BalancedBaggingClassifier¶

class
imblearn.ensemble.
BalancedBaggingClassifier
(base_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 scikitlearn 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], OverBagging [6], or SMOTEBagging [8].
Read more in the User Guide.
 Parameters
 base_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.
 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 outofbag 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 where is the number of samples in the minority class and is the number of samples in the majority class after resampling.Warning
float
is only available for binary classification. An error is raised for multiclass 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 adict
. 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 aRandomUnderSampler
. n_jobsint, default=None
Number of CPU cores used during the crossvalidation loop.
None
means 1 unless in ajoblib.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 theRandomState
instance used bynp.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
) andfit
a base estimator. By default, aRandomUnderSampler
is used.New in version 0.8.
 Attributes
 base_estimator_estimator
The base estimator from which the ensemble is grown.
 n_features_int
The number of features when
fit
is performed. estimators_list of estimators
The collection of fitted base estimators.
estimators_samples_
list of ndarrayThe 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 outofbag estimate.
 oob_decision_function_ndarray of shape (n_samples, n_classes)
Decision function computed with outofbag 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.
See also
BalancedRandomForestClassifier
Random forest applying randomunder sampling to balance the different bootstraps.
EasyEnsembleClassifier
Ensemble of AdaBoost classifier trained on balanced bootstraps.
RUSBoostClassifier
AdaBoost classifier were each bootstrap is balanced using randomunder sampling at each round of boosting.
Notes
This is possible to turn this classifier into a balanced random forest [5] by passing a
DecisionTreeClassifier
withmax_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 online”, Machine Learning, 36(1), 85103, 1999.
 2
L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123140, 1996.
 3
T. Ho, “The random subspace method for constructing decision forests”, Pattern Analysis and Machine Intelligence, 20(8), 832844, 1998.
 4
G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine Learning and Knowledge Discovery in Databases, 346361, 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): 546551.
 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): 412426.
 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
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_params
([deep])Get parameters for this estimator.
predict
(X)Predict class for X.
Predict class logprobabilities for 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.

decision_function
(X)[source]¶ Average of the decision functions of the base classifiers.
 Parameters
 X{arraylike, 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 withk == 1
, otherwisek==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 inbag samples.
Note: the list is recreated 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{arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples.
 yarraylike of shape (n_samples,)
The target values.
 Returns
 selfobject
Returns self.

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{arraylike, 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 logprobabilities for X.
The predicted class logprobabilities of an input sample is computed as the log of the mean predicted class probabilities of the base estimators in the ensemble.
 Parameters
 X{arraylike, 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 logprobabilities 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{arraylike, 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 multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of
self.predict(X)
wrt.y
.

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.