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feat: Added tabular forecasting samples (#128)
* Added predict, get_model_evaluation and create_training_pipeline samples for AutoML Forecasting * Added param handlers * Added headers manually * fix: Improved forecasting sample * Added forecasting test * Added tests for predict and get_model_evaluation * fix: Fixed create_training_pipeline_sample * feat: Added list_model_evaluations_tabular_forecasting_sample and test, fixed get_model_evaluation_tabular_forecasting_sample, and fixed create_training_pipeline_tabular_forecasting_sample * fix: Reverted back to generated BUILD_SPECIFIC_GCLOUD_PROJECT * fix: Fixed name of test * fix: Fixed lint errors * fix: Fixed assertion * fix: Removed predict samples * Consolidated samples * fix: Removed list_model_evaluations_tabular_forecasting * fix: tweaks Co-authored-by: Ivan Cheung <ivanmkc@google.com>
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.sample_configs/param_handlers/create_training_pipeline_tabular_forecasting_sample.py
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| # 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. | ||
| # | ||
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| def make_parent(parent: str) -> str: | ||
| parent = parent | ||
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| return parent | ||
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| 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"}}, | ||
| ] | ||
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| period = {"unit": "day", "quantity": 1} | ||
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| # 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, | ||
| "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, | ||
| } | ||
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| training_task_inputs = to_protobuf_value(training_task_inputs_dict) | ||
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| 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}, | ||
| } | ||
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| return training_pipeline | ||
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samples/snippets/create_training_pipeline_tabular_forecasting_sample.py
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| # 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 | ||
| # | ||
| # 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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| # [START aiplatform_create_training_pipeline_tabular_forecasting_sample] | ||
| from google.cloud import aiplatform | ||
| from google.protobuf import json_format | ||
| from google.protobuf.struct_pb2 import Value | ||
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| def create_training_pipeline_tabular_forecasting_sample( | ||
| project: str, | ||
| 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, | ||
| location: str = "us-central1", | ||
| api_endpoint: str = "us-central1-aiplatform.googleapis.com", | ||
| ): | ||
| client_options = {"api_endpoint": api_endpoint} | ||
| # Initialize client that will be used to create and send requests. | ||
| # This client only needs to be created once, and can be reused for multiple requests. | ||
| client = aiplatform.gapic.PipelineServiceClient(client_options=client_options) | ||
| # 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"}}, | ||
| ] | ||
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| period = {"unit": "day", "quantity": 1} | ||
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| # 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, | ||
| "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, | ||
| } | ||
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| training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value()) | ||
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| 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}, | ||
| } | ||
| parent = f"projects/{project}/locations/{location}" | ||
| response = client.create_training_pipeline( | ||
| parent=parent, training_pipeline=training_pipeline | ||
| ) | ||
| print("response:", response) | ||
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| # [END aiplatform_create_training_pipeline_tabular_forecasting_sample] |
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samples/snippets/create_training_pipeline_tabular_forecasting_sample_test.py
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| # 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 | ||
| # | ||
| # 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 os | ||
| from uuid import uuid4 | ||
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| from google.cloud import aiplatform | ||
| import pytest | ||
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| import cancel_training_pipeline_sample | ||
| import create_training_pipeline_tabular_forecasting_sample | ||
| import delete_training_pipeline_sample | ||
| import helpers | ||
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| PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT") | ||
| DATASET_ID = "3003302817130610688" # COVID Dataset | ||
| DISPLAY_NAME = f"temp_create_training_pipeline_test_{uuid4()}" | ||
| TARGET_COLUMN = "deaths" | ||
| PREDICTION_TYPE = "forecasting" | ||
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| @pytest.fixture | ||
| def shared_state(): | ||
| state = {} | ||
| yield state | ||
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| @pytest.fixture(scope="function", autouse=True) | ||
| def teardown(shared_state): | ||
| yield | ||
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| training_pipeline_id = shared_state["training_pipeline_name"].split("/")[-1] | ||
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| # Stop the training pipeline | ||
| cancel_training_pipeline_sample.cancel_training_pipeline_sample( | ||
| project=PROJECT_ID, training_pipeline_id=training_pipeline_id | ||
| ) | ||
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| client_options = {"api_endpoint": "us-central1-aiplatform.googleapis.com"} | ||
| pipeline_client = aiplatform.gapic.PipelineServiceClient( | ||
| client_options=client_options | ||
| ) | ||
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| # Waiting for training pipeline to be in CANCELLED state | ||
| helpers.wait_for_job_state( | ||
| get_job_method=pipeline_client.get_training_pipeline, | ||
| name=shared_state["training_pipeline_name"], | ||
| ) | ||
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| # Delete the training pipeline | ||
| delete_training_pipeline_sample.delete_training_pipeline_sample( | ||
| project=PROJECT_ID, training_pipeline_id=training_pipeline_id | ||
| ) | ||
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| def test_ucaip_generated_create_training_pipeline_sample(capsys, shared_state): | ||
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| create_training_pipeline_tabular_forecasting_sample.create_training_pipeline_tabular_forecasting_sample( | ||
| project=PROJECT_ID, | ||
| display_name=DISPLAY_NAME, | ||
| dataset_id=DATASET_ID, | ||
| model_display_name="permanent_tabular_forecasting_model", | ||
| target_column=TARGET_COLUMN, | ||
| time_series_identifier_column="county", | ||
| time_column="date", | ||
| static_columns=["state_name"], | ||
| time_variant_past_only_columns=["deaths"], | ||
| time_variant_past_and_future_columns=["date"], | ||
| forecast_window_end=10, | ||
| ) | ||
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| out, _ = capsys.readouterr() | ||
| assert "response:" in out | ||
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| # Save resource name of the newly created training pipeline | ||
| shared_state["training_pipeline_name"] = helpers.get_name(out) |