class imbalanced_ensemble.sampler.over_sampling.ADASYN(*, sampling_strategy='auto', random_state=None, n_neighbors=5, n_jobs=None)

This method is similar to SMOTE but it generates different number of samples depending on an estimate of the local distribution of the class to be oversampled.

Read more in the User Guide.

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 $$\alpha_{os} = N_{rm} / N_{M}$$ 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.

n_neighborsint or estimator object, default=5

If int, number of nearest neighbours to used to construct synthetic samples. If object, an estimator that inherits from KNeighborsMixin that will be used to find the k_neighbors.

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.

SMOTE

Over-sample using SMOTE.

SVMSMOTE

Over-sample using SVM-SMOTE variant.

BorderlineSMOTE

Over-sample using Borderline-SMOTE variant.

Notes

The implementation is based on [1].

Supports multi-class resampling. A one-vs.-rest scheme is used.

References

1

He, Haibo, Yang Bai, Edwardo A. Garcia, and Shutao Li. “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322-1328, 2008.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> 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})
>>> X_res, y_res = ada.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({0: 904, 1: 900})


Methods

 fit(X, y) Check inputs and statistics of the sampler. fit_resample(X, y, *[, sample_weight]) Resample the dataset. get_params([deep]) Get parameters for this estimator. set_params(**params) Set the parameters of this estimator.
fit(X, y)

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, *, sample_weight=None, **kwargs)

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.

sample_weightarray-like of shape (n_samples,), default=None

Corresponding weight for each sample in X.

• If None, perform normal resampling and return (X_resampled, y_resampled).

• If array-like, the given sample_weight will be resampled along with X and y, and the resampled sample weights will be added to returns. The function will return (X_resampled, y_resampled, sample_weight_resampled).

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.

sample_weight_resampledarray-like of shape (n_samples_new,), default=None

The corresponding weight of X_resampled. Only will be returned if input sample_weight is not None.

get_params(deep=True)

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)

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.