def _clean_valid_inputformat(self, name):
"""Ensure field with given name has a valid format.
@param name: name of field to clean
@type name: string
@return: list of integers
@rtype: list of integers
"""
if name in self.cleaned_data:
if not self.cleaned_data[name]:
return None
# list ? datasplit
if type(self.cleaned_data[name])==list:
out = []
splits= []
for split in self.cleaned_data[name]:
if not check_split_str(split):
raise forms.ValidationError('invalid format')
else:
split = [int(x) for x in expand_split_str(split)]
if len(split)>0 and (int(split[-1]) >= self.cleaned_data['data'].num_instances or int(split[0]) < 0):
raise forms.ValidationError('index out of bounds')
splits.append(split)
out.append(split)
dset=['train_idx','val_idx','test_idx']
for d in dset:
if self.data.has_key(d) and d != name:
intersec=check_split_intersec([[expand_split_str(i) for i in self.data.getlist(d)],splits])
if intersec:
raise forms.ValidationError('index intersection in row ' + str(intersec) )
return out
# else input or output variables
else:
if check_split_str(self.cleaned_data[name]):
split=[int(x) for x in expand_split_str(self.cleaned_data[name])]
if len(split)>0:
if (split[-1] >= self.cleaned_data['data'].num_attributes or split[0] < 0):
raise forms.ValidationError('index out of bounds')
dset=['input_variables','output_variables']
#import pdb
#pdb.set_trace()
for d in dset:
if self.data.has_key(d) and d != name:
intersec=check_split_intersec([[int(i) for i in expand_split_str(self.data.getlist(d))],split])
if intersec:
raise forms.ValidationError('index intersection')
else:
raise forms.ValidationError('invalid format')
return split
else:
raise ValidationError(_('Invalid format (example: 0,1,2:5,5 = 0,1,2,3,4,5)'))
def _clean_valid_inputformat(self, name):
"""Ensure field with given name has a valid format.
@param name: name of field to clean
@type name: string
@return: list of integers
@rtype: list of integers
"""
if name in self.cleaned_data:
if not self.cleaned_data[name]:
return None
# list ? datasplit
if type(self.cleaned_data[name]) == list:
dset = ['train_idx', 'val_idx', 'test_idx']
out = []
splits = []
i = 0
# loop through all split parts of the same kind (name)
for split in self.cleaned_data[name]:
# handle percent
if len(split) > 0 and split[-1] == '%':
# setup
frac = float(split[:-1]) / 100.0
numat = self.cleaned_data['data'].num_instances
ints = range(0, numat)
# filter already selected instances
for d in dset:
if self.cleaned_data.has_key(
d) and d != name and len(
self.cleaned_data[d]) >= i:
spl = self.cleaned_data[d][i]
ints = filter(lambda x: not x in spl, ints)
numrest = len(ints)
if (numrest == 0):
raise forms.ValidationError('sum of percents > 1')
# sample adjust sampling ratio to number of instances left
frac = frac * float(numat) / float(numrest)
if (frac > 1):
raise forms.ValidationError('sum of percents > 1')
# sample with given ratio from remaining instances
split = random.sample(ints,
int(math.floor(frac * numrest)))
# save the split
splits.append(split)
out.append(split)
elif not check_split_str(split):
raise forms.ValidationError('invalid format')
else:
# convert python-like string into list of integers
split = [int(x) for x in expand_split_str(split)]
if len(split) > 0 and (
int(split[-1]) >=
self.cleaned_data['data'].num_instances
or int(split[0]) < 0):
raise forms.ValidationError('index out of bounds')
# save the split
splits.append(split)
out.append(split)
i += 1
# check if any of splits intersect with each other
for d in dset:
if self.data.has_key(d) and d != name:
intersec = check_split_intersec([[
expand_split_str(i) for i in self.data.getlist(d)
], splits])
if intersec:
raise forms.ValidationError(
'index intersection in row ' + str(intersec))
return out
# else input or output variables
else:
if check_split_str(self.cleaned_data[name]):
split = [
int(x)
for x in expand_split_str(self.cleaned_data[name])
]
if len(split) > 0:
if (split[-1] >=
self.cleaned_data['data'].num_attributes
or split[0] < 0):
raise forms.ValidationError('index out of bounds')
dset = ['input_variables', 'output_variables']
#import pdb
#pdb.set_trace()
for d in dset:
if self.data.has_key(d) and d != name:
intersec = check_split_intersec([[
int(i)
for i in expand_split_str(self.data.getlist(d))
], split])
if intersec:
raise forms.ValidationError(
'index intersection')
else:
raise forms.ValidationError('invalid format')
return split
else:
raise ValidationError(
_('Invalid format (example: 0,1,2:5,5 = 0,1,2,3,4,5)'))
def _clean_valid_inputformat(self, name):
"""Ensure field with given name has a valid format.
@param name: name of field to clean
@type name: string
@return: list of integers
@rtype: list of integers
"""
if name in self.cleaned_data:
if not self.cleaned_data[name]:
return None
# list ? datasplit
if type(self.cleaned_data[name])==list:
dset=['train_idx','val_idx','test_idx']
out = []
splits= []
i = 0
# loop through all split parts of the same kind (name)
for split in self.cleaned_data[name]:
# handle percent
if len(split) > 0 and split[-1] == '%':
# setup
frac = float(split[:-1]) / 100.0
numat = self.cleaned_data['data'].num_instances
ints = range(0,numat)
# filter already selected instances
for d in dset:
if self.cleaned_data.has_key(d) and d != name and len(self.cleaned_data[d]) >= i:
spl = self.cleaned_data[d][i]
ints = filter(lambda x: not x in spl, ints)
numrest = len(ints)
if (numrest == 0):
raise forms.ValidationError('sum of percents > 1')
# sample adjust sampling ratio to number of instances left
frac = frac * float(numat) / float(numrest)
if (frac > 1):
raise forms.ValidationError('sum of percents > 1')
# sample with given ratio from remaining instances
split = random.sample(ints, int(math.floor(frac*numrest)))
# save the split
splits.append(split)
out.append(split)
elif not check_split_str(split):
raise forms.ValidationError('invalid format')
else:
# convert python-like string into list of integers
split = [int(x) for x in expand_split_str(split)]
if len(split)>0 and (int(split[-1]) >= self.cleaned_data['data'].num_instances or int(split[0]) < 0):
raise forms.ValidationError('index out of bounds')
# save the split
splits.append(split)
out.append(split)
i+=1
# check if any of splits intersect with each other
for d in dset:
if self.data.has_key(d) and d != name:
intersec=check_split_intersec([[expand_split_str(i) for i in self.data.getlist(d)],splits])
if intersec:
raise forms.ValidationError('index intersection in row ' + str(intersec) )
return out
# else input or output variables
else:
if check_split_str(self.cleaned_data[name]):
split=[int(x) for x in expand_split_str(self.cleaned_data[name])]
if len(split)>0:
if (split[-1] >= self.cleaned_data['data'].num_attributes or split[0] < 0):
raise forms.ValidationError('index out of bounds')
dset=['input_variables','output_variables']
#import pdb
#pdb.set_trace()
for d in dset:
if self.data.has_key(d) and d != name:
intersec=check_split_intersec([[int(i) for i in expand_split_str(self.data.getlist(d))],split])
if intersec:
raise forms.ValidationError('index intersection')
else:
raise forms.ValidationError('invalid format')
return split
else:
raise ValidationError(_('Invalid format (example: 0,1,2:5,5 = 0,1,2,3,4,5)'))