updates

Backend/venv/lib/python3.12/site-packages/nltk/classify/tadm.py

@@ -0,0 +1,122 @@
+# Natural Language Toolkit: Interface to TADM Classifier
+#
+# Copyright (C) 2001-2025 NLTK Project
+# Author: Joseph Frazee <jfrazee@mail.utexas.edu>
+# 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()