def check_data(self, _, y):
if y is None and self.iterator_train is DataLoader:
raise ValueError("No y-values are given (y=None). You must "
"implement your own DataLoader for training "
"(and your validation) and supply it using the "
"``iterator_train`` and ``iterator_valid`` "
"parameters respectively.")
elif y is None:
# The user implements its own mechanism for generating y.
return
# The problem with 1-dim float y is that the pytorch DataLoader will
# somehow upcast it to DoubleTensor
if get_dim(y) == 1:
raise ValueError("The target data shouldn't be 1-dimensional; "
"please reshape (e.g. y.reshape(-1, 1).")
def check_data(self, X, y):
if ((y is None) and (not is_dataset(X))
and (self.iterator_train is DataLoader)):
raise ValueError("No y-values are given (y=None). You must "
"implement your own DataLoader for training "
"(and your validation) and supply it using the "
"``iterator_train`` and ``iterator_valid`` "
"parameters respectively.")
elif y is None:
# The user implements its own mechanism for generating y.
return
if get_dim(y) == 1:
msg = (
"The target data shouldn't be 1-dimensional but instead have "
"2 dimensions, with the second dimension having the same size "
"as the number of regression targets (usually 1). Please "
"reshape your target data to be 2-dimensional "
"(e.g. y = y.reshape(-1, 1).")
raise ValueError(msg)
def check_data(self, X, y):
super().check_data(X, y)
if get_dim(y) != 1:
raise ValueError("The target data should be 1-dimensional.")
def check_data(self, X, y):
super().check_data(X, y)
if (not is_dataset(X)) and (get_dim(y) != 1):
raise ValueError("The target data should be 1-dimensional.")
