SMOTENC#

class imblearn.over_sampling.SMOTENC(categorical_features, *, categorical_encoder=None, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=None)[source]#

Synthetic Minority Over-sampling Technique for Nominal and Continuous.

Unlike SMOTE, SMOTE-NC for dataset containing numerical and categorical features. However, it is not designed to work with only categorical features.

Read more in the User Guide.

Added in version 0.4.

Parameters:
categorical_features“infer” or array-like of shape (n_cat_features,) or (n_features,), dtype={bool, int, str}

Specified which features are categorical. Can either be:

  • “auto” (default) to automatically detect categorical features. Only supported when X is a pandas.DataFrame and it corresponds to columns that have a pandas.CategoricalDtype;

  • array of int corresponding to the indices specifying the categorical features;

  • array of str corresponding to the feature names. X should be a pandas pandas.DataFrame in this case.

  • mask array of shape (n_features, ) and bool dtype for which True indicates the categorical features.

categorical_encoderestimator, default=None

One-hot encoder used to encode the categorical features. If None, a OneHotEncoder is used with default parameters apart from handle_unknown which is set to ‘ignore’.

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.

    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:

    '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=5

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.

Deprecated since version 0.10: n_jobs has been deprecated in 0.10 and will be removed in 0.12. It was previously used to set n_jobs of nearest neighbors algorithm. From now on, you can pass an estimator where n_jobs is already set instead.

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.

nn_k_estimator object

Validated k-nearest neighbours created from the k_neighbors parameter.

ohe_OneHotEncoder

One-hot encoder used to encode the categorical features.

categorical_encoder_estimator

The encoder used to encode the categorical features.

categorical_features_ndarray of shape (n_cat_features,), dtype=np.int64

Indices of the categorical features.

continuous_features_ndarray of shape (n_cont_features,), dtype=np.int64

Indices of the continuous features.

median_std_dict of int -> float

Median of the standard deviation of the continuous features for each class to be over-sampled.

n_features_int

Number of features observed at fit.

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

SMOTE

Over-sample using SMOTE.

SMOTEN

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

SVMSMOTE

Over-sample using SVM-SMOTE variant.

BorderlineSMOTE

Over-sample using Borderline-SMOTE variant.

ADASYN

Over-sample using ADASYN.

KMeansSMOTE

Over-sample applying a clustering before to oversample using SMOTE.

Notes

See the original paper [1] for more details.

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1].

See Compare over-sampling samplers, and Sample generator used in SMOTE-like samplers.

References

[1] (1,2)

N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of artificial intelligence research, 321-357, 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_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)[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)[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.

property ohe_#

One-hot encoder used to encode the categorical features.

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.over_sampling.SMOTENC#

Compare over-sampling samplers

Compare over-sampling samplers
On this page
Edit on GitHub
Show Source