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_paramsdict
Additional parameters to be passed to the model fit method.
- Returns:
- JuTransformedTargetModel
The fitted model.
- predict(X)#
Predict using the model.
- Parameters:
- Xpd.DataFrame
The data to predict on.
- Returns:
- DataLike
The predictions.
- score(X, y)#
Score the model.
- Parameters:
- Xpd.DataFrame
The input data.
- yDataLike
The target.
- Returns:
- float
Score for the model.
- 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)#
Transform target.
- Parameters:
- Xpd.DataFrame
The input data.
- yDataLike
The target.
- Returns:
- DataLike
The transformed target.
- can_inverse_transform()#
Check if the target can be inverse transformed.
- Returns:
- bool
True if the target can be inverse transformed, False otherwise.
- 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
MetadataRequestencapsulating 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.