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 to the target y in regression problems. This transformation can be given as a Transformer such as the QuantileTransformer or as a function and its inverse such as np.log and np.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.

New in version 0.20.

Parameters:
regressorobject, default=None

Regressor object such as derived from RegressorMixin. This regressor will automatically be cloned each time prior to fitting. If regressor is None, LinearRegression is created and used.

transformerobject, 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.

funcfunction, default=None

Function to apply to y before passing to fit. Cannot be set at the same time as transformer. If func is None, the function used will be the identity function. If func is set, inverse_func also needs to be provided. The function needs to return a 2-dimensional array.

inverse_funcfunction, default=None

Function to apply to the prediction of the regressor. Cannot be set at the same time as transformer. The inverse function is used to return predictions to the same space of the original training labels. If inverse_func is set, func also needs to be provided. The inverse function needs to return a 2-dimensional array.

check_inversebool, 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.

n_features_in_int

Number of features seen during fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

sklearn.preprocessing.FunctionTransformer

Construct a transformer from an arbitrary callable.

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.

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.])

For a more detailed example use case refer to Effect of transforming the targets in regression model.

Methods

fit(X, y, **fit_params)

Fit the model according to the given training data.

get_metadata_routing()

Raise NotImplementedError.

get_params([deep])

Get parameters for this estimator.

predict(X, **predict_params)

Predict using the base regressor, applying inverse.

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.

fit(X, y, **fit_params)[source]

Fit the model according to the given training data.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

**fit_paramsdict

Parameters passed to the fit method of the underlying regressor.

Returns:
selfobject

Fitted estimator.

get_metadata_routing()[source]

Raise NotImplementedError.

This estimator does not support metadata routing yet.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

property n_features_in_

Number of features seen during fit.

predict(X, **predict_params)[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} of shape (n_samples, n_features)

Samples.

**predict_paramsdict of str -> object

Parameters passed to the predict method of the underlying regressor.

Returns:
y_hatndarray of shape (n_samples,)

Predicted values.

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

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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:
Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
scorefloat

\(R^2\) of self.predict(X) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)[source]

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:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') TransformedTargetRegressor[source]

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

Examples using sklearn.compose.TransformedTargetRegressor

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model