def BinyangUse(sample_path,
model_path='model_nb',
tokenize='False'): #just for test
sents = []
if not os.path.isfile(sample_path):
sbd_util.die('test path [%s] does not exist' % sample_path)
## labeled data
data_root = '/u/dgillick/workspace/sbd/'
brown_data = data_root + 'whiskey/brown.1'
wsj_data = data_root + 'whiskey/satz.1'
poe_data = data_root + 'whiskey/poe.1'
new_wsj_data = data_root + 'whiskey/wsj.1'
## install root
install_root = 'C:\\Python27\\Lib\\splitta\\'
#-----set parameters
out = None
if not model_path.endswith('/'): model_path += '/'
if not os.path.isdir(model_path):
model_path = install_root + model_path
if not os.path.isdir(model_path):
sbd_util.die('model path [%s] does not exist' % model_path)
svm = False
if 'svm' in model_path: svm = True
model = load_sbd_model(model_path, svm)
#-------------------
test = get_data(sample_path, tokenize=True)
test.featurize(model, verbose=True)
model.classify(test, verbose=True)
sents = test.segment(use_preds=True, tokenize=tokenize, output=out)
return sents
def BinyangUse(sample_path,model_path='model_nb',tokenize='False'): #just for test
sents=[]
if not os.path.isfile(sample_path): sbd_util.die('test path [%s] does not exist' %sample_path)
## labeled data
data_root = '/u/dgillick/workspace/sbd/'
brown_data = data_root + 'whiskey/brown.1'
wsj_data = data_root + 'whiskey/satz.1'
poe_data = data_root + 'whiskey/poe.1'
new_wsj_data = data_root + 'whiskey/wsj.1'
## install root
install_root = 'C:\\Python27\\Lib\\splitta\\'
#-----set parameters
out=None
if not model_path.endswith('/'): model_path += '/'
if not os.path.isdir(model_path):
model_path = install_root + model_path
if not os.path.isdir(model_path):
sbd_util.die('model path [%s] does not exist' %model_path)
svm=False
if 'svm' in model_path: svm = True
model = load_sbd_model(model_path, svm)
#-------------------
test = get_data(sample_path, tokenize=True)
test.featurize(model, verbose=True)
model.classify(test, verbose=True)
sents=test.segment(use_preds=True, tokenize=tokenize, output=out)
return sents
def classify(self, doc, verbose=False):
model_file = '%ssvm_model' % self.path
if not self.feats: sbd_util.die('Incomplete model')
if not os.path.isfile(model_file):
sbd_util.die('no model [%s]' % model_file)
## testing data file
if verbose: sys.stderr.write('SVM classifying... ')
lines = []
frag = doc.frag
while frag:
if frag.label == None: svm_label = '0'
elif frag.label: svm_label = '+1'
else: svm_label = '-1'
line = '%s ' % svm_label
feats = [f + '_' + v for f, v in frag.features.items()]
svm_feats = [self.feats[f] for f in feats if f in self.feats]
svm_feats.sort(lambda x, y: x - y)
line += ' '.join(['%d:1' % x for x in svm_feats])
lines.append(line)
frag = frag.next
#print "!----!",get_open_fds()
#unused, test_file = tempfile.mkstemp()
test_file = "tmp1"
fh = open(test_file, 'w')
fh.write('\n'.join(lines) + '\n')
fh.close()
#print "!----!",get_open_fds()
#unused, pred_file = tempfile.mkstemp()
pred_file = "tmp2"
options = '-v 0'
cmd = '%s %s %s %s %s' % (SVM_CLASSIFY, options, test_file, model_file,
pred_file)
os.system(cmd)
## get predictions
total = 0
pf = open(pred_file, 'r')
#print pf
preds = map(float, pf.read().splitlines())
frag = doc.frag
while frag:
frag.pred = sbd_util.logit(preds[total])
frag = frag.next
total += 1
## clean up
pf.close()
os.remove(test_file)
os.remove(pred_file)
if verbose: sys.stderr.write('done!\n')
def classify(self, doc, verbose=False):
model_file = '%ssvm_model' %self.path
if not self.feats: sbd_util.die('Incomplete model')
if not os.path.isfile(model_file): sbd_util.die('no model [%s]' %model_file)
## testing data file
if verbose: sys.stderr.write('SVM classifying... ')
lines = []
frag = doc.frag
while frag:
if frag.label == None: svm_label = '0'
elif frag.label: svm_label = '+1'
else: svm_label = '-1'
line = '%s ' %svm_label
feats = [f+'_'+v for f,v in frag.features.items()]
svm_feats = [self.feats[f] for f in feats if f in self.feats]
svm_feats.sort(lambda x,y: x-y)
line += ' '.join(['%d:1' %x for x in svm_feats])
lines.append(line)
frag = frag.next
#print "!----!",get_open_fds()
#unused, test_file = tempfile.mkstemp()
test_file = "tmp1"
fh = open(test_file, 'w')
fh.write('\n'.join(lines) + '\n')
fh.close()
#print "!----!",get_open_fds()
#unused, pred_file = tempfile.mkstemp()
pred_file = "tmp2"
options = '-v 0'
cmd = '%s %s %s %s %s' %(SVM_CLASSIFY, options, test_file, model_file, pred_file)
os.system(cmd)
## get predictions
total = 0
pf = open(pred_file,'r')
#print pf
preds = map(float, pf.read().splitlines())
frag = doc.frag
while frag:
frag.pred = sbd_util.logit(preds[total])
frag = frag.next
total += 1
## clean up
pf.close()
os.remove(test_file)
os.remove(pred_file)
if verbose: sys.stderr.write('done!\n')
def train(self, doc):
"""
takes training data and a path and creates an svm model
"""
model_file = '%ssvm_model' % self.path
## need integer dictionary for features
sys.stderr.write('training. making feat dict... ')
feat_list = set()
frag = doc.frag
while frag:
feats = [f + '_' + v for f, v in frag.features.items()]
for feat in feats:
feat_list.add(feat)
frag = frag.next
self.feats = dict(zip(feat_list, range(1, len(feat_list) + 1)))
## training data file
sys.stderr.write('writing... ')
lines = []
frag = doc.frag
while frag:
if frag.label == None:
sbd_util.die('expecting labeled data [%s]' % frag)
elif frag.label > 0.5:
svm_label = '+1'
elif frag.label < 0.5:
svm_label = '-1'
else:
continue
line = '%s ' % svm_label
feats = [f + '_' + v for f, v in frag.features.items()]
svm_feats = [self.feats[f] for f in feats]
svm_feats.sort(lambda x, y: x - y)
line += ' '.join(['%d:1' % x for x in svm_feats])
lines.append(line)
frag = frag.next
unused, train_file = tempfile.mkstemp()
fh = open(train_file, 'w')
fh.write('\n'.join(lines) + '\n')
fh.close()
## train an svm model
sys.stderr.write('running svm... ')
options = '-c 1 -v 0'
cmd = '%s %s %s %s' % (SVM_LEARN, options, train_file, model_file)
os.system(cmd)
sys.stderr.write('done!\n')
## clean up
os.remove(train_file)
def train(self, doc):
"""
takes training data and a path and creates an svm model
"""
model_file = '%ssvm_model' %self.path
## need integer dictionary for features
sys.stderr.write('training. making feat dict... ')
feat_list = set()
frag = doc.frag
while frag:
feats = [f+'_'+v for f,v in frag.features.items()]
for feat in feats: feat_list.add(feat)
frag = frag.next
self.feats = dict(zip(feat_list, range(1,len(feat_list)+1)))
## training data file
sys.stderr.write('writing... ')
lines = []
frag = doc.frag
while frag:
if frag.label == None: sbd_util.die('expecting labeled data [%s]' %frag)
elif frag.label > 0.5: svm_label = '+1'
elif frag.label < 0.5: svm_label = '-1'
else: continue
line = '%s ' %svm_label
feats = [f+'_'+v for f,v in frag.features.items()]
svm_feats = [self.feats[f] for f in feats]
svm_feats.sort(lambda x,y: x-y)
line += ' '.join(['%d:1' %x for x in svm_feats])
lines.append(line)
frag = frag.next
unused, train_file = tempfile.mkstemp()
fh = open(train_file, 'w')
fh.write('\n'.join(lines) + '\n')
fh.close()
## train an svm model
sys.stderr.write('running svm... ')
options = '-c 1 -v 0'
cmd = '%s %s %s %s' %(SVM_LEARN, options, train_file, model_file)
os.system(cmd)
sys.stderr.write('done!\n')
## clean up
os.remove(train_file)
type='str',
default=None,
help='train a new model using this labeled data file')
parser.add_option('-c',
'--svm',
dest='svm',
default=False,
action='store_true',
help='use SVM instead of Naive Bayes for training')
(options, args) = parser.parse_args()
## get test file
if len(args) > 0:
options.test = args[0]
if not os.path.isfile(options.test):
sbd_util.die('test path [%s] does not exist' % options.test)
else:
options.test = None
if not options.train:
sbd_util.die('you did not specify either train or test!')
## create model path
if not options.model_path.endswith('/'): options.model_path += '/'
if options.train:
if not os.path.isfile(options.train):
sbd_util.die('model path [%s] does not exist' % options.train)
if os.path.isdir(options.model_path):
sbd_util.die('model path [%s] already exists' % options.model_path)
else:
os.mkdir(options.model_path)
else:
parser.add_option('-t', '--tokenize', dest='tokenize', default=False,
action='store_true', help='write tokenized output')
parser.add_option('-m', '--model', dest='model_path', type='str', default='model_nb',
help='model path')
parser.add_option('-o', '--output', dest='output', type='str', default=None,
help='write sentences to this file')
parser.add_option('-x', '--train', dest='train', type='str', default=None,
help='train a new model using this labeled data file')
parser.add_option('-c', '--svm', dest='svm', default=False,
action='store_true', help='use SVM instead of Naive Bayes for training')
(options, args) = parser.parse_args()
## get test file
if len(args) > 0:
options.test = args[0]
if not os.path.isfile(options.test): sbd_util.die('test path [%s] does not exist' %options.test)
else:
options.test = None
if not options.train: sbd_util.die('you did not specify either train or test!')
## create model path
if not options.model_path.endswith('/'): options.model_path += '/'
if options.train:
if not os.path.isfile(options.train): sbd_util.die('model path [%s] does not exist' %options.train)
if os.path.isdir(options.model_path): sbd_util.die('model path [%s] already exists' %options.model_path)
else: os.mkdir(options.model_path)
else:
if not os.path.isdir(options.model_path):
options.model_path = install_root + options.model_path
if not os.path.isdir(options.model_path):
sbd_util.die('model path [%s] does not exist' %options.model_path)
def poo():
## labeled data
data_root = '/u/dgillick/workspace/sbd/'
brown_data = data_root + 'whiskey/brown.1'
wsj_data = data_root + 'whiskey/satz.1'
poe_data = data_root + 'whiskey/poe.1'
new_wsj_data = data_root + 'whiskey/wsj.1'
## install root
install_root = '/home/chonger/Downloads/splitta/'
## options
from optparse import OptionParser
usage = 'usage: %prog [options] <text_file>'
parser = OptionParser(usage=usage)
parser.add_option('-v', '--verbose', dest='verbose', default=False,
action='store_true', help='verbose output')
parser.add_option('-t', '--tokenize', dest='tokenize', default=False,
action='store_true', help='write tokenized output')
parser.add_option('-m', '--model', dest='model_path', type='str', default='model_nb',
help='model path')
parser.add_option('-o', '--output', dest='output', type='str', default=None,
help='write sentences to this file')
parser.add_option('-x', '--train', dest='train', type='str', default=None,
help='train a new model using this labeled data file')
parser.add_option('-c', '--svm', dest='svm', default=False,
action='store_true', help='use SVM instead of Naive Bayes for training')
(options, args) = parser.parse_args()
## get test file
if len(args) > 0:
options.test = args[0]
if not os.path.isfile(options.test): sbd_util.die('test path [%s] does not exist' %options.test)
else:
options.test = None
if not options.train: sbd_util.die('you did not specify either train or test!')
## create model path
if not options.model_path.endswith('/'): options.model_path += '/'
if options.train:
if not os.path.isfile(options.train): sbd_util.die('model path [%s] does not exist' %options.train)
if os.path.isdir(options.model_path): sbd_util.die('model path [%s] already exists' %options.model_path)
else: os.mkdir(options.model_path)
else:
if not os.path.isdir(options.model_path):
options.model_path = install_root + options.model_path
if not os.path.isdir(options.model_path):
sbd_util.die('model path [%s] does not exist' %options.model_path)
## create a model
if options.train:
model = build_model(options.train, options)
if not options.test: sys.exit()
print options.svm
print options.test
## test
if not options.train:
if 'svm' in options.model_path: options.svm = True
model = load_sbd_model(options.model_path, options.svm)
if options.output: options.output = open(options.output, 'w')
test = get_data(options.test, tokenize=True)
test.featurize(model, verbose=True)
model.classify(test, verbose=True)
print options.tokenize
print options.output
test.segment(use_preds=True, tokenize=options.tokenize, output=options.output)
parser.add_option('-t', '--tokenize', dest='tokenize', default=False,
action='store_true', help='write tokenized output')
parser.add_option('-m', '--model', dest='model_path', type='str', default='model_nb',
help='model path')
parser.add_option('-o', '--output', dest='output', type='str', default=None,
help='write sentences to this file')
parser.add_option('-x', '--train', dest='train', type='str', default=None,
help='train a new model using this labeled data file')
parser.add_option('-c', '--svm', dest='svm', default=False,
action='store_true', help='use SVM instead of Naive Bayes for training')
(options, args) = parser.parse_args()
## get test file
if len(args) > 0:
options.test = args[0]
if not os.path.isfile(options.test): sbd_util.die('test path [%s] does not exist' %options.test)
else:
options.test = None
if not options.train: sbd_util.die('you did not specify either train or test!')
## create model path
if not options.model_path.endswith('/'): options.model_path += '/'
if options.train:
if not os.path.isfile(options.train): sbd_util.die('model path [%s] does not exist' %options.train)
if os.path.isdir(options.model_path): sbd_util.die('model path [%s] already exists' %options.model_path)
else: os.mkdir(options.model_path)
else:
if not os.path.isdir(options.model_path):
options.model_path = install_root + options.model_path
if not os.path.isdir(options.model_path):
sbd_util.die('model path [%s] does not exist' %options.model_path)
def poo():
## labeled data
data_root = '/u/dgillick/workspace/sbd/'
brown_data = data_root + 'whiskey/brown.1'
wsj_data = data_root + 'whiskey/satz.1'
poe_data = data_root + 'whiskey/poe.1'
new_wsj_data = data_root + 'whiskey/wsj.1'
## install root
install_root = '/home/chonger/Downloads/splitta/'
## options
from optparse import OptionParser
usage = 'usage: %prog [options] <text_file>'
parser = OptionParser(usage=usage)
parser.add_option('-v',
'--verbose',
dest='verbose',
default=False,
action='store_true',
help='verbose output')
parser.add_option('-t',
'--tokenize',
dest='tokenize',
default=False,
action='store_true',
help='write tokenized output')
parser.add_option('-m',
'--model',
dest='model_path',
type='str',
default='model_nb',
help='model path')
parser.add_option('-o',
'--output',
dest='output',
type='str',
default=None,
help='write sentences to this file')
parser.add_option('-x',
'--train',
dest='train',
type='str',
default=None,
help='train a new model using this labeled data file')
parser.add_option('-c',
'--svm',
dest='svm',
default=False,
action='store_true',
help='use SVM instead of Naive Bayes for training')
(options, args) = parser.parse_args()
## get test file
if len(args) > 0:
options.test = args[0]
if not os.path.isfile(options.test):
sbd_util.die('test path [%s] does not exist' % options.test)
else:
options.test = None
if not options.train:
sbd_util.die('you did not specify either train or test!')
## create model path
if not options.model_path.endswith('/'): options.model_path += '/'
if options.train:
if not os.path.isfile(options.train):
sbd_util.die('model path [%s] does not exist' % options.train)
if os.path.isdir(options.model_path):
sbd_util.die('model path [%s] already exists' % options.model_path)
else:
os.mkdir(options.model_path)
else:
if not os.path.isdir(options.model_path):
options.model_path = install_root + options.model_path
if not os.path.isdir(options.model_path):
sbd_util.die('model path [%s] does not exist' %
options.model_path)
## create a model
if options.train:
model = build_model(options.train, options)
if not options.test: sys.exit()
print options.svm
print options.test
## test
if not options.train:
if 'svm' in options.model_path: options.svm = True
model = load_sbd_model(options.model_path, options.svm)
if options.output: options.output = open(options.output, 'w')
test = get_data(options.test, tokenize=True)
test.featurize(model, verbose=True)
model.classify(test, verbose=True)
print options.tokenize
print options.output
test.segment(use_preds=True,
tokenize=options.tokenize,
output=options.output)
