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AI::Categorizer::ExperUsertContributed Perl DocuAI::Categorizer::Experiment(3)

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,
       precision, recall, F1, and so on, with both macro-averaging and micro-
       averaging over categories.

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

       Internally, "AI::Categorizer::Experiment" inherits from the
       "Statistics::Contingency".  Please see the documentation	of
       "Statistics::Contingency" 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
	   category 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_precision()".

       add_result($assigned, $correct, $name)
	   Adds	a new result to	the experiment.	 Please	see the
	   "Statistics::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@mathforum.org>

COPYRIGHT
       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.

perl v5.24.1			  2017-07-02	AI::Categorizer::Experiment(3)

NAME | SYNOPSIS | DESCRIPTION | METHODS | AUTHOR | COPYRIGHT

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