type TadmMaxentClassifier

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 - 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 - 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 - The level of diagnostic tracing output to produce. Higher values produce more verbose output.
• 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.
• 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.
• 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

Overrides: MaxentClassifier.train

(inherited documentation)