imblearn.over_sampling.ADASYN

class imblearn.over_sampling.ADASYN(ratio='auto', random_state=None, k=None, n_neighbors=5, n_jobs=1)[source][source]

Perform over-sampling using ADASYN.

Perform over-sampling using Adaptive Synthetic Sampling Approach for Imbalanced Learning.

Parameters:

ratio : str, dict, or callable, optional (default=’auto’)

Ratio to use for resampling the data set.

  • If str, has to be one of: (i) 'minority': resample the minority class; (ii) 'majority': resample the majority class, (iii) 'not minority': resample all classes apart of the minority class, (iv) 'all': resample all classes, and (v) 'auto': correspond to 'all' with for over-sampling methods and 'not minority' for under-sampling methods. The classes targeted will be over-sampled or under-sampled to achieve an equal number of sample with the majority or minority class.
  • If dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples.
  • If callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples.

random_state : int, RandomState instance or None, optional (default=None)

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 : int, optional (default=None)

Number of nearest neighbours to used to construct synthetic samples.

Deprecated since version 0.2: k is deprecated from 0.2 and will be replaced in 0.4 Use n_neighbors instead.

n_neighbors : int int or object, optional (default=5)

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

n_jobs : int, optional (default=1)

Number of threads to run the algorithm when it is possible.

Notes

Supports mutli-class resampling.

The implementation is based on [R30].

References

[R30](1, 2) 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
>>> from imblearn.over_sampling import ADASYN 
>>> 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 {}'.format(Counter(y)))
Original dataset shape Counter({1: 900, 0: 100})
>>> ada = ADASYN(random_state=42)
>>> X_res, y_res = ada.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({0: 904, 1: 900})

Methods

fit(X, y) Find the classes statistics before to perform sampling.
fit_sample(X, y) Fit the statistics and resample the data directly.
get_params([deep]) Get parameters for this estimator.
sample(X, y) Resample the dataset.
set_params(**params) Set the parameters of this estimator.
__init__(ratio='auto', random_state=None, k=None, n_neighbors=5, n_jobs=1)[source][source]

Methods

__init__([ratio, random_state, k, ...])
fit(X, y) Find the classes statistics before to perform sampling.
fit_sample(X, y) Fit the statistics and resample the data directly.
get_params([deep]) Get parameters for this estimator.
sample(X, y) Resample the dataset.
set_params(**params) Set the parameters of this estimator.
fit(X, y)[source]

Find the classes statistics before to perform sampling.

Parameters:

X : ndarray, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.
Returns:

self : object,

Return self.
fit_sample(X, y)[source]

Fit the statistics and resample the data directly.

Parameters:

X : ndarray, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.
Returns:

X_resampled : ndarray, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : ndarray, shape (n_samples_new)

The corresponding label of X_resampled
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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

params : mapping of string to any

Parameter names mapped to their values.
sample(X, y)[source]

Resample the dataset.

Parameters:

X : ndarray, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.
Returns:

X_resampled : ndarray, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : ndarray, shape (n_samples_new)

The corresponding label of X_resampled
set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self