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AutoMLCandidateGenerationConfig

Source code in docs_src/capo_sagemaker/types/auto_ml_candidate_generation_config.py 
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class AutoMLCandidateGenerationConfig(TypedDict, closed=True):
    feature_specification_s3_uri: NotRequired["capo_sagemaker.types.s3_uri.S3Uri"]
    """<p>A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input <code>FeatureAttributeNames</code> (optional) in JSON format as shown below: </p> <p> <code>{ "FeatureAttributeNames":["col1", "col2", ...] }</code>.</p> <p>You can also specify the data type of the feature (optional) in the format shown below:</p> <p> <code>{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }</code> </p> <note> <p>These column keys may not include the target column.</p> </note> <p>In ensembling mode, Autopilot only supports the following data types: <code>numeric</code>, <code>categorical</code>, <code>text</code>, and <code>datetime</code>. In HPO mode, Autopilot can support <code>numeric</code>, <code>categorical</code>, <code>text</code>, <code>datetime</code>, and <code>sequence</code>.</p> <p>If only <code>FeatureDataTypes</code> is provided, the column keys (<code>col1</code>, <code>col2</code>,..) should be a subset of the column names in the input data. </p> <p>If both <code>FeatureDataTypes</code> and <code>FeatureAttributeNames</code> are provided, then the column keys should be a subset of the column names provided in <code>FeatureAttributeNames</code>. </p> <p>The key name <code>FeatureAttributeNames</code> is fixed. The values listed in <code>["col1", "col2", ...]</code> are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.</p>"""
    algorithms_config: NotRequired[
        "capo_sagemaker.types.auto_ml_algorithms_config.AutoMLAlgorithmsConfig"
    ]
    """<p>Stores the configuration information for the selection of algorithms trained on tabular data.</p> <p>The list of available algorithms to choose from depends on the training mode set in <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html"> <code>TabularJobConfig.Mode</code> </a>.</p> <ul> <li> <p> <code>AlgorithmsConfig</code> should not be set if the training mode is set on <code>AUTO</code>.</p> </li> <li> <p>When <code>AlgorithmsConfig</code> is provided, one <code>AutoMLAlgorithms</code> attribute must be set and one only.</p> <p>If the list of algorithms provided as values for <code>AutoMLAlgorithms</code> is empty, <code>CandidateGenerationConfig</code> uses the full set of algorithms for the given training mode.</p> </li> <li> <p>When <code>AlgorithmsConfig</code> is not provided, <code>CandidateGenerationConfig</code> uses the full set of algorithms for the given training mode.</p> </li> </ul> <p>For the list of all algorithms per problem type and training mode, see <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLAlgorithmConfig.html"> AutoMLAlgorithmConfig</a>.</p> <p>For more information on each algorithm, see the <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support">Algorithm support</a> section in Autopilot developer guide.</p>"""

algorithms_config instance-attribute 

algorithms_config: NotRequired[AutoMLAlgorithmsConfig]

Stores the configuration information for the selection of algorithms trained on tabular data.

The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode .

  • AlgorithmsConfig should not be set if the training mode is set on AUTO.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig.

For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

feature_specification_s3_uri instance-attribute 

feature_specification_s3_uri: NotRequired[S3Uri]

A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:

{ "FeatureAttributeNames":["col1", "col2", ...] }.

You can also specify the data type of the feature (optional) in the format shown below:

{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

These column keys may not include the target column.

In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.