OutlierTransformerMixin

class pyoptex.analysis.mixins.fit_mixin.OutlierTransformerMixin(factors=(), Y2X=<function identityY2X>, random_effects=())

Very similar to TransformerMixin, but focused on outlier detection and removal during training. The fit_transform function should remove the outliers from the data.

__init__(factors=(), Y2X=<function identityY2X>, random_effects=())

Creates the regressor

Parameters

factorslist(Factor)

A list of factors to be used during fitting. It contains the categorical encoding, continuous normalization, etc.

Y2Xfunc(Y)

The function to transform a design matrix Y to a model matrix X.

random_effectslist(str)

The names of any random effect columns. Every random effect is interpreted as a string column and encoded using effect encoding.

Methods

OutlierTransformerMixin.fit(X, y)	Fits the data.
OutlierTransformerMixin.fit_transform(X, y)	Fit the transformer to the data and apply the transformation.
OutlierTransformerMixin.preprocess_fit(X, y)	Preprocesses before fitting the data.
OutlierTransformerMixin.set_output(*[, ...])	Set output container.
OutlierTransformerMixin.transform(X, y)	Ignore any transformation as the outlier detection only applies during training.

Attributes

OutlierTransformerMixin.is_fitted

Checks whether the regressor has been fitted.