sklearn.semi_supervised.SelfTrainingClassifier¶

class sklearn.semi_supervised.SelfTrainingClassifier(base_estimator, threshold=0.75, criterion='threshold', k_best=10, max_iter=10, verbose=False)[source]¶

Self-training classifier.

This class allows a given supervised classifier to function as a semi-supervised classifier, allowing it to learn from unlabeled data. It does this by iteratively predicting pseudo-labels for the unlabeled data and adding them to the training set.

The classifier will continue iterating until either max_iter is reached, or no pseudo-labels were added to the training set in the previous iteration.

Read more in the User Guide.

Parameters

base_estimatorestimator object

An estimator object implementing fit and predict_proba. Invoking the fit method will fit a clone of the passed estimator, which will be stored in the base_estimator_ attribute.

thresholdfloat, default=0.75

The decision threshold for use with criterion='threshold'. Should be in [0, 1). When using the ‘threshold’ criterion, a well calibrated classifier should be used.

criterion{‘threshold’, ‘k_best’}, default=’threshold’

The selection criterion used to select which labels to add to the training set. If ‘threshold’, pseudo-labels with prediction probabilities above threshold are added to the dataset. If ‘k_best’, the k_best pseudo-labels with highest prediction probabilities are added to the dataset. When using the ‘threshold’ criterion, a well calibrated classifier should be used.

k_bestint, default=10

The amount of samples to add in each iteration. Only used when criterion is k_best’.

max_iterint or None, default=10

Maximum number of iterations allowed. Should be greater than or equal to 0. If it is None, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled.

verbosebool, default=False

Enable verbose output.

Attributes

base_estimator_estimator object

The fitted estimator.

classes_ndarray or list of ndarray of shape (n_classes,)

Class labels for each output. (Taken from the trained base_estimator_).

transduction_ndarray of shape (n_samples,)

The labels used for the final fit of the classifier, including pseudo-labels added during fit.

labeled_iter_ndarray of shape (n_samples,)

The iteration in which each sample was labeled. When a sample has iteration 0, the sample was already labeled in the original dataset. When a sample has iteration -1, the sample was not labeled in any iteration.

n_iter_int

The number of rounds of self-training, that is the number of times the base estimator is fitted on relabeled variants of the training set.

termination_condition_{‘max_iter’, ‘no_change’, ‘all_labeled’}

The reason that fitting was stopped.

• ‘max_iter’: n_iter_ reached max_iter.

• ‘no_change’: no new labels were predicted.

• ‘all_labeled’: all unlabeled samples were labeled before max_iter was reached.

References

David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL ‘95). Association for Computational Linguistics, Stroudsburg, PA, USA, 189-196. DOI: https://doi.org/10.3115/981658.981684

Examples

>>> import numpy as np
>>> from sklearn import datasets
>>> from sklearn.semi_supervised import SelfTrainingClassifier
>>> from sklearn.svm import SVC
>>> rng = np.random.RandomState(42)
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
>>> iris.target[random_unlabeled_points] = -1
>>> svc = SVC(probability=True, gamma="auto")
>>> self_training_model = SelfTrainingClassifier(svc)
>>> self_training_model.fit(iris.data, iris.target)
SelfTrainingClassifier(...)

Methods

decision_function(X)	Calls decision function of the base_estimator.
fit(X, y)	Fits this SelfTrainingClassifier to a dataset.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict the classes of X.
predict_log_proba(X)	Predict log probability for each possible outcome.
predict_proba(X)	Predict probability for each possible outcome.
score(X, y)	Calls score on the base_estimator.
set_params(**params)	Set the parameters of this estimator.

decision_function(X)[source]¶

Calls decision function of the base_estimator.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Array representing the data.

Returns

yndarray of shape (n_samples, n_features)

Result of the decision function of the base_estimator.

fit(X, y)[source]¶

Fits this SelfTrainingClassifier to a dataset.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Array representing the data.

y{array-like, sparse matrix} of shape (n_samples,)

Array representing the labels. Unlabeled samples should have the label -1.

Returns

selfobject

Returns an instance of self.

get_params(deep=True)[source]¶

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.

predict(X)[source]¶

Predict the classes of X.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Array representing the data.

Returns

yndarray of shape (n_samples,)

Array with predicted labels.

predict_log_proba(X)[source]¶

Predict log probability for each possible outcome.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Array representing the data.

Returns

yndarray of shape (n_samples, n_features)

Array with log prediction probabilities.

predict_proba(X)[source]¶

Predict probability for each possible outcome.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Array representing the data.

Returns

yndarray of shape (n_samples, n_features)

Array with prediction probabilities.

score(X, y)[source]¶

Calls score on the base_estimator.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Array representing the data.

yarray-like of shape (n_samples,)

Array representing the labels.

Returns

scorefloat

Result of calling score on the base_estimator.

set_params(**params)[source]¶

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.

Examples using sklearn.semi_supervised.SelfTrainingClassifier¶