`bqlearn.tradaboost`.TrAdaBoostClassifier¶

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

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]

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

[3]

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

[4]

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

[5]

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_metadata_routing`()	Get metadata routing of this object.
`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_fit_request`(*[, sample_quality, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.
`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_[source]¶: Estimator used to grow the ensemble.

decision_function(X)[source]¶

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_[source]¶

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)[source]¶

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_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest 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(X)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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_fit_request(*, sample_quality: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → TrAdaBoostClassifier[source]¶

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters:

sample_qualitystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_quality parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TrAdaBoostClassifier[source]¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

staged_decision_function(X)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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

bqlearn.tradaboost.TrAdaBoostClassifier¶

`bqlearn.tradaboost`.TrAdaBoostClassifier¶