Consolidated samples

Author: Ivan Cheung

.sample_configs/param_handlers/create_training_pipeline_tabular_forecasting_sample.py

@@ -13,52 +13,44 @@
 # limitations under the License.
 #
 
+
 def make_parent(parent: str) -> str:
     parent = parent
 
     return parent
 
+
 def make_training_pipeline(
-            display_name: str, 
-            dataset_id: str, 
-            model_display_name: str, 
-            target_column: str, 
-            time_series_identifier_column: str,
-            time_column: str,
-            static_columns: str,
-            time_variant_past_only_columns: str,
-            time_variant_past_and_future_columns: str,
-            forecast_window_end: int,
-            ) -> google.cloud.aiplatform_v1alpha1.types.training_pipeline.TrainingPipeline:
+    display_name: str,
+    dataset_id: str,
+    model_display_name: str,
+    target_column: str,
+    time_series_identifier_column: str,
+    time_column: str,
+    static_columns: str,
+    time_variant_past_only_columns: str,
+    time_variant_past_and_future_columns: str,
+    forecast_window_end: int,
+) -> google.cloud.aiplatform_v1alpha1.types.training_pipeline.TrainingPipeline:
     # set the columns used for training and their data types
     transformations = [
         {"auto": {"column_name": "date"}},
         {"auto": {"column_name": "state_name"}},
         {"auto": {"column_name": "county_fips_code"}},
         {"auto": {"column_name": "confirmed_cases"}},
-        {"auto": {"column_name": "deaths"}}
+        {"auto": {"column_name": "deaths"}},
     ]
 
     period = {"unit": "day", "quantity": 1}
 
+    # the inputs should be formatted according to the training_task_definition yaml file
     training_task_inputs_dict = {
         # required inputs
         "targetColumn": target_column,
         "timeSeriesIdentifierColumn": time_series_identifier_column,
         "timeColumn": time_column,
         "transformations": transformations,
         "period": period,
-
-        # Objective function the model is to be optimized towards.
-        # The training process creates a Model that optimizes the value of the objective
-        # function over the validation set. The supported optimization objectives:
-        # "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
-        # "minimize-mae" - Minimize mean-absolute error (MAE).
-        # "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
-        # "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
-        # "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE)
-        # and mean-absolute-error (MAE).
-        # "minimize-quantile-loss" - Minimize the quantile loss at the defined quantiles.
         "optimizationObjective": "minimize-rmse",
         "trainBudgetMilliNodeHours": 8000,
         "staticColumns": static_columns,
@@ -70,20 +62,18 @@ def make_training_pipeline(
     training_task_inputs = to_protobuf_value(training_task_inputs_dict)
 
     training_pipeline = {
-        'display_name': display_name,
-        'training_task_definition': "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_forecasting_1.0.0.yaml",
-        'training_task_inputs': training_task_inputs,
-        'input_data_config': {
-            'dataset_id': dataset_id,
-            'fraction_split': {
-                'training_fraction': 0.8,
-                'validation_fraction': 0.1,
-                'test_fraction': 0.1,
-            }
+        "display_name": display_name,
+        "training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_forecasting_1.0.0.yaml",
+        "training_task_inputs": training_task_inputs,
+        "input_data_config": {
+            "dataset_id": dataset_id,
+            "fraction_split": {
+                "training_fraction": 0.8,
+                "validation_fraction": 0.1,
+                "test_fraction": 0.1,
+            },
         },
-        'model_to_upload': {
-            'display_name': model_display_name
-        }
+        "model_to_upload": {"display_name": model_display_name},
     }
 
     return training_pipeline

.sample_configs/param_handlers/list_model_evaluations_tabular_forecasting_sample.py

@@ -18,6 +18,7 @@ create_batch_prediction_job_custom_image_explain_sample: {}
 create_batch_prediction_job_custom_tabular_explain_sample: {}
 create_batch_prediction_job_sample: {}
 create_batch_prediction_job_tabular_explain_sample: {}
+create_batch_prediction_job_tabular_forecasting_sample: {}
 create_batch_prediction_job_text_classification_sample: {}
 create_batch_prediction_job_text_entity_extraction_sample: {}
 create_batch_prediction_job_text_sentiment_analysis_sample: {}

.sample_configs/process_configs.yaml

@@ -22,6 +22,7 @@ create_batch_prediction_job:
 - custom_image_explain
 - custom_tabular_explain
 - tabular_explain
+- tabular_forecasting
 - text_classification
 - text_entity_extraction
 - text_sentiment_analysis
@@ -132,7 +133,6 @@ get_model_evaluation:
 - image_classification
 - image_object_detection
 - tabular_classification
-- tabular_forecasting
 - tabular_regression
 - text_classification
 - text_entity_extraction
@@ -177,7 +177,7 @@ list_hyperparameter_tuning_jobs:
 list_model_evaluation_slices:
 - ''
 list_model_evaluations:
-- tabular_forecasting
+- ''
 list_models:
 - ''
 list_specialist_pools: