-
Notifications
You must be signed in to change notification settings - Fork 1
/
f19.py
136 lines (96 loc) · 4.52 KB
/
f19.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
import nltk
import random
from nltk.corpus import movie_reviews
from nltk.classify.scikitlearn import SklearnClassifier
import pickle
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC
from nltk.classify import ClassifierI
from statistics import mode
from nltk.tokenize import word_tokenize
import io
class VoteClassifier(ClassifierI):
def __init__(self, *classifiers):
self._classifiers = classifiers
def classify(self, features):
votes = []
for c in self._classifiers:
v = c.classify(features)#iterate through each classifier
votes.append(v)#store vote of each classifier in array
return mode(votes)
def confidence(self, features):
votes = []
for c in self._classifiers:
v = c.classify(features)
votes.append(v)
choice_votes = votes.count(mode(votes))
conf = choice_votes / len(votes)
return conf
with io.open("spam.txt", "r", encoding="UTF-8") as fd:
short_pos = fd.read()
with io.open("ham.txt", "r", encoding="UTF-8") as fd:
short_neg = fd.read()
documents = []
for r in short_pos.split('\n'):#spilt by lines
documents.append( (r, "pos") )#append it in documents line by line
for r in short_neg.split('\n'):
documents.append( (r, "neg") )
#one question? how are we removing the stop words here?
all_words = []
short_pos_words = word_tokenize(short_pos)#tokenise the words
short_neg_words = word_tokenize(short_neg)
for w in short_pos_words:
all_words.append(w.lower())#append the words in all_words
for w in short_neg_words:
all_words.append(w.lower())
all_words = nltk.FreqDist(all_words)#find freq dis of all words
word_features = list(all_words.keys())[:5000]#list the most common 5000 words, plz explain how this works
def find_features(document):
words = word_tokenize(document)
features = {}
for w in word_features:
features[w] = (w in words)#explain this line
return features
#print((find_features(movie_reviews.words('neg/cv000_29416.txt'))))
featuresets = [(find_features(rev), category) for (rev, category) in documents]
random.shuffle(featuresets)
# positive data example:
training_set = featuresets[:10000]
testing_set = featuresets[10000:]
##
### negative data example:
##training_set = featuresets[100:]
##testing_set = featuresets[:100]
classifier = nltk.NaiveBayesClassifier.train(training_set)
print("Original Naive Bayes Algo accuracy percent:", (nltk.classify.accuracy(classifier, testing_set))*100)
classifier.show_most_informative_features(150)
'''MNB_classifier = SklearnClassifier(MultinomialNB())
MNB_classifier.train(training_set)
print("MNB_classifier accuracy percent:", (nltk.classify.accuracy(MNB_classifier, testing_set))*100)
BernoulliNB_classifier = SklearnClassifier(BernoulliNB())
BernoulliNB_classifier.train(training_set)
print("BernoulliNB_classifier accuracy percent:", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)
LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("LogisticRegression_classifier accuracy percent:", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)
SGDClassifier_classifier = SklearnClassifier(SGDClassifier())
SGDClassifier_classifier.train(training_set)
print("SGDClassifier_classifier accuracy percent:", (nltk.classify.accuracy(SGDClassifier_classifier, testing_set))*100)
##SVC_classifier = SklearnClassifier(SVC())
##SVC_classifier.train(training_set)
##print("SVC_classifier accuracy percent:", (nltk.classify.accuracy(SVC_classifier, testing_set))*100)
LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
print("LinearSVC_classifier accuracy percent:", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)
NuSVC_classifier = SklearnClassifier(NuSVC())
NuSVC_classifier.train(training_set)
print("NuSVC_classifier accuracy percent:", (nltk.classify.accuracy(NuSVC_classifier, testing_set))*100)
voted_classifier = VoteClassifier(
NuSVC_classifier,
LinearSVC_classifier,
MNB_classifier,
BernoulliNB_classifier,
LogisticRegression_classifier)
print("voted_classifier accuracy percent:", (nltk.classify.accuracy(voted_classifier, testing_set))*100)
'''