Source code for nltk.classify.tadm

# Natural Language Toolkit: Interface to TADM Classifier
# Copyright (C) 2001-2022 NLTK Project
# Author: Joseph Frazee <>
# URL: <>
# For license information, see LICENSE.TXT

import subprocess
import sys

from nltk.internals import find_binary

    import numpy
except ImportError:

_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="" )
[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: # # # 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, str): 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.maxent import TadmMaxentClassifier from nltk.classify.util import names_demo 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()