module `mlmodel.target_predictors`#

Short summary#

module mlinsights.mlmodel.target_predictors

Implements a slightly different version of the sklearn.compose.TransformedTargetRegressor.

Classes#

class	truncated documentation
`TransformedTargetClassifier2`	Meta-estimator to classify on a transformed target. Useful for applying permutation transformation in classification …
`TransformedTargetRegressor2`	Meta-estimator to regress on a transformed target. Useful for applying a non-linear transformation in regression …

Functions#

function	truncated documentation
`_common_get_transform`

Properties#

property	truncated documentation
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`classes_`	Returns the classes.

Methods#

method	truncated documentation
`__init__`
`__init__`
`_apply`	Calls predict, predict_proba or decision_function using the base classifier, applying inverse.
`_check_is_fitted`
`_more_tags`
`_more_tags`
`decision_function`	Predicts using the base classifier, applying inverse.
`fit`	Fits the model according to the given training data.
`fit`	Fits the model according to the given training data.
`predict`	Predicts using the base classifier, applying inverse.
`predict`	Predicts using the base regressor, applying inverse.
`predict_proba`	Predicts using the base classifier, applying inverse.
`score`	Scores the model with sklearn.metrics.accuracy_score.
`score`	Scores the model with sklearn.metrics.r2_score.

Documentation#

Implements a slightly different version of the sklearn.compose.TransformedTargetRegressor.

source on GitHub

class mlinsights.mlmodel.target_predictors.TransformedTargetClassifier2(classifier=None, transformer=None)#

Bases: BaseEstimator, ClassifierMixin

Meta-estimator to classify on a transformed target. Useful for applying permutation transformation in classification problems.

Parameters#

classifierobject, default=LogisticRegression(): Classifier object such as derived from ClassifierMixin. This classifier will automatically be cloned each time prior to fitting.

transformer : str or object of type BaseReciprocalTransformer

Attributes#

classifier_object: Fitted classifier.
transformer_object: Transformer used in fit, predict, decision_function, predict_proba.

Examples#

<<<

import numpy
from sklearn.linear_model import LogisticRegression
from mlinsights.mlmodel import TransformedTargetClassifier2

tt = TransformedTargetClassifier2(classifier=LogisticRegression(),
                                  transformer='permute')
X = numpy.arange(4).reshape(-1, 1)
y = numpy.array([0, 1, 0, 1])
print(tt.fit(X, y))
print(tt.score(X, y))
print(tt.classifier_.coef_)

>>>

    TransformedTargetClassifier2(classifier=LogisticRegression(),
                                 transformer='permute')
    0.5
    [[-0.453]]

See notebook Transformed Target for a more complete example.

source on GitHub

__init__(classifier=None, transformer=None)#

_apply(X, method)#

Calls predict, predict_proba or decision_function using the base classifier, applying inverse.

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

source on GitHub

_check_is_fitted()#

_more_tags()#

property classes_#

Returns the classes.

source on GitHub

decision_function(X)#

Predicts using the base classifier, applying inverse.

Parameters:: X – {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.
Returns:: raw score : array, shape = (n_samples, ?)

source on GitHub

fit(X, y, sample_weight=None)#

Fits 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

source on GitHub

predict(X)#

Predicts using the base classifier, applying inverse.

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

source on GitHub

predict_proba(X)#

Predicts using the base classifier, applying inverse.

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

source on GitHub

score(X, y, sample_weight=None)#

Scores the model with sklearn.metrics.accuracy_score.

source on GitHub

class mlinsights.mlmodel.target_predictors.TransformedTargetRegressor2(regressor=None, transformer=None)#

Bases: BaseEstimator, RegressorMixin

Meta-estimator to regress on a transformed target. Useful for applying a non-linear transformation in regression problems.

Parameters#

regressorobject, default=LinearRegression(): Regressor object such as derived from RegressorMixin. This regressor will automatically be cloned each time prior to fitting.

transformer : str or object of type BaseReciprocalTransformer

Attributes#

regressor_object: Fitted regressor.
transformer_object: Transformer used in fit and predict.

Examples#

<<<

import numpy
from sklearn.linear_model import LinearRegression
from mlinsights.mlmodel import TransformedTargetRegressor2

tt = TransformedTargetRegressor2(regressor=LinearRegression(),
                                 transformer='log')
X = numpy.arange(4).reshape(-1, 1)
y = numpy.exp(2 * X).ravel()
print(tt.fit(X, y))
print(tt.score(X, y))
print(tt.regressor_.coef_)

>>>

    TransformedTargetRegressor2(regressor=LinearRegression(), transformer='log')
    1.0
    [2.]

See notebook Transformed Target for a more complete example.

source on GitHub

__init__(regressor=None, transformer=None)#

_more_tags()#

fit(X, y, sample_weight=None)#

Fits 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

source on GitHub

predict(X)#

Predicts using the base regressor, applying inverse.

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

source on GitHub

score(X, y, sample_weight=None)#

Scores the model with sklearn.metrics.r2_score.

source on GitHub