SelfTrainingClassifier (original) (raw)
class sklearn.semi_supervised.SelfTrainingClassifier(estimator=None, base_estimator='deprecated', threshold=0.75, criterion='threshold', k_best=10, max_iter=10, verbose=False)[source]#
Self-training classifier.
This metaestimator 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:
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 estimator_ attribute.
Added in version 1.6: estimator was added to replace base_estimator.
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 estimator_ attribute.
Deprecated since version 1.6: base_estimator was deprecated in 1.6 and will be removed in 1.8. Use estimator instead.
thresholdfloat, default=0.75
The decision threshold for use with criterion='threshold'. Should be in [0, 1). When using the 'threshold' criterion, awell 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, awell calibrated classifier should be used.
k_bestint, default=10
The amount of samples to add in each iteration. Only used whencriterion='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:
**estimator_**estimator object
The fitted estimator.
**classes_**ndarray or list of ndarray of shape (n_classes,)
Class labels for each output. (Taken from the trainedestimator_).
**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_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
**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_reachedmax_iter.'no_change': no new labels were predicted.'all_labeled': all unlabeled samples were labeled beforemax_iterwas reached.
References
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(...)
decision_function(X, **params)[source]#
Call decision function of the estimator.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
**paramsdict of str -> object
Parameters to pass to the underlying estimator’sdecision_function method.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Returns:
yndarray of shape (n_samples, n_features)
Result of the decision function of the estimator.
Fit self-training classifier using X, y as training data.
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.
**paramsdict
Parameters to pass to the underlying estimators.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Returns:
selfobject
Fitted estimator.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.6.
Returns:
routingMetadataRouter
A MetadataRouter encapsulating routing information.
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 the classes of X.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
**paramsdict of str -> object
Parameters to pass to the underlying estimator’s predict method.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Returns:
yndarray of shape (n_samples,)
Array with predicted labels.
predict_log_proba(X, **params)[source]#
Predict log probability for each possible outcome.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
**paramsdict of str -> object
Parameters to pass to the underlying estimator’spredict_log_proba method.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Returns:
yndarray of shape (n_samples, n_features)
Array with log prediction probabilities.
predict_proba(X, **params)[source]#
Predict probability for each possible outcome.
Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data.
**paramsdict of str -> object
Parameters to pass to the underlying estimator’spredict_proba method.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Returns:
yndarray of shape (n_samples, n_features)
Array with prediction probabilities.
score(X, y, **params)[source]#
Call score on the 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.
**paramsdict of str -> object
Parameters to pass to the underlying estimator’s score method.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by usingsklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
Returns:
scorefloat
Result of calling score on the estimator.
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.