nltk.classify.megam module

A set of functions used to interface with the external megam maxent optimization package. Before megam can be used, you should tell NLTK where it can find the megam binary, using the config_megam() function. Typical usage:

>>> from nltk.classify import megam
>>> megam.config_megam() # pass path to megam if not found in PATH 
[Found megam: ...]

Use with MaxentClassifier. Example below, see MaxentClassifier documentation for details.

nltk.classify.MaxentClassifier.train(corpus, ‘megam’)

nltk.classify.megam.call_megam(args)[source]

Call the megam binary with the given arguments.

nltk.classify.megam.config_megam(bin=None)[source]

Configure NLTK’s interface to the megam maxent optimization package.

Parameters

bin (str) – The full path to the megam binary. If not specified, then nltk will search the system for a megam binary; and if one is not found, it will raise a LookupError exception.

nltk.classify.megam.parse_megam_weights(s, features_count, explicit=True)[source]

Given the stdout output generated by megam when training a model, return a numpy array containing the corresponding weight vector. This function does not currently handle bias features.

nltk.classify.megam.write_megam_file(train_toks, encoding, stream, bernoulli=True, explicit=True)[source]

Generate an input file for megam based on the given corpus of classified tokens.

Parameters

  • train_toks (list(tuple(dict, str))) – 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.

  • encoding (MaxentFeatureEncodingI) – A feature encoding, used to convert featuresets into feature vectors. May optionally implement a cost() method in order to assign different costs to different class predictions.

  • stream (stream) – The stream to which the megam input file should be written.

  • bernoulli – If true, then use the ‘bernoulli’ format. I.e., all joint features have binary values, and are listed iff they are true. Otherwise, list feature values explicitly. If bernoulli=False, then you must call megam with the -fvals option.

  • explicit – If true, then use the ‘explicit’ format. I.e., list the features that would fire for any of the possible labels, for each token. If explicit=True, then you must call megam with the -explicit option.