def loading_target_CRFs(command):
if command == 'twitter':
# loading target labels
path_ = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF'
name_ = 'labeling_all.txt'
list_line_ = filterTxt_CRF(load_file(path_, name_), command='removeLink')
elif command == 'sgforums':
# loading target labels
path_ = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/sgforums/20152207_singaporebuses_all_posts/labeling_CRF'
name_ = 'Label_all_crf.txt'
list_line_ = load_file(path_, name_)
elif command == 'facebook':
# loading target labels
path_ = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/facebook/BusNews/labeling_CRF'
name_ = 'label.txt'
list_line_ = filterTxt_CRF(load_file(path_, name_), command='removePunc')
Y = np.array(load_target_label(list_line_))
print 'Finish loading target label ' + command
return Y
# path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLinkWordVector/wordVec_30'
# path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLinkWordVector/wordVec_70'
# path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLinkWordVector/wordVec_100'
# path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLinkWordVector/wordVec_150'
path_ftr = "D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLinkWordVector/wordVec_200"
files_ = folder_files(path_ftr)
features = featuers_CRF(files_, path_ftr)
# construct_ftr_CRF_wordVector(features)
X = np.array(construct_ftr_CRF_wordVector(features)) # construct the features for CRF
print X.shape
print "Finish loading features for CRF"
#
# # loading target labels
path_ = "D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF"
name_ = "labeling_all.txt"
list_line_ = filterTxt_CRF(load_file(path_, name_), "removeLink", "model")
Y = np.array(load_target_label(list_line_))
print Y.shape
print "Finish loading target label"
#
# # for index in range(0, len(X)):
# # print len(X[index]), len(Y[index])
#
# # running CRF models
# n_cross_valid_crf(X, Y, K=5, command='metrics_F1') # use to calculate the F1 for classification
n_cross_valid_crf(X, Y, K=5, command="confusion_matrix") # use to calculate the F1 for classification
# # n_cross_valid_crf(X, Y, K=5, command='write_results') # use to calculate the confusion matrix
#
# stop = timeit.default_timer()
# print 'Finish running CRF model %.3f sec' % (stop - start)