NAME
Algorithm::AM - Perl extension for Analogical Modeling using a parallel
algorithm
VERSION
version 2.43
AUTHOR
Theron Stanford <
[email protected]>, Nathan Glenn
<
[email protected]>
COPYRIGHT AND LICENSE
This software is copyright (c) 2013 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.
SYNOPSIS
use Algorithm::AM;
my $p = Algorithm::AM->new('finnverb', -commas => 'no');
$p->classify();
DESCRIPTION
Analogical Modeling is an exemplar-based way to model language usage.
"Algorithm::AM" is a Perl module which analyzes data sets using
Analogical Modeling.
How to create data sets is not explained here. See the appendices in the
"red book", *Analogical Modeling: An exemplar-based approach to
language*, for details on that. See also the "green book", *Analogical
Modeling of Language*, for an explanation of the method in general, and
the "blue book", *Analogy and Structure*, for its mathematical basis.
METHODS
"new"
Arguments: see "Initializing a Project" (TODO: reorganize POD properly)
Creates and returns a subroutine to classify the data in a given
project.
"classify"
Using the analogical modeling algorithm, this method classifies the
instances in the project and prints the results to STDOUT, as well as to
"amcpresults" in the project directory.
"print_summary"
THIS METHOD IS UNDER CONSTRUCTION. Currently it is called by
"classify" to print a summary of the classifcation results.
HISTORY
Initially, Analogical Modeling was implemented as a Pascal program.
Subsequently, it was ported to Perl, with substantial improvements made
in 2000. In 2001, the core of the algorithm was rewritten in C, while
the parsing, printing, and statistical routines remained in C; this was
accomplished by embedding a Perl interpreter into the C code.
In 2004, the algorithm was again rewritten, this time in order to handle
more variables and large data sets. It breaks the supracontextual
lattice into the direct product of four smaller ones, which the
algorithm manipulates individually before recombining them. Because
these lattices could be manipulated in parallel, using the right
hardware, the module was named "AM::Parallel". Later it was renamed
"Algorithm::AM" to fit better into the CPAN ecostystem.
To provide more flexibility and to more closely follow "the Perl way",
the C core is now an XSUB wrapped within a Perl module. Instead of
specifying a configuration file, parameters are passed to the "new()"
function of "Algorithm::AM". The core functionality of the module has
been stripped down; the only reports available are the statistical
summary, the analogical set, and the gang listings. However, hooks are
provided for users to create their own reports. They can also manipulate
various parameters at run time and redirect output.
It is expected that future improvements will maintain a Perl interface
to an XSUB. However, the design will remain simple enough that users
without much programming experience will still be able to use the module
with the least amount of trouble.
PROJECTS
"Algorithm::AM" assumes the existence of a *project*, a directory
containing the data set, the test set, and the outcome file (named, not
surprisingly, data, test, and outcome). Once the project is initialized,
the user can set various parameters and run the algorithm.
If no outcome file is given, one is created using the outcomes which
appear in the data set. If no test set is given, it is assumed that the
data set functions as the test set.
Initializing a Project
A project is initialized using the syntax
*$p* = Algorithm::AM->new(*directory*, -commas => *commas*,
?*options*?);
The first parameter must be the name of the directory where the files
are. It can be an absolute or a relative path. The following parameter
is required:
-commas
Tells how to parse the lines of the data file. May be set to either
"yes" or "no". Any other value will trigger a warning and stop
creation of the project, as will omitting this option entirely. See
details in the "red book" to determine how to set this.
The following options are available:
-nulls
Tells how to treat nulls, i.e., variables marked with an equals sign
"=". Can be "include" or "exclude"; any other value will revert back
to the default. Default: "exclude".
-given
Tells whether or not to include the test item as a data item if it
is found in the data set. Can be "include" or "exclude"; any other
value will revert back to the default. Default: "exclude".
-linear
Determines if the analogical set will be computed using
*occurrences* (linearly) or *pointers* (quadratically). If "-linear"
is set to "yes", the analogical set will be computed using
occurrences; otherwise, it will be computed using pointers. Default:
compute using pointers.
-probability
Sets the probability of including any one data item. Default:
"undef". (TODO: what's undef do here?)
-repeat
Determines how many times each individual test item will be
analyzed. Only makes sense if the probability is less than 1.
Default: 1.
-skipset
Determines whether or not the analogical set is printed. Can be
"yes" or "no"; any other value will revert to the default. Default:
"yes".
-gangs
Determines whether or not gang effects will be printed. Can be one
of the following three values:
* "yes": Prints which contexts affect the result, how many
pointers they contain, and which data items are in them.
* "summary": Prints which contexts affect the result and how
many pointers they contain.
* "no": Omits any information about gang effects.
Any other value will revert to the default. Default: "no".
So, the minimal invocation to initialize a project would be something
like
$p = Algorithm::AM->new('finnverb', -commas => 'no');
while something fancier might be
$p = Algorithm::AM->new('negpre', -commas => 'yes',
-probability => 0.2, -repeat => 5,
-skipset => 'no', -gangs => 'summary');
Initializing a project doesn't do anything more than read in the files
and prepare them for analysis. To actually do any work, read on.
Running a project
To run an already initialized project with the defaults set at
initialization time, use the following:
$p->classify();
Yep, that's all there is to it.
Of course, you can override the defaults. Any of the options set at
initialization can be temporarily overridden. So, for instance, you can
run your project twice, once including nulls and once excluding them, as
follows:
$p->classify(-nulls => 'include');
$p->classify(-nulls => 'exclude');
Or, if you didn't specify a value at initialization time and accepted
the default, you can merely use
$p->classify(-nulls => 'include');
$p->classify();
Or you can play with the probabilities:
$p->classify(-probability => 0.5, -repeat => 2);
$p->classify(-probability => 0.2, -repeat => 5);
$p->classify(-probability => 0.1, -repeat => 10);
Output
Output from the program is appended to the file amcpresults in the
project directory by default. Internally, "Algorithm::AM" opens
amcpresults at the beginning each run and selects its file handle to be
current, so that the output of all "print()" statements gets directed to
it. Directing output elsewhere is possible, but you can't do it the
"obvious" way; the following won't work:
## do not use this code -- it is a BAD example
open FH5, ">results05";
open FH2, ">results02";
open FH1, ">results01";
select FH5;
$p->classify(-probability => 0.5, -repeat => 2);
select FH2;
$p->classify(-probability => 0.2, -repeat => 5);
select FH1;
$p->classify(-probability => 0.1, -repeat => 10);
close FH1;
close FH2;
close FH5;
That's because at the very beginning of each run, the code for $p
reselects the file handle. However, you can do this using a hook; see
"-beginhook" for a simple example of redirected output and
"-beginrepeathook" for a more complicated one.
Warnings and error messages get sent to STDERR. If there are no fatal
errors and the program runs normally, status messages are sent to
STDERR. You can see how long the program has been running, what test
item it's currently on, and even which iteration of an individual test
item it's on if the repeat is set greater than one.
USING HOOKS
"Algorithm::AM" provides *power* and *flexibility*. The *power* is in
the C code; the *flexibility* is in the *hooks* provided for the user to
interact with the algorithm at various stages.
Hook Placement in "Algorithm::AM"
Hooks are just references to subroutines that can be passed to the
project at run time; the subroutine references can be either named or
anonymous. They are passed as any other option. The following hooks are
currently implemented:
-beginhook
This hook is called before any test items are run.
-endhook
This hook is called after all test items are run.
Example: To send all the output from a run to another file, you can
do the following:
$p->classify(-beginhook => sub {open FH, ">myoutput"; select FH;},
-endhook => sub {close FH;});
-begintesthook
This hook is called at the beginning of each new test item. If a
test item will be run more than once, this hook is called just once
before the first iteration.
-endtesthook
This hook is called at the end of each test item. If a test item
will be run more than once, this hook is called just once after the
last iteration.
Example: If each test item is run just once, and you want to keep a
running tally of how many test items are correctly predicted, you
can use the variables $curTestOutcome, $pointermax, and @sum:
$count = 0;
$countsub = sub {
## must use eq instead of == in following statement
++$count if $sum[$curTestOutcome] eq $pointermax;
};
$p->classify(-endtesthook => $countsub,
-endhook => sub {print "Number of correct predictions: $count\n";});
-beginrepeathook
This hook is called at the beginning of each iteration of a test
item.
-endrepeathook
This hook is called at the end of each iteration of a test item.
Example: To vary the probability of each iteration through a test
item, you can use the variables $probability and $pass:
open FH5, ">results05";
open FH2, ">results02";
$repeatsub = sub {
$probability = (0.5, 0.2)[$pass];
select((FH5, FH2)[$pass]);
};
$p->classify(-beginrepeathook => $repeatsub);
Then on iteration 0, the test item is analyzed with the probability
of any data item being included set to 0.5, with output sent to file
results05, while on iteration 1, the test item is analyzed with the
probability of any data item being included set to 0.2, with output
sent to file results02.
-datahook
This hook is called for each data item considered during a test item
run. Unlike other hooks, which receive no arguments, this hook is
passed the index of the data item under consideration. The value of
this index ranges from one less than the number of data items to 0
(data items are considered in reverse order in "Algorithm::AM" for
various reasons not gone into here).
The index passed is not a copy but the actual index variable used in
"Algorithm::AM"; be careful not to change it -- for example, by
assigning to $_[0] -- unless that is what is intended.
This hook should return a true value (in the Perl sense of true) if
the data item should still be included in the test run, and should
return a false value otherwise. To ensure this, it's a good idea to
end the subroutine assigned to the hook with
return 1;
since
return;
returns an undefined value.
If the probability of including any data item is less than one, this
hook is called *before* a call to "rand()" to see whether or not to
include the item. If you don't like this, set "-probability" to 1 in
the option list and call "rand()" yourself somewhere within the
hook.
Example: The results for *sorta-* in the "red book" do not match
what you get when you run finnverb. That's because the "red book"
omitted all data items with outcome *a-oi*. You can do this using
the variables @curTestItem, @outcome, and %outcometonum:
$datasub = sub {
## we use @curTestItem because finnverb/test has no specifiers
return 1 unless join('', @curTestItem) eq 'SO0=SR0=TA';
return 1 unless $outcome[$_[0]] eq $outcometonum{'a-oi'};
return 0;
};
$p->classify(-datahook => $datasub);
Hook Variables
Various variables can be read and even manipulated by the hooks.
Note: All hook variables are exported into package "main". If you don't
know what this means, chances are you don't need to worry about it; if
you *do* know what it means, you'll know how to deal with it.
However, these variables exist in package "main" only while a project is
being run (they are exported using "local()"). Thus, you can only access
them through a hook, and they will not clobber the values of variables
of the same name outside of the run.
Variables Fixed at Initialization
These variables should be considered read-only, unless you're really
sure what you're doing.
@outcomelist
This array lists all possible outcomes. It is generated either from
the outcome file, if it exists, or from the outcomes that appear in
the data file. If there is a "short" version and a "long" version of
each outcome, @outcomelist contains the "long" version.
Outcomes are assigned positive integer values; outcome 0 is reserved
for internal use of "Algorithm::AM". (You'll have to look at the
source code and its documentation for further details, which most
likely you won't need.)
Example: File finnverb/outcome is as follows:
A V-i
B a-oi
C tV-si
During initialization, "Algorithm::AM" makes a series of assignments
equivalent to the following:
@outcomelist = ('', 'V-i', 'a-oi', 'tV-si');
%outcometonum
This hash maps outcome strings (the "long" ones that appear in
@outcomelist) to their respective positions in @outcomelist.
@outcome
$outcome[$i] contains the outcome of data item $i as an integer
index into @outcomelist.
@data
$data[$i] is a reference to an array containing the variables of
data item $i.
@spec
$spec[$i] contains the specifier for data item $i.
Example: Line 80 of file finnverb/data is as follows:
C MU0=SR0=TA MURTA
During initialization, "Algorithm::AM" makes a series of assignments
equivalent to the following:
$outcome[79] = 3;
$data[79] = ['M', 'U', '0', '=', 'S', 'R', '0', '=', 'T', 'A'];
$spec[79] = 'MURTA';
Variables Used for a Specific Test Item
These variables should be considered read-only, unless you're really
sure what you're doing.
$curTestOutcome
Contains the outcome index for the outcome of the current test item,
as determined by @outcomelist, if an outcome has been specified, and
0 otherwise.
@curTestItem
Contains the variables of the current test item.
$curTestSpec
Contains the specifier of the current test item, if one has been
specified, and is empty otherwise.
Variables Used for a Specific Iteration of a Test Item Run
$probability
Setting this changes the likelihood of including any one particular
data item in a test run. Note: If the option "-probability" is not
set at either initialization time or at run time, setting the value
of $probability inside a hook has no effect. (This is an intentional
optimization; see the source code and its documentation for the
reason why.) Therefore, if you plan to change the probability during
test item runs, make sure to specify a value (1 is a good choice)
for the option "-probability".
$pass
This variable indicates the current iteration of a test item run; it
will range from 0 to one less than the number specified by the
"-repeat" option.
Note: You cannot (easily) change the number of repetitions from
within a hook. You can only do this (easily) using the "-repeat"
option at run time. This is because typically you want each test
item to be subjected to the same number of repetitions. (But if for
some reason you really want to do this, you can increase $pass so
that "Algorithm::AM" will skip some passes. You're on your own
figuring out which hook to put this in.)
$datacap
This variable determines how many data items will be considered. It
is initially set to "scalar @data". However, if it is set smaller,
only the first $datacap items in the data file will be considered.
"Algorithm::AM" automatically truncates $datacap if it isn't an
integer, so you don't have to.
Example: It is often of interest to see how results change as the
number of data items considered decreases. Here's one way to do it:
$repeatsub = sub {
$datacap = (1, 0.5, 0.25)[$pass] * scalar @data;
};
$p->classify(-repeat => 3, -beginrepeathook => $repeatsub);
Note that this will give different results than the following:
$repeatsub = sub {
$probability = (1, 0.5, 0.25)[$pass];
};
$p->classify(-probability => 1, -repeat => 3, -beginrepeathook => $repeatsub);
The first way would be useful for modeling how predictions change as
more examples are gathered -- say, as a child grows older (though
the way it's written, it looks like the child is actually growing
younger). The second way would be useful for modeling how
predictions change as memory worsens -- say, as an adult grows
older. Note that option "-probability" must be specified at run time
if it hasn't been at initialization time; otherwise, calling the
hook has no effect.
Variables Available at the End of a Test Run Iteration
Before looking at these variables, it is important to know what they
contain.
"Algorithm::AM" works with really big integers, much larger than what 32
bits can hold. The XSUB uses a special internal format for storing them.
(You can read all about it in the usual place: the source code and its
documentation.) However, when the XSUB has finished its computations, it
converts these integers into something that the Perl code finds more
useful.
The scalar values returned from the XSUB are *dual-valued* scalars; they
have different values depending on the context they're called in. In
string context, you get a string representation of the integer. In
numeric context, you get a double.
For example, if $n and $d are big integers returned from the XSUB, you
can write
print $n/$d;
to see the decimal value of the fraction you get when you divide $n by
$d, because the division will use the numeric values, while
print "$n/$d";
will let you see this fraction expressed as the quotient of two
integers, because the quotation marks will interpolate the string
values.
Because of this, you can't use "==" to test if two big integers have the
same value -- they might be so big that the double representation
doesn't give enough accuracy to distinguish them. Use "eq" to test
equality.
If you need a comparison operator, you can use "bigcmp()".
@sum
Contains the number of pointers for each outcome index. (Remember
that outcome indices start with 1.)
$pointertotal
Contains the total number of pointers.
$pointermax
Contains the maximum value among all the values in @sum.
Note that there is no variable reporting which outcome has the most
pointers. That's because there could be a tie, and different users treat
ties in different ways. So, if you want to see which outcomes have the
highest number of pointers, try something like this:
@winners = ();
for ($i = 1; $i < @sum; ++$i) {
push @winners, $i if $sum[$i] eq $pointermax; ## use eq, not ==
}
For another example using these variables, see "-endtesthook".
Variables Useful for Formatting
You may want to create your own reports. These variables can help your
formatting. (They are also used by "Algorithm::AM" to format the
standard reports.)
$dformat
Leaves enough space to hold an integer equal to the number of data
items. Justifies right.
$sformat
Leaves enough space to hold any of the specifiers in the data set.
Justifies left.
$oformat
Leaves enough space to hold a "long" outcome. Justifies left.
$vformat
Formats a list of variables. Set "-gangs" to "yes" for an example.
$pformat
Leaves enough space to hold the big integer $pointertotal, and thus
is big enough to hold $pointermax or any element of @sum as well.
Justifies right.
Note: This variable changes with each iteration of a test item.
Hook Function
The following function is also exported into package "main" and
available for use in hooks. This is done with "local()", just as with
hook variables, so it is not available outside of hooks.
bigcmp()
Compares two big integers, returning 1, 0, or -1 depending on
whether the first argument is greater than, equal to, or less than
the second argument. Remember that the syntax is different: you must
write
bigcmp($a, $b)
instead of "$a bigcmp $b".
MORE EXAMPLES
Summarizing a Repeated Test Item
Suppose you run each test item 5 times, each with probability 0.005, and
you want to create a statistical analysis summarizing the results for
each test item. Here's one way to do it:
$begintest = sub {
$valid = 0;
@testPct = ();
@testPctSq = ();
$correct = 0;
};
$endrepeat = sub {
return unless $pointertotal;
++$valid;
++$correct if $sum[$curTestOutcome] eq $pointermax;
for ($i = 1; $i < @outcomelist; ++$i) {
$testPct[$i] += $sum[$i]/$pointertotal;
$testPctSq[$i] += ($sum[$i]*$sum[$i])/($pointertotal*$pointertotal);
}
};
$endtest = sub {
print "Summary for test item: $curTestSpec\n";
print "Valid runs: $valid out of 5\n\n";
print "\n" and return unless $valid;
printf "$oformat Avg Std Dev\n", "";
for ($i = 1; $i < @outcomelist; ++$i) {
next unless $testPct[$i];
if ($valid > 1) {
printf "$oformat %7.3f%% %7.3f%%\n",
$outcomelist[$i],
100 * $testPct[$i]/$valid,
100 * sqrt(($testPctSq[$i]-$testPct[$i]*$testPct[$i]/$valid)/($valid-1));
} else {
printf "$oformat %7.3f%%\n",
$outcomelist[$i],
100 * $testPct[$i]/$valid;
}
}
printf "\nCorrect prediction occurred %7.3f%% (%i/5) of the time\n",
100 * $correct / 5,
$correct;
print "\n\n";
};
$p->classify(-probability => 0.005, -repeat => 5,
-begintesthook => $begintest, -endrepeathook => $endrepeat, -endtesthook => $endtest);
Creating a Confusion Matrix
Suppose you want to compare correct outcomes with predicted outcomes.
Here's one way to do it:
$begin = sub {
@confusion = ();
};
$endrepeat = sub {
if (!$pointertotal) {
++$confusion[$curTestOutcome][0];
return;
}
if ($sum[$curTestOutcome] eq $pointermax) {
++$confusion[$curTestOutcome][$curTestOutcome];
return;
}
my @winners = ();
my $i;
for ($i = 1; $i < @outcomelist; ++$i) {
push @winners, $i if $sum[$i] == $pointermax;
}
my $numwinners = scalar @winners;
foreach (@winners) {
$confusion[$curTestOutcome][$_] += 1 / $numwinners;
}
};
$end = sub {
my($i,$j);
for ($i = 1; $i < @outcomelist; ++$i) {
my $total = 0;
foreach (@{$confusion[$i]}) {
$total += $_;
}
next unless $total;
printf "Test items with outcome $oformat were predicted as follows:\n",
$outcomelist[$i];
for ($j = 1; $j < @outcomelist; ++$j) {
my $t;
next unless ($t = $confusion[$i][$j]);
printf "%7.3f%% $oformat (%i/%i)\n", 100 * $t / $total, $outcomelist[$j], $t, $total;
}
if ($t = $confusion[$i][0]) {
printf "%7.3f%% could not be predicted (%i/%i)\n", 100 * $t / $total, $t, $total;
}
print "\n\n";
}
};
$p->classify(-probability => 0.005, -repeat => 5,
-beginhook => $begin, -endrepeathook => $endrepeat, -endhook => $end);
WARNINGS AND ERROR MESSAGES
Project not specified
No project was specified in the call to "Algorithm::AM->new". An
empty subroutine is returned (so that batch scripts do not break).
Project %s has no data file
The project directory has no file named data. An empty subroutine is
returned (so that batch scripts do not break).
Project %s did not specify comma formatting
The required parameter "-commas" was not provided. An empty
subroutine is returned (so that batch scripts do not break).
Project %s did not specify comma formatting correctly
Parameter "-commas" must be either "yes" or "no". An empty
subroutine is returned (so that batch scripts do not break).
Project %s did not specify option -nulls correctly
Parameter "-nulls" must be either "include" or "exclude". Displayed
default value will be used.
Project %s did not specify option -given correctly
Parameter "-given" must be either "include" or "exclude". Displayed
default value will be used.
Project %s did not specify option -skipset correctly
Parameter "-skipset" must be either "yes" or "no". Displayed default
value will be used.
Project %s did not specify option -gangs correctly
Parameter "-gangs" must be either "yes", "summary", or "no".
Displayed default value will be used.
Couldn't open %s/test
Project %s does not have a test file. The data file will be used.
SEE ALSO
The <home page|
http://humanities.byu.edu/am/> for Analogical Modeling
includes information about current research and publications, awell as
sample data sets.
The Wikipedia article <
http://en.wikipedia.org/wiki/Analogical_modeling>
has details and illustrations explaining the utility and inner-workings
of analogical modeling.
AUTHORS
Theron Stanford <
[email protected]>
Nathan Glenn <
[email protected]>
COPYRIGHT
Copyright (C) 2004 by Royal Skousen
