KMeansSMOTE¶

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 oversample using SMOTE.
This is an implementation of the algorithm described in [1].
Read more in the User Guide.
New in version 0.5.
 Parameters
 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 where is the number of samples in the minority class after resampling and is the number of samples in the majority class.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:'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 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
.
 k_neighborsint or object, default=2
If
int
, number of nearest neighbours to used to construct synthetic samples. If object, an estimator that inherits fromKNeighborsMixin
that will be used to find the k_neighbors. 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. 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 featurelength based exponent.
 Attributes
 kmeans_estimator_estimator
The fitted clustering method used before to apply SMOTE.
 nn_k_estimator
The fitted kNN estimator used in SMOTE.
 cluster_balance_threshold_float
The threshold used during
fit
for calling a cluster balanced.
See also
SMOTE
Oversample using SMOTE.
SMOTENC
Oversample using SMOTE for continuous and categorical features.
SMOTEN
Oversample using the SMOTE variant specifically for categorical features only.
SVMSMOTE
Oversample using SVMSMOTE variant.
BorderlineSMOTE
Oversample using BorderlineSMOTE variant.
ADASYN
Oversample using ADASYN.
References
 1
Felix Last, Georgios Douzas, Fernando Bacao, “Oversampling for Imbalanced Learning Based on KMeans and SMOTE” https://arxiv.org/abs/1711.00837
Examples
>>> 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(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
Methods
fit
(X, y)Check inputs and statistics of the sampler.
fit_resample
(X, y)Resample the dataset.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)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. 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
(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
(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.