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 assignment_e_naivebayes_2():
# Use these throughout below. These are really language-specific functions, so it's a huge
# simplification to use these for a language identifier.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
results = []
# Callback for receiving results. Received scores are log-probabilities.
def match_collector(match: dict):
results.append(match)
print("*** WINNER", match["score"], match["category"])
# Replicate Example 13.1 on pages 241 and 242 in the textbook.
china = InMemoryCorpus()
china.add_document(InMemoryDocument(0,
{"body": "Chinese Beijing Chinese"}))
china.add_document(
InMemoryDocument(1, {"body": "Chinese Chinese Shanghai"}))
china.add_document(InMemoryDocument(2, {"body": "Chinese Macao"}))
not_china = InMemoryCorpus()
not_china.add_document(InMemoryDocument(0,
{"body": "Tokyo Japan Chinese"}))
training_set = {"china": china, "not china": not_china}
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
buffer = "Chinese Chinese Chinese Tokyo Japan"
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert len(results) == 2
assert results[0]["category"] == "china"
assert results[1]["category"] == "not china"
assert math.isclose(math.exp(results[0]["score"]), 0.0003, abs_tol=0.00001)
assert math.isclose(math.exp(results[1]["score"]), 0.0001, abs_tol=0.00005)
def classify_tweets(request):
consumer_key="Wb4W1n264iHhcrqcXt54bA"
consumer_secret="2NFs7pO610XKQUOs5hPAz8wCEO4uxmP3111HPhsmgc"
access_token="36641014-28RR3YAp6MxFxJ706gsp5a7bRy0sYDsjLCwixs2iM"
access_token_secret="qOGQg84VvurJKX9qSF3Zgl973BxF6ryt7Yruoxtw"
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
query = request.POST.get('query')
result=api.search(query)
tweets=[]
classification=[]
for tweet in result:
try:
tweets.append(str(tweet.text))
except:
pass
posScore=0
negScore=0
for tweet in tweets:
tokens=tweet.split()
data_preprocess.remove_noise_words(tokens)
data_preprocess.remove_names(tokens)
data_preprocess.remove_links(tokens)
tweet_counts=[]
token_counts=[]
category_counts=defaultdict(lambda:defaultdict(int))
p=tweet_category_count.objects.get(id=1)
tweet_counts.append(p.positive_count)
tweet_counts.append(p.negative_count)
p=token_category_count.objects.get(id=1)
token_counts.append(p.positive_count)
token_counts.append(p.negative_count)
for token in tokens:
try:
p=pos_tokens.objects.get(ptoken=token)
category_counts[token]['pos']=p.pcount
except:
category_counts[token]['pos']=0
for token in tokens:
try:
p=neg_tokens.objects.get(ntoken=token)
category_counts[token]['neg']=p.ncount
except:
category_counts[token]['neg']=0
classifier=NaiveBayesClassifier()
result=classifier.classify(tokens,category_counts,tweet_counts,token_counts)
if(result=='pos'):
posScore+=1
else:
negScore+=1
classification.append(result)
return render_to_response("index.html",{'tweets':tweets,'pos_neg':classification,'posScore':posScore,'negScore':negScore})
def assignment_e_naivebayes_1():
# Use these throughout below. These are really language-specific functions, so it's a huge
# simplification to use these for a language identifier.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
results = []
# Callback for receiving results. Received scores are log-probabilities.
def match_collector(match: dict):
results.append(match)
print("*** WINNER", match["score"], match["category"])
# Use this as the training set for our language identifier.
print("LOADING...")
training_set = {
language: InMemoryCorpus("data/" + language + ".txt")
for language in ["en", "no", "da", "de"]
}
# Assess probabilities from the training set.
print("TRAINING...")
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
# Classify some previously unseen text fragments.
print("CLASSIFYING...")
for (buffer, language) in [
("Mon tro om det riktige språket identifiseres? Dette er norsk bokmål, forøvrig.",
"no"),
("I don't believe that the number of tokens exceeds a billion.", "en"),
("De danske drenge drikker snaps!", "da"),
("Der Kriminalpolizei! Haben sie angst?", "de")
]:
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert results[0]["category"] == language
def test_china_example_from_textbook(self):
import math
from corpus import InMemoryDocument, InMemoryCorpus
from naivebayesclassifier import NaiveBayesClassifier
china = InMemoryCorpus()
china.add_document(
InMemoryDocument(0, {"body": "Chinese Beijing Chinese"}))
china.add_document(
InMemoryDocument(1, {"body": "Chinese Chinese Shanghai"}))
china.add_document(InMemoryDocument(2, {"body": "Chinese Macao"}))
not_china = InMemoryCorpus()
not_china.add_document(
InMemoryDocument(0, {"body": "Tokyo Japan Chinese"}))
training_set = {"china": china, "not china": not_china}
classifier = NaiveBayesClassifier(training_set, ["body"],
self._normalizer, self._tokenizer)
results = []
classifier.classify("Chinese Chinese Chinese Tokyo Japan",
lambda m: results.append(m))
self.assertEqual(len(results), 2)
self.assertEqual(results[0]["category"], "china")
self.assertAlmostEqual(math.exp(results[0]["score"]), 0.0003, 4)
self.assertEqual(results[1]["category"], "not china")
self.assertAlmostEqual(math.exp(results[1]["score"]), 0.0001, 4)
def main():
import os.path
from normalization import BrainDeadNormalizer
from tokenization import BrainDeadTokenizer
from corpus import InMemoryCorpus
from naivebayesclassifier import NaiveBayesClassifier
print("Initializing naive Bayes classifier from news corpora...")
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
languages = ["en", "no", "da", "de"]
training_set = {language: InMemoryCorpus(os.path.join(data_path,f"{language}.txt")) for language in languages}
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer, tokenizer)
print(f"Enter some text and classify it into {languages}.")
print(f"Returned scores are log-probabilities.")
def evaluator(text):
results = []
classifier.classify(text, lambda m: results.append(m))
return results
simple_repl("text", evaluator)
def test_language_detection_trained_on_some_news_corpora(self):
import os.path
from corpus import InMemoryCorpus
from naivebayesclassifier import NaiveBayesClassifier
training_set = {
language: InMemoryCorpus(os.path.join(data_path,
f"{language}.txt"))
for language in ["en", "no", "da", "de"]
}
classifier = NaiveBayesClassifier(training_set, ["body"],
self._normalizer, self._tokenizer)
self._classify_buffer_and_verify_top_categories(
"Vil det riktige språket identifiseres? Dette er bokmål.",
classifier, ["no"])
self._classify_buffer_and_verify_top_categories(
"I don't believe that the number of tokens exceeds a billion.",
classifier, ["en"])
self._classify_buffer_and_verify_top_categories(
"De danske drenge drikker snaps!", classifier, ["da"])
self._classify_buffer_and_verify_top_categories(
"Der Kriminalpolizei! Haben sie angst?", classifier, ["de"])
class NaiveBayesClassifierTest(unittest.TestCase):
def test_predict(self):
STOP_WORDS = set(line.strip().decode('utf-8')
for line in open("stopwords.dic", 'r'))
def tokenize(text):
try:
seg_list = jieba.cut(text, cut_all=False)
return set(
[x.strip() for x in seg_list if x not in STOP_WORDS])
except Exception, e:
print e
return []
classifier = NaiveBayesClassifier(tokenizer=tokenize)
# classifier.fit(u'naive_train_data')
# classifier.dump('naive_classifier.dat')
classifier.load('naive_classifier.dat')
classifier.reduce(400)
start = time()
total = 0.0
errors = 0.0
for root, dirs, files in os.walk(u'naive_test_data/', topdown=True):
for name in files:
if root.startswith('.') or name.startswith('.'):
continue
category = root.split('/')[-1]
text = open(os.path.join(root, name),
'r').read().decode('utf-8')
predict = classifier.predict(text)
total += 1
if category != predict:
errors += 1
print 'predict: %s, actual: %s, errors percentage: %0.2f' % (
predict.encode('utf-8'), category.encode('utf-8'),
100 * errors / total)
print 'testing completed, total: %d, errors: %d, error rate:%0.2f, costs: %0.2f' % (
total, errors, 100 * errors / total, time() - start)
return errors / total
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.ioloop
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))
processed = re.sub(r'—', r"-", line)
processed = re.sub(r'([^\w\s\'])', r' \1 ', line)
processed = processed.lower()
return (processed.split())
#End def
parser = argparse.ArgumentParser()
parser.add_argument('train', help='The filename that points to training set.')
parser.add_argument('test', help='The filename that points to test set.')
args = parser.parse_args()
# Train our classifier
nbc = NaiveBayesClassifier(featurizer, classer, (AGREE_CLASS, DISAGREE_CLASS))
with open(args.train, 'r', encoding='UTF-8') as csv_train:
train_reader = csv.reader(csv_train, delimiter=',')
next(train_reader)
for row in train_reader:
rating = float(row[1])
if rating >= -1 and rating < 1:
continue
nbc.add_sample(row)
#End with
nbc.smooth()
false_counts = Counter()
true_counts = Counter()
real_counts = Counter()
def assignment_e():
# Use these throughout below. These are really language-specific functions, so it's a huge
# simplification to use these for a language identifier.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
results = []
# Callback for receiving results. Received scores are log-probabilities.
def match_collector(match: dict):
results.append(match)
print("*** WINNER", match["score"], match["category"])
# Use this as the training set for our language identifier.
print("LOADING...")
training_set = {
language: InMemoryCorpus("data/" + language + ".txt")
for language in ["en", "no", "da", "de"]
}
# Assess probabilities from the training set.
print("TRAINING...")
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
# Classify some previously unseen text fragments.
print("CLASSIFYING...")
for (buffer, language) in [
("Mon tro om det riktige språket identifiseres? Dette er norsk bokmål, forøvrig.",
"no"),
("I don't believe that the number of tokens exceeds a billion.", "en"),
("De danske drenge drikker snaps!", "da"),
("Der Kriminalpolizei! Haben sie angst?", "de")
]:
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert results[0]["category"] == language
# For demonstration purposes, replicate Example 13.1 on pages 241 and 242 in the textbook.
china = InMemoryCorpus()
china.add_document(InMemoryDocument(0,
{"body": "Chinese Beijing Chinese"}))
china.add_document(
InMemoryDocument(1, {"body": "Chinese Chinese Shanghai"}))
china.add_document(InMemoryDocument(2, {"body": "Chinese Macao"}))
not_china = InMemoryCorpus()
not_china.add_document(InMemoryDocument(0,
{"body": "Tokyo Japan Chinese"}))
training_set = {"china": china, "not china": not_china}
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
buffer = "Chinese Chinese Chinese Tokyo Japan"
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert len(results) == 2
assert results[0]["category"] == "china"
assert results[1]["category"] == "not china"
assert math.isclose(math.exp(results[0]["score"]), 0.0003, abs_tol=0.00001)
assert math.isclose(math.exp(results[1]["score"]), 0.0001, abs_tol=0.00005)
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()
from pymongo import MongoClient
from bson.objectid import ObjectId
import jieba
import re
from bs4 import BeautifulSoup
from naivebayesclassifier import NaiveBayesClassifier
from weighter.informationgain import InformationGain
if __name__ == '__main__':
STOP_WORDS = set(line.strip().decode('utf-8') for line in open("stopwords.dic", 'r'))
def tokenize(text):
try:
# print text.encode('utf-8')
seg_list = jieba.cut(text, cut_all=False)
zh_vocabulaly = re.compile(ur"([\u4E00-\u9FA5]+$)")
return [x.strip() for x in seg_list if zh_vocabulaly.match(x) and x not in STOP_WORDS]
except Exception, e:
print e
return []
client = MongoClient()
documents = client.rss.documents
classifier = NaiveBayesClassifier(tokenizer=tokenize)
classifier.load(u'raw_features_1.dat')
classifier.reduce(max_size=404, weighter=InformationGain)
for x in documents.find({},{'_id':'1','content':1}):
content = BeautifulSoup(x['content']).text.encode('utf-8')
category = classifier.predict_text(content)
print category.encode('utf-8')
documents.update({'_id': x['_id']},{'$set': {'category':category}})
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from naivebayesclassifier import NaiveBayesClassifier
from cmd import Cmd
l = NaiveBayesClassifier('data')
while True:
name = raw_input()
name_unicode = name.decode('utf-8')
final_p = l.classify(name_unicode[-1], force_class_average = True)
best_p = 0
best_ans = -1
for i in final_p:
if final_p[i] > best_p:
best_p = final_p[i]
best_ans = i
print status[best_ans], best_p
if name == 'exit':
break
