deeptables.preprocessing package

Submodules

deeptables.preprocessing.transformer module

class deeptables.preprocessing.transformer.CategorizeEncoder(columns=None, remain_numeric=True)

Bases: object

fit(X)

fit_transform(X)

transform(X)

class deeptables.preprocessing.transformer.DataFrameWrapper(transform, columns=None)

Bases: object

fit(X)

fit_transform(X)

transform(X)

class deeptables.preprocessing.transformer.GaussRankScaler

Bases: object

fit_transform(X)

class deeptables.preprocessing.transformer.LgbmLeavesEncoder(cat_vars, cont_vars, task, **params)

Bases: object

fit(X, y)

fit_transform(X, y)

transform(X)

class deeptables.preprocessing.transformer.MultiKBinsDiscretizer(columns=None, bins=None, strategy='quantile')

Bases: object

fit(X)

fit_transform(X)

transform(X)

class deeptables.preprocessing.transformer.MultiLabelEncoder(columns=None)

Bases: object

fit(X)

fit_transform(X)

transform(X)

class deeptables.preprocessing.transformer.PassThroughEstimator

Bases: object

fit(X)

fit_transform(X)

transform(X)

class deeptables.preprocessing.transformer.SafeLabelEncoder

Bases: sklearn.preprocessing._label.LabelEncoder

transform(y)

Transform labels to normalized encoding.

Parameters:y (array-like of shape [n_samples]) – Target values. Returns:y Return type:array-like of shape [n_samples]

deeptables.preprocessing.utils module

deeptables.preprocessing.utils.target_encoding(train, target, test=None, feat_to_encode=None, smooth=0.2, random_state=9527)

deeptables.preprocessing.utils.target_rate_encodeing(feat_to_encode, target, df, mode='order')

Module contents