imblearn.ensemble.EasyEnsemble

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

Create an ensemble sets by iteratively applying random under-sampling.

This method iteratively select a random subset and make an ensemble of the different sets.

Deprecated since version 0.4: EasyEnsemble is deprecated in 0.4 and will be removed in 0.6. Use EasyEnsembleClassifier instead.

Parameters:
sampling_strategy : float, 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 majority class over the number of samples in the minority class after resampling. Therefore, the ratio is expressed as \alpha_{us} = N_{rM} / N_{m} where N_{rM} and N_{m} are the number of samples in the majority class after resampling and the number of samples in the minority class, respectively.

    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_indices : bool, optional (default=False)

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

random_state : int, 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.
replacement : bool, optional (default=False)

Whether or not to sample randomly with replacement or not.

n_subsets : int, optional (default=10)

Number of subsets to generate.

ratio : str, 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 [1].

Supports multi-class resampling by sampling each class independently.

References

[1](1, 2) 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
>>> from imblearn.ensemble import EasyEnsemble # doctest: +NORMALIZE_WHITESPACE
>>> 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})
>>> ee = EasyEnsemble(random_state=42) # doctest: +SKIP
>>> X_res, y_res = ee.fit_resample(X, y) # doctest: +SKIP
>>> print('Resampled dataset shape %s' % Counter(y_res[0])) # doctest: +SKIP
Resampled dataset shape Counter({0: 100, 1: 100})
__init__(**kwargs)[source]

DEPRECATED: EasyEnsemble is deprecated in 0.4 and will be removed in 0.6. Use EasyEnsembleClassifier instead.

fit(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.

y : array-like, shape (n_samples,)

Target array.

Returns:
self : object

Return the instance itself.

fit_resample(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.

y : array-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_resampled : ndarray, shape (n_subset, n_samples_new)

The corresponding label of X_resampled

fit_sample(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.

y : array-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_resampled : array-like, shape (n_samples_new,)

The corresponding label of X_resampled.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

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

Returns:
params : mapping of string to any

Parameter names mapped to their values.

set_params(**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