view release on metacpan or  search on metacpan

README  view on Meta::CPAN

        data_root
            A shortcut for setting the "training_set", "test_set", and
            "category_file" parameters separately. Sets "training_set" to
            "$data_root/training", "test_set" to "$data_root/test", and
            "category_file" (used by some of the Collection classes) to
            "$data_root/cats.txt".

    learner()
        Returns the Learner object associated with this Categorizer. Before
        "train()", the Learner will of course not be trained yet.

    knowledge_set()
        Returns the KnowledgeSet object associated with this Categorizer. If
        "read_training_set()" has not yet been called, the KnowledgeSet will not
        yet be populated with any training data.

    run_experiment()
        Runs a complete experiment on the training and testing data, reporting
        the results on "STDOUT". Internally, this is just a shortcut for calling
        the "scan_features()", "read_training_set()", "train()", and
        "evaluate_test_set()" methods, then printing the value of the
        "stats_table()" method.

    scan_features()
        Scans the Collection specified in the "test_set" parameter to determine
        the set of features (words) that will be considered when training the
        Learner. Internally, this calls the "scan_features()" method of the
        KnowledgeSet, then saves a list of the KnowledgeSet's features for later
        use.

        This step is not strictly necessary, but it can dramatically reduce
        memory requirements if you scan for features before reading the entire
        corpus into memory.

    read_training_set()
        Populates the KnowledgeSet with the data specified in the "test_set"
        parameter. Internally, this calls the "read()" method of the
        KnowledgeSet. Returns the KnowledgeSet. Also saves the KnowledgeSet
        object for later use.

    train()
        Calls the Learner's "train()" method, passing it the KnowledgeSet
        created during "read_training_set()". Returns the Learner object. Also
        saves the Learner object for later use.

    evaluate_test_set()
        Creates a Collection based on the value of the "test_set" parameter, and
        calls the Learner's "categorize_collection()" method using this
        Collection. Returns the resultant Experiment object. Also saves the
        Experiment object for later use in the "stats_table()" method.

    stats_table()
        Returns the value of the Experiment's (as created by
        "evaluate_test_set()") "stats_table()" method. This is a string that
        shows various statistics about the accuracy/precision/recall/F1/etc. of
        the assignments made during testing.

HISTORY
    This module is a revised and redesigned version of the previous
    "AI::Categorize" module by the same author. Note the added 'r' in the new
    name. The older module has a different interface, and no attempt at backward
    compatibility has been made - that's why I changed the name.

    You can have both "AI::Categorize" and "AI::Categorizer" installed at the
    same time on the same machine, if you want. They don't know about each other
    or use conflicting namespaces.

AUTHOR
    Ken Williams <ken@mathforum.org>

    Discussion about this module can be directed to the perl-AI list at
    <perl-ai@perl.org>. For more info about the list, see
    http://lists.perl.org/showlist.cgi?name=perl-ai

REFERENCES
    An excellent introduction to the academic field of Text Categorization is
    Fabrizio Sebastiani's "Machine Learning in Automated Text Categorization":
    ACM Computing Surveys, Vol. 34, No. 1, March 2002, pp. 1-47.

COPYRIGHT
    Copyright 2000-2003 Ken Williams. All rights reserved.

    This distribution is free software; you can redistribute it and/or modify it
    under the same terms as Perl itself. These terms apply to every file in the
    distribution - if you have questions, please contact the author.