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glassbox.cleaner.outliers

OutlierCapper — clips values outside IQR bounds.

OutlierCapper 

OutlierCapper()

Bases: BaseTransformer

Identifies and caps numerical outliers based on specified bounds.

Source code in glassbox/cleaner/outliers.py 
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def __init__(self):
    self._bounds: Dict[str, Dict[str, float]] = {}

fit 

fit(X)

Detect boundaries for outlier capping from the training data.

Parameters:

Name Type Description Default
X ndarray

Input array of shape (n_samples, n_features).

 required

Returns:

Type Description
Self

Fitted outlier capper instance.
Source code in glassbox/cleaner/outliers.py 
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def fit(self, X: np.ndarray) -> Self:
    """
    Detect boundaries for outlier capping from the training data.

    Parameters
    ----------
    X : np.ndarray
        Input array of shape (n_samples, n_features).

    Returns
    -------
    Self
        Fitted outlier capper instance.
    """
    n_features = X.shape[1]
    for col_idx in range(n_features):
        col = X[:, col_idx]

        if np.issubdtype(col.dtype, np.number):
            col_clean = col[~np.isnan(col)]
        else:
            col_clean = np.array(
                [
                    x
                    for x in col
                    if x is not None and not (isinstance(x, float) and np.isnan(x))
                ]
            )

        if len(col_clean) == 0:
            self._bounds[str(col_idx)] = {"lower": 0.0, "upper": 1.0}
        else:
            lower, upper = calc_iqr(col_clean)
            self._bounds[str(col_idx)] = {"lower": lower, "upper": upper}
    return self

transform 

transform(X)

Cap outliers in the input dataset.

Parameters:

Name Type Description Default
X ndarray

Input array of shape (n_samples, n_features).

 required

Returns:

Type Description
ndarray

Transformed array with outliers capped.
Source code in glassbox/cleaner/outliers.py 
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def transform(self, X: np.ndarray) -> np.ndarray:
    """
    Cap outliers in the input dataset.

    Parameters
    ----------
    X : np.ndarray
        Input array of shape (n_samples, n_features).

    Returns
    -------
    np.ndarray
        Transformed array with outliers capped.
    """
    X_out = X.copy()
    n_features = X.shape[1]
    for col_idx in range(n_features):
        bounds = self._bounds.get(str(col_idx), {"lower": 0.0, "upper": 1.0})
        lower = bounds["lower"]
        upper = bounds["upper"]

        col = X_out[:, col_idx]
        if np.issubdtype(col.dtype, np.number):
            mask_clean = ~np.isnan(col)
            # Cap the values only for non-NaN elements
            X_out[mask_clean, col_idx] = np.clip(col[mask_clean], lower, upper)
        else:
            mask_clean = np.array([v is not None for v in col])
            # Objects might fail if not numbers under the hood
            try:
                X_out[mask_clean, col_idx] = np.clip(
                    col[mask_clean].astype(float), lower, upper
                )
            except ValueError:
                pass
    return X_out