BalancedBaggingClassifier#

class imblearn.ensemble.BalancedBaggingClassifier(estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, sampling_strategy='auto', replacement=False, n_jobs=None, random_state=None, verbose=0, sampler=None)[source]#

A Bagging classifier with additional balancing.

This implementation of Bagging is similar to the scikit-learn implementation. It includes an additional step to balance the training set at fit time using a given sampler.

This classifier can serves as a basis to implement various methods such as Exactly Balanced Bagging [6], Roughly Balanced Bagging [7], Over-Bagging [6], or SMOTE-Bagging [8].

Read more in the User Guide.

Parameters:
estimatorestimator object, default=None

The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.

Added in version 0.10.

n_estimatorsint, default=10

The number of base estimators in the ensemble.

max_samplesint or float, default=1.0

The number of samples to draw from X to train each base estimator.

  • If int, then draw max_samples samples.

  • If float, then draw max_samples * X.shape[0] samples.

max_featuresint or float, default=1.0

The number of features to draw from X to train each base estimator.

  • If int, then draw max_features features.

  • If float, then draw max_features * X.shape[1] features.

bootstrapbool, default=True

Whether samples are drawn with replacement.

Note

Note that this bootstrap will be generated from the resampled dataset.

bootstrap_featuresbool, default=False

Whether features are drawn with replacement.

oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization error.

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble.

sampling_strategyfloat, str, dict, callable, default=’auto’

Sampling information to sample the data set.

  • When float, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as \(\alpha_{us} = N_{m} / N_{rM}\) where \(N_{m}\) is the number of samples in the minority class and \(N_{rM}\) is the number of samples in the majority class after resampling.

    Warning

    float is only available for binary classification. An error is raised for multi-class classification.

  • When str, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:

    'majority': resample only the majority class;

    'not minority': resample all classes but the minority class;

    'not majority': resample all classes but the majority class;

    'all': resample all classes;

    'auto': equivalent to 'not minority'.

  • When dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.

  • When callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

replacementbool, default=False

Whether or not to randomly sample with replacement or not when sampler is None, corresponding to a RandomUnderSampler.

n_jobsint, default=None

Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

random_stateint, RandomState instance, default=None

Control the randomization of the algorithm.

  • If int, random_state is the seed used by the random number generator;

  • If RandomState instance, random_state is the random number generator;

  • If None, the random number generator is the RandomState instance used by np.random.

verboseint, default=0

Controls the verbosity of the building process.

samplersampler object, default=None

The sampler used to balanced the dataset before to bootstrap (if bootstrap=True) and fit a base estimator. By default, a RandomUnderSampler is used.

Added in version 0.8.

Attributes:
estimator_estimator

The base estimator from which the ensemble is grown.

Added in version 0.10.

estimators_list of estimators

The collection of fitted base estimators.

sampler_sampler object

The validate sampler created from the sampler parameter.

estimators_samples_list of ndarray

The subset of drawn samples for each base estimator.

estimators_features_list of ndarray

The subset of drawn features for each base estimator.

classes_ndarray of shape (n_classes,)

The classes labels.

n_classes_int or list

The number of classes.

oob_score_float

Score of the training dataset obtained using an out-of-bag estimate.

oob_decision_function_ndarray of shape (n_samples, n_classes)

Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN.

n_features_in_int

Number of features in the input dataset.

Added in version 0.9.

feature_names_in_ndarray of shape (n_features_in_,)

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

Added in version 0.9.

See also

BalancedRandomForestClassifier

Random forest applying random-under sampling to balance the different bootstraps.

EasyEnsembleClassifier

Ensemble of AdaBoost classifier trained on balanced bootstraps.

RUSBoostClassifier

AdaBoost classifier were each bootstrap is balanced using random-under sampling at each round of boosting.

Notes

This is possible to turn this classifier into a balanced random forest [5] by passing a DecisionTreeClassifier with max_features='auto' as a base estimator.

See Compare ensemble classifiers using resampling.

References

[1]

L. Breiman, “Pasting small votes for classification in large databases and on-line”, Machine Learning, 36(1), 85-103, 1999.

[2]

L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140, 1996.

[3]

T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.

[4]

G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.

[5]

C. Chen Chao, A. Liaw, and L. Breiman. “Using random forest to learn imbalanced data.” University of California, Berkeley 110, 2004.

[6] (1,2)

R. Maclin, and D. Opitz. “An empirical evaluation of bagging and boosting.” AAAI/IAAI 1997 (1997): 546-551.

[7]

S. Hido, H. Kashima, and Y. Takahashi. “Roughly balanced bagging for imbalanced data.” Statistical Analysis and Data Mining: The ASA Data Science Journal 2.5‐6 (2009): 412-426.

[8]

S. Wang, and X. Yao. “Diversity analysis on imbalanced data sets by using ensemble models.” 2009 IEEE symposium on computational intelligence and data mining. IEEE, 2009.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.metrics import confusion_matrix
>>> from imblearn.ensemble import BalancedBaggingClassifier
>>> X, y = make_classification(n_classes=2, class_sep=2,
... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
>>> print('Original dataset shape %s' % Counter(y))
Original dataset shape Counter({1: 900, 0: 100})
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
...                                                     random_state=0)
>>> bbc = BalancedBaggingClassifier(random_state=42)
>>> bbc.fit(X_train, y_train)
BalancedBaggingClassifier(...)
>>> y_pred = bbc.predict(X_test)
>>> print(confusion_matrix(y_test, y_pred))
[[ 23   0]
 [  2 225]]

Methods

decision_function(X)

Average of the decision functions of the base classifiers.

fit(X, y)

Build a Bagging ensemble of estimators 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 class 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_weight])

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.
property base_estimator_#

Attribute for older sklearn version compatibility.

decision_function(X)[source]#

Average of the decision functions of the base classifiers.

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

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:
scorendarray of shape (n_samples, k)

The decision function of the input samples. The columns correspond to the classes in sorted order, as they appear in the attribute classes_. Regression and binary classification are special cases with k == 1, otherwise k==n_classes.

property estimators_samples_#

The subset of drawn samples for each base estimator.

Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples.

Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.

fit(X, y)[source]#

Build a Bagging ensemble of estimators from the training set (X, y).

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

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

yarray-like of shape (n_samples,)

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

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 1.5.

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(X)[source]#

Predict class for X.

The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting.

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

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

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 log of the mean predicted class probabilities of the base estimators in the ensemble.

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

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:
pndarray of shape (n_samples, n_classes)

The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba(X)[source]#

Predict class probabilities for X.

The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a predict_proba method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class.

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

The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns:
pndarray of shape (n_samples, n_classes)

The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

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_weight: bool | None | str = '$UNCHANGED$') → BalancedBaggingClassifier[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.

Added 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. 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 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 = '$UNCHANGED$') → BalancedBaggingClassifier[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.

Added 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. 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.

Examples using imblearn.ensemble.BalancedBaggingClassifier#

Fitting model on imbalanced datasets and how to fight bias

Fitting model on imbalanced datasets and how to fight bias

Bagging classifiers using sampler

Bagging classifiers using sampler

Compare ensemble classifiers using resampling

Compare ensemble classifiers using resampling