imblearn.under_sampling
.ClusterCentroids¶

class
imblearn.under_sampling.
ClusterCentroids
(sampling_strategy='auto', random_state=None, estimator=None, voting='auto', n_jobs=None)[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 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.
 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
.
 estimatorobject, default=KMeans()
Pass a
sklearn.cluster.KMeans
estimator. voting{“hard”, “soft”, “auto”}, default=’auto’
Voting strategy to generate the new samples:
If
'hard'
, the nearestneighbors 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.
New in version 0.3.0.
 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.
See also
EditedNearestNeighbours
Undersampling by editing samples.
CondensedNearestNeighbour
Undersampling by condensing samples.
Notes
Supports multiclass resampling by sampling each class independently.
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> 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(random_state=42) >>> X_res, y_res = cc.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) ... Resampled dataset shape Counter({...})

__init__
(self, sampling_strategy='auto', random_state=None, estimator=None, voting='auto', n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y)[source]¶ Check inputs and statistics of the sampler.
You should use
fit_resample
in all cases. Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Data array.
 yarraylike of shape (n_samples,)
Target array.
 Returns
 selfobject
Return the instance itself.

fit_resample
(self, X, y)[source]¶ Resample the dataset.
 Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
 yarraylike of shape (n_samples,)
Corresponding label for each sample in X.
 Returns
 X_resampled{arraylike, dataframe, sparse matrix} of shape (n_samples_new, n_features)
The array containing the resampled data.
 y_resampledarraylike of shape (n_samples_new,)
The corresponding label of
X_resampled
.

fit_sample
(self, X, y)[source]¶ Resample the dataset.
 Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
 yarraylike of shape (n_samples,)
Corresponding label for each sample in X.
 Returns
 X_resampled{arraylike, dataframe, sparse matrix} of shape (n_samples_new, n_features)
The array containing the resampled data.
 y_resampledarraylike of shape (n_samples_new,)
The corresponding label of
X_resampled
.

get_params
(self, 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
 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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.