mirror of
https://github.com/hanzoai/mail.git
synced 2026-09-23 23:53:02 +00:00
Fork of github.com/mjl-/mox at 5656ca6d1b
(upstream v0.0.15 plus 55 commits).
Rename the module path in go.mod and the import paths in 268 Go files. Also
update the five scripts that "go install" the module (genwebsite.sh,
docker-release.sh, apidiff.sh, test-upgrade.sh, Dockerfile.release), which
would otherwise build upstream rather than this fork.
Add NOTICE recording the upstream project, author, license, and fork point,
and stating which license covers which files: MIT for the mox source and our
changes, MPL-2.0 for publicsuffix/public_suffix_list.txt alone, and the
vendored dependencies' own licenses under licenses/. Embed NOTICE so
"mail licenses" prints the lineage from the running binary. LICENSE.MIT,
LICENSE.MPLv2.0 and the licenses/ tree are preserved verbatim; no code is
relicensed.
The mox command name, config keys, data directory layout and website content
are unchanged. LLM.md records the fork point, the rename, what was left alone,
and the steps to stand the server up.
Co-authored-by: Hanzo Dev <[email protected]>
434 lines
13 KiB
Go
434 lines
13 KiB
Go
package main
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/*
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note: these testdata paths are not in the repo, you should gather some of your
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own ham/spam emails.
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./mox junk train testdata/train/ham testdata/train/spam
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./mox junk train -sent-dir testdata/sent testdata/train/ham testdata/train/spam
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./mox junk check 'testdata/check/ham/mail1'
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./mox junk test testdata/check/ham testdata/check/spam
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./mox junk analyze testdata/train/ham testdata/train/spam
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./mox junk analyze -top-words 10 -train-ratio 0.5 -spam-threshold 0.85 -max-power 0.01 -sent-dir testdata/sent testdata/train/ham testdata/train/spam
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./mox junk play -top-words 10 -train-ratio 0.5 -spam-threshold 0.85 -max-power 0.01 -sent-dir testdata/sent testdata/train/ham testdata/train/spam
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*/
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import (
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"context"
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"flag"
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"fmt"
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"log"
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mathrand "math/rand"
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"os"
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"path/filepath"
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"sort"
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"time"
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"github.com/hanzoai/mail/junk"
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"github.com/hanzoai/mail/message"
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"github.com/hanzoai/mail/mlog"
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"github.com/hanzoai/mail/mox-"
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)
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type junkArgs struct {
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params junk.Params
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spamThreshold float64
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trainRatio float64
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seed bool
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sentDir string
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databasePath, bloomfilterPath string
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debug bool
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}
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func (a junkArgs) SetLogLevel() {
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mox.Conf.Log[""] = mlog.LevelInfo
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if a.debug {
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mox.Conf.Log[""] = mlog.LevelDebug
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}
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mlog.SetConfig(mox.Conf.Log)
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}
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func junkFlags(fs *flag.FlagSet) (a junkArgs) {
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fs.BoolVar(&a.params.Onegrams, "one-grams", false, "use 1-grams, i.e. single words, for scoring")
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fs.BoolVar(&a.params.Twograms, "two-grams", true, "use 2-grams, i.e. word pairs, for scoring")
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fs.BoolVar(&a.params.Threegrams, "three-grams", false, "use 3-grams, i.e. word triplets, for scoring")
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fs.Float64Var(&a.params.MaxPower, "max-power", 0.05, "maximum word power, e.g. min 0.05/max 0.95")
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fs.Float64Var(&a.params.IgnoreWords, "ignore-words", 0.1, "ignore words with ham/spaminess within this distance from 0.5")
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fs.IntVar(&a.params.TopWords, "top-words", 10, "number of top spam and number of top ham words from email to use")
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fs.IntVar(&a.params.RareWords, "rare-words", 1, "words are rare if encountered this number during training, and skipped for scoring")
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fs.BoolVar(&a.debug, "debug", false, "print debug logging when calculating spam probability")
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fs.Float64Var(&a.spamThreshold, "spam-threshold", 0.95, "probability where message is seen as spam")
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fs.Float64Var(&a.trainRatio, "train-ratio", 0.5, "part of data to use for training versus analyzing (for analyze only)")
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fs.StringVar(&a.sentDir, "sent-dir", "", "directory with sent mails, for training")
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fs.BoolVar(&a.seed, "seed", false, "seed prng before analysis")
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fs.StringVar(&a.databasePath, "dbpath", "filter.db", "database file for ham/spam words")
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fs.StringVar(&a.bloomfilterPath, "bloompath", "filter.bloom", "bloom filter for ignoring unique strings")
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return
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}
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func listDir(dir string) (l []string) {
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files, err := os.ReadDir(dir)
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xcheckf(err, "listing directory %q", dir)
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for _, f := range files {
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l = append(l, f.Name())
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}
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return l
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}
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func must(f *junk.Filter, err error) *junk.Filter {
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xcheckf(err, "filter")
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return f
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}
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func cmdJunkTrain(c *cmd) {
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c.unlisted = true
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c.params = "hamdir spamdir"
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c.help = "Train a junk filter with messages from hamdir and spamdir."
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a := junkFlags(c.flag)
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args := c.Parse()
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if len(args) != 2 {
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c.Usage()
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}
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a.SetLogLevel()
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f := must(junk.NewFilter(context.Background(), c.log, a.params, a.databasePath, a.bloomfilterPath))
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defer func() {
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if err := f.Close(); err != nil {
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log.Printf("closing junk filter: %v", err)
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}
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}()
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hamFiles := listDir(args[0])
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spamFiles := listDir(args[1])
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var sentFiles []string
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if a.sentDir != "" {
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sentFiles = listDir(a.sentDir)
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}
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err := f.TrainDirs(args[0], a.sentDir, args[1], hamFiles, sentFiles, spamFiles)
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xcheckf(err, "train")
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}
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func cmdJunkCheck(c *cmd) {
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c.unlisted = true
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c.params = "mailfile"
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c.help = "Check an email message against a junk filter, printing the probability of spam on a scale from 0 to 1."
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a := junkFlags(c.flag)
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args := c.Parse()
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if len(args) != 1 {
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c.Usage()
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}
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a.SetLogLevel()
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f := must(junk.OpenFilter(context.Background(), c.log, a.params, a.databasePath, a.bloomfilterPath, false))
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defer func() {
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if err := f.Close(); err != nil {
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log.Printf("closing junk filter: %v", err)
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}
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}()
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result, err := f.ClassifyMessagePath(context.Background(), args[0])
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xcheckf(err, "testing mail")
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sig := "significant"
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if !result.Significant {
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sig = "not significant"
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}
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fmt.Printf("%.6f, %s\n", result.Probability, sig)
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}
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func cmdJunkTest(c *cmd) {
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c.unlisted = true
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c.params = "hamdir spamdir"
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c.help = "Check a directory with hams and one with spams against the junk filter, and report the success ratio."
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a := junkFlags(c.flag)
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args := c.Parse()
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if len(args) != 2 {
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c.Usage()
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}
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a.SetLogLevel()
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f := must(junk.OpenFilter(context.Background(), c.log, a.params, a.databasePath, a.bloomfilterPath, false))
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defer func() {
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if err := f.Close(); err != nil {
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log.Printf("closing junk filter: %v", err)
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}
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}()
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testDir := func(dir string, ham bool) (int, int) {
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ok, bad := 0, 0
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files, err := os.ReadDir(dir)
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xcheckf(err, "readdir %q", dir)
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for _, fi := range files {
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path := filepath.Join(dir, fi.Name())
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result, err := f.ClassifyMessagePath(context.Background(), path)
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if err != nil {
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log.Printf("classify message %q: %s", path, err)
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continue
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}
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if ham && result.Probability < a.spamThreshold || !ham && result.Probability > a.spamThreshold {
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ok++
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} else {
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bad++
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}
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if ham && result.Probability > a.spamThreshold {
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fmt.Printf("ham %q: %.4f\n", path, result.Probability)
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}
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if !ham && result.Probability < a.spamThreshold {
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fmt.Printf("spam %q: %.4f\n", path, result.Probability)
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}
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}
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return ok, bad
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}
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nhamok, nhambad := testDir(args[0], true)
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nspamok, nspambad := testDir(args[1], false)
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fmt.Printf("total ham, ok %d, bad %d\n", nhamok, nhambad)
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fmt.Printf("total spam, ok %d, bad %d\n", nspamok, nspambad)
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fmt.Printf("specifity (true negatives, hams identified): %.6f\n", float64(nhamok)/(float64(nhamok+nhambad)))
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fmt.Printf("sensitivity (true positives, spams identified): %.6f\n", float64(nspamok)/(float64(nspamok+nspambad)))
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fmt.Printf("accuracy: %.6f\n", float64(nhamok+nspamok)/float64(nhamok+nhambad+nspamok+nspambad))
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}
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func cmdJunkAnalyze(c *cmd) {
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c.unlisted = true
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c.params = "hamdir spamdir"
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c.help = `Analyze a directory with ham messages and one with spam messages.
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A part of the messages is used for training, and remaining for testing. The
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messages are shuffled, with optional random seed.`
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a := junkFlags(c.flag)
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args := c.Parse()
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if len(args) != 2 {
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c.Usage()
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}
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a.SetLogLevel()
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f := must(junk.NewFilter(context.Background(), c.log, a.params, a.databasePath, a.bloomfilterPath))
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defer func() {
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if err := f.Close(); err != nil {
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log.Printf("closing junk filter: %v", err)
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}
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}()
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hamDir := args[0]
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spamDir := args[1]
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hamFiles := listDir(hamDir)
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spamFiles := listDir(spamDir)
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var seed int64
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if a.seed {
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seed = time.Now().UnixMilli()
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}
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// Still at math/rand (v1 instead of v2) for potential comparison to earlier test results.
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rand := mathrand.New(mathrand.NewSource(seed))
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shuffle := func(l []string) {
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count := len(l)
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for i := range l {
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n := rand.Intn(count)
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l[i], l[n] = l[n], l[i]
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}
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}
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shuffle(hamFiles)
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shuffle(spamFiles)
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ntrainham := int(a.trainRatio * float64(len(hamFiles)))
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ntrainspam := int(a.trainRatio * float64(len(spamFiles)))
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trainHam := hamFiles[:ntrainham]
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trainSpam := spamFiles[:ntrainspam]
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testHam := hamFiles[ntrainham:]
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testSpam := spamFiles[ntrainspam:]
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var trainSent []string
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if a.sentDir != "" {
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trainSent = listDir(a.sentDir)
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}
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err := f.TrainDirs(hamDir, a.sentDir, spamDir, trainHam, trainSent, trainSpam)
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xcheckf(err, "train")
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testDir := func(dir string, files []string, ham bool) (ok, bad, malformed int) {
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for _, name := range files {
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path := filepath.Join(dir, name)
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result, err := f.ClassifyMessagePath(context.Background(), path)
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if err != nil {
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// log.Infof("%s: %s", path, err)
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malformed++
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continue
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}
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if ham && result.Probability < a.spamThreshold || !ham && result.Probability > a.spamThreshold {
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ok++
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} else {
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bad++
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}
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if ham && result.Probability > a.spamThreshold {
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fmt.Printf("ham %q: %.4f\n", path, result.Probability)
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}
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if !ham && result.Probability < a.spamThreshold {
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fmt.Printf("spam %q: %.4f\n", path, result.Probability)
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}
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}
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return
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}
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nhamok, nhambad, nmalformedham := testDir(args[0], testHam, true)
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nspamok, nspambad, nmalformedspam := testDir(args[1], testSpam, false)
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fmt.Printf("training done, nham %d, nsent %d, nspam %d\n", ntrainham, len(trainSent), ntrainspam)
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fmt.Printf("total ham, ok %d, bad %d, malformed %d\n", nhamok, nhambad, nmalformedham)
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fmt.Printf("total spam, ok %d, bad %d, malformed %d\n", nspamok, nspambad, nmalformedspam)
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fmt.Printf("specifity (true negatives, hams identified): %.6f\n", float64(nhamok)/(float64(nhamok+nhambad)))
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fmt.Printf("sensitivity (true positives, spams identified): %.6f\n", float64(nspamok)/(float64(nspamok+nspambad)))
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fmt.Printf("accuracy: %.6f\n", float64(nhamok+nspamok)/float64(nhamok+nhambad+nspamok+nspambad))
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}
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func cmdJunkPlay(c *cmd) {
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c.unlisted = true
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c.params = "hamdir spamdir"
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c.help = "Play messages from ham and spam directory according to their time of arrival and report on junk filter performance."
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a := junkFlags(c.flag)
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args := c.Parse()
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if len(args) != 2 {
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c.Usage()
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}
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a.SetLogLevel()
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f := must(junk.NewFilter(context.Background(), c.log, a.params, a.databasePath, a.bloomfilterPath))
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defer func() {
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if err := f.Close(); err != nil {
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log.Printf("closing junk filter: %v", err)
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}
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}()
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// We'll go through all emails to find their dates.
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type msg struct {
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dir, filename string
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ham, sent bool
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t time.Time
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}
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var msgs []msg
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var nbad, nnodate, nham, nspam, nsent int
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scanDir := func(dir string, ham, sent bool) {
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for _, name := range listDir(dir) {
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path := filepath.Join(dir, name)
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mf, err := os.Open(path)
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xcheckf(err, "open %q", path)
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fi, err := mf.Stat()
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xcheckf(err, "stat %q", path)
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p, err := message.EnsurePart(c.log.Logger, false, mf, fi.Size())
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if err != nil {
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nbad++
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if err := mf.Close(); err != nil {
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log.Printf("closing message file: %v", err)
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}
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continue
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}
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if p.Envelope.Date.IsZero() {
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nnodate++
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if err := mf.Close(); err != nil {
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log.Printf("closing message file: %v", err)
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}
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continue
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}
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if err := mf.Close(); err != nil {
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log.Printf("closing message file: %v", err)
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}
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msgs = append(msgs, msg{dir, name, ham, sent, p.Envelope.Date})
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if sent {
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nsent++
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} else if ham {
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nham++
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} else {
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nspam++
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}
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}
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}
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hamDir := args[0]
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spamDir := args[1]
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scanDir(hamDir, true, false)
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scanDir(spamDir, false, false)
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if a.sentDir != "" {
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scanDir(a.sentDir, true, true)
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}
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// Sort the messages, earliest first.
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sort.Slice(msgs, func(i, j int) bool {
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return msgs[i].t.Before(msgs[j].t)
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})
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// Play all messages as if they are coming in. We predict their spaminess, check if
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// we are right. And we train the system with the result.
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var nhamok, nhambad, nspamok, nspambad int
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play := func(msg msg) {
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var words map[string]struct{}
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path := filepath.Join(msg.dir, msg.filename)
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if !msg.sent {
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result, err := f.ClassifyMessagePath(context.Background(), path)
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if err != nil {
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nbad++
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return
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}
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if msg.ham {
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if result.Probability < a.spamThreshold {
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nhamok++
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} else {
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nhambad++
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}
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} else {
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if result.Probability > a.spamThreshold {
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nspamok++
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} else {
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nspambad++
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}
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}
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} else {
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mf, err := os.Open(path)
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xcheckf(err, "open %q", path)
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defer func() {
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if err := mf.Close(); err != nil {
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log.Printf("closing message file: %v", err)
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}
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}()
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fi, err := mf.Stat()
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xcheckf(err, "stat %q", path)
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p, err := message.EnsurePart(c.log.Logger, false, mf, fi.Size())
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if err != nil {
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log.Printf("bad sent message %q: %s", path, err)
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return
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}
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words, err = f.ParseMessage(p)
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if err != nil {
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log.Printf("bad sent message %q: %s", path, err)
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return
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}
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}
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if err := f.Train(context.Background(), msg.ham, words); err != nil {
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log.Printf("train: %s", err)
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}
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}
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for _, m := range msgs {
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play(m)
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}
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err := f.Save()
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xcheckf(err, "saving filter")
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fmt.Printf("completed, nham %d, nsent %d, nspam %d, nbad %d, nwithoutdate %d\n", nham, nsent, nspam, nbad, nnodate)
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fmt.Printf("total ham, ok %d, bad %d\n", nhamok, nhambad)
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fmt.Printf("total spam, ok %d, bad %d\n", nspamok, nspambad)
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fmt.Printf("specifity (true negatives, hams identified): %.6f\n", float64(nhamok)/(float64(nhamok+nhambad)))
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fmt.Printf("sensitivity (true positives, spams identified): %.6f\n", float64(nspamok)/(float64(nspamok+nspambad)))
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fmt.Printf("accuracy: %.6f\n", float64(nhamok+nspamok)/float64(nhamok+nhambad+nspamok+nspambad))
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}
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