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# Natural Language Toolkit: Interface to TADM Classifier
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Joseph Frazee <[email protected]>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
import subprocess
import sys
from nltk.internals import find_binary
try:
import numpy
except ImportError:
pass
_tadm_bin = None
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"
)
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:
#
# https://sf.net/forum/forum.php?thread_id=1391502&forum_id=473054
# https://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)
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")
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!")
def names_demo():
from nltk.classify.maxent import TadmMaxentClassifier
from nltk.classify.util import names_demo
classifier = names_demo(TadmMaxentClassifier.train)
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()
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