Source code for nltk.classify.tadm

# Natural Language Toolkit: Interface to TADM Classifier
#
# Copyright (C) 2001-2014 NLTK Project
# Author: Joseph Frazee <jfrazee@mail.utexas.edu>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import print_function, unicode_literals

import sys
import subprocess

from nltk import compat
from nltk.internals import find_binary
try:
    import numpy
except ImportError:
    numpy = None

_tadm_bin = None
[docs]def config_tadm(bin=None): global _tadm_bin _tadm_bin = find_binary( 'tadm', bin, env_vars=['TADM'], binary_names=['tadm'], url='http://tadm.sf.net')
[docs]def write_tadm_file(train_toks, encoding, stream): """ Generate an input file for ``tadm`` based on the given corpus of classified tokens. :type train_toks: list(tuple(dict, str)) :param train_toks: Training data, represented as a list of pairs, the first member of which is a feature dictionary, and the second of which is a classification label. :type encoding: TadmEventMaxentFeatureEncoding :param encoding: A feature encoding, used to convert featuresets into feature vectors. :type stream: stream :param stream: The stream to which the ``tadm`` input file should be written. """ # See the following for a file format description: # # http://sf.net/forum/forum.php?thread_id=1391502&forum_id=473054 # http://sf.net/forum/forum.php?thread_id=1675097&forum_id=473054 labels = encoding.labels() for featureset, label in train_toks: length_line = '%d\n' % len(labels) stream.write(length_line) for known_label in labels: v = encoding.encode(featureset, known_label) line = '%d %d %s\n' % ( int(label == known_label), len(v), ' '.join('%d %d' % u for u in v) ) stream.write(line)
[docs]def parse_tadm_weights(paramfile): """ Given the stdout output generated by ``tadm`` when training a model, return a ``numpy`` array containing the corresponding weight vector. """ weights = [] for line in paramfile: weights.append(float(line.strip())) return numpy.array(weights, 'd')
[docs]def call_tadm(args): """ Call the ``tadm`` binary with the given arguments. """ if isinstance(args, compat.string_types): raise TypeError('args should be a list of strings') if _tadm_bin is None: config_tadm() # Call tadm via a subprocess cmd = [_tadm_bin] + args p = subprocess.Popen(cmd, stdout=sys.stdout) (stdout, stderr) = p.communicate() # Check the return code. if p.returncode != 0: print() print(stderr) raise OSError('tadm command failed!')
[docs]def names_demo(): from nltk.classify.util import names_demo from nltk.classify.maxent import TadmMaxentClassifier classifier = names_demo(TadmMaxentClassifier.train)
[docs]def encoding_demo(): import sys from nltk.classify.maxent import TadmEventMaxentFeatureEncoding tokens = [({'f0':1, 'f1':1, 'f3':1}, 'A'), ({'f0':1, 'f2':1, 'f4':1}, 'B'), ({'f0':2, 'f2':1, 'f3':1, 'f4':1}, 'A')] encoding = TadmEventMaxentFeatureEncoding.train(tokens) write_tadm_file(tokens, encoding, sys.stdout) print() for i in range(encoding.length()): print('%s --> %d' % (encoding.describe(i), i)) print()
if __name__ == '__main__': encoding_demo() names_demo()