SMOTENC¶

class
imblearn.over_sampling.
SMOTENC
(categorical_features, *, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=None)[source]¶ Synthetic Minority Oversampling Technique for Nominal and Continuous.
Unlike
SMOTE
, SMOTENC for dataset containing numerical and categorical features. However, it is not designed to work with only categorical features.Read more in the User Guide.
New in version 0.4.
 Parameters
 categorical_featuresndarray of shape (n_cat_features,) or (n_features,)
Specified which features are categorical. Can either be:
array of indices specifying the categorical features;
mask array of shape (n_features, ) and
bool
dtype for whichTrue
indicates the categorical features.
 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=5
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.
See also
SMOTE
Oversample using SMOTE.
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.
KMeansSMOTE
Oversample applying a clustering before to oversample using SMOTE.
Notes
See the original paper [1] for more details.
Supports mutliclass resampling. A onevs.rest scheme is used as originally proposed in [1].
See Compare oversampling samplers, and Sample generator used in SMOTElike samplers. # noqa
References
 1(1,2)
N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority oversampling technique,” Journal of artificial intelligence research, 321357, 2002.
Examples
>>> from collections import Counter >>> from numpy.random import RandomState >>> from sklearn.datasets import make_classification >>> from imblearn.over_sampling import SMOTENC >>> 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(f'Original dataset shape {X.shape}') Original dataset shape (1000, 20) >>> print(f'Original dataset samples per class {Counter(y)}') Original dataset samples per class Counter({1: 900, 0: 100}) >>> # simulate the 2 last columns to be categorical features >>> X[:, 2:] = RandomState(10).randint(0, 4, size=(1000, 2)) >>> sm = SMOTENC(random_state=42, categorical_features=[18, 19]) >>> X_res, y_res = sm.fit_resample(X, y) >>> print(f'Resampled dataset samples per class {Counter(y_res)}') Resampled dataset samples per class Counter({0: 900, 1: 900})
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.