mirror of https://github.com/carlostrub/sisyphus
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
131 lines
2.9 KiB
Go
131 lines
2.9 KiB
Go
package sisyphus
|
|
|
|
import (
|
|
"errors"
|
|
|
|
"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
|
|
}
|
|
|
|
// 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) {
|
|
|
|
err = db.View(func(tx *bolt.Tx) error {
|
|
var gN, jN uint64
|
|
|
|
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 = 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 = jWordHLL.Count()
|
|
}
|
|
|
|
p := tx.Bucket([]byte("Statistics"))
|
|
gHLL, err := hllpp.Unmarshal(p.Get([]byte("ProcessedGood")))
|
|
if err != nil {
|
|
return err
|
|
}
|
|
jHLL, err := hllpp.Unmarshal(p.Get([]byte("ProcessedJunk")))
|
|
if err != nil {
|
|
return err
|
|
}
|
|
|
|
gTotal := gHLL.Count()
|
|
if gTotal == 0 {
|
|
return errors.New("no good mails have been classified so far")
|
|
}
|
|
jTotal := jHLL.Count()
|
|
if jTotal == 0 {
|
|
return errors.New("no junk mails have been classified so far")
|
|
}
|
|
|
|
g = float64(gN) / float64(gTotal)
|
|
j = float64(jN) / float64(jTotal)
|
|
|
|
return nil
|
|
})
|
|
|
|
return g, j, nil
|
|
}
|
|
|
|
// 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
|
|
}
|
|
|
|
// Junk returns true if the wordlist is classified as a junk mail using Bayes'
|
|
// rule.
|
|
func Junk(db *bolt.DB, wordlist []string) (bool, error) {
|
|
var probabilities []float64
|
|
|
|
for _, val := range wordlist {
|
|
p, err := classificationWord(db, val)
|
|
if err != nil {
|
|
return false, err
|
|
}
|
|
probabilities = append(probabilities, p)
|
|
}
|
|
|
|
if stat.HarmonicMean(probabilities, nil) < 0.5 {
|
|
return true, nil
|
|
}
|
|
|
|
return false, nil
|
|
}
|