def train_expression_classifier():
# 1) Create the training file from all the features
# Load the feature description
path_feat_desc = my_config_manager.get_feature_desc_filename()
fic = open(path_feat_desc)
fields = fic.read().strip()
fic.close()
separator = '\t'
feat_folder = my_config_manager.get_feature_folder_name()
crf_folder = my_config_manager.get_crf_expression_folder()
# Create all the CRF files calling to the crfutils.extract_features_to_crf
crf_out_files = []
templates_exp = my_config_manager.get_templates_expr()
#possible_classes = my_config_manager.get_possible_expression_values()
possible_classes = [OPINION_EXPRESSION]
##FOR MPQA
possible_classes = ['dse']
# Only set the target class for the tokens of possible_classes
# For others, it's set to O (out sequence)
for feat_file in glob.glob(feat_folder+'/*.feat'):
base_name = os.path.basename(feat_file)
base_name = base_name[:-5]
out_crf = os.path.join(crf_folder,base_name)
logging.debug('Creating crf file in --> '+out_crf)
try:
extract_features_to_crf(feat_file,out_crf,fields,separator,templates_exp,possible_classes)
crf_out_files.append(out_crf)
except:
print>>sys.stderr,'Failed conversion to tab-expression -> CRF: ',feat_file
###########################################################################################
# Concatenate all the crf files just created
out_f = open(my_config_manager.get_training_dataset_exp(),'w')
for crf_file in crf_out_files:
f = open(crf_file)
out_f.write(f.read())
f.close()
out_f.close()
logging.debug('Created training data for crf, op.exp '+my_config_manager.get_training_dataset_exp())
#############################################
#Train the model
crf_params = my_config_manager.get_crfsuite_params()
input_file = my_config_manager.get_training_dataset_exp()
model_file = my_config_manager.get_filename_model_expression()
logging.debug('Training the classifier for opinion expressions (could take a while)')
run_crfsuite(crf_params,input_file,model_file)
def train_expression_classifier():
# 1) Create the training file from all the features
# Load the feature description
path_feat_desc = my_config_manager.get_feature_desc_filename()
fic = open(path_feat_desc)
fields = fic.read().strip()
fic.close()
separator = '\t'
feat_folder = my_config_manager.get_feature_folder_name()
crf_folder = my_config_manager.get_crf_expression_folder()
# Create all the CRF files calling to the crfutils.extract_features_to_crf
crf_out_files = []
templates_exp = my_config_manager.get_templates_expr()
possible_classes = my_config_manager.get_possible_expression_values()
# Only set the target class for the tokens of possible_classes
# For others, it's set to O (out sequence)
for feat_file in glob.glob(feat_folder + '/*.feat'):
base_name = os.path.basename(feat_file)
base_name = base_name[:-5]
out_crf = os.path.join(crf_folder, base_name)
logging.debug('Creating crf file in --> ' + out_crf)
try:
extract_features_to_crf(feat_file, out_crf, fields, separator,
templates_exp, possible_classes)
crf_out_files.append(out_crf)
except:
print >> sys.stderr, 'Failed conversion to tab-expression -> CRF: ', feat_file
###########################################################################################
# Concatenate all the crf files just created
out_f = open(my_config_manager.get_training_dataset_exp(), 'w')
for crf_file in crf_out_files:
f = open(crf_file)
out_f.write(f.read())
f.close()
out_f.close()
logging.debug('Created training data for crf, op.exp ' +
my_config_manager.get_training_dataset_exp())
#############################################
#Train the model
crf_params = my_config_manager.get_crfsuite_params()
input_file = my_config_manager.get_training_dataset_exp()
model_file = my_config_manager.get_filename_model_expression()
logging.debug(
'Training the classifier for opinion expressions (could take a while)')
run_crfsuite(crf_params, input_file, model_file)
def convert_to_crf(input_file,templates):
out_desc = NamedTemporaryFile(delete=False)
out_desc.close()
out_crf = out_desc.name
##Load description of features
path_feat_desc = my_config_manager.get_feature_desc_filename()
fic = open(path_feat_desc)
fields = fic.read().strip()
fic.close()
####
extract_features_to_crf(input_file,out_crf,fields,separator,templates,possible_classes=None)
return out_crf
def convert_to_crf(input_file,templates):
out_desc = NamedTemporaryFile(delete=False)
out_desc.close()
out_crf = out_desc.name
##Load description of features
path_feat_desc = my_config_manager.get_feature_desc_filename()
fic = open(path_feat_desc)
fields = fic.read().strip()
fic.close()
####
extract_features_to_crf(input_file,out_crf,fields,separator,templates,possible_classes=None)
return out_crf
def train_holder_classifier():
# 1) Create the training file from all the features
# Load the feature description
path_feat_desc = my_config_manager.get_feature_desc_filename()
fic = open(path_feat_desc)
fields = fic.read().strip()
fic.close()
separator = '\t'
feat_folder = my_config_manager.get_feature_folder_name()
crf_folder = my_config_manager.get_crf_holder_folder()
# Create all the CRF files calling to the crfutils.extract_features_to_crf
crf_out_files = []
templates_holder = my_config_manager.get_templates_holder()
possible_classes = ['holder']
for feat_file in glob.glob(feat_folder+'/*.feat'):
base_name = os.path.basename(feat_file)
base_name = base_name[:-5]
out_crf = os.path.join(crf_folder,base_name)
logging.debug('Creating crf file in --> '+out_crf)
try:
extract_features_to_crf(feat_file,out_crf,fields,separator,templates_holder,possible_classes)
crf_out_files.append(out_crf)
except:
print>>sys.stderr,'Failed conversion to tab-holder -> CRF: ',feat_file
###########################################################################################
# Concatenate all the crf files just created
out_f = open(my_config_manager.get_training_dataset_holder(),'w')
for crf_file in crf_out_files:
f = open(crf_file)
out_f.write(f.read())
f.close()
out_f.close()
logging.debug('Created training data for crf, op.exp '+my_config_manager.get_training_dataset_holder())
#############################################
#Train the model
crf_params = my_config_manager.get_crfsuite_params()
input_file = my_config_manager.get_training_dataset_holder()
model_file = my_config_manager.get_filename_model_holder()
logging.debug('Training the classifier for opinion holder (could take a while)')
run_crfsuite(crf_params,input_file,model_file)
