class imblearn.over_sampling.KMeansSMOTE(*, sampling_strategy='auto', random_state=None, k_neighbors=2, n_jobs=None, kmeans_estimator=None, cluster_balance_threshold='auto', density_exponent='auto')[source]#

Apply a KMeans clustering before to over-sample using SMOTE.

This is an implementation of the algorithm described in [1].

Read more in the User Guide.

New in version 0.5.

sampling_strategyfloat, str, dict or callable, default=’auto’

Sampling information to resample 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 \(\alpha_{os} = N_{rm} / N_{M}\) where \(N_{rm}\) is the number of samples in the minority class after resampling and \(N_{M}\) is the number of samples in the majority class.


    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:

    'minority': resample only the minority 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 majority'.

  • 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.

k_neighborsint or object, default=2

The nearest neighbors used to define the neighborhood of samples to use to generate the synthetic samples. You can pass:

  • an int corresponding to the number of neighbors to use. A ~sklearn.neighbors.NearestNeighbors instance will be fitted in this case.

  • an instance of a compatible nearest neighbors algorithm that should implement both methods kneighbors and kneighbors_graph. For instance, it could correspond to a NearestNeighbors but could be extended to any compatible class.

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.

kmeans_estimatorint or object, default=None

A KMeans instance or the number of clusters to be used. By default, we used a MiniBatchKMeans which tend to be better with large number of samples.

cluster_balance_threshold“auto” or float, default=”auto”

The threshold at which a cluster is called balanced and where samples of the class selected for SMOTE will be oversampled. If “auto”, this will be determined by the ratio for each class, or it can be set manually.

density_exponent“auto” or float, default=”auto”

This exponent is used to determine the density of a cluster. Leaving this to “auto” will use a feature-length based exponent.


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.


The fitted clustering method used before to apply SMOTE.


The fitted k-NN estimator used in SMOTE.


The threshold used during fit for calling a cluster balanced.


Number of features in the input dataset.

New 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.

New in version 0.10.

See also


Over-sample using SMOTE.


Over-sample using SMOTE for continuous and categorical features.


Over-sample using the SMOTE variant specifically for categorical features only.


Over-sample using SVM-SMOTE variant.


Over-sample using Borderline-SMOTE variant.


Over-sample using ADASYN.



Felix Last, Georgios Douzas, Fernando Bacao, “Oversampling for Imbalanced Learning Based on K-Means and SMOTE”


>>> import numpy as np
>>> from imblearn.over_sampling import KMeansSMOTE
>>> from sklearn.datasets import make_blobs
>>> blobs = [100, 800, 100]
>>> X, y  = make_blobs(blobs, centers=[(-10, 0), (0,0), (10, 0)])
>>> # Add a single 0 sample in the middle blob
>>> X = np.concatenate([X, [[0, 0]]])
>>> y = np.append(y, 0)
>>> # Make this a binary classification problem
>>> y = y == 1
>>> sm = KMeansSMOTE(
...     kmeans_estimator=MiniBatchKMeans(n_init=1, random_state=0), random_state=42
... )
>>> X_res, y_res = sm.fit_resample(X, y)
>>> # Find the number of new samples in the middle blob
>>> n_res_in_middle = ((X_res[:, 0] > -5) & (X_res[:, 0] < 5)).sum()
>>> print("Samples in the middle blob: %s" % n_res_in_middle)
Samples in the middle blob: 801
>>> print("Middle blob unchanged: %s" % (n_res_in_middle == blobs[1] + 1))
Middle blob unchanged: True
>>> print("More 0 samples: %s" % ((y_res == 0).sum() > (y == 0).sum()))
More 0 samples: True


fit(X, y)

Check inputs and statistics of the sampler.

fit_resample(X, y)

Resample the dataset.


Get output feature names for transformation.


Get metadata routing of this object.


Get parameters for this estimator.


Set the parameters of this estimator.

fit(X, y)[source]#

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

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

Data array.

yarray-like of shape (n_samples,)

Target array.


Return the instance itself.

fit_resample(X, y)[source]#

Resample the dataset.

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.

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 output feature names for transformation.

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.

feature_names_outndarray of str objects

Same as input features.


Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.


A MetadataRequest encapsulating routing information.


Get parameters for this estimator.

deepbool, default=True

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


Parameter names mapped to their values.


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.


Estimator parameters.

selfestimator instance

Estimator instance.

Examples using imblearn.over_sampling.KMeansSMOTE#

Compare over-sampling samplers

Compare over-sampling samplers