Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.transformers.target.JuTransformedTargetModel#

class julearn.transformers.target.JuTransformedTargetModel(model, transformer)#

Class that provides a model that supports transforming the target.

This _model_ is a wrapper that will transform the target before fitting.

Parameters:
modelModelLike

The model to be wrapped. Can be a pipeline.

transformerJuTargetPipeline

The transformer to be used to transform the target.

__init__(model, transformer)#
fit(X, y, **fit_params)#

Fit the model.

Parameters:
Xpd.DataFrame

The input data.

yDataLike

The target.

fit_paramsAny

Additional parameters to be passed to the model fit method.

Returns:
selfJuTransformedTargetModel

The fitted model.

predict(X)#

Predict using the model.

Parameters:
Xpd.DataFrame

The data to predict on.

Returns:
DataLike

The predictions.

score(X, y)#
predict_proba(X)#

Compute probabilities of possible outcomes for samples in X.

Parameters:
Xpd.DataFrame

The data to predict on.

Returns:
np.ndarray

Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

decision_function(X)#

Evaluate the decision function for the samples in X.

Parameters:
Xpd.DataFrame

The data to obtain the decision function.

Returns:
Xarray-like of shape (n_samples, n_class * (n_class-1) / 2)

Returns the decision function of the sample for each class in the model.

transform_target(X, y)#
property classes_: ndarray#

Get the classes of the model.

filter_columns(X)#

Get the apply_to columns of a pandas DataFrame.

Parameters:
Xpd.DataFrame

The DataFrame to filter.

Returns:
pd.DataFrame

The DataFrame with only the apply_to columns.

get_apply_to()#

Get the column types the estimator applies to.

Returns:
ColumnTypes

The column types the estimator applies to.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_needed_types()#

Get the column types needed by the estimator.

Returns:
ColumnTypes

The column types needed by the estimator.

get_params(deep=True)#

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.

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:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.