mirror of https://github.com/carlostrub/sisyphus
many new things...
Carlo Strub
7 years ago
parent
237fe45ced
commit
0497999a15
@ -1,100 +1,130 @@
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/*
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Part of this code is borrowed from github.com/jbrukh/bayesian published under a BSD3CLAUSE License
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*/
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package sisyphus
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import (
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"math"
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"strconv"
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"errors"
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"github.com/boltdb/bolt"
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"github.com/gonum/stat"
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"github.com/retailnext/hllpp"
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)
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// classificationPriors returns the prior probabilities for good and junk
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// classificationPrior returns the prior probabilities for good and junk
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// classes.
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func classificationPriors(db *bolt.DB) (g, j float64) {
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func classificationPrior(db *bolt.DB) (g float64, err error) {
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db.View(func(tx *bolt.Tx) error {
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err = db.View(func(tx *bolt.Tx) error {
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b := tx.Bucket([]byte("Wordlists"))
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good := b.Bucket([]byte("Good"))
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gN := float64(good.Stats().KeyN)
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junk := b.Bucket([]byte("Junk"))
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jN := float64(junk.Stats().KeyN)
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// division by zero means there are no learned mails so far
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if (gN + jN) == 0 {
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return errors.New("no mails have been classified so far")
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}
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g = gN / (gN + jN)
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j = jN / (gN + jN)
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return nil
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})
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return
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return g, err
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}
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// classificationWordProb returns P(W|C_j) -- the probability of seeing
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// a particular word W in a document of this class.
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func classificationWordProb(db *bolt.DB, word string) (g, j float64) {
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// classificationLikelihood returns P(W|C_j) -- the probability of seeing a
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// particular word W in a document of this class.
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func classificationLikelihood(db *bolt.DB, word string) (g, j float64, err error) {
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err = db.View(func(tx *bolt.Tx) error {
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var gN, jN uint64
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db.View(func(tx *bolt.Tx) error {
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b := tx.Bucket([]byte("Wordlists"))
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good := b.Bucket([]byte("Good"))
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gNString := string(good.Get([]byte(word)))
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gN, _ := strconv.ParseFloat(gNString, 64)
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gWordRaw := good.Get([]byte(word))
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if len(gWordRaw) != 0 {
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gWordHLL, err := hllpp.Unmarshal(gWordRaw)
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if err != nil {
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return err
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}
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gN = gWordHLL.Count()
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}
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junk := b.Bucket([]byte("Junk"))
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jNString := string(junk.Get([]byte(word)))
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jN, _ := strconv.ParseFloat(jNString, 64)
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p := tx.Bucket([]byte("Processed"))
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counters := p.Bucket([]byte("Counters"))
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jString := string(counters.Get([]byte("Junk")))
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j, _ = strconv.ParseFloat(jString, 64)
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mails := p.Bucket([]byte("Mails"))
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pN := mails.Stats().KeyN
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g = gN / (float64(pN) - j)
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j = jN / j
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jWordRaw := junk.Get([]byte(word))
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if len(jWordRaw) != 0 {
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jWordHLL, err := hllpp.Unmarshal(jWordRaw)
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if err != nil {
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return err
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}
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jN = jWordHLL.Count()
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}
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p := tx.Bucket([]byte("Statistics"))
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gHLL, err := hllpp.Unmarshal(p.Get([]byte("ProcessedGood")))
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if err != nil {
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return err
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}
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jHLL, err := hllpp.Unmarshal(p.Get([]byte("ProcessedJunk")))
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if err != nil {
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return err
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}
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gTotal := gHLL.Count()
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if gTotal == 0 {
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return errors.New("no good mails have been classified so far")
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}
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jTotal := jHLL.Count()
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if jTotal == 0 {
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return errors.New("no junk mails have been classified so far")
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}
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g = float64(gN) / float64(gTotal)
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j = float64(jN) / float64(jTotal)
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return nil
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})
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return g, j
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return g, j, nil
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}
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// classificationWord produces the conditional probability of a word belonging
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// to good or junk using the classic Bayes' rule.
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func classificationWord(db *bolt.DB, word string) (g float64, err error) {
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priorG, err := classificationPrior(db)
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if err != nil {
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return g, err
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}
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likelihoodG, likelihoodJ, err := classificationLikelihood(db, word)
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if err != nil {
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return g, err
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}
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g = (likelihoodG * priorG) / (likelihoodG*priorG + likelihoodJ*(1-priorG))
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return g, nil
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}
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// LogScores produces "log-likelihood"-like scores that can
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// be used to classify documents into classes.
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//
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// The value of the score is proportional to the likelihood,
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// as determined by the classifier, that the given document
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// belongs to the given class. This is true even when scores
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// returned are negative, which they will be (since we are
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// taking logs of probabilities).
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//
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// The index j of the score corresponds to the class given
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// by c.Classes[j].
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//
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// Additionally returned are "inx" and "strict" values. The
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// inx corresponds to the maximum score in the array. If more
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// than one of the scores holds the maximum values, then
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// strict is false.
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//
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// Unlike c.Probabilities(), this function is not prone to
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// floating point underflow and is relatively safe to use.
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func LogScores(db *bolt.DB, wordlist []string) (scoreG, scoreJ float64, junk bool) {
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priorG, priorJ := classificationPriors(db)
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// calculate the scores
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scoreG = math.Log(priorG)
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scoreJ = math.Log(priorJ)
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for _, word := range wordlist {
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gP, jP := classificationWordProb(db, word)
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scoreG += math.Log(gP)
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scoreJ += math.Log(jP)
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// Junk returns true if the wordlist is classified as a junk mail using Bayes'
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// rule.
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func Junk(db *bolt.DB, wordlist []string) (bool, error) {
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var probabilities []float64
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for _, val := range wordlist {
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p, err := classificationWord(db, val)
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if err != nil {
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return false, err
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}
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probabilities = append(probabilities, p)
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}
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if scoreJ == math.Max(scoreG, scoreJ) {
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junk = true
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if stat.HarmonicMean(probabilities, nil) < 0.5 {
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return true, nil
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}
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return scoreG, scoreJ, junk
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return false, nil
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}
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@ -0,0 +1,21 @@
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[
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{"linter":"gas","severity":"warning","path":"bayesian.go","line":18,"col":0,"message":"Errors unhandled.,LOW,HIGH"},
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{"linter":"gas","severity":"warning","path":"bayesian.go","line":38,"col":0,"message":"Errors unhandled.,LOW,HIGH"},
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{"linter":"gas","severity":"warning","path":"bayesian.go","line":42,"col":0,"message":"Errors unhandled.,LOW,HIGH"},
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{"linter":"gas","severity":"warning","path":"bayesian.go","line":45,"col":0,"message":"Errors unhandled.,LOW,HIGH"},
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{"linter":"gas","severity":"warning","path":"bayesian.go","line":50,"col":0,"message":"Errors unhandled.,LOW,HIGH"},
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{"linter":"gas","severity":"warning","path":"daemon.go","line":45,"col":0,"message":"Subprocess launching with variable.,HIGH,HIGH"},
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{"linter":"gas","severity":"warning","path":"daemon.go","line":115,"col":0,"message":"Subprocess launching with variable.,HIGH,HIGH"},
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{"linter":"gas","severity":"warning","path":"daemon.go","line":122,"col":0,"message":"Subprocess launching with variable.,HIGH,HIGH"},
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{"linter":"gocyclo","severity":"warning","path":"mail.go","line":232,"col":0,"message":"cyclomatic complexity 16 of function (*Mail).Classify() is high (\u003e 10)"},
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{"linter":"dupl","severity":"warning","path":"mail_test.go","line":135,"col":0,"message":"duplicate of mail_test.go:160-183"},
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{"linter":"dupl","severity":"warning","path":"mail_test.go","line":160,"col":0,"message":"duplicate of mail_test.go:185-208"},
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{"linter":"dupl","severity":"warning","path":"mail_test.go","line":185,"col":0,"message":"duplicate of mail_test.go:210-233"},
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{"linter":"dupl","severity":"warning","path":"mail_test.go","line":210,"col":0,"message":"duplicate of mail_test.go:235-258"},
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{"linter":"dupl","severity":"warning","path":"mail_test.go","line":235,"col":0,"message":"duplicate of mail_test.go:260-283"},
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{"linter":"dupl","severity":"warning","path":"mail_test.go","line":260,"col":0,"message":"duplicate of mail_test.go:135-158"},
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{"linter":"errcheck","severity":"warning","path":"bayesian.go","line":18,"col":9,"message":"error return value not checked (db.View(func(tx *bolt.Tx) error {)"},
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{"linter":"errcheck","severity":"warning","path":"bayesian.go","line":38,"col":9,"message":"error return value not checked (db.View(func(tx *bolt.Tx) error {)"},
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{"linter":"errcheck","severity":"warning","path":"daemon.go","line":26,"col":18,"message":"error return value not checked (defer file.Close())"},
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{"linter":"errcheck","severity":"warning","path":"mail.go","line":275,"col":11,"message":"error return value not checked (db.Update(func(tx *bolt.Tx) error {)"}
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]
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