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glassbox.inspector.auditor

The DataAuditor — orchestrates the full EDA pipeline.

DataAuditor

Orchestrates the EDA process to generate a complete report.

run_audit 

run_audit(data)

Perform a full audit on the dataset.

Parameters:

Name Type Description Default
data Dataset

The dataset to audit.

 required

Returns:

Type Description
EDAReport

A comprehensive report containing EDA results.
Source code in glassbox/inspector/auditor.py 
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def run_audit(self, data: Dataset) -> EDAReport:
    """
    Perform a full audit on the dataset.

    Parameters
    ----------
    data : Dataset
        The dataset to audit.

    Returns
    -------
    EDAReport
        A comprehensive report containing EDA results.
    """
    auto_typer = AutoTyper()
    outlier_detector = OutlierDetector()
    stat_profiler = StatProfiler()
    association_analyzer = AssociationAnalyzer()

    feature_types = auto_typer.infer_types(data)
    n_samples = data.shape[0]

    missing_values = {}
    for col_name in data.columns:
        col_data = data.get_columns(col_name).data[:, 0]
        if np.issubdtype(col_data.dtype, np.number):
            missing = int(np.isnan(col_data).sum())
        else:
            missing = 0
            for v in col_data:
                if v is None or (isinstance(v, float) and np.isnan(v)):
                    missing += 1
        missing_values[col_name] = MissingInfo(
            count=missing, percentage=missing / n_samples
        )

    numeric_cols = [c for c, t in feature_types.items() if t == FeatureType.NUMERICAL]
    categorical_cols = [
        c
        for c, t in feature_types.items()
        if t in (FeatureType.NOMINAL, FeatureType.ORDINAL, FeatureType.BOOLEAN)
    ]

    outliers_info = outlier_detector.flag_outliers(data, numeric_cols)
    num_stats = stat_profiler.calculate_numeric_stats(data, numeric_cols)
    cat_stats = stat_profiler.calculate_categorical_stats(data, categorical_cols)
    collinearity_map = association_analyzer.build_associations(
        data, numeric_cols, categorical_cols
    )

    summary_stats = {**num_stats, **cat_stats}

    return EDAReport(
        feature_types=feature_types,
        missing_values=missing_values,
        outliers_info=outliers_info,
        summary_stats=summary_stats,
        collinearity_map=collinearity_map,
    )