generate_imbalance_data

imbalanced_ensemble.datasets.generate_imbalance_data(n_samples=200, weights=[0.9, 0.1], test_size=0.5, random_state=None, kwargs={})

Generate a random n-classes imbalanced classification problem.

Returns the training and test data and labels.

Parameters
n_samplesint, default=100

The number of samples.

weightsarray-like of shape (n_classes,), default=[.9,.1]

The proportions of samples assigned to each class, i.e., it determines the imbalance ratio between classes. If None, then classes are balanced. Note that the number of class will be automatically set to the length of weights.

test_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples.

random_stateint, RandomState instance or None, 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.

kwargsdict

Dictionary of additional keyword arguments to pass to sklearn.datasets.make_classification. Please see details here.

Returns
X_train{ndarray, dataframe} of shape (n_samples*(1-test_size), n_features)

The array containing the imbalanced training data.

X_test{ndarray, dataframe} of shape (n_samples*test_size, n_features)

The array containing the imbalanced test data.

y_trainndarray of shape (n_samples*(1-test_size))

The corresponding label of X_train.

y_testndarray of shape (n_samples*test_size)

The corresponding label of X_test.

Examples using imbalanced_ensemble.datasets.generate_imbalance_data