary(
+ show_all_candidate_models=True,
+ )
+ print(summary.peek())
+
+ # Expected output:
+ # row non_seasonal_p	non_seasonal_d	non_seasonal_q	has_drift	log_likelihood	AIC	variance	seasonal_periods	has_holiday_effect	has_spikes_and_dips	has_step_changes	error_message
+ # 0 0 1 3 True -2464.255656	4938.511313 42772.506055 ['WEEKLY'] False False True
+ # 1 2 1 0 False -2473.141651	4952.283303 44942.416463 ['WEEKLY'] False False True
+ # 2 1 1 0 False -2479.880885	4963.76177 46642.953433 ['WEEKLY'] False False True
+ # 3 0 1 1 False -2470.632377	4945.264753 44319.379307 ['WEEKLY'] False False True
+ # 4 2 1 1 True -2463.671247	4937.342493 42633.299513 ['WEEKLY'] False False True
+ # [END bigquery_dataframes_single_timeseries_forecasting_model_tutorial_evaluate]
  assert coef is not None
+ assert summary is not None
  assert model is not None
  assert parsed_date is not None
  assert total_visits is not None

tests/unit/core/test_dtypes.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import bigframes_vendored.ibis.backends.bigquery.datatypes as ibis_bq_types
 import bigframes_vendored.ibis.expr.datatypes as ibis_dtypes
 import bigframes_vendored.ibis.expr.types as ibis_types
 import geopandas as gpd # type: ignore
@@ -152,6 +153,15 @@ def test_ibis_dtype_to_arrow_dtype(ibis_dtype, arrow_dtype):
  assert result == arrow_dtype
 
 
+@pytest.mark.parametrize(
+ ("ibis_dtype", "bigquery_type"),
+ [(ibis_dtypes.String(), "STRING"), (ibis_dtypes.String(nullable=False), "STRING")],
+)
+def test_ibis_dtype_to_bigquery_type(ibis_dtype, bigquery_type):
+ result = ibis_bq_types.BigQueryType.from_ibis(ibis_dtype)
+ assert result == bigquery_type
+
+
 @pytest.mark.parametrize(
  ["bigframes_dtype", "ibis_dtype"],
  [

third_party/bigframes_vendored/ibis/backends/bigquery/datatypes.py

@@ -55,6 +55,8 @@ def from_ibis(cls, dtype: dt.DataType) -> str:
  return "INT64"
  elif dtype.is_binary():
  return "BYTES"
+ elif dtype.is_string():
+ return "STRING"
  elif dtype.is_date():
  return "DATE"
  elif dtype.is_timestamp():