imblearn.datasets.make_imbalance

imblearn.datasets.make_imbalance(X, y, sampling_strategy=None, ratio=None, random_state=None, verbose=False, **kwargs)[source][source]

Turns a dataset into an imbalanced dataset at specific ratio.

A simple toy dataset to visualize clustering and classification algorithms.

Read more in the User Guide.

Parameters:
X : ndarray, shape (n_samples, n_features)

Matrix containing the data to be imbalanced.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.

sampling_strategy : dict, or callable,

Ratio to use for resampling the data set.

  • 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.
ratio : str, dict, or callable

Deprecated since version 0.4: Use the parameter sampling_strategy instead. It will be removed in 0.6.

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.

verbose : bool, optional (default=False)

Show information regarding the sampling.

kwargs : dict, optional

Dictionary of additional keyword arguments to pass to sampling_strategy.

Returns:
X_resampled : ndarray, shape (n_samples_new, n_features)

The array containing the imbalanced data.

y_resampled : ndarray, shape (n_samples_new)

The corresponding label of X_resampled

Notes

See Multiclass classification with under-sampling, make_imbalance function, and Usage of the sampling_strategy parameter for the different algorithms.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import load_iris
>>> from imblearn.datasets import make_imbalance
>>> data = load_iris()
>>> X, y = data.data, data.target
>>> print('Distribution before imbalancing: {}'.format(Counter(y)))
Distribution before imbalancing: Counter({0: 50, 1: 50, 2: 50})
>>> X_res, y_res = make_imbalance(X, y,
...                               sampling_strategy={0: 10, 1: 20, 2: 30},
...                               random_state=42)
>>> print('Distribution after imbalancing: {}'.format(Counter(y_res)))
Distribution after imbalancing: Counter({2: 30, 1: 20, 0: 10})