def __init__(
self,
X,
y=None,
device=None,
length=None,
):
# TODO: Remove warning in release 0.4
if device is not None:
warnings.warn(
"device is no longer needed by Dataset and will be ignored.",
DeprecationWarning)
self.X = X
self.y = y
self.X_indexing = check_indexing(X)
self.y_indexing = check_indexing(y)
self.X_is_ndframe = is_pandas_ndframe(X)
if length is not None:
self._len = length
return
# pylint: disable=invalid-name
len_X = get_len(X)
if y is not None:
len_y = get_len(y)
if len_y != len_X:
raise ValueError("X and y have inconsistent lengths.")
self._len = len_X
def on_train_begin(self, net, X, y, **kwargs):
self.X_indexing_ = check_indexing(X)
self.y_indexing_ = check_indexing(y)
# Looks for the right most index where `*_best` is True
# That index is used to get the best score in `net.history`
with suppress(ValueError, IndexError, KeyError):
best_name_history = net.history[:, '{}_best'.format(self.name_)]
idx_best_reverse = best_name_history[::-1].index(True)
idx_best = len(best_name_history) - idx_best_reverse - 1
self.best_score_ = net.history[idx_best, self.name_]
def __init__(
self,
X,
y=None,
length=None,
):
self.X = X
self.y = y
self.X_indexing = check_indexing(X)
self.y_indexing = check_indexing(y)
self.X_is_ndframe = is_pandas_ndframe(X)
if length is not None:
self._len = length
return
# pylint: disable=invalid-name
len_X = get_len(X)
if y is not None:
len_y = get_len(y)
if len_y != len_X:
raise ValueError("X and y have inconsistent lengths.")
self._len = len_X
def on_train_begin(self, net, X, y, **kwargs):
self.X_indexing_ = check_indexing(X)
self.y_indexing_ = check_indexing(y)
