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MAILFOOT(1) MAILFOOT(1)
NAME
mailfoot - a full-online-ordered-training simulator for use with dbacl.
SYNOPSIS
mailfoot command [ command_arguments ]
DESCRIPTION
mailfoot automates the task of testing email filtering and classification programs such as dbacl(1). Given a set of categorized documents,
mailfoot initiates test runs to estimate the classification errors and thereby permit fine tuning of the parameters of the classifier.
Full Online Ordered Training is a learning method for email classifiers where each incoming email is learned as soon as it arrives, thereby
always keeping category descriptions up to date for the next classification. This directly models the way that some email classifiers are
used in practice.
FOOT'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 an impact on the number of misclassifications. Consequently, mailfoot 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),mailtoe(1)) attempt to capture the behaviour of classification errors in other ways.
To improve and stabilize the error rate calculation, mailfoot performs the FOOT 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.
mailfoot uses the environment variable MAILFOOT_FILTER when executing, which permits the simulation of arbitrary filters, provided these
satisfy the compatibility conditions stated in the ENVIRONMENT section below.
For convenience, mailfoot 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, mailfoot builds a subdirectory named mailfoot.d in the current working directory. All needed calculations are per-
formed inside this subdirectory.
EXIT STATUS
mailfoot returns 0 on success, 1 if a problem occurred.
COMMANDS
prepare size
Prepares a subdirectory named mailfoot.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 mailfoot.d and all its contents.
run Multiplexes randomly from the email streams added earlier, and relearns categories only when a misclassification occurs. The simula-
tion 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
mailfoot/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 mailfoot.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 direc-
tory /usr/share/dbacl/testsuite. Each filter has a rigid interface documented below, and the act of selecting it copies it to the
mailfoot.d/filters directory. Only filters located there are used in the simulations.
testsuite deselect [ FILTER ]...
Removes the named filter(s) from the directory mailfoot.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 mailfoot.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 other-
wise). 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
% mailfoot 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
% mailfoot add spam spam.mbox
% mailfoot add work work.mbox
% mailfoot 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 FOOT simulations as desired. The multiplexed emails are classified and learned one at a time, by executing the
command given in the environment variable MAILFOOT_FILTER. If not set, a default value is used.
% mailfoot run
% mailfoot 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:
% mailfoot testsuite list
Note that the wrapper scripts are NOT the actual email classifiers, which must be installed separately by your system administrator or oth-
erwise. Once this is done, you can select one or more wrappers for the simulation by typing, for example:
% mailfoot 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).
% mailfoot testsuite run
% mailfoot testsuite summarize
Once you are all done, you can delete the working files, log files etc. by typing
% mailfoot clean
SCRIPT INTERFACE
mailfoot 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 mail-
foot 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 writ-
ten 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 Used by mailtoe(1).
foot 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.
ENVIRONMENT
Right after loading, mailfoot reads the hidden file .mailfootrc in the $HOME directory, if it exists, so this would be a good place to
define custom values for environment variables.
MAILFOOT_FILTER
This variable contains a shell command to be executed repeatedly during the running stage. The command should accept an email mes-
sage 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 MAILFOOT_FILTER is undefined, mailfoot 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 mailfoot.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.
FOOT simulations for dbacl(1) are very, very slow (order n squared) and will take all night to perform. This is not easy to improve.
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>
SEE ALSO
bayesol(1) dbacl(1), mailcross(1), mailinspect(1), mailtoe(1), regex(7)
Version 1.12 Bayesian Text Classification Tools MAILFOOT(1)
