func (s *SentimentSuite) TestNewAnalyzer(c *C) {
analyzer := NewAnalyzer()
learned := analyzer.classifier.Learned()
c.Assert(analyzer.classifier, FitsTypeOf, bayesian.NewClassifier(Positive, Negative, Neutral))
c.Assert(learned, Not(Equals), 0)
}
// trainClassifiers trains classifiers for each plugin.
func trainClassifiers() error {
for _, pconf := range PluginsGo {
ss, err := fetchTrainingSentences(pconf.ID, pconf.Name)
if err != nil {
return err
}
// Assemble list of Bayesian classes from all trained intents
// for this plugin. m is used to keep track of the classes
// already taught to each classifier.
m := map[string]struct{}{}
for _, s := range ss {
_, ok := m[s.Intent]
if ok {
continue
}
log.Debug("learning intent", s.Intent)
m[s.Intent] = struct{}{}
pluginIntents[s.PluginID] = append(pluginIntents[s.PluginID],
bayesian.Class(s.Intent))
}
// Build classifier from complete sets of intents
for _, s := range ss {
intents := pluginIntents[s.PluginID]
// Calling bayesian.NewClassifier() with 0 or 1
// classes causes a panic.
if len(intents) == 0 {
break
}
if len(intents) == 1 {
intents = append(intents, bayesian.Class("__no_intent"))
}
c := bayesian.NewClassifier(intents...)
bClassifiers[s.PluginID] = c
}
// With classifiers initialized, train each of them on a
// sentence's stems.
for _, s := range ss {
tokens := TokenizeSentence(s.Sentence)
stems := StemTokens(tokens)
c, exists := bClassifiers[s.PluginID]
if exists {
c.Learn(stems, bayesian.Class(s.Intent))
}
}
}
return nil
}
func main() {
const (
Positive bayesian.Class = "Positive"
Negative bayesian.Class = "Negative"
)
stopWords := createMapOfStopWord("stopwords.txt")
classifier := bayesian.NewClassifier(Positive, Negative)
goodStuff := removeStopWords(stopWords, convFileToListWords("training-1.txt"))
badStuff := removeStopWords(stopWords, convFileToListWords("training-0.txt"))
classifier.Learn(goodStuff, Positive)
classifier.Learn(badStuff, Negative)
classifier.WriteToFile("classifier.gob")
}
func (scanner *Scanner) LoadOrCreate() {
classifier, err := bayesian.NewClassifierFromFile(scanner.BayesianFile())
if err != nil {
if os.IsNotExist(err) {
log.Printf("%s does not exist. Creating db\n", scanner.BayesianFile())
classifier = bayesian.NewClassifier(BAYESIAN_CLASSES...)
}
}
cat, err := catalog.NewCatalogFromFile(scanner.CatalogFile())
if err != nil {
if os.IsNotExist(err) {
cat = &catalog.Catalog{Filename: scanner.CatalogFile(), Files: make([]uint32, 0)}
}
}
scanner.classifier = classifier
scanner.catalog = cat
}
// Sets up and trains a new analyzer to classify sentiment
func NewAnalyzer() Analyzer {
a := Analyzer{}
// Get the training data if not present
_, err := os.Stat(DATA_FILE)
if err != nil {
if os.IsNotExist(err) {
a.downloadDataSet()
}
}
c, err := bayesian.NewClassifierFromFile(DATA_FILE)
if err == nil {
a.classifier = c
} else {
// Note: Nothing will be trained at this point, but we'll still have a classifier that can be trained
a.classifier = bayesian.NewClassifier(Positive, Negative, Neutral)
}
return a
}
func main() {
const (
Good bayesian.Class = "Good"
Bad bayesian.Class = "Bad"
)
classifier := bayesian.NewClassifier(Good, Bad)
goodStuff := []string{"tall", "rich", "handsome"}
badStuff := []string{"poor", "smelly", "ugly"}
classifier.Learn(goodStuff, Good)
classifier.Learn(badStuff, Bad)
scores, likely, _ := classifier.LogScores(
[]string{"tall", "girl"},
)
fmt.Printf("Score: %v, likely: %v\n", scores, likely)
probs, likely, _ := classifier.ProbScores(
[]string{"tall", "girl"},
)
fmt.Printf("Probs: %v, likely: %v\n", probs, likely)
}
func main() {
var ledgerFileName string
var accountSubstring, csvFileName, csvDateFormat string
var negateAmount bool
var fieldDelimiter string
flag.BoolVar(&negateAmount, "neg", false, "Negate amount column value.")
flag.StringVar(&ledgerFileName, "f", "", "Ledger file name (*Required).")
flag.StringVar(&csvDateFormat, "date-format", "01/02/2006", "Date format.")
flag.StringVar(&fieldDelimiter, "delimiter", ",", "Field delimiter.")
flag.Parse()
args := flag.Args()
if len(args) != 2 {
usage()
} else {
accountSubstring = args[0]
csvFileName = args[1]
}
csvFileReader, err := os.Open(csvFileName)
if err != nil {
fmt.Println("CSV: ", err)
return
}
defer csvFileReader.Close()
ledgerFileReader, err := os.Open(ledgerFileName)
if err != nil {
fmt.Println("Ledger: ", err)
return
}
defer ledgerFileReader.Close()
generalLedger, parseError := ledger.ParseLedger(ledgerFileReader)
if parseError != nil {
fmt.Println(parseError)
return
}
var matchingAccount string
matchingAccounts := ledger.GetBalances(generalLedger, []string{accountSubstring})
if len(matchingAccounts) < 1 {
fmt.Println("Unable to find matching account.")
return
} else {
matchingAccount = matchingAccounts[len(matchingAccounts)-1].Name
}
allAccounts := ledger.GetBalances(generalLedger, []string{})
csvReader := csv.NewReader(csvFileReader)
csvReader.Comma, _ = utf8.DecodeRuneInString(fieldDelimiter)
csvRecords, _ := csvReader.ReadAll()
classes := make([]bayesian.Class, len(allAccounts))
for i, bal := range allAccounts {
classes[i] = bayesian.Class(bal.Name)
}
classifier := bayesian.NewClassifier(classes...)
for _, tran := range generalLedger {
payeeWords := strings.Split(tran.Payee, " ")
for _, accChange := range tran.AccountChanges {
if strings.Contains(accChange.Name, "Expense") {
classifier.Learn(payeeWords, bayesian.Class(accChange.Name))
}
}
}
// Find columns from header
var dateColumn, payeeColumn, amountColumn int
dateColumn, payeeColumn, amountColumn = -1, -1, -1
for fieldIndex, fieldName := range csvRecords[0] {
fieldName = strings.ToLower(fieldName)
if strings.Contains(fieldName, "date") {
dateColumn = fieldIndex
} else if strings.Contains(fieldName, "description") {
payeeColumn = fieldIndex
} else if strings.Contains(fieldName, "payee") {
payeeColumn = fieldIndex
} else if strings.Contains(fieldName, "amount") {
amountColumn = fieldIndex
} else if strings.Contains(fieldName, "expense") {
amountColumn = fieldIndex
}
}
if dateColumn < 0 || payeeColumn < 0 || amountColumn < 0 {
fmt.Println("Unable to find columns required from header field names.")
return
}
expenseAccount := ledger.Account{Name: "unknown:unknown", Balance: new(big.Rat)}
csvAccount := ledger.Account{Name: matchingAccount, Balance: new(big.Rat)}
for _, record := range csvRecords[1:] {
inputPayeeWords := strings.Split(record[payeeColumn], " ")
csvDate, _ := time.Parse(csvDateFormat, record[dateColumn])
if !existingTransaction(generalLedger, csvDate, inputPayeeWords[0]) {
// Classify into expense account
_, likely, _ := classifier.LogScores(inputPayeeWords)
if likely >= 0 {
expenseAccount.Name = string(classifier.Classes[likely])
}
// Negate amount if required
expenseAccount.Balance.SetString(record[amountColumn])
if negateAmount {
expenseAccount.Balance.Neg(expenseAccount.Balance)
}
// Csv amount is the negative of the expense amount
csvAccount.Balance.Neg(expenseAccount.Balance)
// Create valid transaction for print in ledger format
trans := &ledger.Transaction{Date: csvDate, Payee: record[payeeColumn]}
trans.AccountChanges = []ledger.Account{csvAccount, expenseAccount}
PrintTransaction(trans, 80)
}
}
}
func NewClassifier() *bayesian.Classifier {
return bayesian.NewClassifier(Boy, Girl, None)
}
func main() {
anaconda.SetConsumerKey("")
anaconda.SetConsumerSecret("")
api := anaconda.NewTwitterApi("", "")
t := time.NewTicker(time.Second * 10)
l := fmt.Sprintf("%d", time.Now().UnixNano())
te := bayesian.Class("tech")
ot := bayesian.Class("other")
fparts := strings.Split(*files, ",")
b, err := ioutil.ReadFile(fparts[0])
if err != nil {
panic(err.Error())
}
words := strings.Split(string(b), "\n")
c := bayesian.NewClassifier(te, ot)
c.Learn(words, te)
posts := make(chan string, 10)
post := time.NewTicker(time.Minute * 30)
var cur []string
var max float64
var idx int
go func() {
for {
select {
case <-post.C:
idx = -1
max = -1000.0
for i, p := range cur {
parts := strings.Split(strings.ToLower(p), " ")
scores, class, _ := c.LogScores(parts)
if class != 0 {
continue
}
if len(scores) == 0 {
continue
}
if scores[0] > max {
max = scores[0]
idx = i
}
}
if idx >= 0 {
api.PostTweet(cur[idx], url.Values{})
}
cur = []string{}
case p := <-posts:
cur = append(cur, p)
}
}
}()
for _ = range t.C {
r, err := http.Get("http://127.0.0.1:8889/objects?stream=tech&last=" + l)
if err != nil {
fmt.Println(err)
continue
}
b, err := ioutil.ReadAll(r.Body)
r.Body.Close()
if err != nil {
fmt.Println(err)
continue
}
var objects []Object
err = json.Unmarshal(b, &objects)
if err != nil {
fmt.Println(err)
continue
}
for _, object := range objects {
text := object.Text
parts := strings.Split(object.Text, " ")
for _, part := range parts {
m := getMetadata(part)
if m != nil && len(m.Title) > 0 && len(m.Url) > 0 {
text = m.Title + " " + m.Url
}
}
posts <- text
}
if len(objects) > 0 {
l = fmt.Sprintf("%d", objects[len(objects)-1].Created)
}
}
}
