(
    X,
    y,
    *,
    sample_weight=None,
    sampler=None,
    batch_size=32,
    keep_sparse=False,
    random_state=None,
)
return tf_bbg(
        X=X,
        y=y,
        sample_weight=sample_weight,
        sampler=sampler,
        batch_size=batch_size,
        keep_sparse=keep_sparse,
        random_state=random_state,
    )
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_``.

    Parameters
    ----------
    X : ndarray of shape (n_samples, n_features)
        Original imbalanced dataset.

    y : ndarray of shape (n_samples,) or (n_samples, n_classes)
        Associated targets.

    sample_weight : ndarray of shape (n_samples,), default=None
        Sample weight.

    sampler : sampler object, default=None
        A sampler instance which has an attribute ``sample_indices_``.
        By default, the sampler used is a
        :class:`~imblearn.under_sampling.RandomUnderSampler`.

    batch_size : int, default=32
        Number of samples per gradient update.

    keep_sparse : bool, 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_state}

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

    steps_per_epoch : int
        The number of samples per epoch. Required by ``fit_generator`` in
        keras.

    Examples
    --------
    >>> 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 = model.fit(training_generator,
    ...                              steps_per_epoch=steps_per_epoch,
    ...                              epochs=10, verbose=0)
Create a balanced batch generator to train keras model.
Create a balanced batch generator to train keras model .
def balanced_batch_generator(
    X,
    y,
    *,
    sample_weight=None,
    sampler=None,
    batch_size=32,
    keep_sparse=False,
    random_state=None,
):
    """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_``.

    Parameters
    ----------
    X : ndarray of shape (n_samples, n_features)
        Original imbalanced dataset.

    y : ndarray of shape (n_samples,) or (n_samples, n_classes)
        Associated targets.

    sample_weight : ndarray of shape (n_samples,), default=None
        Sample weight.

    sampler : sampler object, default=None
        A sampler instance which has an attribute ``sample_indices_``.
        By default, the sampler used is a
        :class:`~imblearn.under_sampling.RandomUnderSampler`.

    batch_size : int, default=32
        Number of samples per gradient update.

    keep_sparse : bool, 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_state}

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

    steps_per_epoch : int
        The number of samples per epoch. Required by ``fit_generator`` in
        keras.

    Examples
    --------
    >>> 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 = model.fit(training_generator,
    ...                              steps_per_epoch=steps_per_epoch,
    ...                              epochs=10, verbose=0)
    """

    return tf_bbg(
        X=X,
        y=y,
        sample_weight=sample_weight,
        sampler=sampler,
        batch_size=batch_size,
        keep_sparse=keep_sparse,
        random_state=random_state,
    )
def balanced_batch_generator ( X , y , * , sample_weight = None , sampler = None , batch_size = 32 , keep_sparse = False , random_state = None , ) : return tf_bbg ( X = X , y = y , sample_weight = sample_weight , sampler = sampler , batch_size = batch_size , keep_sparse = keep_sparse , random_state = random_state , )