AI::Categorizer::Experiment--3pm

AI::Categorizer::ExperUsertContributed Perl DoAI::Categorizer::Experiment(3pm)



NAME
       AI::Categorizer::Experiment - Coordinate experimental results

SYNOPSIS
        use AI::Categorizer::Experiment;
        my $e = new AI::Categorizer::Experiment(categories => \%categories);
        my $l = AI::Categorizer::Learner->restore_state(...path...);

        while (my $d = ... get document ...) {
          my $h = $l->categorize($d); # A Hypothesis
          $e->add_hypothesis($h, [map $_->name, $d->categories]);
        }

        print "Micro F1: ", $e->micro_F1, "\n"; # Access a single statistic
        print $e->stats_table; # Show several stats in table form

DESCRIPTION
       The "AI::Categorizer::Experiment" class helps you organize the results
       of categorization experiments.  As you get lots of categorization
       results (Hypotheses) back from the Learner, you can feed these results
       to the Experiment class, along with the correct answers.  When all
       results have been collected, you can get a report on accuracy, preci-
       sion, recall, F1, and so on, with both macro-averaging and micro-aver-
       aging over categories.

METHODS
       The general execution flow when using this class is to create an Exper-
       iment object, add a bunch of Hypotheses to it, and then report on the
       results.

       Internally, "AI::Categorizer::Experiment" inherits from the "Statis-
       tics::Contingency".  Please see the documentation of "Statistics::Con-
       tingency" for a description of its interface.  All of its methods are
       available here, with the following additions:

       new( categories => \%categories )
       new( categories => \@categories, verbose => 1, sig_figs => 2 )
           Returns a new Experiment object.  A required "categories" parameter
           specifies the names of all categories in the data set.  The cate-
           gory names may be specified either the keys in a reference to a
           hash, or as the entries in a reference to an array.

           The "new()" method accepts a "verbose" parameter which will cause
           some status/debugging information to be printed to "STDOUT" when
           "verbose" is set to a true value.

           A "sig_figs" indicates the number of significant figures that
           should be used when showing the results in the "results_table()"
           method.  It does not affect the other methods like "micro_preci-
           sion()".

       add_result($assigned, $correct, $name)
           Adds a new result to the experiment.  Please see the "Statis-
           tics::Contingency" documentation for a description of this method.

       add_hypothesis($hypothesis, $correct_categories)
           Adds a new result to the experiment.  The first argument is a
           "AI::Categorizer::Hypothesis" object such as one generated by a
           Learner's "categorize()" method.  The list of correct categories
           can be given as an array of category names (strings), as a hash
           whose keys are the category names and whose values are anything
           logically true, or as a single string if there is only one
           category.  For example, all of the following are legal:

            $e->add_hypothesis($h, "sports");
            $e->add_hypothesis($h, ["sports", "finance"]);
            $e->add_hypothesis($h, {sports => 1, finance => 1});

AUTHOR
       Ken Williams <ken AT mathforum.org>

COPYRIGHT
       This distribution is free software; you can redistribute it and/or mod-
       ify 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.



perl v5.8.7                       2002-11-24  AI::Categorizer::Experiment(3pm)