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Data source for explainability analysis using a sample from a dataset. DatasetDataSource allows you to perform explainability analysis on a random sample of data from a specified environment/dataset. This is useful for understanding general model behavior, analyzing feature importance patterns across multiple instances, or getting representative explanations. This data source type is ideal for exploratory analysis, understanding overall model behavior, or when you want to analyze explanations across a representative sample rather than specific instances.

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Examples

Creating a dataset data source for production sampling: 
dataset_source = DatasetDataSource(
    env_type="PRODUCTION",
    num_samples=100,
    env_id="prod_dataset_uuid"
)
Creating a dataset data source for validation analysis: 
validation_source = DatasetDataSource(
    env_type="VALIDATION",
    num_samples=50
)
Creating a dataset data source with default sampling: 
default_source = DatasetDataSource(
    env_type="PRODUCTION"
)

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source_type

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env_type

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num_samples

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env_id

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model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].