Ubuntu Manpage: mailtoe - a train-on-error simulator for use with dbacl.

NAME

       mailtoe - a train-on-error simulator for use with dbacl.

SYNOPSIS

       mailtoe command [ command_arguments ]

DESCRIPTION

mailtoe automates the task of testing email filtering and classification programs such as dbacl(1).
Given a set of categorized documents, mailtoe initiates test runs to estimate the classification errors
and thereby permit fine tuning of the parameters of the classifier.

Train-on-error (TOE) is a learning method which is sometimes advocated for email classifiers. Given an
incoming email stream, the method consists in reusing a fixed set of category databases until the first
misclassification occurs. At that point, the offending email is used to relearn the relevant category,
until the next misclassification. In this way, categories are only updated when errors occur. This
directly models the way that some email classifiers are used in practice.

TOE's error rates depend directly on the order in which emails are seen. A small change in ordering, as
might happen due to networking delays, can have a large impact on the number of misclassifications.
Consequently, mailtoe does not give meaningful results, unless the sample emails are chosen carefully.
However, as this method is commonly used by spam filters, it is still worth computing to foster
comparisons. Other methods (see mailcross(1),mailfoot(1)) attempt to capture the behaviour of
classification errors in other ways.

To improve and stabilize the error rate calculation, mailtoe performs the TOE simulations several times
on slightly reordered email streams, and averages the results. The reorderings occur by multiplexing the
emails from each category mailbox in random order. Thus if there are three categories, the first email
classified is chosen randomly from the front of the sample email streams of each type. The second email
is also chosen randomly among the three types, from the front of the
streams after the first email was removed. Simulation stops when all sample streams are exhausted.

mailtoe uses the environment variable MAILTOE_FILTER when executing, which permits the simulation of
arbitrary filters, provided these satisfy the compatibility conditions stated in the ENVIRONMENT section
below.

For convenience, mailtoe implements a testsuite framework with predefined wrappers for several open
source classifiers. This permits the direct comparison of dbacl(1) with competing classifiers on the same
set of email samples. See the USAGE section below.

During preparation, mailtoe builds a subdirectory named mailtoe.d in the current working directory. All
needed calculations are performed inside this subdirectory.

EXIT STATUS

       mailtoe returns 0 on success, 1 if a problem occurred.

COMMANDS

prepare size
Prepares a subdirectory named mailtoe.d in the current working directory, and populates it with
empty subdirectories for exactly size subsets.

add category [ FILE ]...
Takes a set of emails from either FILE if specified, or STDIN, and associates them with category.
The ordering of emails within FILE is preserved, and subsequent FILEs are appended to the first in
each category. This command can be repeated several times, but should be executed at least once.

clean Deletes the directory mailtoe.d and all its contents.

run Multiplexes randomly from the email streams added earlier, and relearns categories only when a
misclassification occurs. The simulation is repeated size times.

summarize
Prints average error rates for the simulations.

plot [ ps | logscale ]...
Plots the number of errors over simulation time. The "ps" option, if present, writes the plot to a
postscript file in the directory mailtoe/plots, instead of being shown on-screen. The "logscale"
option, if present, causes the plot to be on the log scale for both ordinates.

review truecat predcat
Scans the last run statistics and extracts all the messages which belong to category truecat but
have been classified into category predcat. The extracted messages are copied to the directory
mailtoe.d/review for perusal.

testsuite list
Shows a list of available filters/wrapper scripts which can be selected.

testsuite select [ FILTER ]...
Prepares the filter(s) named FILTER to be used for simulation. The filter name is the name of a
wrapper script located in the directory /usr/share/dbacl/testsuite. Each filter has a rigid
interface documented below, and the act of selecting it copies it to the mailtoe.d/filters
directory. Only filters located there are used in the simulations.

testsuite deselect [ FILTER ]...
Removes the named filter(s) from the directory mailtoe.d/filters so that they are not used in the
simulation.

testsuite run [ plots ]
Invokes every selected filter on the datasets added previously, and calculates misclassification
rates. If the "plots" option is present, each filter simulation is plotted as a postscript file in
the directory mailtoe.d/plots.

testsuite status
Describes the scheduled simulations.

testsuite summarize
Shows the cross validation results for all filters. Only makes sense after the run command.

USAGE

The normal usage pattern is the following: first, you should separate your email collection into several
categories (manually or otherwise). Each category should be associated with one or more folders, but each
folder should not contain more than one category. Next, you should decide how many runs to use, say 10.
The more runs you use, the better the predicted error rates. However, more runs take more time. Now you
can type

% mailtoe prepare 10

Next, for every category, you must add every folder associated with this category. Suppose you have three
categories named spam, work, and play, which are associated with the mbox files spam.mbox, work.mbox, and
play.mbox respectively. You would type

% mailtoe add spam spam.mbox
% mailtoe add work work.mbox
% mailtoe add play play.mbox

You should aim for a similar number of emails in each category, as the random multiplexing will be
unbalanced otherwise. The ordering of the email messages in each *.mbox file is important, and is
preserved during each simulation. If you repeatedly add to the same category, the later mailboxes will be
appended to the first, preserving the implied ordering.

You can now perform as many TOE simulations as desired. The multiplexed emails are classified and learned
one at a time, by executing the command given in the environment variable MAILTOE_FILTER. If not set, a
default value is used.

% mailtoe run
% mailtoe summarize

The testsuite commands are designed to simplify the above steps and allow comparison of a wide range of
email classifiers, including but not limited to dbacl. Classifiers are supported through wrapper
scripts, which are located in the /usr/share/dbacl/testsuite directory.

The first stage when using the testsuite is deciding which classifiers to compare. You can view a list
of available wrappers by typing:

% mailtoe testsuite list

Note that the wrapper scripts are NOT the actual email classifiers, which must be installed separately by
your system administrator or otherwise. Once this is done, you can select one or more wrappers for the
simulation by typing, for example:

% mailtoe testsuite select dbaclA ifile

If some of the selected classifiers cannot be found on the system, they are not selected. Note also that
some wrappers can have hard-coded category names, e.g. if the classifier only supports binary
classification. Heed the warning messages.

It remains only to run the simulation. Beware, this can take a long time (several hours depending on the
classifier).

% mailtoe testsuite run
% mailtoe testsuite summarize

Once you are all done, you can delete the working files, log files etc. by typing

% mailtoe clean

SCRIPT INTERFACE

mailtoe testsuite takes care of learning and classifying your prepared email corpora for each selected
classifier. Since classifiers have widely varying interfaces, this is only possible by wrapping those
interfaces individually into a standard form which can be used by mailtoe testsuite.

Each wrapper script is a command line tool which accepts a single command followed by zero or more
optional arguments, in the standard form:

wrapper command [argument]...

Each wrapper script also makes use of STDIN and STDOUT in a well defined way. If no behaviour is
described, then no output or input should be used. The possible commands are described below:

filter In this case, a single email is expected on STDIN, and a list of category filenames is expected in
$2, $3, etc. The script writes the category name corresponding to the input email on STDOUT. No
trailing newline is required or expected.

learn In this case, a standard mbox stream is expected on STDIN, while a suitable category file name is
expected in $2. No output is written to STDOUT.

clean In this case, a directory is expected in $2, which is examined for old database information. If
any old databases are found, they are purged or reset. No output is written to STDOUT.

describe
IN this case, a single line of text is written to STDOUT, describing the filter's functionality.
The line should be kept short to prevent line wrapping on a terminal.

bootstrap
In this case, a directory is expected in $2. The wrapper script first checks for the existence of
its associated classifier, and other prerequisites. If the check is successful, then the wrapper
is cloned into the supplied directory. A courtesy notification should be given on STDOUT to
express success or failure. It is also permissible to give longer descriptions caveats.

toe In this case, a list of categories is expected in $3, $4, etc. Every possible category must be
listed. Preceding this list, the true category is given in $2.

foot Used by mailfoot(1).

ENVIRONMENT

       Right  after  loading,  mailtoe reads the hidden file .mailtoerc in the $HOME directory, if it exists, so
       this would be a good place to define custom values for environment variables.

       MAILTOE_FILTER
              This variable contains a shell command to be executed repeatedly during the  running  stage.   The
              command  should  accept  an  email  message  on STDIN and output a resulting category name. On the
              command line, it should also accept first the true category name, then  a  list  of  all  possible
              category  file  names.  If the output category does not match the true category, then the relevant
              categories are assumed to have been silently updated/relearned.  If MAILTOE_FILTER  is  undefined,
              mailtoe uses a default value.

       TEMPDIR
              This  directory  is  exported  for  the  benefit  of wrapper scripts. Scripts which need to create
              temporary files should place them a the location given in TEMPDIR.

NOTES

       The subdirectory mailtoe.d can grow quite large. It contains a full copy of the training corpora, as well
       as learning files for size times all the added categories, and various log files.

       While  TOE simulations for dbacl(1) can be used to compare with other classifiers, TOE should not be used
       for real world classifications. This is because, unlike many  other  filters,  dbacl(1)  learns  evidence
       weights  in  a nonlinear way, and does not preserve relative weights between tokens, even if those tokens
       aren't seen in new emails.

WARNING

       Because the ordering of emails within the added mailboxes matters, the estimated error rates are not well
       defined  or  even  meaningful  in  an objective sense.  However, if the sample emails represent an actual
       snapshot of a user's incoming email, then the error rates are somewhat meaningful.  The  simulations  can
       then  be  interpreted as alternate realities where a given classifier would have intercepted the incoming
       mail.

SOURCE

       The source code for the latest version of this program is available at the following locations:

       http://www.lbreyer.com/gpl.html
       http://dbacl.sourceforge.net

AUTHOR

       Laird A. Breyer <laird@lbreyer.com>