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.TargetConfoundRemover#
- class julearn.transformers.target.TargetConfoundRemover(model_confound=None, confounds='confound', threshold=None)#
Remove confounds from the target.
- Parameters:
- model_confoundModelLike, optional
Sklearn compatible model used to predict specified features independently using the confounds as features. The predictions of these models are then subtracted from each of the specified features, defaults to LinearRegression().
- confoundsstr or list of str, optional
The name of the ‘confounds’ type(s), i.e. which column type(s) represents the confounds. By default this is set to ‘confounds’.
- thresholdfloat, optional
All residual values after confound removal which fall under the threshold will be set to 0. None (default) means that no threshold will be applied.
- __init__(model_confound=None, confounds='confound', threshold=None)#
- fit(X, y)#
Fit ConfoundRemover.
- Parameters:
- Xpd.DataFrame
Training data for the confound remover.
- ypd.Series
Training target values.
- Returns:
- TargetConfoundRemover
The fitted target confound remover.
- transform(X, y)#
Remove confounds from the target.
- Parameters:
- Xpd.DataFrame
Testing data for the confound remover.
- ypd.Series
Target values.
- Returns:
- pd.Series
The target with confounds removed.
- fit_transform(X, y)#
Fit and transform the target.
- Parameters:
- Xpd.DataFrame
The input data.
- yDataLike
The target.
- Returns:
- DataLike
The transformed target.