# imblearn.ensemble.BalanceCascade¶

class imblearn.ensemble.BalanceCascade(**kwargs)[source]

Create an ensemble of balanced sets by iteratively under-sampling the imbalanced dataset using an estimator.

This method iteratively select subset and make an ensemble of the different sets. The selection is performed using a specific classifier.

Parameters
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.

return_indicesbool, optional (default=True)

Whether or not to return the indices of the samples randomly selected from the majority class.

random_stateint, RandomState instance or None, optional (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.

n_max_subsetint or None, optional (default=None)

Maximum number of subsets to generate. By default, all data from the training will be selected that could lead to a large number of subsets. We can probably deduce this number empirically.

estimatorobject, optional (default=KNeighborsClassifier())

An estimator inherited from sklearn.base.ClassifierMixin and having an attribute predict_proba.

bootstrapbool, optional (default=True)

Whether to bootstrap the data before each iteration.

ratiostr, dict, or callable

Deprecated since version 0.4: Use the parameter sampling_strategy instead. It will be removed in 0.6.

Notes

The method is described in [R67d578f59cad-1].

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [R67d578f59cad-1].

References

X. Y. Liu, J. Wu and Z. H. Zhou, “Exploratory Undersampling for Class-Imbalance Learning,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539-550, April 2009.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> 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_res, y_res = bc.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res[0]))
Resampled dataset shape Counter({...})

__init__(*args, **kwargs)[source]

DEPRECATED: BalanceCascade is deprecated in 0.4 and will be removed in 0.6.

fit(self, X, y)[source]

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

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

Data array.

yarray-like, shape (n_samples,)

Target array.

Returns
selfobject

Return the instance itself.

fit_resample(self, X, y)[source]

Resample the dataset.

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

Matrix containing the data which have to be sampled.

yarray-like, shape (n_samples,)

Corresponding label for each sample in X.

Returns
X_resampled{ndarray, sparse matrix}, shape (n_subset, n_samples_new, n_features)

The array containing the resampled data.

y_resampledndarray, shape (n_subset, n_samples_new)

The corresponding label of X_resampled

fit_sample(self, X, y)[source]

Resample the dataset.

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

Matrix containing the data which have to be sampled.

yarray-like, shape (n_samples,)

Corresponding label for each sample in X.

Returns
X_resampled{array-like, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampledarray-like, shape (n_samples_new,)

The corresponding label of X_resampled.

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns
self