# Source code for nltk.classify.maxent

```
# Natural Language Toolkit: Maximum Entropy Classifiers
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# Dmitry Chichkov <dchichkov@gmail.com> (TypedMaxentFeatureEncoding)
# URL: <https://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.
"""
try:
import numpy
except ImportError:
pass
import os
import tempfile
from collections import defaultdict
from nltk.classify.api import ClassifierI
from nltk.classify.megam import call_megam, parse_megam_weights, write_megam_file
from nltk.classify.tadm import call_tadm, parse_tadm_weights, write_tadm_file
from nltk.classify.util import CutoffChecker, accuracy, log_likelihood
from nltk.data import gzip_open_unicode
from nltk.probability import DictionaryProbDist
from nltk.util import OrderedDict
__docformat__ = "epytext en"
######################################################################
# { Classifier Model
######################################################################
[docs]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))
"""
[docs] 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)
[docs] 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)
[docs] def weights(self):
"""
:return: The feature weight vector for this classifier.
:rtype: list of float
"""
return self._weights
[docs] 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)
[docs] 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)
)
[docs] def most_informative_features(self, n=10):
"""
Generates the ranked list of informative features from most to least.
"""
if hasattr(self, "_most_informative_features"):
return self._most_informative_features[:n]
else:
self._most_informative_features = sorted(
list(range(len(self._weights))),
key=lambda fid: abs(self._weights[fid]),
reverse=True,
)
return self._most_informative_features[:n]
[docs] def show_most_informative_features(self, n=10, show="all"):
"""
:param show: all, neg, or pos (for negative-only or positive-only)
:type show: str
:param n: The no. of top features
:type n: int
"""
# Use None the full list of ranked features.
fids = self.most_informative_features(None)
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(f"{self._weights[fid]:8.3f} {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"]
[docs] @classmethod
def train(
cls,
train_toks,
algorithm=None,
trace=3,
encoding=None,
labels=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
######################################################################
[docs]class MaxentFeatureEncodingI:
"""
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.
"""
[docs] 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()
[docs] def length(self):
"""
:return: The size of the fixed-length joint-feature vectors
that are generated by this encoding.
:rtype: int
"""
raise NotImplementedError()
[docs] 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()
[docs] 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()
[docs] 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()
[docs]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.
"""
[docs] 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
[docs]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.
"""
[docs] 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 = {
label: i + self._length for (i, label) in enumerate(labels)
}
self._length += len(self._alwayson)
if unseen_features:
fnames = {fname for (fname, fval, label) in mapping}
self._unseen = {fname: i + self._length for (i, fname) in enumerate(fnames)}
self._length += len(fnames)
[docs] 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
[docs] def describe(self, f_id):
# Inherit docs.
if not isinstance(f_id, int):
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 f"{fname}=={fval!r} and label is {label!r}"
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")
[docs] @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)
[docs]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.
"""
[docs] 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({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
[docs] 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
[docs] def describe(self, f_id):
if f_id == BinaryMaxentFeatureEncoding.length(self):
return "Correction feature (%s)" % self._C
else:
return BinaryMaxentFeatureEncoding.describe(self, f_id)
[docs]class TadmEventMaxentFeatureEncoding(BinaryMaxentFeatureEncoding):
[docs] 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
)
[docs] 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
[docs] def describe(self, fid):
for (feature, label) in self._mapping:
if self._mapping[(feature, label)] == fid:
return (feature, label)
[docs] @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)
[docs]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.
"""
[docs] 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 = {
label: i + self._length for (i, label) in enumerate(labels)
}
self._length += len(self._alwayson)
if unseen_features:
fnames = {fname for (fname, fval, label) in mapping}
self._unseen = {fname: i + self._length for (i, fname) in enumerate(fnames)}
self._length += len(fnames)
[docs] def encode(self, featureset, label):
# Inherit docs.
encoding = []
# Convert input-features to joint-features:
for fname, fval in featureset.items():
if isinstance(fval, (int, 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
[docs] def describe(self, f_id):
# Inherit docs.
if not isinstance(f_id, int):
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 f"{fname}=={fval!r} and label is {label!r}"
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")
[docs] @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
######################################################################
[docs]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
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(f" Final {ll:14.5f} {acc:9.3f}")
# Return the classifier.
return classifier
[docs]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
[docs]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
######################################################################
[docs]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(" ---------------------------------------")
# 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(f" Final {ll:14.5f} {acc:9.3f}")
# Return the classifier.
return classifier
[docs]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 {nf: i for (i, nf) in enumerate(nfset)}
[docs]def calculate_deltas(
train_toks,
classifier,
unattested,
ffreq_empirical,
nfmap,
nfarray,
nftranspose,
encoding,
):
r"""
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.
[docs]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-")
with open(trainfile_name, "w") as trainfile:
write_megam_file(
train_toks, encoding, trainfile, explicit=explicit, bernoulli=bernoulli
)
os.close(fd)
except (OSError, ValueError) as e:
raise ValueError("Error while creating megam training file: %s" % e) from 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 https://users.umiacs.umd.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 as e:
print(f"Warning: unable to delete {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
######################################################################
[docs]class TadmMaxentClassifier(MaxentClassifier):
[docs] @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_unicode(trainfile_name, "w")
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) 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
######################################################################
[docs]def demo():
from nltk.classify.util import names_demo
classifier = names_demo(MaxentClassifier.train)
if __name__ == "__main__":
demo()
```