(
    X,
    y,
    *,
    sample_weight=None,
    sampler=None,
    batch_size=32,
    keep_sparse=False,
    random_state=None,
)
return (
        generator(X, y, sample_weight, indices, batch_size),
        int(indices.size // batch_size),
    )
Create a balanced batch generator to train tensorflow model.

    Returns a generator --- as well as the number of step per epoch --- to
    iterate to get the mini-batches. The sampler defines the sampling strategy
    used to balance the dataset ahead of creating the batch. The sampler should
    have an attribute ``sample_indices_``.

    .. versionadded:: 0.4

    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 ``X``. 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.
Create a balanced batch generator to train tensorflow model.
Create a balanced batch generator to train tensorflow 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 tensorflow model.

    Returns a generator --- as well as the number of step per epoch --- to
    iterate to get the mini-batches. The sampler defines the sampling strategy
    used to balance the dataset ahead of creating the batch. The sampler should
    have an attribute ``sample_indices_``.

    .. versionadded:: 0.4

    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 ``X``. 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.
    """

    random_state = check_random_state(random_state)
    if sampler is None:
        sampler_ = RandomUnderSampler(random_state=random_state)
    else:
        sampler_ = clone(sampler)
    sampler_.fit_resample(X, y)
    if not hasattr(sampler_, "sample_indices_"):
        raise ValueError("'sampler' needs to have an attribute 'sample_indices_'.")
    indices = sampler_.sample_indices_
    # shuffle the indices since the sampler are packing them by class
    random_state.shuffle(indices)

    def generator(X, y, sample_weight, indices, batch_size):
        while True:
            for index in range(0, len(indices), batch_size):
                X_res = _safe_indexing(X, indices[index : index + batch_size])
                y_res = _safe_indexing(y, indices[index : index + batch_size])
                if issparse(X_res) and not keep_sparse:
                    X_res = X_res.toarray()
                if sample_weight is None:
                    yield X_res, y_res
                else:
                    sw_res = _safe_indexing(
                        sample_weight, indices[index : index + batch_size]
                    )
                    yield X_res, y_res, sw_res

    return (
        generator(X, y, sample_weight, indices, batch_size),
        int(indices.size // batch_size),
    )
def balanced_batch_generator ( X , y , * , sample_weight = None , sampler = None , batch_size = 32 , keep_sparse = False , random_state = None , ) : random_state = check_random_state ( random_state ) if sampler is None : sampler_ = RandomUnderSampler ( random_state = random_state ) else : sampler_ = clone ( sampler ) sampler_ . fit_resample ( X , y ) if not hasattr ( sampler_ , "sample_indices_" ) : raise ValueError ( "'sampler' needs to have an attribute 'sample_indices_'." ) indices = sampler_ . sample_indices_ # shuffle the indices since the sampler are packing them by class random_state . shuffle ( indices ) def generator ( X , y , sample_weight , indices , batch_size ) : while True : for index in range ( 0 , len ( indices ) , batch_size ) : X_res = _safe_indexing ( X , indices [ index : index + batch_size ] ) y_res = _safe_indexing ( y , indices [ index : index + batch_size ] ) if issparse ( X_res ) and not keep_sparse : X_res = X_res . toarray ( ) if sample_weight is None : yield X_res , y_res else : sw_res = _safe_indexing ( sample_weight , indices [ index : index + batch_size ] ) yield X_res , y_res , sw_res return ( generator ( X , y , sample_weight , indices , batch_size ) , int ( indices . size // batch_size ) , )