# Natural Language Toolkit: Maximum Entropy Classifiers
#
# Copyright (C) 2001-2013 NLTK Project
# Author: Edward Loper <edloper@gradient.cis.upenn.edu>
#         Dmitry Chichkov <dchichkov@gmail.com> (TypedMaxentFeatureEncoding)
# URL: <http://www.nltk.org/>
# For license information, see LICENSE.TXT

"""
A classifier model based on maximum entropy modeling framework.  This
framework considers all of the probability distributions that are
empirically consistent with the training data; and chooses the
distribution with the highest entropy.  A probability distribution is
"empirically consistent" with a set of training data if its estimated
frequency with which a class and a feature vector value co-occur is
equal to the actual frequency in the data.

Terminology: 'feature'
======================
The term *feature* is usually used to refer to some property of an
unlabeled token.  For example, when performing word sense
disambiguation, we might define a ``'prevword'`` feature whose value is
the word preceding the target word.  However, in the context of
maxent modeling, the term *feature* is typically used to refer to a
property of a "labeled" token.  In order to prevent confusion, we
will introduce two distinct terms to disambiguate these two different
concepts:

  - An "input-feature" is a property of an unlabeled token.
  - A "joint-feature" is a property of a labeled token.

In the rest of the ``nltk.classify`` module, the term "features" is
used to refer to what we will call "input-features" in this module.

In literature that describes and discusses maximum entropy models,
input-features are typically called "contexts", and joint-features
are simply referred to as "features".

Converting Input-Features to Joint-Features
-------------------------------------------
In maximum entropy models, joint-features are required to have numeric
values.  Typically, each input-feature ``input_feat`` is mapped to a
set of joint-features of the form:

|   joint_feat(token, label) = { 1 if input_feat(token) == feat_val
|                              {      and label == some_label
|                              {
|                              { 0 otherwise

For all values of ``feat_val`` and ``some_label``.  This mapping is
performed by classes that implement the ``MaxentFeatureEncodingI``
interface.
"""
from __future__ import print_function, unicode_literals
__docformat__ = 'epytext en'

try:
    import numpy
except ImportError:
    pass

import time
import tempfile
import os
import gzip
from collections import defaultdict

from nltk import compat
from nltk.util import OrderedDict
from nltk.probability import DictionaryProbDist

from nltk.classify.api import ClassifierI
from nltk.classify.util import CutoffChecker, accuracy, log_likelihood
from nltk.classify.megam import call_megam, write_megam_file, parse_megam_weights
from nltk.classify.tadm import call_tadm, write_tadm_file, parse_tadm_weights

######################################################################
#{ Classifier Model
######################################################################

@compat.python_2_unicode_compatible
class MaxentClassifier(ClassifierI):
    """
    A maximum entropy classifier (also known as a "conditional
    exponential classifier").  This classifier is parameterized by a
    set of "weights", which are used to combine the joint-features
    that are generated from a featureset by an "encoding".  In
    particular, the encoding maps each ``(featureset, label)`` pair to
    a vector.  The probability of each label is then computed using
    the following equation::

                                dotprod(weights, encode(fs,label))
      prob(fs|label) = ---------------------------------------------------
                       sum(dotprod(weights, encode(fs,l)) for l in labels)

    Where ``dotprod`` is the dot product::

      dotprod(a,b) = sum(x*y for (x,y) in zip(a,b))
    """
    def __init__(self, encoding, weights, logarithmic=True):
        """
        Construct a new maxent classifier model.  Typically, new
        classifier models are created using the ``train()`` method.

        :type encoding: MaxentFeatureEncodingI
        :param encoding: An encoding that is used to convert the
            featuresets that are given to the ``classify`` method into
            joint-feature vectors, which are used by the maxent
            classifier model.

        :type weights: list of float
        :param weights:  The feature weight vector for this classifier.

        :type logarithmic: bool
        :param logarithmic: If false, then use non-logarithmic weights.
        """
        self._encoding = encoding
        self._weights = weights
        self._logarithmic = logarithmic
        #self._logarithmic = False
        assert encoding.length() == len(weights)

    def labels(self):
        return self._encoding.labels()

    def set_weights(self, new_weights):
        """
        Set the feature weight vector for this classifier.
        :param new_weights: The new feature weight vector.
        :type new_weights: list of float
        """
        self._weights = new_weights
        assert (self._encoding.length() == len(new_weights))

    def weights(self):
        """
        :return: The feature weight vector for this classifier.
        :rtype: list of float
        """
        return self._weights

    def classify(self, featureset):
        return self.prob_classify(featureset).max()

    def prob_classify(self, featureset):
        prob_dict = {}
        for label in self._encoding.labels():
            feature_vector = self._encoding.encode(featureset, label)

            if self._logarithmic:
                total = 0.0
                for (f_id, f_val) in feature_vector:
                    total += self._weights[f_id] * f_val
                prob_dict[label] = total

            else:
                prod = 1.0
                for (f_id, f_val) in feature_vector:
                    prod *= self._weights[f_id] ** f_val
                prob_dict[label] = prod

        # Normalize the dictionary to give a probability distribution
        return DictionaryProbDist(prob_dict, log=self._logarithmic,
                                  normalize=True)

    def explain(self, featureset, columns=4):
        """
        Print a table showing the effect of each of the features in
        the given feature set, and how they combine to determine the
        probabilities of each label for that featureset.
        """
        descr_width = 50
        TEMPLATE = '  %-'+str(descr_width-2)+'s%s%8.3f'

        pdist = self.prob_classify(featureset)
        labels = sorted(pdist.samples(), key=pdist.prob, reverse=True)
        labels = labels[:columns]
        print('  Feature'.ljust(descr_width)+''.join(
            '%8s' % (("%s" % l)[:7]) for l in labels))
        print('  '+'-'*(descr_width-2+8*len(labels)))
        sums = defaultdict(int)
        for i, label in enumerate(labels):
            feature_vector = self._encoding.encode(featureset, label)
            feature_vector.sort(key=lambda fid__: abs(self._weights[fid__[0]]),
                                reverse=True)
            for (f_id, f_val) in feature_vector:
                if self._logarithmic: score = self._weights[f_id] * f_val
                else: score = self._weights[f_id] ** f_val
                descr = self._encoding.describe(f_id)
                descr = descr.split(' and label is ')[0] # hack
                descr += ' (%s)' % f_val                 # hack
                if len(descr) > 47: descr = descr[:44]+'...'
                print(TEMPLATE % (descr, i*8*' ', score))
                sums[label] += score
        print('  '+'-'*(descr_width-1+8*len(labels)))
        print('  TOTAL:'.ljust(descr_width)+''.join(
            '%8.3f' % sums[l] for l in labels))
        print('  PROBS:'.ljust(descr_width)+''.join(
            '%8.3f' % pdist.prob(l) for l in labels))

    def show_most_informative_features(self, n=10, show='all'):
        """
        :param show: all, neg, or pos (for negative-only or positive-only)
        """
        fids = sorted(list(range(len(self._weights))),
                      key=lambda fid: abs(self._weights[fid]),
                      reverse=True)
        if show == 'pos':
            fids = [fid for fid in fids if self._weights[fid]>0]
        elif show == 'neg':
            fids = [fid for fid in fids if self._weights[fid]<0]
        for fid in fids[:n]:
            print('%8.3f %s' % (self._weights[fid],
                                self._encoding.describe(fid)))

    def __repr__(self):
        return ('<ConditionalExponentialClassifier: %d labels, %d features>' %
                (len(self._encoding.labels()), self._encoding.length()))

    #: A list of the algorithm names that are accepted for the
    #: ``train()`` method's ``algorithm`` parameter.
    ALGORITHMS = ['GIS', 'IIS', 'MEGAM', 'TADM']

    @classmethod
    def train(cls, train_toks, algorithm=None, trace=3, encoding=None,
              labels=None, sparse=None, gaussian_prior_sigma=0, **cutoffs):
        """
        Train a new maxent classifier based on the given corpus of
        training samples.  This classifier will have its weights
        chosen to maximize entropy while remaining empirically
        consistent with the training corpus.

        :rtype: MaxentClassifier
        :return: The new maxent classifier

        :type train_toks: list
        :param train_toks: Training data, represented as a list of
            pairs, the first member of which is a featureset,
            and the second of which is a classification label.

        :type algorithm: str
        :param algorithm: A case-insensitive string, specifying which
            algorithm should be used to train the classifier.  The
            following algorithms are currently available.

            - Iterative Scaling Methods: Generalized Iterative Scaling (``'GIS'``),
              Improved Iterative Scaling (``'IIS'``)
            - External Libraries (requiring megam):
              LM-BFGS algorithm, with training performed by Megam (``'megam'``)

            The default algorithm is ``'IIS'``.

        :type trace: int
        :param trace: The level of diagnostic tracing output to produce.
            Higher values produce more verbose output.
        :type encoding: MaxentFeatureEncodingI
        :param encoding: A feature encoding, used to convert featuresets
            into feature vectors.  If none is specified, then a
            ``BinaryMaxentFeatureEncoding`` will be built based on the
            features that are attested in the training corpus.
        :type labels: list(str)
        :param labels: The set of possible labels.  If none is given, then
            the set of all labels attested in the training data will be
            used instead.
        :param gaussian_prior_sigma: The sigma value for a gaussian
            prior on model weights.  Currently, this is supported by
            ``megam``. For other algorithms, its value is ignored.
        :param cutoffs: Arguments specifying various conditions under
            which the training should be halted.  (Some of the cutoff
            conditions are not supported by some algorithms.)

            - ``max_iter=v``: Terminate after ``v`` iterations.
            - ``min_ll=v``: Terminate after the negative average
              log-likelihood drops under ``v``.
            - ``min_lldelta=v``: Terminate if a single iteration improves
              log likelihood by less than ``v``.
        """
        if algorithm is None:
            algorithm = 'iis'
        for key in cutoffs:
            if key not in ('max_iter', 'min_ll', 'min_lldelta',
                           'max_acc', 'min_accdelta', 'count_cutoff',
                           'norm', 'explicit', 'bernoulli'):
                raise TypeError('Unexpected keyword arg %r' % key)
        algorithm = algorithm.lower()
        if algorithm == 'iis':
            return train_maxent_classifier_with_iis(
                train_toks, trace, encoding, labels, **cutoffs)
        elif algorithm == 'gis':
            return train_maxent_classifier_with_gis(
                train_toks, trace, encoding, labels, **cutoffs)
        elif algorithm == 'megam':
            return train_maxent_classifier_with_megam(
                train_toks, trace, encoding, labels,
                gaussian_prior_sigma, **cutoffs)
        elif algorithm == 'tadm':
            kwargs = cutoffs
            kwargs['trace'] = trace
            kwargs['encoding'] = encoding
            kwargs['labels'] = labels
            kwargs['gaussian_prior_sigma'] = gaussian_prior_sigma
            return TadmMaxentClassifier.train(train_toks, **kwargs)
        else:
            raise ValueError('Unknown algorithm %s' % algorithm)


#: Alias for MaxentClassifier.
ConditionalExponentialClassifier = MaxentClassifier


######################################################################
#{ Feature Encodings
######################################################################

class MaxentFeatureEncodingI(object):
    """
    A mapping that converts a set of input-feature values to a vector
    of joint-feature values, given a label.  This conversion is
    necessary to translate featuresets into a format that can be used
    by maximum entropy models.

    The set of joint-features used by a given encoding is fixed, and
    each index in the generated joint-feature vectors corresponds to a
    single joint-feature.  The length of the generated joint-feature
    vectors is therefore constant (for a given encoding).

    Because the joint-feature vectors generated by
    ``MaxentFeatureEncodingI`` are typically very sparse, they are
    represented as a list of ``(index, value)`` tuples, specifying the
    value of each non-zero joint-feature.

    Feature encodings are generally created using the ``train()``
    method, which generates an appropriate encoding based on the
    input-feature values and labels that are present in a given
    corpus.
    """
    def encode(self, featureset, label):
        """
        Given a (featureset, label) pair, return the corresponding
        vector of joint-feature values.  This vector is represented as
        a list of ``(index, value)`` tuples, specifying the value of
        each non-zero joint-feature.

        :type featureset: dict
        :rtype: list(tuple(int, int))
        """
        raise NotImplementedError()

    def length(self):
        """
        :return: The size of the fixed-length joint-feature vectors
            that are generated by this encoding.
        :rtype: int
        """
        raise NotImplementedError()

    def labels(self):
        """
        :return: A list of the \"known labels\" -- i.e., all labels
            ``l`` such that ``self.encode(fs,l)`` can be a nonzero
            joint-feature vector for some value of ``fs``.
        :rtype: list
        """
        raise NotImplementedError()

    def describe(self, fid):
        """
        :return: A string describing the value of the joint-feature
            whose index in the generated feature vectors is ``fid``.
        :rtype: str
        """
        raise NotImplementedError()

    def train(cls, train_toks):
        """
        Construct and return new feature encoding, based on a given
        training corpus ``train_toks``.

        :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.
        """
        raise NotImplementedError()

class FunctionBackedMaxentFeatureEncoding(MaxentFeatureEncodingI):
    """
    A feature encoding that calls a user-supplied function to map a
    given featureset/label pair to a sparse joint-feature vector.
    """
    def __init__(self, func, length, labels):
        """
        Construct a new feature encoding based on the given function.

        :type func: (callable)
        :param func: A function that takes two arguments, a featureset
             and a label, and returns the sparse joint feature vector
             that encodes them::

                 func(featureset, label) -> feature_vector

             This sparse joint feature vector (``feature_vector``) is a
             list of ``(index,value)`` tuples.

        :type length: int
        :param length: The size of the fixed-length joint-feature
            vectors that are generated by this encoding.

        :type labels: list
        :param labels: A list of the \"known labels\" for this
            encoding -- i.e., all labels ``l`` such that
            ``self.encode(fs,l)`` can be a nonzero joint-feature vector
            for some value of ``fs``.
        """
        self._length = length
        self._func = func
        self._labels = labels

    def encode(self, featureset, label):
        return self._func(featureset, label)

    def length(self):
        return self._length

    def labels(self):
        return self._labels

    def describe(self, fid):
        return 'no description available'

class BinaryMaxentFeatureEncoding(MaxentFeatureEncodingI):
    """
    A feature encoding that generates vectors containing a binary
    joint-features of the form:

    |  joint_feat(fs, l) = { 1 if (fs[fname] == fval) and (l == label)
    |                      {
    |                      { 0 otherwise

    Where ``fname`` is the name of an input-feature, ``fval`` is a value
    for that input-feature, and ``label`` is a label.

    Typically, these features are constructed based on a training
    corpus, using the ``train()`` method.  This method will create one
    feature for each combination of ``fname``, ``fval``, and ``label``
    that occurs at least once in the training corpus.

    The ``unseen_features`` parameter can be used to add "unseen-value
    features", which are used whenever an input feature has a value
    that was not encountered in the training corpus.  These features
    have the form:

    |  joint_feat(fs, l) = { 1 if is_unseen(fname, fs[fname])
    |                      {      and l == label
    |                      {
    |                      { 0 otherwise

    Where ``is_unseen(fname, fval)`` is true if the encoding does not
    contain any joint features that are true when ``fs[fname]==fval``.

    The ``alwayson_features`` parameter can be used to add "always-on
    features", which have the form::

    |  joint_feat(fs, l) = { 1 if (l == label)
    |                      {
    |                      { 0 otherwise

    These always-on features allow the maxent model to directly model
    the prior probabilities of each label.
    """
    def __init__(self, labels, mapping, unseen_features=False,
                 alwayson_features=False):
        """
        :param labels: A list of the \"known labels\" for this encoding.

        :param mapping: A dictionary mapping from ``(fname,fval,label)``
            tuples to corresponding joint-feature indexes.  These
            indexes must be the set of integers from 0...len(mapping).
            If ``mapping[fname,fval,label]=id``, then
            ``self.encode(..., fname:fval, ..., label)[id]`` is 1;
            otherwise, it is 0.

        :param unseen_features: If true, then include unseen value
           features in the generated joint-feature vectors.

        :param alwayson_features: If true, then include always-on
           features in the generated joint-feature vectors.
        """
        if set(mapping.values()) != set(range(len(mapping))):
            raise ValueError('Mapping values must be exactly the '
                             'set of integers from 0...len(mapping)')

        self._labels = list(labels)
        """A list of attested labels."""

        self._mapping = mapping
        """dict mapping from (fname,fval,label) -> fid"""

        self._length = len(mapping)
        """The length of generated joint feature vectors."""

        self._alwayson = None
        """dict mapping from label -> fid"""

        self._unseen = None
        """dict mapping from fname -> fid"""

        if alwayson_features:
            self._alwayson = dict((label,i+self._length)
                                   for (i,label) in enumerate(labels))
            self._length += len(self._alwayson)

        if unseen_features:
            fnames = set(fname for (fname, fval, label) in mapping)
            self._unseen = dict((fname, i+self._length)
                                 for (i, fname) in enumerate(fnames))
            self._length += len(fnames)

    def encode(self, featureset, label):
        # Inherit docs.
        encoding = []

        # Convert input-features to joint-features:
        for fname, fval in featureset.items():
            # Known feature name & value:
            if (fname, fval, label) in self._mapping:
                encoding.append((self._mapping[fname, fval, label], 1))

            # Otherwise, we might want to fire an "unseen-value feature".
            elif self._unseen:
                # Have we seen this fname/fval combination with any label?
                for label2 in self._labels:
                    if (fname, fval, label2) in self._mapping:
                        break # we've seen this fname/fval combo
                # We haven't -- fire the unseen-value feature
                else:
                    if fname in self._unseen:
                        encoding.append((self._unseen[fname], 1))

        # Add always-on features:
        if self._alwayson and label in self._alwayson:
            encoding.append((self._alwayson[label], 1))

        return encoding

    def describe(self, f_id):
        # Inherit docs.
        if not isinstance(f_id, compat.integer_types):
            raise TypeError('describe() expected an int')
        try:
            self._inv_mapping
        except AttributeError:
            self._inv_mapping = [-1]*len(self._mapping)
            for (info, i) in self._mapping.items():
                self._inv_mapping[i] = info

        if f_id < len(self._mapping):
            (fname, fval, label) = self._inv_mapping[f_id]
            return '%s==%r and label is %r' % (fname, fval, label)
        elif self._alwayson and f_id in self._alwayson.values():
            for (label, f_id2) in self._alwayson.items():
                if f_id==f_id2: return 'label is %r' % label
        elif self._unseen and f_id in self._unseen.values():
            for (fname, f_id2) in self._unseen.items():
                if f_id==f_id2: return '%s is unseen' % fname
        else:
            raise ValueError('Bad feature id')

    def labels(self):
        # Inherit docs.
        return self._labels

    def length(self):
        # Inherit docs.
        return self._length

    @classmethod
    def train(cls, train_toks, count_cutoff=0, labels=None, **options):
        """
        Construct and return new feature encoding, based on a given
        training corpus ``train_toks``.  See the class description
        ``BinaryMaxentFeatureEncoding`` for a description of the
        joint-features that will be included in this encoding.

        :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 count_cutoff: int
        :param count_cutoff: A cutoff value that is used to discard
            rare joint-features.  If a joint-feature's value is 1
            fewer than ``count_cutoff`` times in the training corpus,
            then that joint-feature is not included in the generated
            encoding.

        :type labels: list
        :param labels: A list of labels that should be used by the
            classifier.  If not specified, then the set of labels
            attested in ``train_toks`` will be used.

        :param options: Extra parameters for the constructor, such as
            ``unseen_features`` and ``alwayson_features``.
        """
        mapping = {}              # maps (fname, fval, label) -> fid
        seen_labels = set()       # The set of labels we've encountered
        count = defaultdict(int)  # maps (fname, fval) -> count

        for (tok, label) in train_toks:
            if labels and label not in labels:
                raise ValueError('Unexpected label %s' % label)
            seen_labels.add(label)

            # Record each of the features.
            for (fname, fval) in tok.items():

                # If a count cutoff is given, then only add a joint
                # feature once the corresponding (fname, fval, label)
                # tuple exceeds that cutoff.
                count[fname,fval] += 1
                if count[fname,fval] >= count_cutoff:
                    if (fname, fval, label) not in mapping:
                        mapping[fname, fval, label] = len(mapping)

        if labels is None: labels = seen_labels
        return cls(labels, mapping, **options)

class GISEncoding(BinaryMaxentFeatureEncoding):
    """
    A binary feature encoding which adds one new joint-feature to the
    joint-features defined by ``BinaryMaxentFeatureEncoding``: a
    correction feature, whose value is chosen to ensure that the
    sparse vector always sums to a constant non-negative number.  This
    new feature is used to ensure two preconditions for the GIS
    training algorithm:

      - At least one feature vector index must be nonzero for every
        token.
      - The feature vector must sum to a constant non-negative number
        for every token.
    """
    def __init__(self, labels, mapping, unseen_features=False,
                 alwayson_features=False, C=None):
        """
        :param C: The correction constant.  The value of the correction
            feature is based on this value.  In particular, its value is
            ``C - sum([v for (f,v) in encoding])``.
        :seealso: ``BinaryMaxentFeatureEncoding.__init__``
        """
        BinaryMaxentFeatureEncoding.__init__(
            self, labels, mapping, unseen_features, alwayson_features)
        if C is None:
            C = len(set(fname for (fname,fval,label) in mapping))+1
        self._C = C

    @property
    def C(self):
        """The non-negative constant that all encoded feature vectors
        will sum to."""
        return self._C

    def encode(self, featureset, label):
        # Get the basic encoding.
        encoding = BinaryMaxentFeatureEncoding.encode(self, featureset, label)
        base_length = BinaryMaxentFeatureEncoding.length(self)

        # Add a correction feature.
        total = sum(v for (f,v) in encoding)
        if total >= self._C:
            raise ValueError('Correction feature is not high enough!')
        encoding.append( (base_length, self._C-total) )

        # Return the result
        return encoding

    def length(self):
        return BinaryMaxentFeatureEncoding.length(self) + 1

    def describe(self, f_id):
        if f_id == BinaryMaxentFeatureEncoding.length(self):
            return 'Correction feature (%s)' % self._C
        else:
            return BinaryMaxentFeatureEncoding.describe(self, f_id)


class TadmEventMaxentFeatureEncoding(BinaryMaxentFeatureEncoding):
    def __init__(self, labels, mapping, unseen_features=False,
                       alwayson_features=False):
        self._mapping = OrderedDict(mapping)
        self._label_mapping = OrderedDict()
        BinaryMaxentFeatureEncoding.__init__(self, labels, self._mapping,
                                                   unseen_features,
                                                   alwayson_features)

    def encode(self, featureset, label):
        encoding = []
        for feature, value in featureset.items():
            if (feature, label) not in self._mapping:
                self._mapping[(feature, label)] = len(self._mapping)
            if value not in self._label_mapping:
                if not isinstance(value, int):
                    self._label_mapping[value] = len(self._label_mapping)
                else:
                    self._label_mapping[value] = value
            encoding.append((self._mapping[(feature, label)],
                             self._label_mapping[value]))
        return encoding

    def labels(self):
        return self._labels

    def describe(self, fid):
        for (feature, label) in self._mapping:
            if self._mapping[(feature, label)] == fid:
                return (feature, label)

    def length(self):
        return len(self._mapping)

    @classmethod
    def train(cls, train_toks, count_cutoff=0, labels=None, **options):
        mapping = OrderedDict()
        if not labels:
            labels = []

        # This gets read twice, so compute the values in case it's lazy.
        train_toks = list(train_toks)

        for (featureset, label) in train_toks:
            if label not in labels:
                labels.append(label)

        for (featureset, label) in train_toks:
            for label in labels:
                for feature in featureset:
                    if (feature, label) not in mapping:
                        mapping[(feature, label)] = len(mapping)

        return cls(labels, mapping, **options)


class TypedMaxentFeatureEncoding(MaxentFeatureEncodingI):
    """
    A feature encoding that generates vectors containing integer,
    float and binary joint-features of the form:

    Binary (for string and boolean features):

    |  joint_feat(fs, l) = { 1 if (fs[fname] == fval) and (l == label)
    |                      {
    |                      { 0 otherwise

    Value (for integer and float features):

    |  joint_feat(fs, l) = { fval if     (fs[fname] == type(fval))
    |                      {         and (l == label)
    |                      {
    |                      { not encoded otherwise

    Where ``fname`` is the name of an input-feature, ``fval`` is a value
    for that input-feature, and ``label`` is a label.

    Typically, these features are constructed based on a training
    corpus, using the ``train()`` method.

    For string and boolean features [type(fval) not in (int, float)]
    this method will create one feature for each combination of
    ``fname``, ``fval``, and ``label`` that occurs at least once in the
    training corpus.

    For integer and float features [type(fval) in (int, float)] this
    method will create one feature for each combination of ``fname``
    and ``label`` that occurs at least once in the training corpus.

    For binary features the ``unseen_features`` parameter can be used
    to add "unseen-value features", which are used whenever an input
    feature has a value that was not encountered in the training
    corpus.  These features have the form:

    |  joint_feat(fs, l) = { 1 if is_unseen(fname, fs[fname])
    |                      {      and l == label
    |                      {
    |                      { 0 otherwise

    Where ``is_unseen(fname, fval)`` is true if the encoding does not
    contain any joint features that are true when ``fs[fname]==fval``.

    The ``alwayson_features`` parameter can be used to add "always-on
    features", which have the form:

    |  joint_feat(fs, l) = { 1 if (l == label)
    |                      {
    |                      { 0 otherwise

    These always-on features allow the maxent model to directly model
    the prior probabilities of each label.
    """
    def __init__(self, labels, mapping, unseen_features=False,
                 alwayson_features=False):
        """
        :param labels: A list of the \"known labels\" for this encoding.

        :param mapping: A dictionary mapping from ``(fname,fval,label)``
            tuples to corresponding joint-feature indexes.  These
            indexes must be the set of integers from 0...len(mapping).
            If ``mapping[fname,fval,label]=id``, then
            ``self.encode({..., fname:fval, ...``, label)[id]} is 1;
            otherwise, it is 0.

        :param unseen_features: If true, then include unseen value
           features in the generated joint-feature vectors.

        :param alwayson_features: If true, then include always-on
           features in the generated joint-feature vectors.
        """
        if set(mapping.values()) != set(range(len(mapping))):
            raise ValueError('Mapping values must be exactly the '
                             'set of integers from 0...len(mapping)')

        self._labels = list(labels)
        """A list of attested labels."""

        self._mapping = mapping
        """dict mapping from (fname,fval,label) -> fid"""

        self._length = len(mapping)
        """The length of generated joint feature vectors."""

        self._alwayson = None
        """dict mapping from label -> fid"""

        self._unseen = None
        """dict mapping from fname -> fid"""

        if alwayson_features:
            self._alwayson = dict((label, i+self._length)
                                  for (i,label) in enumerate(labels))
            self._length += len(self._alwayson)

        if unseen_features:
            fnames = set(fname for (fname, fval, label) in mapping)
            self._unseen = dict((fname, i+self._length)
                                for (i, fname) in enumerate(fnames))
            self._length += len(fnames)

    def encode(self, featureset, label):
        # Inherit docs.
        encoding = []

        # Convert input-features to joint-features:
        for fname, fval in featureset.items():
            if isinstance(fval, (compat.integer_types, float)):
                # Known feature name & value:
                if (fname, type(fval), label) in self._mapping:
                    encoding.append((self._mapping[fname, type(fval), label], fval))
            else:
                # Known feature name & value:
                if (fname, fval, label) in self._mapping:
                    encoding.append((self._mapping[fname, fval, label], 1))

                # Otherwise, we might want to fire an "unseen-value feature".
                elif self._unseen:
                    # Have we seen this fname/fval combination with any label?
                    for label2 in self._labels:
                        if (fname, fval, label2) in self._mapping:
                            break # we've seen this fname/fval combo
                    # We haven't -- fire the unseen-value feature
                    else:
                        if fname in self._unseen:
                            encoding.append((self._unseen[fname], 1))


        # Add always-on features:
        if self._alwayson and label in self._alwayson:
            encoding.append((self._alwayson[label], 1))

        return encoding

    def describe(self, f_id):
        # Inherit docs.
        if not isinstance(f_id, compat.integer_types):
            raise TypeError('describe() expected an int')
        try:
            self._inv_mapping
        except AttributeError:
            self._inv_mapping = [-1]*len(self._mapping)
            for (info, i) in self._mapping.items():
                self._inv_mapping[i] = info

        if f_id < len(self._mapping):
            (fname, fval, label) = self._inv_mapping[f_id]
            return '%s==%r and label is %r' % (fname, fval, label)
        elif self._alwayson and f_id in self._alwayson.values():
            for (label, f_id2) in self._alwayson.items():
                if f_id==f_id2: return 'label is %r' % label
        elif self._unseen and f_id in self._unseen.values():
            for (fname, f_id2) in self._unseen.items():
                if f_id==f_id2: return '%s is unseen' % fname
        else:
            raise ValueError('Bad feature id')

    def labels(self):
        # Inherit docs.
        return self._labels

    def length(self):
        # Inherit docs.
        return self._length

    @classmethod
    def train(cls, train_toks, count_cutoff=0, labels=None, **options):
        """
        Construct and return new feature encoding, based on a given
        training corpus ``train_toks``.  See the class description
        ``TypedMaxentFeatureEncoding`` for a description of the
        joint-features that will be included in this encoding.

        Note: recognized feature values types are (int, float), over
        types are interpreted as regular binary features.

        :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 count_cutoff: int
        :param count_cutoff: A cutoff value that is used to discard
            rare joint-features.  If a joint-feature's value is 1
            fewer than ``count_cutoff`` times in the training corpus,
            then that joint-feature is not included in the generated
            encoding.

        :type labels: list
        :param labels: A list of labels that should be used by the
            classifier.  If not specified, then the set of labels
            attested in ``train_toks`` will be used.

        :param options: Extra parameters for the constructor, such as
            ``unseen_features`` and ``alwayson_features``.
        """
        mapping = {}              # maps (fname, fval, label) -> fid
        seen_labels = set()       # The set of labels we've encountered
        count = defaultdict(int)  # maps (fname, fval) -> count

        for (tok, label) in train_toks:
            if labels and label not in labels:
                raise ValueError('Unexpected label %s' % label)
            seen_labels.add(label)

            # Record each of the features.
            for (fname, fval) in tok.items():
                if(type(fval) in (int, float)): fval = type(fval)
                # If a count cutoff is given, then only add a joint
                # feature once the corresponding (fname, fval, label)
                # tuple exceeds that cutoff.
                count[fname,fval] += 1
                if count[fname,fval] >= count_cutoff:
                    if (fname, fval, label) not in mapping:
                        mapping[fname, fval, label] = len(mapping)

        if labels is None: labels = seen_labels
        return cls(labels, mapping, **options)




######################################################################
#{ Classifier Trainer: Generalized Iterative Scaling
######################################################################

def train_maxent_classifier_with_gis(train_toks, trace=3, encoding=None,
                                     labels=None, **cutoffs):
    """
    Train a new ``ConditionalExponentialClassifier``, using the given
    training samples, using the Generalized Iterative Scaling
    algorithm.  This ``ConditionalExponentialClassifier`` will encode
    the model that maximizes entropy from all the models that are
    empirically consistent with ``train_toks``.

    :see: ``train_maxent_classifier()`` for parameter descriptions.
    """
    cutoffs.setdefault('max_iter', 100)
    cutoffchecker = CutoffChecker(cutoffs)

    # Construct an encoding from the training data.
    if encoding is None:
        encoding = GISEncoding.train(train_toks, labels=labels)

    if not hasattr(encoding, 'C'):
        raise TypeError('The GIS algorithm requires an encoding that '
                        'defines C (e.g., GISEncoding).')

    # Cinv is the inverse of the sum of each joint feature vector.
    # This controls the learning rate: higher Cinv (or lower C) gives
    # faster learning.
    Cinv = 1.0/encoding.C

    # Count how many times each feature occurs in the training data.
    empirical_fcount = calculate_empirical_fcount(train_toks, encoding)

    # Check for any features that are not attested in train_toks.
    unattested = set(numpy.nonzero(empirical_fcount==0)[0])

    # Build the classifier.  Start with weight=0 for each attested
    # feature, and weight=-infinity for each unattested feature.
    weights = numpy.zeros(len(empirical_fcount), 'd')
    for fid in unattested: weights[fid] = numpy.NINF
    classifier = ConditionalExponentialClassifier(encoding, weights)

    # Take the log of the empirical fcount.
    log_empirical_fcount = numpy.log2(empirical_fcount)
    del empirical_fcount

    # Old log-likelihood and accuracy; used to check if the change
    # in log-likelihood or accuracy is sufficient to indicate convergence.
    ll_old = None
    acc_old = None

    if trace > 0: print('  ==> Training (%d iterations)' % cutoffs['max_iter'])
    if trace > 2:
        print()
        print('      Iteration    Log Likelihood    Accuracy')
        print('      ---------------------------------------')

    # Train the classifier.
    try:
        while True:
            if trace > 2:
                ll = cutoffchecker.ll or log_likelihood(classifier, train_toks)
                acc = cutoffchecker.acc or accuracy(classifier, train_toks)
                iternum = cutoffchecker.iter
                print('     %9d    %14.5f    %9.3f' % (iternum, ll, acc))

            # Use the model to estimate the number of times each
            # feature should occur in the training data.
            estimated_fcount = calculate_estimated_fcount(
                classifier, train_toks, encoding)

            # Take the log of estimated fcount (avoid taking log(0).)
            for fid in unattested: estimated_fcount[fid] += 1
            log_estimated_fcount = numpy.log2(estimated_fcount)
            del estimated_fcount

            # Update the classifier weights
            weights = classifier.weights()
            weights += (log_empirical_fcount - log_estimated_fcount) * Cinv
            classifier.set_weights(weights)

            # Check the log-likelihood & accuracy cutoffs.
            if cutoffchecker.check(classifier, train_toks):
                break

    except KeyboardInterrupt:
        print('      Training stopped: keyboard interrupt')
    except:
        raise

    if trace > 2:
        ll = log_likelihood(classifier, train_toks)
        acc = accuracy(classifier, train_toks)
        print('         Final    %14.5f    %9.3f' % (ll, acc))

# Return the classifier.
    return classifier

def calculate_empirical_fcount(train_toks, encoding):
    fcount = numpy.zeros(encoding.length(), 'd')

    for tok, label in train_toks:
        for (index, val) in encoding.encode(tok, label):
            fcount[index] += val

    return fcount

def calculate_estimated_fcount(classifier, train_toks, encoding):
    fcount = numpy.zeros(encoding.length(), 'd')

    for tok, label in train_toks:
        pdist = classifier.prob_classify(tok)
        for label in pdist.samples():
            prob = pdist.prob(label)
            for (fid, fval) in encoding.encode(tok, label):
                fcount[fid] += prob*fval

    return fcount


######################################################################
#{ Classifier Trainer: Improved Iterative Scaling
######################################################################

def train_maxent_classifier_with_iis(train_toks, trace=3, encoding=None,
                                     labels=None, **cutoffs):
    """
    Train a new ``ConditionalExponentialClassifier``, using the given
    training samples, using the Improved Iterative Scaling algorithm.
    This ``ConditionalExponentialClassifier`` will encode the model
    that maximizes entropy from all the models that are empirically
    consistent with ``train_toks``.

    :see: ``train_maxent_classifier()`` for parameter descriptions.
    """
    cutoffs.setdefault('max_iter', 100)
    cutoffchecker = CutoffChecker(cutoffs)

    # Construct an encoding from the training data.
    if encoding is None:
        encoding = BinaryMaxentFeatureEncoding.train(train_toks, labels=labels)

    # Count how many times each feature occurs in the training data.
    empirical_ffreq = (calculate_empirical_fcount(train_toks, encoding) /
                       len(train_toks))

    # Find the nf map, and related variables nfarray and nfident.
    # nf is the sum of the features for a given labeled text.
    # nfmap compresses this sparse set of values to a dense list.
    # nfarray performs the reverse operation.  nfident is
    # nfarray multiplied by an identity matrix.
    nfmap = calculate_nfmap(train_toks, encoding)
    nfarray = numpy.array(sorted(nfmap, key=nfmap.__getitem__), 'd')
    nftranspose = numpy.reshape(nfarray, (len(nfarray), 1))

    # Check for any features that are not attested in train_toks.
    unattested = set(numpy.nonzero(empirical_ffreq==0)[0])

    # Build the classifier.  Start with weight=0 for each attested
    # feature, and weight=-infinity for each unattested feature.
    weights = numpy.zeros(len(empirical_ffreq), 'd')
    for fid in unattested: weights[fid] = numpy.NINF
    classifier = ConditionalExponentialClassifier(encoding, weights)

    if trace > 0: print('  ==> Training (%d iterations)' % cutoffs['max_iter'])
    if trace > 2:
        print()
        print('      Iteration    Log Likelihood    Accuracy')
        print('      ---------------------------------------')

    # Old log-likelihood and accuracy; used to check if the change
    # in log-likelihood or accuracy is sufficient to indicate convergence.
    ll_old = None
    acc_old = None

    # Train the classifier.
    try:
        while True:
            if trace > 2:
                ll = cutoffchecker.ll or log_likelihood(classifier, train_toks)
                acc = cutoffchecker.acc or accuracy(classifier, train_toks)
                iternum = cutoffchecker.iter
                print('     %9d    %14.5f    %9.3f' % (iternum, ll, acc))

            # Calculate the deltas for this iteration, using Newton's method.
            deltas = calculate_deltas(
                train_toks, classifier, unattested, empirical_ffreq,
                nfmap, nfarray, nftranspose, encoding)

            # Use the deltas to update our weights.
            weights = classifier.weights()
            weights += deltas
            classifier.set_weights(weights)

            # Check the log-likelihood & accuracy cutoffs.
            if cutoffchecker.check(classifier, train_toks):
                break

    except KeyboardInterrupt:
        print('      Training stopped: keyboard interrupt')
    except:
        raise


    if trace > 2:
        ll = log_likelihood(classifier, train_toks)
        acc = accuracy(classifier, train_toks)
        print('         Final    %14.5f    %9.3f' % (ll, acc))

    # Return the classifier.
    return classifier

def calculate_nfmap(train_toks, encoding):
    """
    Construct a map that can be used to compress ``nf`` (which is
    typically sparse).

    *nf(feature_vector)* is the sum of the feature values for
    *feature_vector*.

    This represents the number of features that are active for a
    given labeled text.  This method finds all values of *nf(t)*
    that are attested for at least one token in the given list of
    training tokens; and constructs a dictionary mapping these
    attested values to a continuous range *0...N*.  For example,
    if the only values of *nf()* that were attested were 3, 5, and
    7, then ``_nfmap`` might return the dictionary ``{3:0, 5:1, 7:2}``.

    :return: A map that can be used to compress ``nf`` to a dense
        vector.
    :rtype: dict(int -> int)
    """
    # Map from nf to indices.  This allows us to use smaller arrays.
    nfset = set()
    for tok, _ in train_toks:
        for label in encoding.labels():
            nfset.add(sum(val for (id,val) in encoding.encode(tok,label)))
    return dict((nf, i) for (i, nf) in enumerate(nfset))

def calculate_deltas(train_toks, classifier, unattested, ffreq_empirical,
                     nfmap, nfarray, nftranspose, encoding):
    """
    Calculate the update values for the classifier weights for
    this iteration of IIS.  These update weights are the value of
    ``delta`` that solves the equation::

      ffreq_empirical[i]
             =
      SUM[fs,l] (classifier.prob_classify(fs).prob(l) *
                 feature_vector(fs,l)[i] *
                 exp(delta[i] * nf(feature_vector(fs,l))))

    Where:
        - *(fs,l)* is a (featureset, label) tuple from ``train_toks``
        - *feature_vector(fs,l)* = ``encoding.encode(fs,l)``
        - *nf(vector)* = ``sum([val for (id,val) in vector])``

    This method uses Newton's method to solve this equation for
    *delta[i]*.  In particular, it starts with a guess of
    ``delta[i]`` = 1; and iteratively updates ``delta`` with:

    | delta[i] -= (ffreq_empirical[i] - sum1[i])/(-sum2[i])

    until convergence, where *sum1* and *sum2* are defined as:

    |    sum1[i](delta) = SUM[fs,l] f[i](fs,l,delta)
    |    sum2[i](delta) = SUM[fs,l] (f[i](fs,l,delta).nf(feature_vector(fs,l)))
    |    f[i](fs,l,delta) = (classifier.prob_classify(fs).prob(l) .
    |                        feature_vector(fs,l)[i] .
    |                        exp(delta[i] . nf(feature_vector(fs,l))))

    Note that *sum1* and *sum2* depend on ``delta``; so they need
    to be re-computed each iteration.

    The variables ``nfmap``, ``nfarray``, and ``nftranspose`` are
    used to generate a dense encoding for *nf(ltext)*.  This
    allows ``_deltas`` to calculate *sum1* and *sum2* using
    matrices, which yields a significant performance improvement.

    :param train_toks: The set of training tokens.
    :type train_toks: list(tuple(dict, str))
    :param classifier: The current classifier.
    :type classifier: ClassifierI
    :param ffreq_empirical: An array containing the empirical
        frequency for each feature.  The *i*\ th element of this
        array is the empirical frequency for feature *i*.
    :type ffreq_empirical: sequence of float
    :param unattested: An array that is 1 for features that are
        not attested in the training data; and 0 for features that
        are attested.  In other words, ``unattested[i]==0`` iff
        ``ffreq_empirical[i]==0``.
    :type unattested: sequence of int
    :param nfmap: A map that can be used to compress ``nf`` to a dense
        vector.
    :type nfmap: dict(int -> int)
    :param nfarray: An array that can be used to uncompress ``nf``
        from a dense vector.
    :type nfarray: array(float)
    :param nftranspose: The transpose of ``nfarray``
    :type nftranspose: array(float)
    """
    # These parameters control when we decide that we've
    # converged.  It probably should be possible to set these
    # manually, via keyword arguments to train.
    NEWTON_CONVERGE = 1e-12
    MAX_NEWTON = 300

    deltas = numpy.ones(encoding.length(), 'd')

    # Precompute the A matrix:
    # A[nf][id] = sum ( p(fs) * p(label|fs) * f(fs,label) )
    # over all label,fs s.t. num_features[label,fs]=nf
    A = numpy.zeros((len(nfmap), encoding.length()), 'd')

    for tok, label in train_toks:
        dist = classifier.prob_classify(tok)

        for label in encoding.labels():
            # Generate the feature vector
            feature_vector = encoding.encode(tok,label)
            # Find the number of active features
            nf = sum(val for (id, val) in feature_vector)
            # Update the A matrix
            for (id, val) in feature_vector:
                A[nfmap[nf], id] += dist.prob(label) * val
    A /= len(train_toks)

    # Iteratively solve for delta.  Use the following variables:
    #   - nf_delta[x][y] = nfarray[x] * delta[y]
    #   - exp_nf_delta[x][y] = exp(nf[x] * delta[y])
    #   - nf_exp_nf_delta[x][y] = nf[x] * exp(nf[x] * delta[y])
    #   - sum1[i][nf] = sum p(fs)p(label|fs)f[i](label,fs)
    #                       exp(delta[i]nf)
    #   - sum2[i][nf] = sum p(fs)p(label|fs)f[i](label,fs)
    #                       nf exp(delta[i]nf)
    for rangenum in range(MAX_NEWTON):
        nf_delta = numpy.outer(nfarray, deltas)
        exp_nf_delta = 2 ** nf_delta
        nf_exp_nf_delta = nftranspose * exp_nf_delta
        sum1 = numpy.sum(exp_nf_delta * A, axis=0)
        sum2 = numpy.sum(nf_exp_nf_delta * A, axis=0)

        # Avoid division by zero.
        for fid in unattested: sum2[fid] += 1

        # Update the deltas.
        deltas -= (ffreq_empirical - sum1) / -sum2

        # We can stop once we converge.
        n_error = (numpy.sum(abs((ffreq_empirical-sum1)))/
                   numpy.sum(abs(deltas)))
        if n_error < NEWTON_CONVERGE:
            return deltas

    return deltas

######################################################################
#{ Classifier Trainer: megam
######################################################################

# [xx] possible extension: add support for using implicit file format;
# this would need to put requirements on what encoding is used.  But
# we may need this for other maxent classifier trainers that require
# implicit formats anyway.
def train_maxent_classifier_with_megam(train_toks, trace=3, encoding=None,
                                       labels=None, gaussian_prior_sigma=0,
                                       **kwargs):
    """
    Train a new ``ConditionalExponentialClassifier``, using the given
    training samples, using the external ``megam`` library.  This
    ``ConditionalExponentialClassifier`` will encode the model that
    maximizes entropy from all the models that are empirically
    consistent with ``train_toks``.

    :see: ``train_maxent_classifier()`` for parameter descriptions.
    :see: ``nltk.classify.megam``
    """

    explicit = True
    bernoulli = True
    if 'explicit' in kwargs: explicit = kwargs['explicit']
    if 'bernoulli' in kwargs: bernoulli = kwargs['bernoulli']

    # Construct an encoding from the training data.
    if encoding is None:
        # Count cutoff can also be controlled by megam with the -minfc
        # option. Not sure where the best place for it is.
        count_cutoff = kwargs.get('count_cutoff', 0)
        encoding = BinaryMaxentFeatureEncoding.train(train_toks, count_cutoff,
                                                     labels=labels,
                                                     alwayson_features=True)
    elif labels is not None:
        raise ValueError('Specify encoding or labels, not both')

    # Write a training file for megam.
    try:
        fd, trainfile_name = tempfile.mkstemp(prefix='nltk-', suffix='.gz')
        trainfile = gzip.open(trainfile_name, 'wb')
        write_megam_file(train_toks, encoding, trainfile, \
                            explicit=explicit, bernoulli=bernoulli)
        trainfile.close()
    except (OSError, IOError, ValueError) as e:
        raise ValueError('Error while creating megam training file: %s' % e)

    # Run megam on the training file.
    options = []
    options += ['-nobias', '-repeat', '10']
    if explicit:
        options += ['-explicit']
    if not bernoulli:
        options += ['-fvals']
    if gaussian_prior_sigma:
        # Lambda is just the precision of the Gaussian prior, i.e. it's the
        # inverse variance, so the parameter conversion is 1.0/sigma**2.
        # See http://www.cs.utah.edu/~hal/docs/daume04cg-bfgs.pdf.
        inv_variance = 1.0 / gaussian_prior_sigma**2
    else:
        inv_variance = 0
    options += ['-lambda', '%.2f' % inv_variance, '-tune']
    if trace < 3:
        options += ['-quiet']
    if 'max_iter' in kwargs:
        options += ['-maxi', '%s' % kwargs['max_iter']]
    if 'll_delta' in kwargs:
        # [xx] this is actually a perplexity delta, not a log
        # likelihood delta
        options += ['-dpp', '%s' % abs(kwargs['ll_delta'])]
    if hasattr(encoding, 'cost'):
        options += ['-multilabel']  # each possible la
    options += ['multiclass', trainfile_name]
    stdout = call_megam(options)
    # print './megam_i686.opt ', ' '.join(options)
    # Delete the training file
    try: os.remove(trainfile_name)
    except (OSError, IOError) as e:
        print('Warning: unable to delete %s: %s' % (trainfile_name, e))

    # Parse the generated weight vector.
    weights = parse_megam_weights(stdout, encoding.length(), explicit)

    # Convert from base-e to base-2 weights.
    weights *= numpy.log2(numpy.e)

    # Build the classifier
    return MaxentClassifier(encoding, weights)

######################################################################
#{ Classifier Trainer: tadm
######################################################################

class TadmMaxentClassifier(MaxentClassifier):
    @classmethod
    def train(cls, train_toks, **kwargs):
        algorithm = kwargs.get('algorithm', 'tao_lmvm')
        trace = kwargs.get('trace', 3)
        encoding = kwargs.get('encoding', None)
        labels = kwargs.get('labels', None)
        sigma = kwargs.get('gaussian_prior_sigma', 0)
        count_cutoff = kwargs.get('count_cutoff', 0)
        max_iter = kwargs.get('max_iter')
        ll_delta = kwargs.get('min_lldelta')

        # Construct an encoding from the training data.
        if not encoding:
            encoding = TadmEventMaxentFeatureEncoding.train(train_toks,
                                                            count_cutoff,
                                                            labels=labels)

        trainfile_fd, trainfile_name = \
            tempfile.mkstemp(prefix='nltk-tadm-events-', suffix='.gz')
        weightfile_fd, weightfile_name = \
            tempfile.mkstemp(prefix='nltk-tadm-weights-')

        trainfile = gzip.open(trainfile_name, 'wb')
        write_tadm_file(train_toks, encoding, trainfile)
        trainfile.close()

        options = []
        options.extend(['-monitor'])
        options.extend(['-method', algorithm])
        if sigma:
            options.extend(['-l2', '%.6f' % sigma**2])
        if max_iter:
            options.extend(['-max_it', '%d' % max_iter])
        if ll_delta:
            options.extend(['-fatol', '%.6f' % abs(ll_delta)])
        options.extend(['-events_in', trainfile_name])
        options.extend(['-params_out', weightfile_name])
        if trace < 3:
            options.extend(['2>&1'])
        else:
            options.extend(['-summary'])

        call_tadm(options)

        with open(weightfile_name, 'rb') as weightfile:
            weights = parse_tadm_weights(weightfile)

        os.remove(trainfile_name)
        os.remove(weightfile_name)

        # Convert from base-e to base-2 weights.
        weights *= numpy.log2(numpy.e)

        # Build the classifier
        return cls(encoding, weights)

######################################################################
#{ Demo
######################################################################
def demo():
    from nltk.classify.util import names_demo
    classifier = names_demo(MaxentClassifier.train)

if __name__ == '__main__':
    demo()
