nltk.classify.megam

module documentation

(source)

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 # doctest: +SKIP
[Found megam: ...]

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

nltk.classify.MaxentClassifier.train(corpus, 'megam')

Function	`call_megam`	Call the `megam` binary with the given arguments.
Function	`config_megam`	Configure NLTK's interface to the `megam` maxent optimization package.
Function	`parse_megam_weights`	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.
Function	`write_megam_file`	Generate an input file for `megam` based on the given corpus of classified tokens.
Function	`_write_megam_features`	Undocumented
Variable	`_megam_bin`	Undocumented

def call_megam(args): (source) ¶

Call the megam binary with the given arguments.

def 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.

def 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.

def 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.

def _write_megam_features(vector, stream, bernoulli): (source) ¶

Undocumented

_megam_bin = (source) ¶

Undocumented