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:

model (ModelLike) – The model to be wrapped. Can be a pipeline.
transformer (JuTargetPipeline) – The transformer to be used to transform the target.

__init__(model, transformer)¶

fit(X, y, **fit_params)¶

Fit the model.

Parameters:

X (DataFrame) – The input data.
y (ndarray | DataFrame | Series) – The target.
**fit_params (Any) – Additional parameters to be passed to the model fit method.

Returns:

The fitted model.

predict(X)¶

Predict using the model.

Parameters:: X (DataFrame) – The data to predict on.
Returns:: The predictions.

score(X, y)¶

Score the model.

Parameters:

X (DataFrame) – The input data.
y (ndarray | DataFrame | Series) – The target.

Returns:

Score for the model.

predict_proba(X)¶

Compute probabilities of possible outcomes for samples in X.

Parameters:: X (DataFrame) – The data to predict on.
Returns:: 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:: X (DataFrame) – The data to obtain the decision function.
Returns:: Returns the decision function of the sample for each class in the model.

transform_target(X, y)¶

Transform target.

Parameters:

X (DataFrame) – The input data.
y (ndarray | DataFrame | Series) – The target.

Returns:

The transformed target.

can_inverse_transform()¶

Check if the target can be inverse transformed.

Returns:: True if the target can be inverse transformed, False otherwise.

property classes_: ndarray¶: Get the classes of the model.

filter_columns(X)¶

Get the apply_to columns of a pandas DataFrame.

Parameters:: X (DataFrame) – The DataFrame to filter.
Returns:: The DataFrame with only the apply_to columns.

get_apply_to()¶

Get the column types the estimator applies to.

Returns:: 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:: A MetadataRequest encapsulating routing information.

get_needed_types()¶

Get the column types needed by the estimator.

Returns:: The column types needed by the estimator.

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:: 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:: **params (dict) – Estimator parameters.
Returns:: Estimator instance.