This is a multi-part message in MIME format. --Boundary_(ID_zzsknvVMvbB6Q6ljBH81DQ) Content-type: text/plain; charset=iso-8859-1 Content-transfer-encoding: 7BIT [Tim, predicting a false-positive rate] > I expect we can end up below 0.1% here, and with a generous > meaning for "not spam", We're there now, and still ignoring the headers. > but I think *some* of these examples show that the only way to get > a 0% false-positive rate is to recode spamprob like so: > > def spamprob(self, wordstream, evidence=False): > return 0.0 Likewise. I'll check in what I've got after this. Changes included: + Using the email pkg to decode (only) text parts of msgs, and, given multipart/alternative with both text/plain and text/html branches, ignoring the HTML part (else a newbie will never get a msg thru: all HTML decorations have monster-high spam probabilities). + Boosting MAX_DISCRIMINATORS, from 15 to 16. + Ignoring very short and very long "words" (this is Eurocentric). + Neither counting unique words once nor an unbounded number of times in the scoring. A word is counted at most twice now. This helps otherwise spamish msgs that have *some* highly relevant content, but doesn't, e.g., let spam through just because it says "Python" 80 times at the start. It helps the false negative rate more, although that may really be due to that UNKNOWN_SPAMPROB is too low (UNKNOWN_SPAMPROB is irrelevant to any of the false positives remaining, so I haven't run any tests varying that yet). I'll attach a complete listing of all false positives across the 20,000 ham msgs I've been using. People using c.l.py as an HTML clinic are out of luck. I'd personally call at least 5 of them spam, but I've been very reluctant to throw msgs out of the "good" archive -- nobody would question the ones I did throw out and replace. The false negative rate is still relatively high. In part that comes from getting the false positive rate so low (this is very much a tradeoff when both get low!), and in part because the spam corpus has a surprising number of msgs with absolutely nothing in the bodies. The latter generate no tokens, so end up with "probability" 0.5. The only thing I tried that cut the false negative rate in a major way was the special parsing+tagging of URLs in the body (see earlier msg), and that was a highly significant aid (it cut the false negative rate in half). There's good reason to hope that adding headers into the scoring would slash the false negative rate. Full results across all 20 runs; floats are percentages: Training on Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams false positive: 0.025 false negative: 2.10909090909 testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams false positive: 0.05 false negative: 2.47272727273 testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams false positive: 0.1 false negative: 2.50909090909 testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams false positive: 0.0500125031258 false negative: 2.8 Training on Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams false positive: 0.05 false negative: 2.8 testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams false positive: 0.075 false negative: 2.47272727273 testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams false positive: 0.15 false negative: 2.36363636364 testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams false positive: 0.0500125031258 false negative: 2.43636363636 Training on Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams false positive: 0.075 false negative: 3.16363636364 testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams false positive: 0.075 false negative: 2.43636363636 testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams false positive: 0.15 false negative: 2.90909090909 testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams false positive: 0.0750187546887 false negative: 2.61818181818 Training on Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams false positive: 0.1 false negative: 2.65454545455 testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams false positive: 0.1 false negative: 1.81818181818 testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams false positive: 0.1 false negative: 2.25454545455 testing against Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams false positive: 0.0750187546887 false negative: 2.50909090909 Training on Data/Ham/Set5 & Data/Spam/Set5 ... 3999 hams & 2750 spams testing against Data/Ham/Set1 & Data/Spam/Set1 ... 4000 hams & 2750 spams false positive: 0.075 false negative: 2.94545454545 testing against Data/Ham/Set2 & Data/Spam/Set2 ... 4000 hams & 2750 spams false positive: 0.05 false negative: 2.07272727273 testing against Data/Ham/Set3 & Data/Spam/Set3 ... 4000 hams & 2750 spams false positive: 0.1 false negative: 2.58181818182 testing against Data/Ham/Set4 & Data/Spam/Set4 ... 4000 hams & 2750 spams false positive: 0.15 false negative: 2.83636363636 The false positive rates vary by a factor of 6. This isn't significant, because the absolute numbers are so small; 0.025% is a single message, and it never gets higher than 0.150%. At these rates, I'd need test coropora about 10x larger to draw any fine distinction among false positive rates with high confidence. --Boundary_(ID_zzsknvVMvbB6Q6ljBH81DQ) Content-type: application/x-zip-compressed; name=fp.zip Content-transfer-encoding: base64 Content-disposition: attachment; filename=fp.zip UEsDBBQAAAAIAO4UHy0xMjhb1XoAABZuAQAGAAAAZnAudHh07FtZd9vGkn7XOfoPJSdnIt8IEHaA jBdREmTREZdLUl7GxycHJJokImwXiyTm4b7N/75VjYUgKdnOXCeTmTESi2Qv1dXVXVVfVTf+9rev +ezvnTuZc3zpBMdjlinHqmrJYnaf7e/FSTSF5yCJrfWjFMWHP4y7E/uHp0W1obc0raoYTH9ls6ys klRVMxWrqjtlSZY4oVvVylXFxWBUETNbmll3mFzWg5imoqt1ebJa3LIdMgPfu/VYslPe7eMAvc6k O+hX5FpfUtcbjOrxLU2y1vxeX13ttH5rn+6UnQ36k87ZZKf8TRcFuFPqeuksT1MvCtOj3cldjxod 9vcukihow5PJYHA1hrMonLOEhTOWPoFnWRT56Qn/K86i4MX+Xp/dpYskyuO0DVgSi74TLsQpy5yj 9c8gcnPfURsl0ZQlUdgoiFniiwGy2SxbZcuNRqmTLVnSKMhm/v7eOOc7ow0Fx3aeRDGDH1qtBvN8 M7I2jPPwCBQVes4KZNx2IJttxWqrEgiSKUn7e4Nk4YTeb06GsmrDiC3w0/Gh4/ueg3RgHiXQDfFv 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