def __init__(self, saved_model_name, threshold_value=85):
pass
self._instance = POSContextSequenceModeler()
coll = saved_model_name.split("/")
saved_model_name = coll[-1:][0]
self._instance.load_model(name=saved_model_name)
self.threshold = threshold_value
def model(self):
self.arg_obj.parse(sys.argv)
if not self.check_params():
self.show_help()
_feature_set_location = self.arg_obj.args["feature_set_location"]
if os.path.isfile(_feature_set_location):
# if given a file path and not provided the model name to save as
if not self.arg_obj.args.has_key("model_name"):
self.show_help()
_model_name = self.arg_obj.args["model_name"]
_instance = POSContextSequenceModeler(
feature_set_location=_feature_set_location)
_instance.train()
_instance.save_model(name=_model_name, location="trained_models")
print "ModelingStub: modeling done for given feature set file."
if os.path.isdir(_feature_set_location):
print "ModelingStub: looking into feature set directory..."
# filter only feature set files with .txt extension
file_list = [
fn for fn in os.listdir(_feature_set_location)
if fn.endswith(('.txt'))
]
for _file in file_list:
_path = _feature_set_location + "/" + _file
_coll = _file.split(".")
_model_name = _coll[0] + ".model"
_instance = POSContextSequenceModeler(
feature_set_location=_path)
_instance.train()
print "ModelingStub: trained the model.about to save."
_instance.save_model(name=_model_name,
location="trained_models")
print "ModelingStub: modeling done for:", _file
print "ModelingStub: modeling done for all files in directory provided."
def __init__(self, saved_model_name):
pass
self._instance = POSContextSequenceModeler()
coll = saved_model_name.split("/")
saved_model_name = coll[-1:][0]
self._instance.load_model(name=saved_model_name)
