03 Apr 2021 09:28:33 UTC
- Distribution: Algorithm-AM
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- Latest versionNGLENN Nathan Gary Glennand 3 contributors
- Theron Stanford, Nathan Glenn <firstname.lastname@example.org>
- Nathan Glenn
- Nick Logan
analogize - classify data with AM from the command line
analogize --format <format> [--exemplars <file>] [--test <file>] [--project <dir>] [--print <config_info,statistical_summary, analogical_set_summary,gang_summary,gang_detailed>] [--help]
Classify data with analogical modeling from the command line. Required arguments are format and either exemplars or project. You can use old AM::Parallel projects (a directory containing
testfiles) or specify individual data and test files. By default, only the accuracy of the predicted outcomes is printed. More detail may be printed using the print option.
specify either commas or nocommas format for exemplar and test data files (
=should be used for "null" variables). See "dataset_from_file" in Algorithm::AM::DataSet for details on the two formats.
path to the file containing the examplar/training data
path to an AM::Parallel-style project (ignores 'outcome' file); this should be a directory containing a file called
datacontaining known exemplars and
testcontaining test exemplars. If the
testfile does not exist, then a leave-one-out scheme is used for testing using the exemplars in the
path to the file containing the test data. If none is specified, performs leave-one-out classification with the exemplar set.
reports to print, separated by commas (be careful not to add spaces between report names!). For example,
--print analogical_set_summary,gang_summarywould print analogical sets and gang summaries.
Available options are:
Describes the configuration used and some simple information about the data, i.e. cardinality, etc.
A statistical summary of the classification results, including all predicted outcomes with their scores and percentages and the total score for all outcomes. Whether the predicted class is correct, incorrect, or a tie is also included, if the test item had a known class.
The analogical set, showing all items that contributed to the predicted outcome, along with the amount contributed by each item (score and percentage overall).
A summary of the gang effects on the outcome prediction.
gang_summary, but also includes lists of exemplars for each gang.
Allow a test item to be included in the data set during classification. If false (default), test items will be removed from the dataset during classification.
Treat null variables in a test item as regular variables. If false (default), these variables will be excluded and not considered during classification.
Calculate scores using occurrences (linearly) instead of using pointers (quadratically).
print help message
This distribution comes with a sample dataset in the
datasets/soybeandirectory. Data exemplars are in
dataand a single test exemplar is in
test. The files are in the
commasformat. The following two commands are equivalent and will analyze the test exemplar and output a summary of gang effects to
analogize --exemplars datasets/soybean/data --test datasets/soybean/test --format commas --print gang_summary > gang.txt analogize --project datasets/soybean --format commas --print gang_summary > gang.txt
The resulting files are best viewed in a text editor with word wrap turned off.
Theron Stanford <email@example.com>, Nathan Glenn <firstname.lastname@example.org>
This software is copyright (c) 2021 by Royal Skousen.
This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.
Module Install Instructions
To install Algorithm::AM, copy and paste the appropriate command in to your terminal.
perl -MCPAN -e shell install Algorithm::AM
For more information on module installation, please visit the detailed CPAN module installation guide.