john_toolbox.preprocessing.pandas_transformers.DebugTransformer¶

class john_toolbox.preprocessing.pandas_transformers.DebugTransformer[source]¶

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

This class save information between steps in sklearn pipeline and is used for debug purposes.

See also

SelectColumnsTransformer: Keep columns from DataFrame.
DropColumnsTransformer: Drop columns from DataFrame.
EncoderTransformer: Drop columns from DataFrame.
FunctionTransformer: Use of standard Encoder from sklearn.

Methods

`fit`
`fit_transform`	Fit to data, then transform it.
`get_params`	Get parameters for this estimator.
`set_params`	Set the parameters of this estimator.
`transform`

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 (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance