本文整理汇总了Python中text.classifiers.NaiveBayesClassifier类的典型用法代码示例。如果您正苦于以下问题:Python NaiveBayesClassifier类的具体用法?Python NaiveBayesClassifier怎么用?Python NaiveBayesClassifier使用的例子?那么, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了NaiveBayesClassifier类的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: nb
def nb(data):
# check out params
# divide data into 4 = 3 + 1, 3 for train, 1 for test
train = data[0: (len(data) / 4) * 3]
test = data[(len(data) / 4) * 3:]
print "Training ..."
classifier = NaiveBayesClassifier(train)
print "Testing ..."
print "Accuracy: ", classifier.accuracy(test)
"""
示例2: test_Textblog
def test_Textblog():
train = [
('I love this sandwich.', 'pos'),
('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')
]
test = [
('The beer was good.', 'pos'),
('I do not enjoy my job', 'neg'),
("I ain't feeling dandy today.", 'neg'),
("I feel amazing!", 'pos'),
('Gary is a friend of mine.', 'pos'),
("I can't believe I'm doing this.", 'neg')
]
cl = NaiveBayesClassifier(train)
#print cl.classify("Their burgers are amazing") # "pos"
#print cl.classify("I don't like their pizza.") # "neg"
import nltk
new_train = []
for item in train:
token_sent = nltk.word_tokenize(item[0])
item = list(item)
item[0] = token_sent
item[1] = item[1]
item = tuple(item)
new_train.append(item)
print new_train
cl = NaiveBayesClassifier(new_train)
new_test = nltk.word_tokenize("I don't like their pizza.")
print new_test, cl.classify(new_test)
示例3: setUp
def setUp(self):
self.train_set = [
('I love this car', 'positive'),
('This view is amazing', 'positive'),
('I feel great this morning', 'positive'),
('I am so excited about the concert', 'positive'),
('He is my best friend', 'positive'),
('I do not like this car', 'negative'),
('This view is horrible', 'negative'),
('I feel tired this morning', 'negative'),
('I am not looking forward to the concert', 'negative'),
('He is my enemy', 'negative')
]
self.classifier = NaiveBayesClassifier(self.train_set)
self.test_set = [('I feel happy this morning', 'positive'),
('Larry is my friend.', 'positive'),
('I do not like that man.', 'negative'),
('My house is not great.', 'negative'),
('Your song is annoying.', 'negative')]
示例4: NaiveBayesClassifier
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')
]
test = [
('The beer was good.', 'pos'),
('I do not enjoy my job', 'neg'),
("I ain't feeling dandy today.", 'neg'),
("I feel amazing!", 'pos'),
('Gary is a friend of mine.', 'pos'),
("I can't believe I'm doing this.", 'neg')
]
print 'initial training going on....'
cl = NaiveBayesClassifier(train)
print 'initial training done.'
# Grab some movie review data
print 'now gathering reviews...'
reviews = [(list(movie_reviews.words(fileid)), category)
for category in movie_reviews.categories()
for fileid in movie_reviews.fileids(category)]
random.shuffle(reviews)
new_train = reviews[0:200]
print 'reviews gathered.'
# Update the classifier with the new training data
print 'now training using the new data...'
cl.update(new_train)
print 'trained and ready!'
print cl.classify("I hated the movie and hated the food")
# Compute accuracy
示例5: test_init_with_json_file
def test_init_with_json_file(self):
cl = NaiveBayesClassifier(JSON_FILE, format="json")
assert_equal(cl.classify("I feel happy this morning"), 'pos')
training_sentence = cl.train_set[0][0]
assert_true(isinstance(training_sentence, unicode))
示例6: range
#dev - years
inputfile = codecs.open("years-dev.txt", 'r', 'utf-8')
dev_train = inputfile.readlines()
inputfile.close()
#dev - content
inputfile = codecs.open("contents-dev.txt", 'r', 'utf-8')
contents_dev = inputfile.readlines()
inputfile.close()
#training set
train_set = []
g = range(0, 4000, 2)
for i in g:
train_set.append((contents_train[i], years_train[i/2]))
print "tu się robi"
cl = NaiveBayesClassifier(train_set)
print "a tu się zrobiło"
outputfile = open("classified.txt", "w")
g = range(0, len(contents_dev), 2)
for i in g:
result = cl.classify(contents_dev[i])
print i
outputfile.write(str(result))
print "zmieliło"
outputfile.close()
示例7: TextBlob
msg = TextBlob(tabsep[1])
try:
words=msg.words
except:
continue
for word in words:
if word not in stopwords.words() and not word.isdigit():
list_tuples.append((word.lower(),tabsep[0]))
c+=1
if c==500:
break
return list_tuples
print 'importing data...'
a = time.time()
entire_data = get_list_tuples("/home/anish/Documents/DataSci/DataSets/sms/SMSSpamCollection")
print "It took "+str(time.time()-a)+" seconds to import data"
print 'data imported'
random.seed(1)
random.shuffle(entire_data)
train = entire_data[:250]
test = entire_data[251:500]
print 'training data'
a = time.time()
cl = NaiveBayesClassifier(train)
print "It took "+str(time.time()-a)+" seconds to train data"
print 'data trained, now checking accuracy:'
accuracy = cl.accuracy(test)
print "accuracy: "+str(accuracy)
print cl.classify("Hey bud, what's up") #ham
print cl.classify("Get a brand new mobile phone by being an agent of The Mob! Plus loads more goodies! For more info just text MAT to 87021") #spam
示例8: test_init_with_csv_file_without_format_specifier
def test_init_with_csv_file_without_format_specifier(self):
cl = NaiveBayesClassifier(CSV_FILE)
assert_equal(cl.classify("I feel happy this morning"), 'pos')
training_sentence = cl.train_set[0][0]
assert_true(isinstance(training_sentence, unicode))
示例9: test_custom_feature_extractor
def test_custom_feature_extractor(self):
cl = NaiveBayesClassifier(self.train_set, custom_extractor)
cl.classify("Yay! I'm so happy it works.")
assert_equal(cl.train_features[0][1], 'positive')
示例10: open_workbook
train = []
book = open_workbook('C:/Documents and Settings/rojin.varghese/Desktop/LargeTest/One_Category_Train.xls')
sheet1 = book.sheet_by_index(0)
print "Training.............\n"
for j in range(sheet1.nrows):
line1 = sheet1.cell_value(j,1)
line1 = re.sub('[\-*>]', '', line1)
line1 = re.sub('[\n]', '', line1)
line2 = sheet1.cell_value(j,2)
stored = [(line1, line2)]
train = train + stored
print "Training algo....\n"
cl = NaiveBayesClassifier(train)
book = open_workbook('C:/Documents and Settings/rojin.varghese/Desktop/LargeTest/One_Category_Test.xls')
sheet = book.sheet_by_index(0)
book1 = xlwt.Workbook()
sh = book1.add_sheet("sheet")
print "Classifying..........."
for j in range(sheet.nrows):
id = sheet.cell_value(j,0)
line = sheet.cell_value(j,1)
line = re.sub('[-*>]', '', line)
line = re.sub('[\n]', '', line)
a = cl.classify(line)
示例11: test_train_from_lists_of_words
def test_train_from_lists_of_words(self):
# classifier can be trained on lists of words instead of strings
train = [(doc.split(), label) for doc, label in train_set]
classifier = NaiveBayesClassifier(train)
assert_equal(classifier.accuracy(test_set),
self.classifier.accuracy(test_set))
示例12: open
infile = "data/yelp_academic_dataset_review.json"
# read the first 1000 reviews
i = 0
fin = open(infile, 'r')
data = []
for line in fin:
review = json.loads(line)
data.append((review['text'], float(review['stars'])))
if i == 1000:
break
i += 1
fin.close()
k = 500
training_set, test_set = data[:k], data[k:]
print "building classifier"
cl = NaiveBayesClassifier(training_set)
print "built classifier"
# Compute accuracy
print "computing accuracy"
print("Accuracy: {0}".format(cl.accuracy(test_set)))
print "computed accuracy"
# Show 5 most informative features
print "showing features"
cl.show_informative_features(5)
print "done :)"
示例13: TestNaiveBayesClassifier
class TestNaiveBayesClassifier(unittest.TestCase):
def setUp(self):
self.classifier = NaiveBayesClassifier(train_set)
def test_basic_extractor(self):
text = "I feel happy this morning."
feats = basic_extractor(text, train_set)
assert_true(feats["contains(feel)"])
assert_true(feats['contains(morning)'])
assert_false(feats["contains(amazing)"])
def test_default_extractor(self):
text = "I feel happy this morning."
assert_equal(self.classifier.extract_features(text), basic_extractor(text, train_set))
def test_classify(self):
res = self.classifier.classify("I feel happy this morning")
assert_equal(res, 'positive')
assert_equal(len(self.classifier.train_set), len(train_set))
def test_classify_a_list_of_words(self):
res = self.classifier.classify(["I", "feel", "happy", "this", "morning"])
assert_equal(res, "positive")
def test_train_from_lists_of_words(self):
# classifier can be trained on lists of words instead of strings
train = [(doc.split(), label) for doc, label in train_set]
classifier = NaiveBayesClassifier(train)
assert_equal(classifier.accuracy(test_set),
self.classifier.accuracy(test_set))
def test_prob_classify(self):
res = self.classifier.prob_classify("I feel happy this morning")
assert_equal(res.max(), "positive")
assert_true(res.prob("positive") > res.prob("negative"))
def test_accuracy(self):
acc = self.classifier.accuracy(test_set)
assert_true(isinstance(acc, float))
def test_update(self):
res1 = self.classifier.prob_classify("lorem ipsum")
original_length = len(self.classifier.train_set)
self.classifier.update([("lorem ipsum", "positive")])
new_length = len(self.classifier.train_set)
res2 = self.classifier.prob_classify("lorem ipsum")
assert_true(res2.prob("positive") > res1.prob("positive"))
assert_equal(original_length + 1, new_length)
def test_labels(self):
labels = self.classifier.labels()
assert_true("positive" in labels)
assert_true("negative" in labels)
def test_show_informative_features(self):
feats = self.classifier.show_informative_features()
def test_informative_features(self):
feats = self.classifier.informative_features(3)
assert_true(isinstance(feats, list))
assert_true(isinstance(feats[0], tuple))
def test_custom_feature_extractor(self):
cl = NaiveBayesClassifier(train_set, custom_extractor)
cl.classify("Yay! I'm so happy it works.")
assert_equal(cl.train_features[0][1], 'positive')
def test_init_with_csv_file(self):
cl = NaiveBayesClassifier(CSV_FILE, format="csv")
assert_equal(cl.classify("I feel happy this morning"), 'pos')
training_sentence = cl.train_set[0][0]
assert_true(isinstance(training_sentence, unicode))
def test_init_with_csv_file_without_format_specifier(self):
cl = NaiveBayesClassifier(CSV_FILE)
assert_equal(cl.classify("I feel happy this morning"), 'pos')
training_sentence = cl.train_set[0][0]
assert_true(isinstance(training_sentence, unicode))
def test_init_with_json_file(self):
cl = NaiveBayesClassifier(JSON_FILE, format="json")
assert_equal(cl.classify("I feel happy this morning"), 'pos')
training_sentence = cl.train_set[0][0]
assert_true(isinstance(training_sentence, unicode))
def test_init_with_json_file_without_format_specifier(self):
cl = NaiveBayesClassifier(JSON_FILE)
assert_equal(cl.classify("I feel happy this morning"), 'pos')
training_sentence = cl.train_set[0][0]
assert_true(isinstance(training_sentence, unicode))
def test_accuracy_on_a_csv_file(self):
a = self.classifier.accuracy(CSV_FILE)
assert_true(isinstance(a, float))
def test_accuracy_on_json_file(self):
a = self.classifier.accuracy(JSON_FILE)
assert_true(isinstance(a, float))
#.........这里部分代码省略.........
示例14: setUp
def setUp(self):
self.classifier = NaiveBayesClassifier(train_set)
示例15: open
train.append((val, "english"))
with open("spanish.txt", "r") as span:
for ind, val in enumerate(span):
try:
val = val.encode("ascii", "ignore")
val = val.replace("\t", "")
val = val.replace("\n", "")
val = val.replace("\r", "")
except UnicodeDecodeError:
continue
train.append((val, "spanish"))
cl = NaiveBayesClassifier(train)
english_links = open("english_links.txt", "w")
spanish_links = open("spanish_links.txt", "w")
for link in classes:
r = requests.get(link)
html = lxml.html.fromstring(r.text)
obj = html.xpath('//div[@class="postingBody"]')
post_body = [elem.text_content() for elem in obj]
if post_body != []:
text = post_body[0]
try:
text = text.encode("ascii", "ignore")
text = text.replace("\t", "")
text = text.replace("\n", "")