module `mlmodel.transfer_transformer`#

Short summary#

module mlinsights.mlmodel.transfer_transformer

Implements a transformer which wraps a predictor to do transfer learning.

class	truncated documentation
`TransferTransformer`	Wraps a predictor or a transformer in a transformer. This model is frozen: it cannot be trained and only computes …

property	truncated documentation
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …

Implements a transformer which wraps a predictor to do transfer learning.

class mlinsights.mlmodel.transfer_transformer.TransferTransformer(estimator, method=None, copy_estimator=True, trainable=False)#

Wraps a predictor or a transformer in a transformer. This model is frozen: it cannot be trained and only computes the predictions.

Parameters:

estimator – estimator to wrap in a transformer, it is cloned with the training data (deep copy) when fitted
method – if None, guess what method should be called, transform for a transformer, predict_proba for a classifier, decision_function if found, predict otherwiser
copy_estimator – copy the model instead of taking a reference
trainable – the transfered model must be trained

__init__(estimator, method=None, copy_estimator=True, trainable=False)#

Parameters:

estimator – estimator to wrap in a transformer, it is cloned with the training data (deep copy) when fitted
method – if None, guess what method should be called, transform for a transformer, predict_proba for a classifier, decision_function if found, predict otherwiser
copy_estimator – copy the model instead of taking a reference
trainable – the transfered model must be trained

fit(X=None, y=None, sample_weight=None)#

The function does nothing.

Parameters:

Returns:

self: returns an instance of self.

Fitted attributes:

transform(X)#

Runs the predictions.

Parameters:: X – numpy array or sparse matrix of shape [n_samples,n_features] Training data
Returns:: tranformed X