179 lines
6.2 KiB
Python
179 lines
6.2 KiB
Python
# Natural Language Toolkit: Interface to Megam Classifier
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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A set of functions used to interface with the external megam_ maxent
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optimization package. Before megam can be used, you should tell NLTK where it
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can find the megam binary, using the ``config_megam()`` function. Typical
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usage:
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>>> from nltk.classify import megam
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>>> megam.config_megam() # pass path to megam if not found in PATH # doctest: +SKIP
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[Found megam: ...]
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Use with MaxentClassifier. Example below, see MaxentClassifier documentation
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for details.
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nltk.classify.MaxentClassifier.train(corpus, 'megam')
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.. _megam: http://www.umiacs.umd.edu/~hal/megam/index.html
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"""
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from __future__ import print_function
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import subprocess
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from six import string_types
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from nltk import compat
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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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numpy = None
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######################################################################
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#{ Configuration
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######################################################################
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_megam_bin = None
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def config_megam(bin=None):
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"""
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Configure NLTK's interface to the ``megam`` maxent optimization
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package.
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:param bin: The full path to the ``megam`` binary. If not specified,
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then nltk will search the system for a ``megam`` binary; and if
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one is not found, it will raise a ``LookupError`` exception.
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:type bin: str
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"""
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global _megam_bin
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_megam_bin = find_binary(
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'megam', bin,
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env_vars=['MEGAM'],
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binary_names=['megam.opt', 'megam', 'megam_686', 'megam_i686.opt'],
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url='http://www.umiacs.umd.edu/~hal/megam/index.html')
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######################################################################
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#{ Megam Interface Functions
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######################################################################
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def write_megam_file(train_toks, encoding, stream,
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bernoulli=True, explicit=True):
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"""
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Generate an input file for ``megam`` 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: MaxentFeatureEncodingI
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:param encoding: A feature encoding, used to convert featuresets
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into feature vectors. May optionally implement a cost() method
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in order to assign different costs to different class predictions.
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:type stream: stream
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:param stream: The stream to which the megam input file should be
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written.
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:param bernoulli: If true, then use the 'bernoulli' format. I.e.,
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all joint features have binary values, and are listed iff they
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are true. Otherwise, list feature values explicitly. If
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``bernoulli=False``, then you must call ``megam`` with the
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``-fvals`` option.
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:param explicit: If true, then use the 'explicit' format. I.e.,
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list the features that would fire for any of the possible
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labels, for each token. If ``explicit=True``, then you must
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call ``megam`` with the ``-explicit`` option.
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"""
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# Look up the set of labels.
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labels = encoding.labels()
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labelnum = dict((label, i) for (i, label) in enumerate(labels))
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# Write the file, which contains one line per instance.
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for featureset, label in train_toks:
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# First, the instance number (or, in the weighted multiclass case, the cost of each label).
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if hasattr(encoding, 'cost'):
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stream.write(':'.join(str(encoding.cost(featureset, label, l))
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for l in labels))
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else:
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stream.write('%d' % labelnum[label])
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# For implicit file formats, just list the features that fire
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# for this instance's actual label.
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if not explicit:
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_write_megam_features(encoding.encode(featureset, label),
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stream, bernoulli)
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# For explicit formats, list the features that would fire for
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# any of the possible labels.
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else:
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for l in labels:
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stream.write(' #')
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_write_megam_features(encoding.encode(featureset, l),
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stream, bernoulli)
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# End of the instance.
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stream.write('\n')
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def parse_megam_weights(s, features_count, explicit=True):
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"""
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Given the stdout output generated by ``megam`` when training a
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model, return a ``numpy`` array containing the corresponding weight
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vector. This function does not currently handle bias features.
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"""
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if numpy is None:
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raise ValueError('This function requires that numpy be installed')
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assert explicit, 'non-explicit not supported yet'
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lines = s.strip().split('\n')
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weights = numpy.zeros(features_count, 'd')
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for line in lines:
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if line.strip():
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fid, weight = line.split()
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weights[int(fid)] = float(weight)
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return weights
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def _write_megam_features(vector, stream, bernoulli):
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if not vector:
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raise ValueError('MEGAM classifier requires the use of an '
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'always-on feature.')
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for (fid, fval) in vector:
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if bernoulli:
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if fval == 1:
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stream.write(' %s' % fid)
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elif fval != 0:
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raise ValueError('If bernoulli=True, then all'
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'features must be binary.')
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else:
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stream.write(' %s %s' % (fid, fval))
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def call_megam(args):
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"""
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Call the ``megam`` binary with the given arguments.
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"""
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if isinstance(args, string_types):
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raise TypeError('args should be a list of strings')
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if _megam_bin is None:
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config_megam()
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# Call megam via a subprocess
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cmd = [_megam_bin] + args
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p = subprocess.Popen(cmd, stdout=subprocess.PIPE)
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(stdout, stderr) = p.communicate()
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# Check the return code.
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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('megam command failed!')
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if isinstance(stdout, string_types):
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return stdout
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else:
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return stdout.decode('utf-8')
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