ClusterCentroids#

class imblearn.under_sampling.ClusterCentroids(*, sampling_strategy='auto', random_state=None, estimator=None, voting='auto')[source]#

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

A scikit-learn compatible clustering method that exposes a n_clusters parameter and a cluster_centers_ fitted attribute. 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.

Added in version 0.3.0.

Attributes:
sampling_strategy_dict

Dictionary containing the information to sample the dataset. The keys corresponds to the class labels from which to sample and the values are the number of samples to sample.

estimator_estimator object

The validated estimator created from the estimator parameter.

voting_str

The validated voting strategy.

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 0.10.

See also

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 sklearn.cluster import MiniBatchKMeans
>>> from imblearn.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(
...     estimator=MiniBatchKMeans(n_init=1, random_state=0), 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, **params)

Check inputs and statistics of the sampler.

fit_resample(X, y, **params)

Resample the dataset.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.
fit(X, y, **params)[source]#

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, **params)[source]#

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.

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.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest 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.

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.

Examples using imblearn.under_sampling.ClusterCentroids#

Compare under-sampling samplers

Compare under-sampling samplers