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docs: Add Time series Dense Encoder (TiDE) model code sample.
PiperOrigin-RevId: 524368116
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‎samples/model-builder/conftest.py

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@@ -279,6 +279,21 @@ def mock_run_automl_forecasting_tft_training_job(mock_forecasting_training_job):
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yield mock
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@pytest.fixture
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def mock_get_automl_forecasting_tide_training_job(mock_forecasting_training_job):
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with patch.object(
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aiplatform, "TimeSeriesDenseEncoderForecastingTrainingJob"
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) as mock:
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mock.return_value = mock_forecasting_training_job
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yield mock
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@pytest.fixture
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def mock_run_automl_forecasting_tide_training_job(mock_forecasting_training_job):
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with patch.object(mock_forecasting_training_job, "run") as mock:
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yield mock
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@pytest.fixture
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def mock_get_automl_image_training_job(mock_image_training_job):
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with patch.object(aiplatform, "AutoMLImageTrainingJob") as mock:

‎samples/model-builder/create_training_pipeline_forecasting_tide_sample.py

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# Copyright 2022 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# 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_tide_sample]
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def create_training_pipeline_forecasting_time_series_dense_encoder_sample(
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project: str,
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display_name: str,
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dataset_id: str,
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location: str = "us-central1",
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model_display_name: str = "my_model",
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target_column: str = "target_column",
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time_column: str = "date",
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time_series_identifier_column: str = "time_series_id",
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unavailable_at_forecast_columns: List[str] = [],
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available_at_forecast_columns: List[str] = [],
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forecast_horizon: int = 1,
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data_granularity_unit: str = "week",
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data_granularity_count: int = 1,
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training_fraction_split: float = 0.8,
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validation_fraction_split: float = 0.1,
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test_fraction_split: float = 0.1,
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budget_milli_node_hours: int = 8000,
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timestamp_split_column_name: str = "timestamp_split",
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weight_column: str = "weight",
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time_series_attribute_columns: List[str] = [],
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context_window: int = 0,
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export_evaluated_data_items: bool = False,
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export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None,
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export_evaluated_data_items_override_destination: bool = False,
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quantiles: Optional[List[float]] = None,
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validation_options: Optional[str] = None,
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predefined_split_column_name: Optional[str] = None,
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sync: bool = True,
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):
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aiplatform.init(project=project, location=location)
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# Create training job
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forecasting_tide_job = aiplatform.TimeSeriesDenseEncoderForecastingTrainingJob(
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display_name=display_name,
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optimization_objective="minimize-rmse",
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)
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# Retrieve existing dataset
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dataset = aiplatform.TimeSeriesDataset(dataset_id)
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# Run training job
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model = forecasting_tide_job.run(
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dataset=dataset,
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target_column=target_column,
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time_column=time_column,
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time_series_identifier_column=time_series_identifier_column,
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unavailable_at_forecast_columns=unavailable_at_forecast_columns,
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available_at_forecast_columns=available_at_forecast_columns,
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forecast_horizon=forecast_horizon,
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data_granularity_unit=data_granularity_unit,
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data_granularity_count=data_granularity_count,
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training_fraction_split=training_fraction_split,
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validation_fraction_split=validation_fraction_split,
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test_fraction_split=test_fraction_split,
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predefined_split_column_name=predefined_split_column_name,
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timestamp_split_column_name=timestamp_split_column_name,
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weight_column=weight_column,
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time_series_attribute_columns=time_series_attribute_columns,
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context_window=context_window,
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export_evaluated_data_items=export_evaluated_data_items,
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export_evaluated_data_items_bigquery_destination_uri=export_evaluated_data_items_bigquery_destination_uri,
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export_evaluated_data_items_override_destination=export_evaluated_data_items_override_destination,
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quantiles=quantiles,
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validation_options=validation_options,
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budget_milli_node_hours=budget_milli_node_hours,
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model_display_name=model_display_name,
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sync=sync,
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)
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model.wait()
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print(model.display_name)
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print(model.resource_name)
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print(model.uri)
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return model
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# [END aiplatform_sdk_create_training_pipeline_forecasting_tide_sample]

‎samples/model-builder/create_training_pipeline_forecasting_tide_sample_test.py

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# Copyright 2022 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import create_training_pipeline_forecasting_tide_sample
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import test_constants as constants
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def test_create_training_pipeline_forecasting_tide_sample(
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mock_sdk_init,
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mock_time_series_dataset,
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mock_get_automl_forecasting_tide_training_job,
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mock_run_automl_forecasting_tide_training_job,
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mock_get_time_series_dataset,
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):
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create_training_pipeline_forecasting_tide_sample.create_training_pipeline_forecasting_time_series_dense_encoder_sample(
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project=constants.PROJECT,
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display_name=constants.DISPLAY_NAME,
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dataset_id=constants.RESOURCE_ID,
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model_display_name=constants.DISPLAY_NAME_2,
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target_column=constants.TABULAR_TARGET_COLUMN,
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training_fraction_split=constants.TRAINING_FRACTION_SPLIT,
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validation_fraction_split=constants.VALIDATION_FRACTION_SPLIT,
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test_fraction_split=constants.TEST_FRACTION_SPLIT,
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budget_milli_node_hours=constants.BUDGET_MILLI_NODE_HOURS_8000,
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timestamp_split_column_name=constants.TIMESTAMP_SPLIT_COLUMN_NAME,
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weight_column=constants.WEIGHT_COLUMN,
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time_series_attribute_columns=constants.TIME_SERIES_ATTRIBUTE_COLUMNS,
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context_window=constants.CONTEXT_WINDOW,
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export_evaluated_data_items=constants.EXPORT_EVALUATED_DATA_ITEMS,
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export_evaluated_data_items_bigquery_destination_uri=constants.EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
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export_evaluated_data_items_override_destination=constants.EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
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quantiles=constants.QUANTILES,
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validation_options=constants.VALIDATION_OPTIONS,
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predefined_split_column_name=constants.PREDEFINED_SPLIT_COLUMN_NAME,
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)
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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(
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project=constants.PROJECT, location=constants.LOCATION
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)
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mock_get_automl_forecasting_tide_training_job.assert_called_once_with(
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display_name=constants.DISPLAY_NAME,
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optimization_objective="minimize-rmse",
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)
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mock_run_automl_forecasting_tide_training_job.assert_called_once_with(
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dataset=mock_time_series_dataset,
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target_column=constants.TABULAR_TARGET_COLUMN,
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time_column=constants.FORECASTNG_TIME_COLUMN,
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time_series_identifier_column=constants.FORECASTNG_TIME_SERIES_IDENTIFIER_COLUMN,
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unavailable_at_forecast_columns=constants.FORECASTNG_UNAVAILABLE_AT_FORECAST_COLUMNS,
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available_at_forecast_columns=constants.FORECASTNG_AVAILABLE_AT_FORECAST_COLUMNS,
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forecast_horizon=constants.FORECASTNG_FORECAST_HORIZON,
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data_granularity_unit=constants.DATA_GRANULARITY_UNIT,
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data_granularity_count=constants.DATA_GRANULARITY_COUNT,
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training_fraction_split=constants.TRAINING_FRACTION_SPLIT,
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validation_fraction_split=constants.VALIDATION_FRACTION_SPLIT,
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test_fraction_split=constants.TEST_FRACTION_SPLIT,
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budget_milli_node_hours=constants.BUDGET_MILLI_NODE_HOURS_8000,
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model_display_name=constants.DISPLAY_NAME_2,
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timestamp_split_column_name=constants.TIMESTAMP_SPLIT_COLUMN_NAME,
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weight_column=constants.WEIGHT_COLUMN,
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time_series_attribute_columns=constants.TIME_SERIES_ATTRIBUTE_COLUMNS,
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context_window=constants.CONTEXT_WINDOW,
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export_evaluated_data_items=constants.EXPORT_EVALUATED_DATA_ITEMS,
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export_evaluated_data_items_bigquery_destination_uri=constants.EXPORT_EVALUATED_DATA_ITEMS_BIGQUERY_DESTINATION_URI,
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export_evaluated_data_items_override_destination=constants.EXPORT_EVALUATED_DATA_ITEMS_OVERRIDE_DESTINATION,
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quantiles=constants.QUANTILES,
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validation_options=constants.VALIDATION_OPTIONS,
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predefined_split_column_name=constants.PREDEFINED_SPLIT_COLUMN_NAME,
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sync=True,
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)

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