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 adict
. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
- When
- 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})