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Data source for explainability analysis using a single row of data. RowDataSource allows you to perform explainability analysis on a specific data row by providing the row data directly. This is useful when you want to explain a particular prediction or analyze feature importance for a specific instance without referencing stored data. This data source type is ideal for real-time explanations, ad-hoc analysis, or when you have specific data points that you want to analyze independently of your stored datasets.

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Examples

Creating a row data source for a loan application: 
row_source = RowDataSource(
    row={
        "age": 35,
        "income": 75000,
        "credit_score": 720,
        "employment_years": 8,
        "loan_amount": 250000
    }
)
Creating a row data source for image classification: 
image_row_source = RowDataSource(
    row={
        "image_features": [0.1, 0.5, 0.3, ...],
        "metadata": "product_image_001.jpg",
        "category": "electronics"
    }
)

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source_type

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row

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model_config

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