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