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docs: Add temporal fusion transformer (TFT) model code sample.
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samples/model-builder/create_training_pipeline_forecasting_tft_sample.py
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| # Copyright 2022 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 | ||
| # | ||
| # https://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. | ||
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| from typing import List, Optional | ||
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| from google.cloud import aiplatform | ||
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| # [START aiplatform_sdk_create_training_pipeline_forecasting_tft_sample] | ||
| def create_training_pipeline_forecasting_temporal_fusion_transformer_sample( | ||
| project: str, | ||
| display_name: str, | ||
| dataset_id: str, | ||
| location: str = "us-central1", | ||
| model_display_name: str = "my_model", | ||
| target_column: str = "target_column", | ||
| time_column: str = "date", | ||
| time_series_identifier_column: str = "time_series_id", | ||
| unavailable_at_forecast_columns: List[str] = [], | ||
| available_at_forecast_columns: List[str] = [], | ||
| forecast_horizon: int = 1, | ||
| data_granularity_unit: str = "week", | ||
| data_granularity_count: int = 1, | ||
| training_fraction_split: float = 0.8, | ||
| validation_fraction_split: float = 0.1, | ||
| test_fraction_split: float = 0.1, | ||
| budget_milli_node_hours: int = 8000, | ||
| timestamp_split_column_name: str = "timestamp_split", | ||
| weight_column: str = "weight", | ||
| time_series_attribute_columns: List[str] = [], | ||
| context_window: int = 0, | ||
| export_evaluated_data_items: bool = False, | ||
| export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None, | ||
| export_evaluated_data_items_override_destination: bool = False, | ||
| validation_options: Optional[str] = None, | ||
| predefined_split_column_name: Optional[str] = None, | ||
| sync: bool = True, | ||
| ): | ||
| aiplatform.init(project=project, location=location) | ||
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| # Create training job | ||
| forecasting_tft_job = aiplatform.TemporalFusionTransformerForecastingTrainingJob( | ||
| display_name=display_name, | ||
| optimization_objective="minimize-rmse", | ||
| ) | ||
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| # Retrieve existing dataset | ||
| dataset = aiplatform.TimeSeriesDataset(dataset_id) | ||
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| # Run training job | ||
| model = forecasting_tft_job.run( | ||
| dataset=dataset, | ||
| target_column=target_column, | ||
| time_column=time_column, | ||
| time_series_identifier_column=time_series_identifier_column, | ||
| unavailable_at_forecast_columns=unavailable_at_forecast_columns, | ||
| available_at_forecast_columns=available_at_forecast_columns, | ||
| forecast_horizon=forecast_horizon, | ||
| data_granularity_unit=data_granularity_unit, | ||
| data_granularity_count=data_granularity_count, | ||
| training_fraction_split=training_fraction_split, | ||
| validation_fraction_split=validation_fraction_split, | ||
| test_fraction_split=test_fraction_split, | ||
| predefined_split_column_name=predefined_split_column_name, | ||
| timestamp_split_column_name=timestamp_split_column_name, | ||
| weight_column=weight_column, | ||
| time_series_attribute_columns=time_series_attribute_columns, | ||
| context_window=context_window, | ||
| export_evaluated_data_items=export_evaluated_data_items, | ||
| export_evaluated_data_items_bigquery_destination_uri=export_evaluated_data_items_bigquery_destination_uri, | ||
| export_evaluated_data_items_override_destination=export_evaluated_data_items_override_destination, | ||
| validation_options=validation_options, | ||
| budget_milli_node_hours=budget_milli_node_hours, | ||
| model_display_name=model_display_name, | ||
| sync=sync, | ||
| ) | ||
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| model.wait() | ||
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| print(model.display_name) | ||
| print(model.resource_name) | ||
| print(model.uri) | ||
| return model | ||
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| # [END aiplatform_sdk_create_training_pipeline_forecasting_tft_sample] |
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samples/model-builder/create_training_pipeline_forecasting_tft_sample_test.py
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| # Copyright 2022 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 | ||
| # | ||
| # https://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. | ||
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| import create_training_pipeline_forecasting_tft_sample | ||
| import test_constants as constants | ||
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| def test_create_training_pipeline_forecasting_tft_sample( | ||
| mock_sdk_init, | ||
| mock_time_series_dataset, | ||
| mock_get_automl_forecasting_tft_training_job, | ||
| mock_run_automl_forecasting_tft_training_job, | ||
| mock_get_time_series_dataset, | ||
| ): | ||
| create_training_pipeline_forecasting_tft_sample.create_training_pipeline_forecasting_temporal_fusion_transformer_sample( | ||
| project=constants.PROJECT, | ||
| display_name=constants.DISPLAY_NAME, | ||
| dataset_id=constants.RESOURCE_ID, | ||
| model_display_name=constants.DISPLAY_NAME_2, | ||
| target_column=constants.TABULAR_TARGET_COLUMN, | ||
| training_fraction_split=constants.TRAINING_FRACTION_SPLIT, | ||
| validation_fraction_split=constants.VALIDATION_FRACTION_SPLIT, | ||
| test_fraction_split=constants.TEST_FRACTION_SPLIT, | ||
| budget_milli_node_hours=constants.BUDGET_MILLI_NODE_HOURS_8000, | ||
| timestamp_split_column_name=constants.TIMESTAMP_SPLIT_COLUMN_NAME, | ||
| weight_column=constants.WEIGHT_COLUMN, | ||
| time_series_attribute_columns=constants.TIME_SERIES_ATTRIBUTE_COLUMNS, | ||
| context_window=constants.CONTEXT_WINDOW, | ||
| export_evaluated_data_items=constants.EXPORT_EVALUATED_DATA_ITEMS, | ||
| export_evaluated_data_items_bigquery_destination_uri=constants.EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI, | ||
| export_evaluated_data_items_override_destination=constants.EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION, | ||
| validation_options=constants.VALIDATION_OPTIONS, | ||
| predefined_split_column_name=constants.PREDEFINED_SPLIT_COLUMN_NAME, | ||
| ) | ||
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| mock_get_time_series_dataset.assert_called_once_with(constants.RESOURCE_ID) | ||
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| mock_sdk_init.assert_called_once_with( | ||
| project=constants.PROJECT, location=constants.LOCATION | ||
| ) | ||
| mock_get_automl_forecasting_tft_training_job.assert_called_once_with( | ||
| display_name=constants.DISPLAY_NAME, | ||
| optimization_objective="minimize-rmse", | ||
| ) | ||
| mock_run_automl_forecasting_tft_training_job.assert_called_once_with( | ||
| dataset=mock_time_series_dataset, | ||
| target_column=constants.TABULAR_TARGET_COLUMN, | ||
| time_column=constants.FORECASTNG_TIME_COLUMN, | ||
| time_series_identifier_column=constants.FORECASTNG_TIME_SERIES_IDENTIFIER_COLUMN, | ||
| unavailable_at_forecast_columns=constants.FORECASTNG_UNAVAILABLE_AT_FORECAST_COLUMNS, | ||
| available_at_forecast_columns=constants.FORECASTNG_AVAILABLE_AT_FORECAST_COLUMNS, | ||
| forecast_horizon=constants.FORECASTNG_FORECAST_HORIZON, | ||
| data_granularity_unit=constants.DATA_GRANULARITY_UNIT, | ||
| data_granularity_count=constants.DATA_GRANULARITY_COUNT, | ||
| training_fraction_split=constants.TRAINING_FRACTION_SPLIT, | ||
| validation_fraction_split=constants.VALIDATION_FRACTION_SPLIT, | ||
| test_fraction_split=constants.TEST_FRACTION_SPLIT, | ||
| budget_milli_node_hours=constants.BUDGET_MILLI_NODE_HOURS_8000, | ||
| model_display_name=constants.DISPLAY_NAME_2, | ||
| timestamp_split_column_name=constants.TIMESTAMP_SPLIT_COLUMN_NAME, | ||
| weight_column=constants.WEIGHT_COLUMN, | ||
| time_series_attribute_columns=constants.TIME_SERIES_ATTRIBUTE_COLUMNS, | ||
| context_window=constants.CONTEXT_WINDOW, | ||
| export_evaluated_data_items=constants.EXPORT_EVALUATED_DATA_ITEMS, | ||
| export_evaluated_data_items_bigquery_destination_uri=constants.EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI, | ||
| export_evaluated_data_items_override_destination=constants.EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION, | ||
| validation_options=constants.VALIDATION_OPTIONS, | ||
| predefined_split_column_name=constants.PREDEFINED_SPLIT_COLUMN_NAME, | ||
| sync=True, | ||
| ) |