Add bayes classifier stubs, string normalizer, and deps, update sample config
This commit is contained in:
eleksir
557
vendor/github.com/jbrukh/bayesian/bayesian.go
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vendored
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557
vendor/github.com/jbrukh/bayesian/bayesian.go
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package bayesian
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import (
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"encoding/gob"
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"errors"
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"io"
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"math"
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"os"
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"path/filepath"
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"sync/atomic"
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)
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// defaultProb is the tiny non-zero probability that a word
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// we have not seen before appears in the class.
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const defaultProb = 0.00000000001
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// ErrUnderflow is returned when an underflow is detected.
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var ErrUnderflow = errors.New("possible underflow detected")
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// Class defines a class that the classifier will filter:
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// C = {C_1, ..., C_n}. You should define your classes as a
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// set of constants, for example as follows:
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//
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// const (
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// Good Class = "Good"
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// Bad Class = "Bad
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// )
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//
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// Class values should be unique.
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type Class string
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// Classifier implements the Naive Bayesian Classifier.
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type Classifier struct {
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Classes []Class
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learned int // docs learned
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seen int32 // docs seen
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datas map[Class]*classData
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tfIdf bool
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DidConvertTfIdf bool // we can't classify a TF-IDF classifier if we haven't yet
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// called ConverTermsFreqToTfIdf
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}
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// serializableClassifier represents a container for
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// Classifier objects whose fields are modifiable by
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// reflection and are therefore writeable by gob.
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type serializableClassifier struct {
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Classes []Class
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Learned int
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Seen int
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Datas map[Class]*classData
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TfIdf bool
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DidConvertTfIdf bool
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}
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// classData holds the frequency data for words in a
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// particular class. In the future, we may replace this
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// structure with a trie-like structure for more
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// efficient storage.
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type classData struct {
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Freqs map[string]float64
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FreqTfs map[string][]float64
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Total int
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}
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// newClassData creates a new empty classData node.
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func newClassData() *classData {
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return &classData{
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Freqs: make(map[string]float64),
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FreqTfs: make(map[string][]float64),
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}
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}
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// getWordProb 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 (d *classData) getWordProb(word string) float64 {
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value, ok := d.Freqs[word]
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if !ok {
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return defaultProb
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}
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return float64(value) / float64(d.Total)
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}
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// getWordsProb returns P(D|C_j) -- the probability of seeing
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// this set of words in a document of this class.
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//
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// Note that words should not be empty, and this method of
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// calulation is prone to underflow if there are many words
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// and their individual probabilties are small.
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func (d *classData) getWordsProb(words []string) (prob float64) {
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prob = 1
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for _, word := range words {
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prob *= d.getWordProb(word)
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}
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return
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}
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// NewClassifierTfIdf returns a new classifier. The classes provided
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// should be at least 2 in number and unique, or this method will
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// panic.
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func NewClassifierTfIdf(classes ...Class) (c *Classifier) {
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n := len(classes)
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// check size
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if n < 2 {
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panic("provide at least two classes")
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}
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// check uniqueness
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check := make(map[Class]bool, n)
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for _, class := range classes {
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check[class] = true
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}
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if len(check) != n {
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panic("classes must be unique")
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}
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// create the classifier
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c = &Classifier{
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Classes: classes,
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datas: make(map[Class]*classData, n),
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tfIdf: true,
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}
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for _, class := range classes {
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c.datas[class] = newClassData()
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}
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return
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}
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// NewClassifier returns a new classifier. The classes provided
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// should be at least 2 in number and unique, or this method will
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// panic.
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func NewClassifier(classes ...Class) (c *Classifier) {
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n := len(classes)
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// check size
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if n < 2 {
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panic("provide at least two classes")
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}
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// check uniqueness
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check := make(map[Class]bool, n)
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for _, class := range classes {
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check[class] = true
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}
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if len(check) != n {
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panic("classes must be unique")
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}
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// create the classifier
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c = &Classifier{
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Classes: classes,
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datas: make(map[Class]*classData, n),
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tfIdf: false,
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DidConvertTfIdf: false,
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}
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for _, class := range classes {
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c.datas[class] = newClassData()
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}
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return
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}
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// NewClassifierFromFile loads an existing classifier from
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// file. The classifier was previously saved with a call
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// to c.WriteToFile(string).
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func NewClassifierFromFile(name string) (c *Classifier, err error) {
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file, err := os.Open(name)
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if err != nil {
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return nil, err
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}
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defer file.Close()
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return NewClassifierFromReader(file)
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}
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// NewClassifierFromReader: This actually does the deserializing of a Gob encoded classifier
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func NewClassifierFromReader(r io.Reader) (c *Classifier, err error) {
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dec := gob.NewDecoder(r)
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w := new(serializableClassifier)
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err = dec.Decode(w)
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return &Classifier{w.Classes, w.Learned, int32(w.Seen), w.Datas, w.TfIdf, w.DidConvertTfIdf}, err
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}
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// getPriors returns the prior probabilities for the
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// classes provided -- P(C_j).
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//
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// TODO: There is a way to smooth priors, currently
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// not implemented here.
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func (c *Classifier) getPriors() (priors []float64) {
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n := len(c.Classes)
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priors = make([]float64, n, n)
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sum := 0
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for index, class := range c.Classes {
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total := c.datas[class].Total
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priors[index] = float64(total)
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sum += total
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}
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if sum != 0 {
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for i := 0; i < n; i++ {
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priors[i] /= float64(sum)
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}
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}
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return
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}
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// Learned returns the number of documents ever learned
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// in the lifetime of this classifier.
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func (c *Classifier) Learned() int {
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return c.learned
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}
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// Seen returns the number of documents ever classified
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// in the lifetime of this classifier.
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func (c *Classifier) Seen() int {
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return int(atomic.LoadInt32(&c.seen))
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}
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// IsTfIdf returns true if we are a classifier of type TfIdf
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func (c *Classifier) IsTfIdf() bool {
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return c.tfIdf
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}
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// WordCount returns the number of words counted for
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// each class in the lifetime of the classifier.
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func (c *Classifier) WordCount() (result []int) {
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result = make([]int, len(c.Classes))
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for inx, class := range c.Classes {
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data := c.datas[class]
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result[inx] = data.Total
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}
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return
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}
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// Observe should be used when word-frequencies have been already been learned
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// externally (e.g., hadoop)
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func (c *Classifier) Observe(word string, count int, which Class) {
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data := c.datas[which]
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data.Freqs[word] += float64(count)
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data.Total += count
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}
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// Learn will accept new training documents for
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// supervised learning.
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func (c *Classifier) Learn(document []string, which Class) {
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// If we are a tfidf classifier we first need to get terms as
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// terms frequency and store that to work out the idf part later
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// in ConvertToIDF().
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if c.tfIdf {
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if c.DidConvertTfIdf {
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panic("Cannot call ConvertTermsFreqToTfIdf more than once. Reset and relearn to reconvert.")
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}
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// Term Frequency: word count in document / document length
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docTf := make(map[string]float64)
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for _, word := range document {
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docTf[word]++
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}
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docLen := float64(len(document))
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for wIndex, wCount := range docTf {
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docTf[wIndex] = wCount / docLen
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// add the TF sample, after training we can get IDF values.
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c.datas[which].FreqTfs[wIndex] = append(c.datas[which].FreqTfs[wIndex], docTf[wIndex])
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}
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}
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data := c.datas[which]
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for _, word := range document {
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data.Freqs[word]++
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data.Total++
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}
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c.learned++
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}
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// ConvertTermsFreqToTfIdf uses all the TF samples for the class and converts
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// them to TF-IDF https://en.wikipedia.org/wiki/Tf%E2%80%93idf
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// once we have finished learning all the classes and have the totals.
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func (c *Classifier) ConvertTermsFreqToTfIdf() {
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if c.DidConvertTfIdf {
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panic("Cannot call ConvertTermsFreqToTfIdf more than once. Reset and relearn to reconvert.")
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}
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for className := range c.datas {
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for wIndex := range c.datas[className].FreqTfs {
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tfIdfAdder := float64(0)
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for tfSampleIndex := range c.datas[className].FreqTfs[wIndex] {
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// we always want a possitive TF-IDF score.
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tf := c.datas[className].FreqTfs[wIndex][tfSampleIndex]
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c.datas[className].FreqTfs[wIndex][tfSampleIndex] = math.Log1p(tf) * math.Log1p(float64(c.learned)/float64(c.datas[className].Total))
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tfIdfAdder += c.datas[className].FreqTfs[wIndex][tfSampleIndex]
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}
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// convert the 'counts' to TF-IDF's
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c.datas[className].Freqs[wIndex] = tfIdfAdder
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}
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}
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// sanity check
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c.DidConvertTfIdf = true
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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 (c *Classifier) LogScores(document []string) (scores []float64, inx int, strict bool) {
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if c.tfIdf && !c.DidConvertTfIdf {
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panic("Using a TF-IDF classifier. Please call ConvertTermsFreqToTfIdf before calling LogScores.")
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}
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n := len(c.Classes)
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scores = make([]float64, n, n)
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priors := c.getPriors()
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// calculate the score for each class
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for index, class := range c.Classes {
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data := c.datas[class]
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// c is the sum of the logarithms
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// as outlined in the refresher
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score := math.Log(priors[index])
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for _, word := range document {
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score += math.Log(data.getWordProb(word))
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}
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scores[index] = score
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}
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inx, strict = findMax(scores)
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atomic.AddInt32(&c.seen, 1)
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return scores, inx, strict
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}
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// ProbScores works the same as LogScores, but delivers
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// actual probabilities as discussed above. Note that float64
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// underflow is possible if the word list contains too
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// many words that have probabilities very close to 0.
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//
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// Notes on underflow: underflow is going to occur when you're
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// trying to assess large numbers of words that you have
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// never seen before. Depending on the application, this
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// may or may not be a concern. Consider using SafeProbScores()
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// instead.
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func (c *Classifier) ProbScores(doc []string) (scores []float64, inx int, strict bool) {
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if c.tfIdf && !c.DidConvertTfIdf {
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panic("Using a TF-IDF classifier. Please call ConvertTermsFreqToTfIdf before calling ProbScores.")
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}
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n := len(c.Classes)
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scores = make([]float64, n, n)
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priors := c.getPriors()
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sum := float64(0)
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// calculate the score for each class
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for index, class := range c.Classes {
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data := c.datas[class]
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// c is the sum of the logarithms
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// as outlined in the refresher
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score := priors[index]
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for _, word := range doc {
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score *= data.getWordProb(word)
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}
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scores[index] = score
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sum += score
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}
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for i := 0; i < n; i++ {
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scores[i] /= sum
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}
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inx, strict = findMax(scores)
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atomic.AddInt32(&c.seen, 1)
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return scores, inx, strict
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}
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// SafeProbScores works the same as ProbScores, but is
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// able to detect underflow in those cases where underflow
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// results in the reverse classification. If an underflow is detected,
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// this method returns an ErrUnderflow, allowing the user to deal with it as
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// necessary. Note that underflow, under certain rare circumstances,
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// may still result in incorrect probabilities being returned,
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// but this method guarantees that all error-less invokations
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// are properly classified.
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//
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// Underflow detection is more costly because it also
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// has to make additional log score calculations.
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func (c *Classifier) SafeProbScores(doc []string) (scores []float64, inx int, strict bool, err error) {
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if c.tfIdf && !c.DidConvertTfIdf {
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panic("Using a TF-IDF classifier. Please call ConvertTermsFreqToTfIdf before calling SafeProbScores.")
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}
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n := len(c.Classes)
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scores = make([]float64, n, n)
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logScores := make([]float64, n, n)
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priors := c.getPriors()
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sum := float64(0)
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// calculate the score for each class
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for index, class := range c.Classes {
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data := c.datas[class]
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// c is the sum of the logarithms
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// as outlined in the refresher
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score := priors[index]
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logScore := math.Log(priors[index])
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for _, word := range doc {
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p := data.getWordProb(word)
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score *= p
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logScore += math.Log(p)
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}
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scores[index] = score
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logScores[index] = logScore
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sum += score
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}
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for i := 0; i < n; i++ {
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scores[i] /= sum
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}
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inx, strict = findMax(scores)
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logInx, logStrict := findMax(logScores)
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// detect underflow -- the size
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// relation between scores and logScores
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// must be preserved or something is wrong
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if inx != logInx || strict != logStrict {
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err = ErrUnderflow
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}
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atomic.AddInt32(&c.seen, 1)
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return scores, inx, strict, err
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}
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// WordFrequencies returns a matrix of word frequencies that currently
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// exist in the classifier for each class state for the given input
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// words. In other words, if you obtain the frequencies
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//
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// freqs := c.WordFrequencies(/* [j]string */)
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//
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// then the expression freq[i][j] represents the frequency of the j-th
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// word within the i-th class.
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func (c *Classifier) WordFrequencies(words []string) (freqMatrix [][]float64) {
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n, l := len(c.Classes), len(words)
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freqMatrix = make([][]float64, n)
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for i := range freqMatrix {
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arr := make([]float64, l)
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data := c.datas[c.Classes[i]]
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for j := range arr {
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arr[j] = data.getWordProb(words[j])
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}
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freqMatrix[i] = arr
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}
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return
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}
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// WordsByClass returns a map of words and their probability of
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// appearing in the given class.
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func (c *Classifier) WordsByClass(class Class) (freqMap map[string]float64) {
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freqMap = make(map[string]float64)
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for word, cnt := range c.datas[class].Freqs {
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freqMap[word] = float64(cnt) / float64(c.datas[class].Total)
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}
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return freqMap
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}
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// WriteToFile serializes this classifier to a file.
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func (c *Classifier) WriteToFile(name string) (err error) {
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file, err := os.OpenFile(name, os.O_WRONLY|os.O_CREATE, 0644)
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if err != nil {
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return err
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||||
}
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defer file.Close()
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return c.WriteTo(file)
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}
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|
||||
// WriteClassesToFile writes all classes to files.
|
||||
func (c *Classifier) WriteClassesToFile(rootPath string) (err error) {
|
||||
for name := range c.datas {
|
||||
c.WriteClassToFile(name, rootPath)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// WriteClassToFile writes a single class to file.
|
||||
func (c *Classifier) WriteClassToFile(name Class, rootPath string) (err error) {
|
||||
data := c.datas[name]
|
||||
fileName := filepath.Join(rootPath, string(name))
|
||||
file, err := os.OpenFile(fileName, os.O_WRONLY|os.O_CREATE, 0644)
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
defer file.Close()
|
||||
|
||||
enc := gob.NewEncoder(file)
|
||||
err = enc.Encode(data)
|
||||
return
|
||||
}
|
||||
|
||||
|
||||
// WriteTo serializes this classifier to GOB and write to Writer.
|
||||
func (c *Classifier) WriteTo(w io.Writer) (err error) {
|
||||
enc := gob.NewEncoder(w)
|
||||
err = enc.Encode(&serializableClassifier{c.Classes, c.learned, int(c.seen), c.datas, c.tfIdf, c.DidConvertTfIdf})
|
||||
|
||||
return
|
||||
}
|
||||
|
||||
// ReadClassFromFile loads existing class data from a
|
||||
// file.
|
||||
func (c *Classifier) ReadClassFromFile(class Class, location string) (err error) {
|
||||
fileName := filepath.Join(location, string(class))
|
||||
file, err := os.Open(fileName)
|
||||
|
||||
if err != nil {
|
||||
return err
|
||||
}
|
||||
defer file.Close()
|
||||
|
||||
dec := gob.NewDecoder(file)
|
||||
w := new(classData)
|
||||
err = dec.Decode(w)
|
||||
|
||||
c.learned++
|
||||
c.datas[class] = w
|
||||
return
|
||||
}
|
||||
|
||||
// findMax finds the maximum of a set of scores; if the
|
||||
// maximum is strict -- that is, it is the single unique
|
||||
// maximum from the set -- then strict has return value
|
||||
// true. Otherwise it is false.
|
||||
func findMax(scores []float64) (inx int, strict bool) {
|
||||
inx = 0
|
||||
strict = true
|
||||
for i := 1; i < len(scores); i++ {
|
||||
if scores[inx] < scores[i] {
|
||||
inx = i
|
||||
strict = true
|
||||
} else if scores[inx] == scores[i] {
|
||||
strict = false
|
||||
}
|
||||
}
|
||||
return
|
||||
}
|
||||
Reference in New Issue
Block a user