imblearn.keras.balanced_batch_generator(X, y, *, sample_weight=None, sampler=None, batch_size=32, keep_sparse=False, random_state=None)[source]#

Create a balanced batch generator to train keras model.

Returns a generator — as well as the number of step per epoch — which is given to fit. The sampler defines the sampling strategy used to balance the dataset ahead of creating the batch. The sampler should have an attribute sample_indices_.

Xndarray of shape (n_samples, n_features)

Original imbalanced dataset.

yndarray of shape (n_samples,) or (n_samples, n_classes)

Associated targets.

sample_weightndarray of shape (n_samples,), default=None

Sample weight.

samplersampler object, default=None

A sampler instance which has an attribute sample_indices_. By default, the sampler used is a RandomUnderSampler.

batch_sizeint, default=32

Number of samples per gradient update.

keep_sparsebool, default=False

Either or not to conserve or not the sparsity of the input (i.e. X, y, sample_weight). By default, the returned batches will be dense.

random_stateint, RandomState instance, default=None

Control the randomization of the algorithm.

  • 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.

generatorgenerator of tuple

Generate batch of data. The tuple generated are either (X_batch, y_batch) or (X_batch, y_batch, sampler_weight_batch).


The number of samples per epoch. Required by fit_generator in keras.


>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> from imblearn.datasets import make_imbalance
>>> class_dict = dict()
>>> class_dict[0] = 30; class_dict[1] = 50; class_dict[2] = 40
>>> from imblearn.datasets import make_imbalance
>>> X, y = make_imbalance(X, y, sampling_strategy=class_dict)
>>> import tensorflow
>>> y = tensorflow.keras.utils.to_categorical(y, 3)
>>> model = tensorflow.keras.models.Sequential()
>>> model.add(
...     tensorflow.keras.layers.Dense(
...         y.shape[1], input_dim=X.shape[1], activation='softmax'
...     )
... )
>>> model.compile(optimizer='sgd', loss='categorical_crossentropy',
...               metrics=['accuracy'])
>>> from imblearn.keras import balanced_batch_generator
>>> from imblearn.under_sampling import NearMiss
>>> training_generator, steps_per_epoch = balanced_batch_generator(
...     X, y, sampler=NearMiss(), batch_size=10, random_state=42)
>>> callback_history =,
...                              steps_per_epoch=steps_per_epoch,
...                              epochs=10, verbose=0)