def run(self):
if not self.usedev:
for grams in self.allgrams:
c = NaiveBayesClassifier(self.rawfname,
grams=grams)
c.trainClassifier()
self.stdout = True
self.evaluate(c)
return
for grams in self.allgrams:
c = NaiveBayesClassifier(self.rawfname,
grams=grams)
c.trainClassifier()
for w in self.allweights:
c.setWeight(w)
for t1 in self.allthresholds:
for t2 in self.allthresholds:
c.setThresholds(neg=t1, pos=t2)
cinfo, accpos, accneg, accall, corrall = self.evaluate(c)
self.results.append([cinfo, accpos, accneg,
accall, corrall])
if self.csvout:
self.flushToCSV()
def run(self):
if not self.usedev:
for grams in self.allgrams:
c = NaiveBayesClassifier(self.rawfname, grams=grams)
c.trainClassifier()
self.stdout = True
self.evaluate(c)
return
for grams in self.allgrams:
c = NaiveBayesClassifier(self.rawfname, grams=grams)
c.trainClassifier()
for w in self.allweights:
c.setWeight(w)
for t1 in self.allthresholds:
for t2 in self.allthresholds:
c.setThresholds(neg=t1, pos=t2)
cinfo, accpos, accneg, accall, corrall = self.evaluate(
c)
self.results.append(
[cinfo, accpos, accneg, accall, corrall])
if self.csvout:
self.flushToCSV()
def run(self):
for grams in self.allgrams:
c = NaiveBayesClassifier(self.rawfname,
grams=grams)
c.trainClassifier()
self.stdout = False
return self.evaluate(c)
for grams in self.allgrams:
c = NaiveBayesClassifier(self.rawfname,
grams=grams)
c.trainClassifier()
for w in self.allweights:
c.setWeight(w)
for t1 in self.allthresholds:
for t2 in self.allthresholds:
c.setThresholds(neg=t1, pos=t2)
cinfo, accpos, accneg, accall, corrall = self.evaluate(c)
self.results.append([cinfo, accpos, accneg, accall, corrall])
from maxentclassifier import MaximumEntropyClassifier
from naivebayesclassifier import NaiveBayesClassifier
import random
import csv
fname = 'training.csv'
nb = NaiveBayesClassifier(fname, grams=[1, 2])
nb.setThresholds(neg=1.0, pos=20.0)
nb.setWeight(0.000000000005)
nb.trainClassifier()
ment = MaximumEntropyClassifier(fname)
ment.trainClassifier()
classifiers = [nb, ment]
def csvdata_to_list(data):
d=[]
for row in data:
d.append(row)
return d
def search(text,data):
output = []
i=0
for d in data:
if d[0].lower().find(text) != -1:
output.append([])
output[i].append(d[0])
import tornado.web
import urllib
import tweepy
import os
from maxentclassifier import MaximumEntropyClassifier
from naivebayesclassifier import NaiveBayesClassifier
# name of training set file
fname = 'trainingandtestdata/training.csv'
# train classifiers here first
nb = NaiveBayesClassifier(fname, grams=[1,2])
nb.setThresholds(neg=1.0, pos=20.0)
nb.setWeight(0.000000000005)
nb.trainClassifier()
ment = MaximumEntropyClassifier(fname)
ment.trainClassifier()
classifiers = [nb, ment]
class MainHandler(tornado.web.RequestHandler):
'''
Handles request to main page
'''
def get(self):
query = self.get_argument("query", "").strip()
cchosen = int(self.get_argument("classifier-type", 0))
auth = tweepy.OAuthHandler ("Yd1EFIv3psmpXdhR3lPVjUXva","WcmeKDjoaD3suYMQbgIyXTTtKcaDvws4h5cFwmlBy7jgDMxO9E")
