def __init__(self, X, y, batch_size, process_fn=None):
"""A `Sequence` implementation that returns balanced `y` by undersampling majority class.
Args:
X: The numpy array of inputs.
y: The numpy array of targets.
batch_size: The generator mini-batch size.
process_fn: The preprocessing function to apply on `X`
"""
self.X = X
self.y = y
self.batch_size = batch_size
self.process_fn = process_fn or (lambda x: x)
self.pos_indices = np.where(y == 1)[0]
self.neg_indices = np.where(y == 0)[0]
self.n = min(len(self.pos_indices), len(self.neg_indices))
self._index_array = None
python类Sequence()的实例源码
def __getitem__(self, index):
assert self.sequence, "This transformer {} has not been called with a Sequence object".format(
self.__class__.__name__)
batch = self.sequence[index]
if self.batch_size is None:
# The first batch should be the maximum batch_size i.e. not the last.
self.batch_size = get_batch_size(batch)
args = self.get_args()
for arg in args:
arg.update(self.common_args)
return apply_fun(batch, self.apply_transformation, self.mask, transformation=self.transformation, args=args)
def __init__(self, X, y, batch_size, process_fn=None):
"""A `Sequence` implementation that can pre-process a mini-batch via `process_fn`
Args:
X: The numpy array of inputs.
y: The numpy array of targets.
batch_size: The generator mini-batch size.
process_fn: The preprocessing function to apply on `X`
"""
self.X = X
self.y = y
self.batch_size = batch_size
self.process_fn = process_fn or (lambda x: x)
