TargetTransformer¶

class hcrystalball.preprocessing.TargetTransformer(estimator, y_transformer, omit_inverse_transformation=False)[source]¶

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

Enable transformation of the target.

Wrapper for applying an estimator to a transformed version of the target y and automatically transforming back predictions

Methods Summary

`fit`(X[, y])	Fit after reshaping and rescaling the target
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`named_steps`()	Provide access to named steps for `Pipeline`
`predict`(X[, y])	Ensure correct estimator.predict with scaled target values
`score`(X[, y])	Ensures correct estimator.score with scaled target values
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X[, y])	Transforms the features

Methods Documentation

fit(X, y=None)[source]¶

Fit after reshaping and rescaling the target

Reshape target to 2d, call fit_transform on 2d, return to 1d form and fit estimator on transformed target

Parameters

X (Any) – Ignored.
y (numpy.ndarray) – Target values.

Returns

Fitted target transformer

Return type

TargetTransformer

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, sparse matrix, dataframe} of shape (n_samples, n_features)) –
y (ndarray of shape (n_samples,), default=None) – Target values.
**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: mapping of string to any

named_steps()[source]¶

Provide access to named steps for Pipeline

Returns: Dictionary of steps
Return type: dict

predict(X, y=None)[source]¶

Ensure correct estimator.predict with scaled target values

Parameters

X (numpy.ndarray) – Input features.
y (numpy.ndarray) – Target values.

Returns

Results of estimators prediction

Return type

pandas.DataFrame

score(X, y=None)[source]¶

Ensures correct estimator.score with scaled target values

Parameters

X (numpy.ndarray) – Input features.
y (numpy.ndarray) – Target values.

Returns

Results of estimators score function

Return type

Any

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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: object

transform(X, y=None)[source]¶

Transforms the features

Parameters

X (numpy.ndarray) – Input features.
y (Any) – Ignored.

Returns

Result of estimator transform

Return type

numpy.ndarray