Class: 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.

Constructors

new SelfTrainingClassifier()

new SelfTrainingClassifier(opts?): SelfTrainingClassifier

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.base_estimator`?	`any`	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.
`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.
`opts.k_best`?	`number`	The amount of samples to add in each iteration. Only used when `criterion='k_best'`.
`opts.max_iter`?	`number`	Maximum 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.
`opts.threshold`?	`number`	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.
`opts.verbose`?	`boolean`	Enable verbose output.

Returns SelfTrainingClassifier

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/semi_supervised/SelfTrainingClassifier.ts:25
`_isInitialized`	`boolean`	`false`	generated/semi_supervised/SelfTrainingClassifier.ts:24
`_py`	`PythonBridge`	`undefined`	generated/semi_supervised/SelfTrainingClassifier.ts:23
`id`	`string`	`undefined`	generated/semi_supervised/SelfTrainingClassifier.ts:20
`opts`	`any`	`undefined`	generated/semi_supervised/SelfTrainingClassifier.ts:21

Accessors

base_estimator_

Get Signature

get base_estimator_(): Promise<any>

The fitted estimator.

Returns Promise<any>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:399

classes_

Get Signature

get classes_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:426

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:534

labeled_iter_

Get Signature

get labeled_iter_(): Promise<ArrayLike>

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.

Returns Promise<ArrayLike>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:480

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:507

n_iter_

Get Signature

get n_iter_(): Promise<number>

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.

Returns Promise<number>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:561

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:72

termination_condition_

Get Signature

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

The reason that fitting was stopped.

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

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:588

transduction_

Get Signature

get transduction_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:453

Methods

decision_function()

decision_function(opts): Promise<ArrayLike[]>

Call decision function of the base_estimator.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Array representing the data.

Returns Promise<ArrayLike[]>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:145

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

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

Returns Promise<void>

Defined in generated/semi_supervised/SelfTrainingClassifier.ts:128

fit()

fit(opts): Promise<any>

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

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Array representing the data.
`opts.y`?	`any`	Array representing the labels. Unlabeled samples should have the label -1.