bqlearn.tradaboost.TrAdaBoostClassifier

class bqlearn.tradaboost.TrAdaBoostClassifier(estimator=None, *, n_estimators=50, learning_rate=1.0, random_state=None)

A TrAdaBoost classifier.

A TrAdaBoost [1] classifier is a meta-estimator that adapts Adaboost [2] to transfert learning. For the trusted dataset, TrAdaBoost works exactly the same way as AdaBoost, the misclassified examples get a higher weight for estimators to focus on them. For the untrusted dataset, TrAdaBoost works the opposite way as in WMA [3], the misclassified examples are deemed useless for the task and thus see their weights decreased.

This class implements a modified TrAdaBoost for multi-class classification in the fashion of AdaBoost-SAMME [4] with weight drift correction from Dynamic TrAdaBoost [5].

Parameters:

estimatorobject, default=None

The base estimator from which the reversed boosted ensemble is built. Support for sample weighting is required. If None, then the base estimator is LinearSVC.

n_estimatorsint

The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.

learning_ratefloat, default=1.0

Learning rate shrinks the contribution of each classifier by learning_rate. There is a trade-off between learning_rate and n_estimators

random_stateint or RandomState, default=None

Controls the random seed given at each estimator at each boosting iteration. Pass an int for reproducible output across multiple function calls.

Attributes:

estimator_estimator

The base estimator from which the ensemble is grown.

estimators_list of classifiers

The collection of fitted sub-estimators.

n_features_in_int

The number of features seen during fit().

classes_ndarray of shape (n_classes,)

The classes labels.

n_classes_int

The number of classes.

estimator_weights_ndarray of floats

Weights for each estimator in the boosted ensemble.

estimator_errors_ndarray of floats

Classification error for each estimator in the boosted ensemble.

References

[1]

Wenyuan Dai, Qiang Yang, Gui-Rong Xue, Yong Yu, “Boosting for Transfer Learning”, 2007.

[2]

25. Freund, R. Schapire, “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting”, 1995.

[3]

14. Littlestone, M.K. Warmuth, “The Weighted Majority Algorithm”, 1994.

[4]

10. Zhu, H. Zou, S. Rosset, T. Hastie, “Multi-class AdaBoost”, 2009.

[5]

19. Al-Stouhi and C. K. Reddy, “Adaptive boosting for transfer learning using dynamic updates”, ECML, 2011.

Methods

decision_function(X)	Compute the decision function of X.
fit(X, y[, sample_weight, sample_quality])	Build a boosted classifier/regressor from the training set (X, y).
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict classes for X.
predict_log_proba(X)	Predict class log-probabilities for X.
predict_proba(X)	Predict class probabilities for X.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.
staged_decision_function(X)	Compute decision function of X for each boosting iteration.
staged_predict(X)	Return staged predictions for X.
staged_predict_proba(X)	Predict class probabilities for X.
staged_score(X, y[, sample_weight])	Return staged scores for X, y.

property base_estimator_

Estimator used to grow the ensemble.

decision_function(X)

Compute the decision function of X.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

scorendarray of shape of (n_samples, k)

The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with k == 1, otherwise k==n_classes. For binary classification, values closer to -1 or 1 mean more like the first or second class in classes_, respectively.

property feature_importances_

The impurity-based feature importances.

The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance() as an alternative.

Returns:

feature_importances_ndarray of shape (n_features,)

The feature importances.

fit(X, y, sample_weight=None, sample_quality=None, **fit_params)

Build a boosted classifier/regressor from the training set (X, y).

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

yarray-like of shape (n_samples,)

The target values (class labels in classification, real numbers in regression).

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. If None, the sample weights are initialized to 1 / n_samples.

sample_qualityarray-like, shape (n_samples,)

Sample qualities.

Returns:

selfobject

get_params(deep=True)

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)

Predict classes for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

yndarray of shape (n_samples,)

The predicted classes.

predict_log_proba(X)

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

pndarray of shape (n_samples, n_classes)

The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

predict_proba(X)

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Returns:

pndarray of shape (n_samples, n_classes)

The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)

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.

staged_decision_function(X)

Compute decision function of X for each boosting iteration.

This method allows monitoring (i.e. determine error on testing set) after each boosting iteration.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields:

scoregenerator of ndarray of shape (n_samples, k)

The decision function of the input samples. The order of outputs is the same of that of the classes_ attribute. Binary classification is a special cases with k == 1, otherwise k==n_classes. For binary classification, values closer to -1 or 1 mean more like the first or second class in classes_, respectively.

staged_predict(X)

Return staged predictions for X.

The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble.

This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine the prediction on a test set after each boost.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields:

ygenerator of ndarray of shape (n_samples,)

The predicted classes.

staged_predict_proba(X)

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble.

This generator method yields the ensemble predicted class probabilities after each iteration of boosting and therefore allows monitoring, such as to determine the predicted class probabilities on a test set after each boost.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

Yields:

pgenerator of ndarray of shape (n_samples,)

The class probabilities of the input samples. The order of outputs is the same of that of the classes_ attribute.

staged_score(X, y, sample_weight=None)

Return staged scores for X, y.

This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost.

Parameters:

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

The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.

yarray-like of shape (n_samples,)

Labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Yields:

zfloat