# ClusterCentroids

class imbalanced_ensemble.sampler.under_sampling.ClusterCentroids(*, sampling_strategy='auto', random_state=None, estimator=None, voting='auto', n_jobs='deprecated')

Undersample by generating centroids based on clustering methods.

Method that under samples the majority class by replacing a cluster of majority samples by the cluster centroid of a KMeans algorithm. This algorithm keeps N majority samples by fitting the KMeans algorithm with N cluster to the majority class and using the coordinates of the N cluster centroids as the new majority samples.

Read more in the User Guide.

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.

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.

estimatorestimator object, default=None

Pass a KMeans estimator. By default, it will be a default KMeans estimator.

voting{“hard”, “soft”, “auto”}, default=’auto’

Voting strategy to generate the new samples:

• If 'hard', the nearest-neighbors of the centroids found using the clustering algorithm will be used.

• If 'soft', the centroids found by the clustering algorithm will be used.

• If 'auto', if the input is sparse, it will default on 'hard' otherwise, 'soft' will be used.

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.

Deprecated since version 0.7: n_jobs was deprecated in 0.7 and will be removed in 0.9.

EditedNearestNeighbours

Under-sampling by editing samples.

CondensedNearestNeighbour

Under-sampling by condensing samples.

Notes

Supports multi-class resampling by sampling each class independently.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imbalanced_ensemble.sampler.under_sampling import ClusterCentroids
>>> 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})
>>> cc = ClusterCentroids(random_state=42)
>>> X_res, y_res = cc.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
...
Resampled dataset shape Counter({...})


Methods

 fit(X, y) Check inputs and statistics of the sampler. fit_resample(X, y, *[, sample_weight]) Resample the dataset. get_params([deep]) Get parameters for this estimator. set_params(**params) Set the parameters of this estimator.
fit(X, y)

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

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

Data array.

yarray-like of shape (n_samples,)

Target array.

Returns
selfobject

Return the instance itself.

fit_resample(X, y, *, sample_weight=None, **kwargs)

Resample the dataset.

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

Matrix containing the data which have to be sampled.

yarray-like of shape (n_samples,)

Corresponding label for each sample in X.

sample_weightarray-like of shape (n_samples,), default=None

Corresponding weight for each sample in X.

• If None, perform normal resampling and return (X_resampled, y_resampled).

• If array-like, the given sample_weight will be resampled along with X and y, and the resampled sample weights will be added to returns. The function will return (X_resampled, y_resampled, sample_weight_resampled).

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

The array containing the resampled data.

y_resampledarray-like of shape (n_samples_new,)

The corresponding label of X_resampled.

sample_weight_resampledarray-like of shape (n_samples_new,), default=None

The corresponding weight of X_resampled. Only will be returned if input sample_weight is not None.

get_params(deep=True)

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.

set_params(**params)

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.