MAILTOE(1)MAILTOE(1)NAMEmailtoe - a train-on-error simulator for use with dbacl.
SYNOPSISmailtoe command [ command_arguments ]
DESCRIPTIONmailtoe automates the task of testing email filtering and classifica‐
tion 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 con‐
sists in reusing a fixed set of category databases until the first mis‐
classification 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 sam‐
ple emails are chosen carefully. However, as this method is commonly
used by spam filters, it is still worth computing to foster compar‐
isons. 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 sat‐
isfy the compatibility conditions stated in the ENVIRONMENT section
below.
For convenience, mailtoe implements a testsuite framework with prede‐
fined 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 STATUSmailtoe 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 sev‐
eral 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/local/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 dur‐
ing 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 com‐
mand 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/local/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 oth‐
erwise. 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 cat‐
egory names, e.g. if the classifier only supports binary classifica‐
tion. 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 INTERFACEmailtoe 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‐
toe testsuite.
Each wrapper script is a command line tool which accepts a single com‐
mand 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 wrap‐
per is cloned into the supplied directory. A courtesy notifica‐
tion 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 rele‐
vant 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 reali‐
ties 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 ALSObayesol(1)dbacl(1), mailinspect(1), mailcross(1), mailfoot(1),
regex(7)Version 1.14.1 Bayesian Text Classification Tools MAILTOE(1)
