add seq2seq e2e tests

tests/system/aiplatform/test_e2e_forecasting.py

@@ -37,10 +37,7 @@ class TestEndToEndForecasting(e2e_base.TestEndToEnd):
 
     def test_end_to_end_forecasting(self, shared_state):
         """Builds a dataset, trains models, and gets batch predictions."""
-        ds = None
-        automl_job = None
-        automl_model = None
-        automl_batch_prediction_job = None
+        resources = []
 
         aiplatform.init(
             project=e2e_base._PROJECT,
@@ -69,12 +66,17 @@ def test_end_to_end_forecasting(self, shared_state):
             }
 
             # Define both training jobs
-            # TODO(humichael): Add seq2seq job.
             automl_job = aiplatform.AutoMLForecastingTrainingJob(
                 display_name=self._make_display_name("train-housing-automl"),
                 optimization_objective="minimize-rmse",
                 column_specs=column_specs,
             )
+            seq2seq_job = aiplatform.SequenceToSequencePlusForecastingTrainingJob(
+                display_name=self._make_display_name("train-housing-seq2seq"),
+                optimization_objective="minimize-rmse",
+                column_specs=column_specs,
+            )
+            resources.extend([automl_job, seq2seq_job])
 
             # Kick off both training jobs, AutoML job will take approx one hour
             # to run.
@@ -94,6 +96,23 @@ def test_end_to_end_forecasting(self, shared_state):
                 model_display_name=self._make_display_name("automl-liquor-model"),
                 sync=False,
             )
+            seq2seq_model = seq2seq_job.run(
+                dataset=ds,
+                target_column=target_column,
+                time_column=time_column,
+                time_series_identifier_column=time_series_identifier_column,
+                available_at_forecast_columns=[time_column],
+                unavailable_at_forecast_columns=[target_column],
+                time_series_attribute_columns=["city", "zip_code", "county"],
+                forecast_horizon=30,
+                context_window=30,
+                data_granularity_unit="day",
+                data_granularity_count=1,
+                budget_milli_node_hours=1000,
+                model_display_name=self._make_display_name("seq2seq-liquor-model"),
+                sync=False,
+            )
+            resources.extend([automl_model, seq2seq_model])
 
             automl_batch_prediction_job = automl_model.batch_predict(
                 job_display_name=self._make_display_name("automl-liquor-model"),
@@ -105,8 +124,22 @@ def test_end_to_end_forecasting(self, shared_state):
                 ),
                 sync=False,
             )
+            seq2seq_batch_prediction_job = seq2seq_model.batch_predict(
+                job_display_name=self._make_display_name("seq2seq-liquor-model"),
+                instances_format="bigquery",
+                machine_type="n1-standard-4",
+                bigquery_source=_PREDICTION_DATASET_BQ_PATH,
+                gcs_destination_prefix=(
+                    f'gs://{shared_state["staging_bucket_name"]}/bp_results/'
+                ),
+                sync=False,
+            )
+            resources.extend(
+                [automl_batch_prediction_job, seq2seq_batch_prediction_job]
+            )
 
             automl_batch_prediction_job.wait()
+            seq2seq_batch_prediction_job.wait()
 
             assert (
                 automl_job.state
@@ -117,11 +150,5 @@ def test_end_to_end_forecasting(self, shared_state):
                 == job_state.JobState.JOB_STATE_SUCCEEDED
             )
         finally:
-            if ds is not None:
-                ds.delete()
-            if automl_job is not None:
-                automl_job.delete()
-            if automl_model is not None:
-                automl_model.delete()
-            if automl_batch_prediction_job is not None:
-                automl_batch_prediction_job.delete()
+            for resource in resources:
+                resource.delete()