create_training_pipeline_tabular_forecasting_sample.py

# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# 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:
    # 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"}}
    ]

    period = {"unit": "day", "quantity": 1}

    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,
        "timeVariantPastOnlyColumns": time_variant_past_only_columns,
        "timeVariantPastAndFutureColumns": time_variant_past_and_future_columns,
        "forecastWindowEnd": forecast_window_end,
    }

    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,
            }
        },
        'model_to_upload': {
            'display_name': model_display_name
        }
    }

    return training_pipeline