package sisyphus import ( "errors" "log" "os" "strconv" "github.com/boltdb/bolt" "github.com/gonum/stat" "github.com/retailnext/hllpp" ) // classificationPrior returns the prior probabilities for good and junk // classes. func classificationPrior(db *bolt.DB) (g float64, err error) { err = db.View(func(tx *bolt.Tx) error { b := tx.Bucket([]byte("Wordlists")) good := b.Bucket([]byte("Good")) gN := float64(good.Stats().KeyN) junk := b.Bucket([]byte("Junk")) jN := float64(junk.Stats().KeyN) // division by zero means there are no learned mails so far if (gN + jN) == 0 { return errors.New("no mails have been classified so far") } g = gN / (gN + jN) return nil }) return g, err } // classificationLikelihoodWordcounts gets wordcounts from database to be used // in Likelihood calculation func classificationLikelihoodWordcounts(db *bolt.DB, word string) (gN, jN float64, err error) { err = db.View(func(tx *bolt.Tx) error { b := tx.Bucket([]byte("Wordlists")) good := b.Bucket([]byte("Good")) gWordRaw := good.Get([]byte(word)) if len(gWordRaw) > 0 { gWordHLL, err := hllpp.Unmarshal(gWordRaw) if err != nil { return err } gN = float64(gWordHLL.Count()) } junk := b.Bucket([]byte("Junk")) jWordRaw := junk.Get([]byte(word)) if len(jWordRaw) > 0 { jWordHLL, err := hllpp.Unmarshal(jWordRaw) if err != nil { return err } jN = float64(jWordHLL.Count()) } return nil }) return gN, jN, err } // classificationLikelihoodStatistics gets global statistics from database to // be used in Likelihood calculation func classificationLikelihoodStatistics(db *bolt.DB, word string) (gTotal, jTotal float64, err error) { err = db.View(func(tx *bolt.Tx) error { p := tx.Bucket([]byte("Statistics")) gRaw := p.Get([]byte("ProcessedGood")) if len(gRaw) > 0 { gHLL, err := hllpp.Unmarshal(gRaw) if err != nil { return err } gTotal = float64(gHLL.Count()) } jRaw := p.Get([]byte("ProcessedJunk")) if len(jRaw) > 0 { jHLL, err := hllpp.Unmarshal(jRaw) if err != nil { return err } jTotal = float64(jHLL.Count()) } if gTotal == 0 { return errors.New("no good mails have yet been classified") } if jTotal == 0 { return errors.New("no junk mails have yet been classified") } return nil }) return gTotal, jTotal, err } // classificationLikelihood returns P(W|C_j) -- the probability of seeing a // particular word W in a document of this class. func classificationLikelihood(db *bolt.DB, word string) (g, j float64, err error) { gN, jN, err := classificationLikelihoodWordcounts(db, word) if err != nil { return g, j, err } gTotal, jTotal, err := classificationLikelihoodStatistics(db, word) if err != nil { return g, j, err } g = gN / gTotal j = jN / jTotal return g, j, err } // classificationWord produces the conditional probability of a word belonging // to good or junk using the classic Bayes' rule. func classificationWord(db *bolt.DB, word string) (g float64, err error) { priorG, err := classificationPrior(db) if err != nil { return g, err } likelihoodG, likelihoodJ, err := classificationLikelihood(db, word) if err != nil { return g, err } g = (likelihoodG * priorG) / (likelihoodG*priorG + likelihoodJ*(1-priorG)) return g, nil } // Classify analyses a new mail (a mail that arrived in the "new" directory), // decides whether it is junk and -- if so -- moves it to the Junk folder. If // it is not junk, the mail is untouched so it can be handled by the mail // client. func (m *Mail) Classify(db *bolt.DB) error { list, err := m.cleanWordlist() if err != nil { return err } junk, _, err := Junk(db, list) if err != nil { return err } log.Print("Classified " + m.Key + " as Junk=" + strconv.FormatBool(m.Junk)) // Move mail around if junk. if junk { m.Junk = junk err := os.Rename("./new/"+m.Key, "./.Junk/cur/"+m.Key) if err != nil { return err } log.Print("Moved " + m.Key + " from new to Junk folder") } return nil } // Junk returns true if the wordlist is classified as a junk mail using Bayes' // rule. If required, it also returns the calculated probability of being junk, // but this is typically not needed. func Junk(db *bolt.DB, wordlist []string) (junk bool, prob float64, err error) { var probabilities []float64 for _, val := range wordlist { p, err := classificationWord(db, val) if err != nil { return false, prob, err } probabilities = append(probabilities, p) } prob = stat.HarmonicMean(probabilities, nil) if prob < 0.5 { return true, (1 - prob), nil } return false, (1 - prob), nil }