imblearn.datasets.make_imbalance

imblearn.datasets.make_imbalance(X, y, ratio, min_c_=None, random_state=None, **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.

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

Ratio to use for resampling the data set.

  • If dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples. All samples will be passed through if the class is not specified.
  • 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.

min_c_ : str or int, optional (default=None)

The identifier of the class to be the minority class. If None, min_c_ is set to be the current minority class. Only used when ratio is a float for back-compatibility.

Deprecated since version 0.2: min_c_ is deprecated in 0.2 and will be removed in 0.4. Use ratio by passing a dict instead.

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.

kwargs : dict, optional

Dictionary of additional keyword arguments to pass to ratio.

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 ratio parameter for the different algorithm.

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, ratio={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})