def upload_csv_button(self):
self.file_path = tkFileDialog.askopenfilename(
initialdir='/',
title='Select file',
filetypes=(('csv files', '*.csv'), ('all files', '*.*')))
with open(self.file_path) as f:
s = f.read()
my_d = repr(s)
docclass.getwords(my_d)
c1 = docclass.classifier(docclass.getwords)
docclass.sampletrain(c1)
def get_article_words_count(feedlist):
allwords = {}
articlewords = []
articletitles = set()
ec = 0
# loop over every feed
for feed in feedlist:
f = feedparser.parse(feed)
for e in f.entries:
# ignore identical articles
if e.title in articletitles: continue
# extract the words
txt = e.title.encode('utf-8') + stripHTML(e.description.encode('utf-8'))
words = getwords(txt)
articlewords.append({})
articletitles.add(e.title)
# inc counts of all words in an article in allwords, articlewords
for word in words:
allwords.setdefault(word, 0)
allwords[word] += 1
articlewords[ec].setdefault(word, 0)
articlewords[ec][word] += 1
ec = ec + 1
return allwords, articlewords, list(articletitles)
def parse_netflix_data(feed_file, con, predicted):
# Get all features from database and saved into array
cur = con.execute('select * from fc');
features = [f[0] for f in cur]
titles = []
data = []
feeds = feedparser.parse(feed_file)
entries = feeds['entries']
for e in range(len(entries)):
entry = entries[e]
title = ''
publisher = ''
summary = ''
if 'title' in entry:
title = entry['title']
if 'publisher' in entry:
publisher = entry['publisher']
if 'summary' in entry:
summary = entry['summary']
fulltext = '{} {} {}'.format(title, publisher, summary)
words = docclass.getwords(fulltext)
# Calculate word count
wc = {}
for w in words:
wc.setdefault(w, 0)
wc[w] += 1
arr_wc = []
# Convert wc to colon-separated term:value
for term, value in wc.items():
# Find index of term in entries
if term in features:
arr_wc.append('{}:{}'.format(features.index(term), value))
# Get actual category, then compare it with predicted
# Insert result (-1 or +1) at the 1st column
(actual,) = con.execute('select actual_category from entry where title =?', (title,)).fetchone();
if actual:
if actual == predicted:
arr_wc.insert(0, '+1')
else:
arr_wc.insert(0, '-1')
else:
arr_wc.insert(0, '-1')
# Join array into space separated value
str_wc = ' '.join(arr_wc)
titles.append(title)
data.append(str_wc)
return (titles, features, data)
def analysis(self, event):
self.text.delete(1.0, END)
index = self.listbox2.curselection()
choice = self.listbox2.get(index)
choice2 = "Genre: " + choice + '\n'
GenresBooks = self.BooksDATA[choice]
totalB = "Total Number Of Books: " + str(len(GenresBooks))
self.text.insert(END, choice2)
self.text.insert(END, totalB)
predictedGenre = []
Books = self.BooksDATA[choice]
for i in Books:
self.selectedBook = i[1]
# categories = self.clasifier.categories()
estimates = []
for cat in self.clasifier.categories():
prob = 0
for word in docclass.getwords(self.selectedBook):
prob += self.clasifier.fprob(word, cat, default_prob=0)
if prob != 0:
estimates.append([cat, prob])
estimates = sorted(estimates, key=lambda x: x[1], reverse=True)
estimates = estimates[0]
predictedGenre.append(estimates)
print predictedGenre
c = len(self.BooksDATA[choice])
wrong = 0
for j in predictedGenre:
if j[0] != choice:
c -= 1
wrong += 1
correct = '\nCorrectly predicted Books: ' + str(c) + '\n'
wrongg = 'Wrongly Predicted Books: ' + str(wrong) + '\n'
accuracy = 'Accuracy: ' + str(
(float(c) / len(self.BooksDATA[choice])) * 100)
self.text.insert(END, correct)
self.text.insert(END, wrongg)
self.text.insert(END, accuracy)
def testStripDuplicates(self):
self.assertEquals(['mail', 'spam'],
sorted(list(docclass.getwords('spam mail spam'))))
def listClicked(self, event):
self.labelp1.config(text=' ', bg='wheat2')
self.labelp2.config(text=' ', bg='wheat2')
self.labelp3.config(text=' ', bg='wheat2')
index = self.listBox1.curselection()
temp = self.listBox1.get(index[0])
catt = temp.split('-')[2]
temp = temp.split('-')[1]
temp = temp[1:]
self.selectedBook = temp
# categories = self.clasifier.categories()
estimates = []
for cat in self.clasifier.categories():
prob = 0
for word in docclass.getwords(self.selectedBook):
prob += self.clasifier.fprob(word, cat, default_prob=0)
if prob != 0:
estimates.append([cat, prob])
estimates = sorted(estimates, key=lambda x: x[1], reverse=True)
if len(estimates) == 1:
line = str(estimates[0][0]) + " -->" + str(estimates[0][1])
if estimates[0][0] == catt:
self.labelp1.config(text=line, bg='green')
else:
print estimates
self.labelp1.config(text=line, bg='red')
elif len(estimates) == 2:
line = str(estimates[0][0]) + " -->" + str(estimates[0][1])
line2 = str(estimates[1][0]) + " -->" + str(estimates[1][1])
if estimates[0][0] == catt:
self.labelp1.config(text=line, bg='green')
else:
print estimates
self.labelp1.config(text=line, bg='red')
if estimates[1][0] == catt:
self.labelp2.config(text=line2, bg='green')
else:
self.labelp2.config(text=line2, bg='red')
elif len(estimates) == 0:
print 'wait what'
else:
line = str(estimates[0][0]) + " -->" + str(estimates[0][1])
line2 = str(estimates[1][0]) + " -->" + str(estimates[1][1])
line3 = str(estimates[2][0]) + " -->" + str(estimates[2][1])
if estimates[0][0] == catt:
self.labelp1.config(text=line, bg='green')
else:
print estimates
self.labelp1.config(text=line, bg='red')
if estimates[1][0] == catt:
self.labelp2.config(text=line2, bg='green')
else:
self.labelp2.config(text=line2, bg='red')
if estimates[2][0] == catt:
self.labelp3.config(text=line3, bg='green')
else:
self.labelp3.config(text=line3, bg='red')
def test_incf(self):
clas = docclass.classifier(docclass.getwords)
origin_str = 'the quick brown for jumps over the lazy dog'
clas.train('the quick brown for jumps over the lazy dog', 'good')
str_tobe = docclass.getwords(origin_str)
self.assertDictEqual(clas.fc, str_tobe)
def test_getwords(self):
words_source = 'disk*jdhs&342nhek[]989c12cnjshdsjkv774*j'
words_result_dict = dict([('disk', 1), ('jdhs', 1),
('nhek', 1), ('cnjshdsjkv', 1)])
self.assertDictEqual(words_result_dict,
docclass.getwords(words_source))
def test_incf(self):
clas = docclass.classifier(docclass.getwords)
origin_str = 'the quick brown for jumps over the lazy dog'
clas.train('the quick brown for jumps over the lazy dog', 'good')
str_tobe = docclass.getwords(origin_str)
self.assertDictEqual(clas.fc, str_tobe)
def test_getwords(self):
words_source = 'disk*jdhs&342nhek[]989c12cnjshdsjkv774*j'
words_result_dict = dict([('disk', 1), ('jdhs', 1), ('nhek', 1),
('cnjshdsjkv', 1)])
self.assertDictEqual(words_result_dict,
docclass.getwords(words_source))
import docclass
docclass.getwords('python is a dynamic language')
cl = docclass.naivebayes(docclass.getwords)
cl.setdb('test.db')
cl.train('pythons are constrictors', 'snake')
cl.train('python has dynamic types', 'language')
cl.train('python was developed as scripting language', 'language')
cl.classify('dynamic programming')
cl.classify('boa constrictors')
exit()