Using hit-frequencies

Once you've run MassCheck, you have a "ham.log" and a "spam.log" file. To turn those into a useful summary, you run "hit-frequencies" to generate a "freqs report". Here's how – run:

    make clean
    make freqs

That will take "ham.log" and "spam.log" and generate a "freqs" file from the data. This gives you the frequencies that each rule hits and details of its accuracy in hitting spam vs. ham. Its format looks like this:

OVERALL%   SPAM%     HAM%     S/O    RANK   SCORE  NAME
   6317     2614     3703    0.414   0.00    0.00  (all messages)
100.000  41.3804  58.6196    0.414   0.00    0.00  (all messages as %)
  2.153   5.2028   0.0000    1.000   1.00    4.30  RCVD_IN_OPM_HTTP
  1.219   0.0000   2.0794    0.000   1.00   -0.10  RCVD_IN_BSP_OTHER
  0.364   0.8799   0.0000    1.000   0.99    4.30  RCVD_IN_OPM_SOCKS
  0.332   0.0000   0.5671    0.000   0.99   -4.30  RCVD_IN_BSP_TRUSTED
  0.063   0.1530   0.0000    1.000   0.99    4.30  RCVD_IN_OPM_WINGATE
  1.061   2.5249   0.0270    0.989   0.96    0.64  RCVD_IN_NJABL_SPAM
  0.697   1.6067   0.0540    0.967   0.90    1.10  RCVD_IN_SORBS_SMTP
  1.520   3.4430   0.1620    0.955   0.87    1.10  RCVD_IN_SORBS_HTTP

The columns are:

• OVERALL%: the percentage of mail overall that the test hits
• SPAM%: the percentage of spam mails hit by the rule
• HAM%: the percentage of ham mails hit by the rule
• S/O: "spam over overall" – the Bayesian probability that, when the rule fires, it hits on a spam message
• RANK: an artificial number indicating how "good" the rule is
• SCORE: the score listed in the "../rules/50_scores.cf" file for that rule
• NAME: the rule's name
  

The first two lines list the number of messages in the corpora, and the percentage makeup of the corpus as ham vs. spam (so in this example, the corpus is 41.38% spam vs 58.61% ham).

"freqs" is the best way to determine a rule's usefulness, since it immediately shows up any false-positive issues. The development team run a nightly mass-check and freqs report from the rules in CVS to test them, with several people scanning their corpora, and the results are put up at: http://www.pathname.com/~corpus/ .

The S/O Ratio

S/O needs more explanation, as it's a key figure. A rule with S/O 1.0 is very very accurate at hitting spam without hitting ham; a rule with S/O 0.0 hits only ham, no spam; but a rule with 0.5 hits both evenly (and is therefore pretty useless).

A good rule has a very extreme S/O (near as possible to 1.0 or 0.0) and a high percentage of hits in the correct category. In other words, RCVD_IN_OPM_HTTP is a very good rule in the example above, because it hits 5.2028% of all spam mails without hitting any ham at all (no false positives).

S/O stands for "spam / overall", in other words, the proportion of the total hits that were spam messages. As such, it is equivalent to Bayesian probability, or 'positive predictive value' in pattern discovery in bioinformatics.

Measuring Rule Overlap

There's one more tool to determine how much 2 rules overlap with each other – "overlap". This is occasionally useful if you suspect that two rules are redundant, checking the same data or hitting exactly the same messages as each other. Take a look at the comments at the top of the "masses/overlap" script for details on how to run this against one or more "mass-check" output log files.

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