toad.transform module

class toad.transform.Transformer

Bases: sklearn.base.TransformerMixin, toad.utils.mixin.SaveMixin

Base class for transformers

fit()

fit method, see details in fit_ method

transform(X, *args, **kwargs)

transform method, see details in transform_ method

export(**kwargs)

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (numpy array of shape [n_samples, n_features]) – Training set.
• y (numpy array of shape [n_samples]) – Target values.
• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]

load(rules, update=False, **kwargs)

class toad.transform.WOETransformer

Bases: toad.transform.Transformer

WOE transformer

fit_(X, y)

fit WOE transformer

Parameters:

• X (DataFrame|array-like) –
• y (str|array-like) –
• select_dtypes (str|numpy.dtypes) – ‘object’, ‘number’ etc. only selected dtypes will be transform

transform_(rule, X, default='min')

transform function for single feature

Parameters:

• X (array-like) –
• default (str) – ‘min’(default), ‘max’ - the strategy to be used for unknown group

Returns:

array-like

export(**kwargs)

fit()

fit method, see details in fit_ method

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (numpy array of shape [n_samples, n_features]) – Training set.
• y (numpy array of shape [n_samples]) – Target values.
• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]

load(rules, update=False, **kwargs)

transform(X, *args, **kwargs)

transform method, see details in transform_ method

class toad.transform.Combiner

Bases: toad.transform.Transformer

Combiner for merge data

fit_(X, y=None, method='chi', empty_separate=False, **kwargs)

fit combiner

Parameters:

• X (DataFrame|array-like) – features to be combined
• y (str|array-like) – target data or name of target in X
• method (str) – the strategy to be used to merge X, same as .merge, default is chi
• n_bins (int) – counts of bins will be combined
• empty_separate (bool) – if need to combine empty values into a separate group

transform_(rule, X, labels=False, **kwargs)

transform X by combiner

Parameters:

• X (DataFrame|array-like) – features to be transformed
• labels (bool) – if need to use labels for resulting bins, False by default

Returns:

array-like

set_rules(map, reset=False)

set rules for combiner

Parameters:

• map (dict|array-like) – map of splits
• reset (bool) – if need to reset combiner

Returns:

self

export(**kwargs)

fit()

fit method, see details in fit_ method

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (numpy array of shape [n_samples, n_features]) – Training set.
• y (numpy array of shape [n_samples]) – Target values.
• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]

load(rules, update=False, **kwargs)

transform(X, *args, **kwargs)

transform method, see details in transform_ method

class toad.transform.GBDTTransformer

Bases: toad.transform.Transformer

GBDT transformer

fit_(X, y, **kwargs)

fit GBDT transformer

Parameters:

• X (DataFrame|array-like) –
• y (str|array-like) –
• select_dtypes (str|numpy.dtypes) – ‘object’, ‘number’ etc. only selected dtypes will be transform,

transform_(rules, X)

transform woe

Parameters:X (DataFrame|array-like) – Returns:array-like

export(**kwargs)

fit()

fit method, see details in fit_ method

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (numpy array of shape [n_samples, n_features]) – Training set.
• y (numpy array of shape [n_samples]) – Target values.
• **fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]

load(rules, update=False, **kwargs)

transform(X, *args, **kwargs)

transform method, see details in transform_ method