Google Cloud - Vertex_AI - AutoML - Tables - Added the "Train_model" …

…component

There is a hack to work around the issue googleapis/python-aiplatform#589 that I fixed in googleapis/python-aiplatform#590

components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.py

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+from typing import NamedTuple
+
+from kfp.components import create_component_from_func
+
+def train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML(
+    # AutoMLTabularTrainingJob.run required parameters
+    dataset_name: 'GoogleCloudVertexAiTabularDatasetName',
+    target_column: str,
+
+    # AutoMLTabularTrainingJob.__init__ required parameters
+    # display_name: str,
+    optimization_prediction_type: str,
+
+    # AutoMLTabularTrainingJob.run parameters
+    training_fraction_split: float = 0.8,
+    validation_fraction_split: float = 0.1,
+    test_fraction_split: float = 0.1,
+    predefined_split_column_name: str = None,
+    weight_column: str = None,
+    budget_milli_node_hours: int = 1000,
+    model_display_name: str = None,
+    disable_early_stopping: bool = False,
+
+    # AutoMLTabularTrainingJob.__init__ parameters
+    optimization_objective: str = None,
+    #column_transformations: Union[Dict, List[Dict], NoneType] = None,
+    optimization_objective_recall_value: float = None,
+    optimization_objective_precision_value: float = None,
+
+    project: str = None,
+    location: str = 'us-central1',
+    #training_encryption_spec_key_name: str = None,
+    #model_encryption_spec_key_name: str = None,
+    encryption_spec_key_name: str = None,
+) -> NamedTuple('Outputs', [
+    ('model_name', 'GoogleCloudVertexAiModelName'),
+    ('model_dict', dict),
+]):
+    '''Trains model using Google Cloud Vertex AI AutoML.
+
+    Data fraction splits:
+    Any of ``training_fraction_split``, ``validation_fraction_split`` and
+    ``test_fraction_split`` may optionally be provided, they must sum to up to 1. If
+    the provided ones sum to less than 1, the remainder is assigned to sets as
+    decided by Vertex AI. If none of the fractions are set, by default roughly 80%
+    of data will be used for training, 10% for validation, and 10% for test.
+
+    Annotations:
+        author: Alexey Volkov <alexey.volkov@ark-kun.com>
+
+    Args:
+        dataset_name:
+            Required. The full name of dataset  (datasets.TabularDataset) within the same Project from which data will be used to train the Model. The
+            Dataset must use schema compatible with Model being trained,
+            and what is compatible should be described in the used
+            TrainingPipeline's [training_task_definition]
+            [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
+            For tabular Datasets, all their data is exported to
+            training, to pick and choose from.
+        target_column (str):
+            Required. The name of the column values of which the Model is to predict.
+        training_fraction_split (float):
+            Required. The fraction of the input data that is to be
+            used to train the Model. This is ignored if Dataset is not provided.
+        validation_fraction_split (float):
+            Required. The fraction of the input data that is to be
+            used to validate the Model. This is ignored if Dataset is not provided.
+        test_fraction_split (float):
+            Required. The fraction of the input data that is to be
+            used to evaluate the Model. This is ignored if Dataset is not provided.
+        predefined_split_column_name (str):
+            Optional. The key is a name of one of the Dataset's data
+            columns. The value of the key (either the label's value or
+            value in the column) must be one of {``training``,
+            ``validation``, ``test``}, and it defines to which set the
+            given piece of data is assigned. If for a piece of data the
+            key is not present or has an invalid value, that piece is
+            ignored by the pipeline.
+
+            Supported only for tabular and time series Datasets.
+        weight_column (str):
+            Optional. Name of the column that should be used as the weight column.
+            Higher values in this column give more importance to the row
+            during Model training. The column must have numeric values between 0 and
+            10000 inclusively, and 0 value means that the row is ignored.
+            If the weight column field is not set, then all rows are assumed to have
+            equal weight of 1.
+        budget_milli_node_hours (int):
+            Optional. The train budget of creating this Model, expressed in milli node
+            hours i.e. 1,000 value in this field means 1 node hour.
+            The training cost of the model will not exceed this budget. The final
+            cost will be attempted to be close to the budget, though may end up
+            being (even) noticeably smaller - at the backend's discretion. This
+            especially may happen when further model training ceases to provide
+            any improvements.
+            If the budget is set to a value known to be insufficient to train a
+            Model for the given training set, the training won't be attempted and
+            will error.
+            The minimum value is 1000 and the maximum is 72000.
+        model_display_name (str):
+            Optional. If the script produces a managed Vertex AI Model. The display name of
+            the Model. The name can be up to 128 characters long and can be consist
+            of any UTF-8 characters.
+
+            If not provided upon creation, the job's display_name is used.
+        disable_early_stopping (bool):
+            Required. If true, the entire budget is used. This disables the early stopping
+            feature. By default, the early stopping feature is enabled, which means
+            that training might stop before the entire training budget has been
+            used, if further training does no longer brings significant improvement
+            to the model.
+
+        optimization_prediction_type (str):
+            The type of prediction the Model is to produce.
+            "classification" - Predict one out of multiple target values is
+            picked for each row.
+            "regression" - Predict a value based on its relation to other values.
+            This type is available only to columns that contain
+            semantically numeric values, i.e. integers or floating
+            point number, even if stored as e.g. strings.
+        optimization_objective (str):
+            Optional. Objective function the Model is to be optimized towards. The training
+            task creates a Model that maximizes/minimizes the value of the objective
+            function over the validation set.
+
+            The supported optimization objectives depend on the prediction type, and
+            in the case of classification also the number of distinct values in the
+            target column (two distint values -> binary, 3 or more distinct values
+            -> multi class).
+            If the field is not set, the default objective function is used.
+
+            Classification (binary):
+            "maximize-au-roc" (default) - Maximize the area under the receiver
+                                        operating characteristic (ROC) curve.
+            "minimize-log-loss" - Minimize log loss.
+            "maximize-au-prc" - Maximize the area under the precision-recall curve.
+            "maximize-precision-at-recall" - Maximize precision for a specified
+                                            recall value.
+            "maximize-recall-at-precision" - Maximize recall for a specified
+                                            precision value.
+
+            Classification (multi class):
+            "minimize-log-loss" (default) - Minimize log loss.
+
+            Regression:
+            "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).
+        column_transformations (Optional[Union[Dict, List[Dict]]]):
+            Optional. Transformations to apply to the input columns (i.e. columns other
+            than the targetColumn). Each transformation may produce multiple
+            result values from the column's value, and all are used for training.
+            When creating transformation for BigQuery Struct column, the column
+            should be flattened using "." as the delimiter.
+            If an input column has no transformations on it, such a column is
+            ignored by the training, except for the targetColumn, which should have
+            no transformations defined on.
+        optimization_objective_recall_value (float):
+            Optional. Required when maximize-precision-at-recall optimizationObjective was
+            picked, represents the recall value at which the optimization is done.
+
+            The minimum value is 0 and the maximum is 1.0.
+        optimization_objective_precision_value (float):
+            Optional. Required when maximize-recall-at-precision optimizationObjective was
+            picked, represents the precision value at which the optimization is
+            done.
+
+            The minimum value is 0 and the maximum is 1.0.
+
+    Returns:
+        model_name: Model name (fully-qualified)
+        model_dict: Model metadata in JSON format
+    '''
+
+    import datetime
+    import logging
+
+    from google.cloud import aiplatform
+    from google.protobuf import json_format
+
+    logging.getLogger().setLevel(logging.INFO)
+
+    if not model_display_name:
+        model_display_name = 'TablesModel_' + datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S")
+
+    aiplatform.init(
+        project=project,
+        location=location,
+        encryption_spec_key_name=encryption_spec_key_name,
+    )
+
+    model = aiplatform.AutoMLTabularTrainingJob(
+        display_name='AutoMLTabularTrainingJob_' + datetime.datetime.utcnow().strftime("%Y_%m_%d_%H_%M_%S"),
+        optimization_prediction_type=optimization_prediction_type,
+        optimization_objective=optimization_objective,
+        #column_transformations=column_transformations,
+        optimization_objective_recall_value=optimization_objective_recall_value,
+        optimization_objective_precision_value=optimization_objective_precision_value,
+    ).run(
+        dataset=aiplatform.TabularDataset(dataset_name=dataset_name),
+        target_column=target_column,
+        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,
+        weight_column=weight_column,
+        budget_milli_node_hours=budget_milli_node_hours,
+        model_display_name=model_display_name,
+        disable_early_stopping=disable_early_stopping,
+    )
+
+    (_, model_project, _, model_location, _, model_id) = model.resource_name.split('/')
+    model_web_url = f'https://console.cloud.google.com/vertex-ai/locations/{model_location}/models/{model_id}/evaluate?project={model_project}'
+    logging.info(f'Created model {model.name}.')
+    logging.info(f'Link: {model_web_url}')
+    model_json = json_format.MessageToJson(model._gca_resource._pb)
+    print(model_json)
+    return (model.resource_name, model_json, model_web_url)
+
+
+if __name__ == '__main__':
+    train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML_op = create_component_from_func(
+        train_tabular_model_using_Google_Cloud_Vertex_AI_AutoML,
+        base_image='python:3.9',
+        # TODO: Update after my fix for TabularDataset.column_names bug is released https://github.com/googleapis/python-aiplatform/pull/590
+        packages_to_install=['git+https://github.com/googleapis/python-aiplatform.git@refs/pull/590/head'],
+        output_component_file='component.yaml',
+        annotations={
+            "author": "Alexey Volkov <alexey.volkov@ark-kun.com>",
+            "canonical_location": "https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/google-cloud/Vertex_AI/AutoML/Tables/Train_model/component.yaml",
+        },
+    )