GSP
Quick Navigator

Search Site

Unix VPS
A - Starter
B - Basic
C - Preferred
D - Commercial
MPS - Dedicated
Previous VPSs
* Sign Up! *

Support
Contact Us
Online Help
Handbooks
Domain Status
Man Pages

FAQ
Virtual Servers
Pricing
Billing
Technical

Network
Facilities
Connectivity
Topology Map

Miscellaneous
Server Agreement
Year 2038
Credits
 

USA Flag

 

 

Man Pages
AI::Categorizer::Experiment(3) User Contributed Perl Documentation AI::Categorizer::Experiment(3)

AI::Categorizer::Experiment - Coordinate experimental results

 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

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.

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});
    

Ken Williams <ken@mathforum.org>

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.
2022-04-08 perl v5.32.1

Search for    or go to Top of page |  Section 3 |  Main Index

Powered by GSP Visit the GSP FreeBSD Man Page Interface.
Output converted with ManDoc.