Package nltk :: Package classify :: Module maxent :: Class MaxentClassifier
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type MaxentClassifier

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     object --+    
              |    
api.ClassifierI --+
                  |
                 MaxentClassifier
Known Subclasses:

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))
Instance Methods [hide private]
 
__init__(self, encoding, weights, logarithmic=True)
Construct a new maxent classifier model.
source code
list of (immutable)
labels(self)
Returns: the list of category labels used by this classifier.
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set_weights(self, new_weights)
Set the feature weight vector for this classifier.
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list of float
weights(self)
Returns: The feature weight vector for this classifier.
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label
classify(self, featureset)
Returns: the most appropriate label for the given featureset.
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ProbDistI
prob_classify(self, featureset)
Returns: a probability distribution over labels for the given featureset.
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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.
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show_most_informative_features(self, n=10, show='all') source code
 
__repr__(self) source code

Inherited from api.ClassifierI: batch_classify, batch_prob_classify

    Deprecated

Inherited from api.ClassifierI: batch_probdist, probdist

Class Methods [hide private]
MaxentClassifier
train(cls, train_toks, algorithm=None, trace=3, encoding=None, labels=None, sparse=True, gaussian_prior_sigma=0, **cutoffs)
Train a new maxent classifier based on the given corpus of training samples.
source code
Class Variables [hide private]
  ALGORITHMS = ['GIS', 'IIS', 'CG', 'BFGS', 'Powell', 'LBFGSB', ...
A list of the algorithm names that are accepted for the train() method's algorithm parameter.
  _SCIPY_ALGS = {'bfgs': 'BFGS', 'cg': 'CG', 'lbfgsb': 'LBFGSB',...
Method Details [hide private]

__init__(self, encoding, weights, logarithmic=True)
(Constructor)

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Construct a new maxent classifier model. Typically, new classifier models are created using the train() method.

Parameters:
  • encoding (MaxentFeatureEncodingI) - 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.
  • weights (list of float) - The feature weight vector for this classifier.
  • logarithmic (bool) - If false, then use non-logarithmic weights.
Overrides: object.__init__

labels(self)

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Returns: list of (immutable)
the list of category labels used by this classifier.
Overrides: api.ClassifierI.labels
(inherited documentation)

set_weights(self, new_weights)

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Set the feature weight vector for this classifier.

Parameters:
  • new_weights (list of float) - The new feature weight vector.

weights(self)

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Returns: list of float
The feature weight vector for this classifier.

classify(self, featureset)

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Returns: label
the most appropriate label for the given featureset.
Overrides: api.ClassifierI.classify
(inherited documentation)

prob_classify(self, featureset)

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Returns: ProbDistI
a probability distribution over labels for the given featureset.
Overrides: api.ClassifierI.prob_classify
(inherited documentation)

show_most_informative_features(self, n=10, show='all')

source code 
Parameters:
  • show - all, neg, or pos (for negative-only or positive-only)

__repr__(self)
(Representation operator)

source code 
Overrides: object.__repr__
(inherited documentation)

train(cls, train_toks, algorithm=None, trace=3, encoding=None, labels=None, sparse=True, gaussian_prior_sigma=0, **cutoffs)
Class Method

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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.

Parameters:
  • train_toks (list) - 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.
  • algorithm (str) - A case-insensitive string, specifying which algorithm should be used to train the classifier. The following algorithms are currently available.
    • Iterative Scaling Methods
      • 'GIS': Generalized Iterative Scaling
      • 'IIS': Improved Iterative Scaling
    • Optimization Methods (require scipy)
      • 'CG': Conjugate gradient
      • 'BFGS': Broyden-Fletcher-Goldfarb-Shanno algorithm
      • 'Powell': Powell agorithm
      • 'LBFGSB': A limited-memory variant of the BFGS algorithm
      • 'Nelder-Mead': The Nelder-Mead algorithm
    • External Libraries
      • 'megam': LM-BFGS algorithm, with training performed by an megam. (requires that megam be installed.)

    The default algorithm is 'CG' if 'scipy' is installed; and 'iis' otherwise.

  • trace (int) - The level of diagnostic tracing output to produce. Higher values produce more verbose output.
  • encoding (MaxentFeatureEncodingI) - 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.
  • labels (list of str) - The set of possible labels. If none is given, then the set of all labels attested in the training data will be used instead.
  • sparse - If true, then use sparse matrices instead of dense matrices. Currently, this is only supported by the scipy (optimization method) algorithms. For other algorithms, its value is ignored.
  • gaussian_prior_sigma - The sigma value for a gaussian prior on model weights. Currently, this is supported by the scipy (optimization method) algorithms and megam. For other algorithms, its value is ignored.
  • 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.
    • tolerance=v: Terminate a scipy optimization method when improvement drops below a tolerance level v. The exact meaning of this tolerance depends on the scipy algorithm used. See scipy documentation for more info. Default values: 1e-3 for CG, 1e-5 for LBFGSB, and 1e-4 for other algorithms. (scipy only)
Returns: MaxentClassifier
The new maxent classifier

Class Variable Details [hide private]

ALGORITHMS

A list of the algorithm names that are accepted for the train() method's algorithm parameter.

Value:
['GIS',
 'IIS',
 'CG',
 'BFGS',
 'Powell',
 'LBFGSB',
 'Nelder-Mead',
 'MEGAM',
...

_SCIPY_ALGS

Value:
{'bfgs': 'BFGS',
 'cg': 'CG',
 'lbfgsb': 'LBFGSB',
 'nelder-mead': 'Nelder-Mead',
 'powell': 'Powell'}