def loading_ftr_CRFs(command):
##############################################################################
if command == 'twitter_vs_sgforums_twitter_training':
# loading CRF features for training
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLink'
elif command == 'twitter_vs_sgforums_sgforums_testing':
# loading CRF features for testing
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/CRFs_compareModel/sgforums/ftr_twitter'
##############################################################################
if command == 'twitter_vs_facebook_twitter_training':
# loading CRF features for training
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLink'
elif command == 'twitter_vs_facebook_facebook_testing':
# loading CRF features for testing
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/CRFs_compareModel/facebook/ftr_twitter'
##############################################################################
elif command == 'sgforums_vs_twitter_sgforums_training':
# features for training
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/sgforums/20152207_singaporebuses_all_posts/labeling_CRF/crf_features/features'
elif command == 'sgforums_vs_twitter_twitter_testing':
# features for testing
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/CRFs_compareModel/twitter/ftr_sgforums'
##############################################################################
elif command == 'sgforums_vs_facebook_sgforums_training':
# features for training
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/sgforums/20152207_singaporebuses_all_posts/labeling_CRF/crf_features/features'
elif command == 'sgforums_vs_facebook_facebook_testing':
# features for testing
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/CRFs_compareModel/facebook/ftr_sgforums'
##############################################################################
elif command == 'facebook_vs_twitter_facebook_training':
# features for training
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/facebook/BusNews/labeling_CRF/crf_features/features'
elif command == 'facebook_vs_twitter_twitter_testing':
# features for testing
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/CRFs_compareModel/twitter/ftr_facebook'
##############################################################################
elif command == 'facebook_vs_sgforums_facebook_training':
# features for training
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/facebook/BusNews/labeling_CRF/crf_features/features'
elif command == 'facebook_vs_sgforums_sgforums_testing':
# features for testing
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/CRFs_compareModel/sgforums/ftr_facebook'
files_ = folder_files(path_ftr)
features = featuers_CRF(files_, path_ftr)
X = np.array(construct_ftr_CRF(features)) # construct the features for CRF
print 'Finish loading features for CRF ' + command
return X
# # running CRF models
# n_cross_valid_crf(X, Y, K=2, command='metrics_F1') # use to calculate the F1 for classification
# # n_cross_valid_crf(X, Y, K=2, command='confusion_matrix') # use to calculate the F1 for classification
# # n_cross_valid_crf(X, Y, K=2, command='write_results') # use to calculate the confusion matrix
#
# stop = timeit.default_timer()
# print 'Finish running CRF model %.3f sec' % (stop - start)
############################################################################
start = timeit.default_timer() # get the start time
# loading CRF features
path_ftr = 'D:/Project/Transportation_SMU-NEC_collaboration/Data/twitter/labeling_CRF/crf_features/features_rmLink'
files_ = folder_files(path_ftr)
features = featuers_CRF(files_, path_ftr)
X = np.array(construct_ftr_CRF(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
