"""
All rights reserved.
--Yang Song, Apr 7th, 2020
"""
from sklearn.model_selection import StratifiedKFold, LeaveOneOut
from FAE.DataContainer.DataContainer import DataContainer
class BaseCrossValidation(object):
def __init__(self, n_split='', description=''):
self._description = description
if n_split == 'all':
self._cv = LeaveOneOut()
self._name = 'LeaveOneOut'
else:
self._cv = StratifiedKFold(int(n_split), shuffle=False)
self._name = '{}-Fold'.format(int(n_split))
pass
def GetName(self):
return self._name
def Generate(self, data_container):
array, label = data_container.GetArray(), data_container.GetLabel()
feature_name, case_name = data_container.GetFeatureName(), data_container.GetCaseName()
for train_index, val_index in self._cv.split(array, label):
train_array, train_label = array[train_index, :], label[train_index]
val_array, val_label = array[val_index, :], label[val_index]
sub_train_container = DataContainer(array=train_array, label=train_label, feature_name=feature_name,
case_name=[case_name[index] for index in train_index])
sub_val_container = DataContainer(array=val_array, label=val_label, feature_name=feature_name,
case_name=[case_name[index] for index in val_index])
yield (sub_train_container, sub_val_container)
def GetDescription(self):
return self._description
CrossValidation5Fold = BaseCrossValidation(n_split='5',
description="To determine the hyper-parameter (e.g. the number of "
"features) of model, we applied cross validation with 5-fold "
"on the training data set. The hyper-parameters were set "
"according to the model performance on the validation data set. "
)
CrossValidation10Fold = BaseCrossValidation(n_split='10',
description="To determine the hyper-parameter (e.g. the number of "
"features) of model, we applied cross validation with 10-fold "
"on the training data set. The hyper-parameters were set "
"according to the model performance on the validation data set. "
)
CrossValidationLOO = BaseCrossValidation(n_split='all',
description="To determine the hyper-parameter (e.g. the number of features) "
"of model, we applied cross validation with leave-one-out on the "
"training data set. The hyper-parameters were set according to "
"the model performance on the validation data set. ")
if __name__ == '__main__':
import numpy as np
data = np.random.random((100, 10))
label = np.concatenate((np.ones((60,)), np.zeros((40,))), axis=0)
cv = LeaveOneOut()
for train, val in cv.split(data, label):
print(train)
print(val)
print('')
