`qolmat.imputations.preprocessing`.BinTransformer¶

class qolmat.imputations.preprocessing.BinTransformer(cols: Optional[List] = None)[source]¶

BinTransformer class.

Learn the possible values of the provided numerical feature, allowing to transform new values to the closest existing one.

__init__(cols: Optional[List] = None)[source]¶

fit(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]], y: Optional[ndarray[tuple[int, ...], dtype[_ScalarType_co]]] = None) → BinTransformer[source]¶

Fit the BinTransformer to X.

Parameters

Xarray-like of shape (n_samples, n_features): The data to determine the unique values.
yNone: Ignored. This parameter exists only for compatibility with Pipeline.

Returns

selfobject: Fitted transformer.

inverse_transform(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → ndarray[tuple[int, ...], dtype[_ScalarType_co]][source]¶

Transform X to existing values learned during fit.

Parameters

Xarray-like of shape (n_samples, n_features): The data to transform.

Returns

X_outndarray of shape (n_samples,): Transformed input.

transform(X: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → ndarray[tuple[int, ...], dtype[_ScalarType_co]][source]¶

Transform X to existing values learned during fit.

Parameters

Xarray-like of shape (n_samples,): The data to transform.

Returns

X_outndarray of shape (n_samples,): Transformed input.

Examples using `qolmat.imputations.preprocessing.BinTransformer`¶

Benchmark for categorical data

Benchmark for categorical data