def feature_count(dataset):
'''
This method saves the number of features that can be expected in a given
observation with respect to 'id_entity'.
@dataset, we assume that validation has occurred, and safe to assume the
data associated with the first dataset instance is identical to any
instance n within the overall collection of dataset(s).
@dataset['count_features'], is defined within the 'dataset_to_dict' method.
Note: this method needs to execute after 'dataset_to_dict'
'''
db_save = Save_Feature({
'id_entity': dataset['id_entity'],
'count_features': dataset['count_features']
})
# save dataset element, append error(s)
db_return = db_save.save_count()
if db_return['error']:
return {'error': db_return['error']}
else:
return {'error': None}
def feature_count(dataset):
'''@feature_count
This method saves the number of features that can be expected in a given
observation with respect to 'id_entity'.
@dataset, we assume that validation has occurred, and safe to assume the
data associated with the first dataset instance is identical to any
instance n within the overall collection of dataset(s).
@dataset['count_features'], is defined within the 'dataset_to_dict' method.
Note: this method needs to execute after 'dataset_to_dict'
'''
db_save = Save_Feature({
'id_entity': dataset['id_entity'],
'count_features': dataset['count_features']
})
# save dataset element, append error(s)
db_return = db_save.save_count()
if db_return['error']:
return {'error': db_return['error']}
else:
return {'error': None}
def save_svm_info(self):
"""@save_svm_info
This method saves the number of features that can be expected in a
given observation with respect to 'id_entity'.
@self.dataset[0], we assume that validation has occurred, and safe to
assume the data associated with the first dataset instance is
identical to any instance n within the overall collection of
dataset(s).
@self.dataset['count_features'], is defined within the
'dataset_to_dict' method.
Note: this method needs to execute after 'dataset_to_dict'
"""
svm_data = self.dataset[0]
db_save = Save_Feature({
'id_entity': svm_data['id_entity'],
'count_features': svm_data['count_features']
})
# save dataset element, append error(s)
db_return = db_save.save_count()
if db_return['error']:
self.list_error.append(db_return['error'])
def save_feature_count(self):
'''@save_feature_count
This method saves the number of features that can be expected in a
given observation with respect to 'id_entity'.
@self.dataset[0], we assume that validation has occurred, and safe to
assume the data associated with the first dataset instance is
identical to any instance n within the overall collection of
dataset(s).
@self.dataset['count_features'], is defined within the
'dataset_to_dict' method.
Note: this method needs to execute after 'dataset_to_dict'
'''
premodel_data = self.dataset[0]
db_save = Save_Feature({
'id_entity': premodel_data['id_entity'],
'count_features': premodel_data['count_features']
})
# save dataset element, append error(s)
db_return = db_save.save_count()
if db_return['error']:
self.list_error.append(db_return['error'])
def save_svm_info(self):
svm_data = self.dataset[0]
db_save = Save_Feature({'id_entity': svm_data['id_entity'], 'count_features': svm_data['count_features']})
# save dataset element, append error(s)
db_return = db_save.save_count()
if db_return['error']: self.list_error.append(db_return['error'])
