def __init__(self, parserpath, savedir):
self.savedir = savedir
self.parserpath = parserpath
self.kwargs = {}
# Parser configuration file
configfilepath = self.parserpath + '/' + self.savedir + '/config.cfg'
self.kwargs['config_file'] = configfilepath
# Pre-trained parser model
self.kwargs['default'] = {'save_dir': self.parserpath + '/' + self.savedir}
self.kwargs['is_evaluation'] = True
self.network = Network(**self.kwargs)
def parse(save_dir, **kwargs):
""""""
kwargs['config_file'] = os.path.join(save_dir, 'config.cfg')
files = kwargs.pop('files')
output_file = kwargs.pop('output_file', None)
output_dir = kwargs.pop('output_dir', None)
if len(files) > 1 and output_file is not None:
raise ValueError('Cannot provide a value for --output_file when parsing multiple files')
kwargs['is_evaluation'] = True
network = Network(**kwargs)
network.parse(files, output_file=output_file, output_dir=output_dir)
return
def train(save_dir, **kwargs):
""""""
kwargs['config_file'] = kwargs.pop('config_file', '')
load = kwargs.pop('load')
try:
if not load and os.path.isdir(save_dir):
raw_input('Save directory already exists. Press <Enter> to continue or <Ctrl-c> to abort.')
if os.path.isfile(os.path.join(save_dir, 'config.cfg')):
os.remove(os.path.join(save_dir, 'config.cfg'))
except KeyboardInterrupt:
print()
sys.exit(0)
network = Network(**kwargs)
network.train(load=load)
return
def network_init(save_dir, **kwargs):
kwargs['config_file'] = os.path.join(save_dir, 'config.cfg')
short = kwargs.pop('short', False)
if short:
kwargs['config_file'] = os.path.join(save_dir, 'config_short.cfg')
kwargs['is_evaluation'] = True
network = Network(**kwargs)
return network
def train(save_dir, **kwargs):
""""""
load = kwargs.pop('load')
try:
if not load and os.path.isdir(save_dir):
raw_input(
'Save directory already exists. Press <Enter> to continue or <Ctrl-c> to abort.'
)
if os.path.isfile(os.path.join(save_dir, 'config.cfg')):
os.remove(os.path.join(save_dir, 'config.cfg'))
except KeyboardInterrupt:
sys.exit(0)
#print (kwargs)
#print ("train files:",kwargs['train_files'])
print("initializing")
network = Network(**kwargs)
print("initialized")
network.train(load=load)
return
class UnstableParser:
"""
Perform parsing using the UnstableParser (universal dependency parser
"""
def __init__(self, parserpath, savedir):
self.savedir = savedir
self.parserpath = parserpath
self.kwargs = {}
# Parser configuration file
configfilepath = self.parserpath + '/' + self.savedir + '/config.cfg'
self.kwargs['config_file'] = configfilepath
# Pre-trained parser model
self.kwargs['default'] = {'save_dir': self.parserpath + '/' + self.savedir}
self.kwargs['is_evaluation'] = True
self.network = Network(**self.kwargs)
def parse(self, outputdir, files):
"""
Parse the files using a pre-trained UnstableParser model
"""
self.network.parse(files, output_dir=outputdir)
@property
def vocabs(self):
return self._vocabs
@property
def datasets(self):
return self._datasets
@property
def optimizer(self):
return self._optimizer
@property
def save_vars(self):
return filter(lambda x: u'Pretrained/Embeddings:0' != x.name, tf.global_variables())
@property
def non_save_vars(self):
return filter(lambda x: u'Pretrained/Embeddings:0' == x.name, tf.global_variables())
@property
def global_step(self):
return self._global_step
@property
def global_epoch(self):
return self._global_epoch
#***************************************************************
if __name__ == '__main__':
""""""
from parser import Network
configurable = Configurable()
network = Network.from_configurable(configurable)
network.train()