quantificationlib.binary.debias module

De-Bias quantifier (just for binary quantification)

class DeBias(estimator_test=None, estimator_train=None, verbose=0)[source]

Bases: UsingClassifiers

Binary quantifier based on De-Bias estimate proposed by Friedman

prevalence (positives) = prior(positives) + ( prevalence_PCC - prior(positives) ) / Vt

where

Vt = [ 1/|T| sum_{x in D} (P(h(x)==+1|x) - prior(positives) )^2 ]

/ (prior(positives) * prior(negatives))

This class works in two different ways:

  1. An estimator is used to classify the examples of the testing bag (the estimator can be already trained)

  2. You can directly provide the predictions for the examples in the predict method. This is useful for synthetic/artificial experiments

Parameters:

  • estimator_train (estimator object (default=None)) – An estimator object implementing fit and predict_proba. It is used to classify the examples of the training set and to compute the confusion matrix

  • estimator_test (estimator object (default=None)) – An estimator object implementing fit and predict_proba. It is used to classify the examples of the testing set and to obtain the confusion matrix of the testing set. For some experiments both estimators could be the same

  • verbose (int, optional, (default=0)) – The verbosity level. The default value, zero, means silent mode

estimator_train

Estimator used to classify the examples of the training set.

Type:

estimator

estimator_test

Estimator used to classify the examples of the testing bag

Type:

estimator

predictions_train_

Predictions of the examples in the training set

Type:

ndarray, shape (n_examples, n_classes) (probabilistic estimator)

predictions_test_

Predictions of the examples in the testing bag

Type:

ndarray, shape (n_examples, n_classes) (probabilistic estimator)

probabilistic_predictions

This means that predictions_train_/predictions_test_ contain probabilistic predictions

Type:

bool, True

needs_predictions_train

It is True because DeBias quantifiers need to estimate the training distribution

Type:

bool, True

classes_

Class labels

Type:

ndarray, shape (n_classes, )

y_ext_

True labels of the training set

Type:

ndarray, shape(n_examples, )

train_prevs_

Prevalence of each class in the training set

Type:

ndarray, shape (n_classes, )

Vt_
The value of equation
Vt = [ 1/|T| sum_{x in D} (P(h(x)==+1|x) - train_prevs_[1])^2 ]

/ (train_prevs_[1] * train_prevs_[0])

applied over the training examples D

Type:

float

verbose

The verbosity level

Type:

int

Notes

Notice that at least one between estimator_train/predictions_train and estimator_test/predictions_test must be not None. If both are None a ValueError exception will be raised. If both are not None, predictions_train/predictions_test are used

References

Jerome Friedman. Class counts in future unlabeled samples. Presentation at MIT CSAIL Big Data Event, 2014.

fit(X, y, predictions_train=None)[source]

This method performs the following operations: 1) fits the estimators for the training set and the testing set (if needed), and 2) computes predictions_train_ (probabilities) if needed. Both operations are performed by the fit method of its superclass.

Finally the method computes the value of Vt

Vt = [ 1/|T| sum_{x in D} (P(h(x)==+1|x) - prior(positives) )^2 ]

/ (prior(positives) * prior(negatives))

Parameters:

  • X (array-like, shape (n_examples, n_features)) – Data

  • y (array-like, shape (n_examples, )) – True classes

  • predictions_train (ndarray, shape (n_examples, n_classes)) – Predictions of the training set

Raises:

  • ValueError – When estimator_train and predictions_train are both None

  • AttributeError – When the number of classes > 2

predict(X, predictions_test=None)[source]

Predict the class distribution of a testing bag

The prevalence for the positive class is

prevalence (positives) = prior(positives) + ( prevalence_PCC - prior(positives) ) / Vt

Parameters:

  • X ((sparse) array-like, shape (n_examples, n_features)) – Data

  • predictions_test (ndarray, shape (n_examples, n_classes) (default=None)) –

    They must be probabilities (the estimator used must have a predict_proba method)

    If predictions_test is not None they are copied on predictions_test_ and used. If predictions_test is None, predictions for the testing examples are computed using the predict method of estimator_test (it must be an actual estimator)

Raises:

ValueError – When estimator_test and predictions_test are both None

Returns:

prevalences

Return type:

An ndarray, shape(n_classes, ) with the prevalence for each class

set_fit_request(*, predictions_train='$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • predictions_train (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for predictions_train parameter in fit.

  • self (DeBias) –

Returns:

self – The updated object.

Return type:

object

set_predict_request(*, predictions_test='$UNCHANGED$')

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • predictions_test (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for predictions_test parameter in predict.

  • self (DeBias) –

Returns:

self – The updated object.

Return type:

object