sklearn.compose.TransformedTargetRegressor

class sklearn.compose.TransformedTargetRegressor(regressor=None, transformer=None, func=None, inverse_func=None, check_inverse=True) [source]

Meta-estimator to regress on a transformed target.

Useful for applying a non-linear transformation in regression problems. This transformation can be given as a Transformer such as the QuantileTransformer or as a function and its inverse such as log and exp.

The computation during fit is:

regressor.fit(X, func(y))

or:

regressor.fit(X, transformer.transform(y))

The computation during predict is:

inverse_func(regressor.predict(X))

or:

transformer.inverse_transform(regressor.predict(X))

Read more in the User Guide.

Parameters:

regressor : object, default=LinearRegression() Regressor object such as derived from RegressorMixin. This regressor will automatically be cloned each time prior to fitting. transformer : object, default=None Estimator object such as derived from TransformerMixin. Cannot be set at the same time as func and inverse_func. If transformer is None as well as func and inverse_func, the transformer will be an identity transformer. Note that the transformer will be cloned during fitting. Also, the transformer is restricting y to be a numpy array. func : function, optional Function to apply to y before passing to fit. Cannot be set at the same time as transformer. The function needs to return a 2-dimensional array. If func is None, the function used will be the identity function. inverse_func : function, optional Function to apply to the prediction of the regressor. Cannot be set at the same time as transformer as well. The function needs to return a 2-dimensional array. The inverse function is used to return predictions to the same space of the original training labels. check_inverse : bool, default=True Whether to check that transform followed by inverse_transform or func followed by inverse_func leads to the original targets.

Attributes:

regressor_ : object Fitted regressor. transformer_ : object Transformer used in fit and predict.

Notes

Internally, the target y is always converted into a 2-dimensional array to be used by scikit-learn transformers. At the time of prediction, the output will be reshaped to a have the same number of dimensions as y.

See examples/compose/plot_transformed_target.py.

Examples

>>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.compose import TransformedTargetRegressor
>>> tt = TransformedTargetRegressor(regressor=LinearRegression(),
...                                 func=np.log, inverse_func=np.exp)
>>> X = np.arange(4).reshape(-1, 1)
>>> y = np.exp(2 * X).ravel()
>>> tt.fit(X, y) 
TransformedTargetRegressor(...)
>>> tt.score(X, y)
1.0
>>> tt.regressor_.coef_
array([2.])

Methods

fit(X, y[, sample_weight])	Fit the model according to the given training data.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the base regressor, applying inverse.
score(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(regressor=None, transformer=None, func=None, inverse_func=None, check_inverse=True) [source]

fit(X, y, sample_weight=None) [source]

Fit the model according to the given training data.

Parameters:	X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples,) Target values. sample_weight : array-like, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
Returns:	self : object

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

predict(X) [source]

Predict using the base regressor, applying inverse.

The regressor is used to predict and the inverse_func or inverse_transform is applied before returning the prediction.

Parameters:	X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.
Returns:	y_hat : array, shape = (n_samples,) Predicted values.

score(X, y, sample_weight=None) [source]

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters:

X : array-like, shape = (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.

Returns:

score : float R^2 of self.predict(X) wrt. y.

set_params(**params) [source]

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.

Returns:	self

Examples using sklearn.compose.TransformedTargetRegressor