Documentation
Classes
SelfTrainingClassifier

SelfTrainingClassifier

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.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new SelfTrainingClassifier(opts?: object): SelfTrainingClassifier;

Parameters

NameTypeDescription
opts?object-
opts.base_estimator?anyAn 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.
opts.criterion?"threshold" | "k_best"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. Default Value 'threshold'
opts.k_best?numberThe amount of samples to add in each iteration. Only used when criterion='k\_best'. Default Value 10
opts.max_iter?numberMaximum number of iterations allowed. Should be greater than or equal to 0. If it is undefined, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled. Default Value 10
opts.threshold?numberThe decision threshold for use with criterion='threshold'. Should be in \0, 1). When using the 'threshold' criterion, a [well calibrated classifier should be used. Default Value 0.75
opts.verbose?booleanEnable verbose output. Default Value false

Returns

SelfTrainingClassifier

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:23 (opens in a new tab)

id

string

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:20 (opens in a new tab)

opts

any

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:21 (opens in a new tab)

Accessors

base_estimator_

The fitted estimator.

Signature

base_estimator_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:385 (opens in a new tab)

classes_

Class labels for each output. (Taken from the trained base\_estimator\_).

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:412 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:520 (opens in a new tab)

labeled_iter_

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.

Signature

labeled_iter_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:466 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:493 (opens in a new tab)

n_iter_

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.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:547 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:72 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:76 (opens in a new tab)

termination_condition_

The reason that fitting was stopped.

Signature

termination_condition_(): Promise<"max_iter" | "no_change" | "all_labeled">;

Returns

Promise<"max_iter" | "no_change" | "all_labeled">

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:574 (opens in a new tab)

transduction_

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

Signature

transduction_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:439 (opens in a new tab)

Methods

decision_function()

Call decision function of the base\_estimator.

Signature

decision_function(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeArray representing the data.

Returns

Promise<ArrayLike[]>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:150 (opens in a new tab)

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:133 (opens in a new tab)

fit()

Fit self-training classifier using X, y as training data.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeArray representing the data.
opts.y?anyArray representing the labels. Unlabeled samples should have the label -1.

Returns

Promise<any>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:188 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:85 (opens in a new tab)

predict()

Predict the classes of X.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeArray representing the data.

Returns

Promise<ArrayLike>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:230 (opens in a new tab)

predict_log_proba()

Predict log probability for each possible outcome.

Signature

predict_log_proba(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeArray representing the data.

Returns

Promise<ArrayLike[]>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:267 (opens in a new tab)

predict_proba()

Predict probability for each possible outcome.

Signature

predict_proba(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeArray representing the data.

Returns

Promise<ArrayLike[]>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:305 (opens in a new tab)

score()

Call score on the base\_estimator.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeArray representing the data.
opts.y?ArrayLikeArray representing the labels.

Returns

Promise<number>

Defined in: generated/semi_supervised/SelfTrainingClassifier.ts:343 (opens in a new tab)