def cross_validate(content_set, times, words, amount_of_words):
incr = len(content_set) // times
document_extraction = Doc_extract(words, amount_of_words)
for i in xrange(times):
train_set = apply_features(document_extraction,
content_set[:i * incr] + content_set[(i + 1) * incr:])
test_set = apply_features(document_extraction,
content_set[i * incr: min((i + 1) * incr, len(content_set))])
classifier = get_trained_classifier(train_set)
acc = accuracy(classifier, test_set)
print('\n{0} classifier\n\tAccuracy: {1:.6}'.format(i + 1, acc))
print('\tPrecision: {0:.6}\n\tRecall: {1:.6}\n\tF_measure: {2:.6}'.format(*get_f_measure(classifier, test_set)))
def runTests(the_names):
feature_sets = [(genderFeatures(n), g) for (n,g) in the_names]
if False:
train_set, test_set = feature_sets[500:], feature_sets[:500]
else:
train_set = apply_features(genderFeatures, the_names[500:])
test_set = apply_features(genderFeatures, the_names[:500])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print '-----------------------------'
if True:
def getGender(name):
return classifier.classify(genderFeatures(name))
def testClassifier(name):
print name, 'is', getGender(name)
test_names = ['Peter', 'Catherine', 'John', 'Madeline', 'Mark', 'Tom',
'Matt', 'Matthew', 'David', 'Julie',
'Chris', 'Morgan', 'Riley']
for name in test_names:
testClassifier(name)
print '-----------------------------'
if False:
for (n,g) in the_names[:500]:
predicted = getGender(n)
print '%10s is' % n, '%6s' % predicted, '(%6s)' % g, '***' if predicted != g else ''
if False:
def getDesc(a_list):
return (len(a_list), sorted(a_list))
print 'Correct (male) =', getDesc([n for (n,g) in the_names[:500] if getGender(n)==g and g =='male'])
print '---------------------------------------------------'
print 'Correct (female) =', getDesc([n for (n,g) in the_names[:500] if getGender(n)==g and g =='female'])
print '===================================================='
print 'Incorrect (male) =', getDesc([n for (n,g) in the_names[:500] if getGender(n)!=g and g =='male'])
print '---------------------------------------------------'
print 'Incorrect (female) =', getDesc([n for (n,g) in the_names[:500] if getGender(n)!=g and g =='female'])
num_correct = len([1 for (n,g) in the_names[:500] if getGender(n)==g])
total = len(the_names[:500])
print num_correct, total
accuracy = num_correct/total
print 'accuracy = %.02f' % accuracy
print 'accuracy = %.02f' % nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(10)
def cross_validate(a_classiffier, content_set, number_folds, all_feats, amount_feats, type_feats):
with open('result.txt', 'a') as result_file:
print('\n\n\nClassiffier {0}, features is {1}'.format(a_classiffier[1], type_feats[8:]), file=result_file) # запись в файл file=result_file
document_extraction = Doc_feat_extractor(all_feats, amount_feats, type_feats)
incr = len(content_set) // number_folds
for i in xrange(number_folds):
train_set = apply_features(document_extraction,
content_set[:i * incr] + content_set[(i + 1) * incr:])
test_set = apply_features(document_extraction,
content_set[i * incr: min((i + 1) * incr, len(content_set))])
classifier = a_classiffier[0].train(train_set)
y_true = [l for (fs, l) in test_set]
y_pred = classifier.batch_classify([fs for (fs, l) in test_set])
print('\n{0} attempt\n'.format(i + 1), classification_report(y_true, y_pred,), file=result_file)
result_file.flush()
def naive_bayes_gender_classifier():
from nltk.corpus import names
names = ([(name, "male") for name in names.words("male.txt")] +
[(name, "female") for name in names.words("female.txt")])
random.shuffle(names)
# featuresets = [(_gender_features(n), g) for (n,g) in names]
# train_set, test_set = featuresets[500:], featuresets[:500]
# advisable to stream the sets in for large data set.
train_set = apply_features(_gender_features, names[500:])
test_set = apply_features(_gender_features, names[:500])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print "Neo is ", classifier.classify(_gender_features("Neo"))
print "Trinity is", classifier.classify(_gender_features("Trinity"))
# calculate the accuracy of the classifier
print nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(5)
def analyzeErrors(the_names):
train_names = the_names[1500:]
validation_names = the_names[500:1500]
test_names = the_names[:500]
train_set, validation_set, test_set = [apply_features(genderFeatures, n)
for n in [train_names, validation_names, test_names]]
classifier = nltk.NaiveBayesClassifier.train(train_set)
print 'validation accuracy = %.02f' % nltk.classify.accuracy(classifier, validation_set)
print ' test accuracy = %.02f' % nltk.classify.accuracy(classifier, test_set)
def getPrediction(name):
return classifier.classify(genderFeatures(name))
validation_results = [(n,g,getPrediction(n)) for (n,g) in validation_names]
validation_results_incorrect = [(n,g,p) for (n,g,p) in validation_results if g!=p]
validation_results_incorrect_male = [n for (n,g,p) in validation_results_incorrect if g=='male']
validation_results_incorrect_female = [n for (n,g,p) in validation_results_incorrect if g=='female']
print '--- %d males classified incorrectly ----------------------------------------' % len(validation_results_incorrect_male)
print sorted(validation_results_incorrect_male)
print '--- %d females classified incorrectly --------------------------------------' % len(validation_results_incorrect_female)
print sorted(validation_results_incorrect_female)
print len(validation_results_incorrect), 'incorrect in', len(validation_results)
classifier.show_most_informative_features(20)
def showPrediction(name):
print name, 'is', getPrediction(name)
showPrediction('madeline')
def initTrainingSet(self):
self.getTweetText()
self.getTerms()
# The apply_features func processes a set of labeled tweet strings using the passed extractFeatures func
self.trainingSet = apply_features(self.extractFeatures, self.labeledTweets)
# End func return
return
def train(self, num_samples):
"""uses connection to mongodb to read in tweets that contain :) and :(
the size of the training data is 2 * num_samples"""
print "Querying DB"
# read samples from DB
posTweets = self.db.get_N_results("\\:\\)",num_samples)
negTweets = self.db.get_N_results("\\:\\(",num_samples)
print "Query Returned"
# read query results into memory
posTweets = [(t["text"].split(" "),'positive') for t in posTweets]
negTweets = [(t["text"].split(" "),'negative') for t in negTweets]
labeled_tweets = posTweets + negTweets
random.shuffle(labeled_tweets)
print "compiling all words list"
# extract all unique words from the tweets
# these will be used as features
self.all_words = self.get_all_words(labeled_tweets)
# remove stop words
self.all_words = self.all_words.difference(self.stop_words)
print "num words: %d"%len(self.all_words)
test_size = int(len(labeled_tweets) * TEST_SET_PROPORTION)
# apply_features is a lazy loader, so that features are
# computed as necessary, instead of being loaded into memory
# all at once
train_set = apply_features(self.document_features,labeled_tweets[:len(labeled_tweets) - test_size])
test_set = apply_features(self.document_features,labeled_tweets[len(labeled_tweets) - test_size:])
print "training"
self.classifier = nltk.NaiveBayesClassifier.train(train_set)
#print accuracy on test set
print "Accuracy on "
print(nltk.classify.accuracy(self.classifier, test_set))
def category_by_name():
from nltk import NaiveBayesClassifier
from nltk import classify
from nltk.corpus import names
from nltk.classify import apply_features
import random
names = ([(name, 'male') for name in names.words('male.txt')] +[(name, 'female') for name in
names.words('female.txt')])
random.shuffle(names)
def gender_features(word):
return {'last_letter':word[-1]}
train_set = apply_features(gender_features, names[500:])
test_set = apply_features(gender_features, names[:500])
classifier = NaiveBayesClassifier.train(train_set)
print classifier.classify(gender_features('Neo'))
print classify.accuracy(classifier, train_set)
def main():
global best_words
tweets = get_tweets_from_db()
tweet_list = tweets[1000:1599000]
test_list = tweets[:1000]+ tweets[1599000:]
word_scores = create_word_scores()
best_words = find_best_words(word_scores, 500000)
f = open('bestwords.pickle', 'wb')
pickle.dump(best_words, f)
f.close()
training_set = classify.apply_features(best_word_features, tweet_list)
print "extracted features"
# train the classifier with the training set
classifier = NaiveBayesClassifier.train(training_set)
print "trained classifier"
# create the pickle file
f = open('NBclassifier_new.pickle', 'wb')
pickle.dump(classifier, f)
f.close()
print "created pickle"
# test for precision and recall
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
test_set = classify.apply_features(best_word_features, test_list)
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print 'neg precision:', metrics.precision(refsets['0'], testsets['0'])
print 'neg recall:', metrics.recall(refsets['0'], testsets['0'])
print 'pos precision:', metrics.precision(refsets['4'], testsets['4'])
print 'pos recall:', metrics.recall(refsets['4'], testsets['4'])
# test_set = classify.apply_features(extract_features, test_list)
# print "extracted features"
print classify.accuracy(classifier, test_set)
print classifier.show_most_informative_features(30)
def get_train_set(self):
"""Documentar
"""
if self._featuresets is None:
self.get_featuresets()
print "-- Recuperando train_set."
# Para não ocupar toda a memória RAM,
# não armazena todos os documentos de uma vez nesta.
# self._train_set = apply_features(Documento.get_features, self._documentos[100:])
self._train_set = apply_features(Documento.get_features, self._documentos)
return self._train_set
def classify(training_set, test_set):
training_set = apply_features(
paragraph_features,
training_set
)
test_set = apply_features(
paragraph_features,
test_set
)
print '\nTraining...'
classifier = nltk.NaiveBayesClassifier.train(
training_set
)
global count
count = 0
print '\nTesting...'
results = nltk.classify.accuracy(classifier, test_set)
print '\nAccuracy:', results
def __init__(self, golden_data, feature_extractor):
self.feature_extractor = feature_extractor
train_set = []
train_set = apply_features(feature_extractor, golden_data)
"""numtimes = 0
for (data, bio_type) in golden_data:
numtimes += 1
if numtimes % 100 == 0:
print numtimes
featureset = self.feature_extractor(data)
train_set.append( (featureset, bio_type) )
print "about to train"""
self.classifier = nltk.NaiveBayesClassifier.train(train_set)
print "done training"
def get_featuresets(self):
"""Configura os featuresets que são construídos na
seguinte estrutura:
(features_do_documento, categoria)
Retorna uma lista de featuresets
"""
if self._featuresets is None:
if self._documentos is None:
self.get_documentos()
print "-- Recuperando featuresets."
self._featuresets = apply_features(Documento.get_features, self._documentos)
return self._featuresets
def get_words_in_tweets(tweet):
all_words = []
for words, sentiment in tweet:
all_words.extend(words)
return all_words
def get_words_features(wordlist):
wordlist = FreqDist(wordlist)
return wordlist.keys()
word_features = get_words_features(get_words_in_tweets(tweet))
#print word_features
def extract_features(document):
document_words = set(document)
features = {}
for word in word_features:
features['contains(%s)' % word] = (word in document_words)
return features
training_set = classify.apply_features(extract_features, tweet)
#print training_set
classifier = NaiveBayesClassifier.train(training_set)
#print classifier.show_most_informative_features(32)
print classify.accuracy(classifier, training_set)
t = 'Larry is not my friend'
print classifier.classify(extract_features(t.split()))
def __init__(
self,
dir,
testDir=None,
doTest=True,
ignoreKlass=[],
includeKlass=None,
extractor="ArticleExtractor",
useHtml=False,
):
RssDataReader.__init__(self, dir, testDir)
logger.info("Start building " + self.__class__.__name__)
self.__mutex = threading.Semaphore()
freqDists = {}
ignore = stopwords.words("english")
features = set()
klassSize = {}
documentsWithLabel = []
for klassId in self.klasses(ignoreKlass, includeKlass):
freqDist = FreqDist()
size = 0
for url, document in self.documents(klassId, useHtml):
try:
txt = document if not useHtml else Extractor(extractor=extractor, html=document).getText()
documentsWithLabel.append((txt, klassId))
txt = tokenize(txt)
size += 1
for part in txt:
if part.isalnum() and part not in ignore:
freqDist.inc(part)
features.add(part)
# for bigram in nltk.bigrams(txt):
# freqDist.inc(bigram)
# featureFd.inc(bigram)
except:
logger.exception(u"Url: " + url)
freqDists[klassId] = freqDist
klassSize[klassId] = size
random.shuffle(documentsWithLabel)
self.__featuresGenerator = FeatureGenerator(freqDists, features, klassSize)
trainset = apply_features(self.__featuresGenerator, documentsWithLabel)
self.__classifier = NaiveBayesClassifier.train(trainset)
logger.info(u"Classifier learned (set size=" + unicode(len(trainset)) + u")")
if doTest:
ref = []
test = []
testDocumentsWithLabel = [
(
document if not useHtml else Extractor(extractor=extractor, html=document).getText(),
correctKlass,
url,
)
for correctKlass in self.klasses(ignoreKlass, includeKlass)
for url, document in self._testDocuments(correctKlass, useHtml)
]
for doc, cat, url in testDocumentsWithLabel:
ans = self.__classifier.classify(self.__featuresGenerator(doc))
ref.append(cat)
test.append(ans)
if ans != cat:
logger.info(u"Wrong " + ans + u"(" + cat + u"):\t" + url + u" " + doc.replace("\n", " "))
# for correctKlass, klass, featuresWithLabel in zip(ref, test, testset):
# if correctKlass != klass:
# pd = self.__classifier.prob_classify(dict(featuresWithLabel[0]))
# labelProbList = sorted( [(sample, pd.logprob(sample)) for sample in pd.samples()], key=lambda x: x[1], reverse=True)
# logger.info( correctKlass + " as " + klass + ": " + str([(correctKlass, "%.2f" % prob) for correctKlass, prob in labelProbList]))
# logger.info([(key, value)for key, value in featuresWithLabel[0].items() if value > 0])
# logger.info(self.__findDocumentByKlassAndFeatures(correctKlass, featuresWithLabel[0]))
logger.info("\n" + ConfusionMatrix(ref, test).pp())
# testset = apply_features(self.__featuresGenerator, testDocumentsWithLabel
# logger.info("Accuracy: " + str(nltk.classify.accuracy(self.__classifier, testset)))
self.__classifier.show_most_informative_features(n=300)
]
return msg_words
def get_features(msg):
features = {}
for w in get_msg_words(msg, stopwords):
features[w] = True
return features
training = []
for file in glob.glob("data/lissa_train/*"):
training.append((open(file, "r").read(), "lissa"))
for file in glob.glob("data/random_train/*"):
training.append((open(file, "r").read(), "not_lissa"))
testing = []
for file in glob.glob("data/lissa_test/*"):
testing.append((open(file, "r").read(), "lissa"))
for file in glob.glob("data/random_test/*"):
testing.append((open(file, "r").read(), "not_lissa"))
train_set = apply_features(get_features, training)
test_set = apply_features(get_features, testing)
cl = NaiveBayesClassifier.train(train_set)
print cl.show_most_informative_features(100)
print nltk.classify.accuracy(cl, test_set)
featuresets = [(gender_features_better(n), g) for (n,g) in names]
train_set, test_set = featuresets[2500:], featuresets[:2500]
classifier = nltk.NaiveBayesClassifier.train(train_set)
classifier.classify(gender_features_better('Neo'))
classifier.classify(gender_features_better('Trinity'))
# pg. 224
print nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(5)
from nltk.classify import apply_features
train_set = apply_features(gender_features, names[2500:])
test_set = apply_features(gender_features, names[:2500])
# pg. 225
def gender_features2(name):
features = {}
features["firstletter"] = name[0].lower()
features["lastletter"] = name[-1].lower()
for letter in 'abcdefghijklmnopqrstuvwxyz':
features["count(%s)" % letter] = name.lower().count(letter)
features["has(%s)" % letter] = (letter in name.lower())
return features
gender_features2('John')
import random
import mongo_db
from nltk.classify import apply_features
import re
from matplotlib import pyplot as plt
def gender_features(word):
return {'suffix1': word[-1:],
'suffix2': word[-2:]}
names = ([(name, 'male') for name in names.words('male.txt')] +
[(name, 'female') for name in names.words('female.txt')])
user_names = [user['id'] for user in mongo_db.load_from_mongo("project3", "reciprocal_user_features")]
random.shuffle(names)
featuresets = [(gender_features(n), g) for (n,g) in names]
train_set = apply_features(gender_features, names)
test_set2 = apply_features(gender_features, names[:500])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print nltk.classify.accuracy(classifier, test_set2)
count = 0
for username in user_names:
if username.startswith('San') or username.startswith('Francisco') or username.startswith('SF') or username.endswith('Francisco') or username.endswith('SF') :
count +=1
user_names.remove(username)
print count
re.sub(ur'[^\x00-\x7F\x80-\xFF\u0100-\u017F\u0180-\u024F\u1E00-\u1EFF]', u'', username)
for username in user_names:
#word_features = get_words_in_tweets(training)
training = joblib.load('models/training_compressed.pkl')
word_features = get_word_features(get_words_in_tweets(training))
joblib.dump(word_features, 'models/word_features_compressed.pkl', 3)
#print word_features
training = joblib.load('models/training_compressed.pkl')
word_features = joblib.load('models/word_features_compressed.pkl')
def extract_features(document):
"""
checks if the passed list of words
is contained in the list 'word_features'
"""
document_words = set(document)
features = {}
global word_features
for word in word_features:
features['contains(%s)' % word] = (word in document_words)
return features
#print extract_features(training[0][0])
training_set = classify.apply_features(extract_features, training)
#print training_set
classifier = NaiveBayesClassifier.train(training_set)
joblib.dump(classifier, 'models/classifier_compressed.pkl', 3)
def getAccuracy(self, documents):
from nltk.classify import apply_features
from nltk.classify.util import accuracy
testSet = apply_features(Classifier.extractFeatures, list(documents))
return accuracy(self.classifier, testSet)
def gender_features(word):
return { 'last_letter' : word[-1] }
#end
import random
import nltk
names = ([(name,'male') for name in ('D:\Project_files_notes\Gender-Identifier\male.txt')] + [(name,'female') for name in ('D:\Project_files_notes\Gender-Identifier\female.txt')])
random.shuffle(names)
featuresets = [(gender_features(n),g) for (n,g) in names]
from nltk.classify import apply_features
train_set = apply_features(gender_features,names[86:])
test_set = apply_features(gender_features, names[:85])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print classifier.classify(gender_features('ravi'))
print nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(5)
gender_features = lambda word:{'last_letter':word[-1]}
from nltk.corpus import names
names = [(name, 'male') for name in names.words('male.txt')] + [(name, 'female') for name in names.words('female.txt')]
from sklearn.model_selection import train_test_split
feature_sets = [(gender_features(name), gender) for name, gender in names]
train_set, test_set = train_test_split(feature_sets, test_size=0.33)
clf = nltk.NaiveBayesClassifier.train(train_set)
clf.classify(gender_features("Neo"))
print(nltk.classify.accuracy(clf, test_set))
clf.show_most_informative_features(5)
from nltk.classify import apply_features
train_set = apply_features(gender_features, names[:500])
from nltk.corpus import movie_reviews
all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())
words_features = list(all_words.keys())[:2000]
def document_features(document):
document_words = set(document)
features = {}
for word in words_features:
features['contains(%s)' % word] = (word in document_words)
return features
print(document_features(movie_reviews.words('pos/cv957_8737.txt')))
from nltk.corpus import movie_reviews
documents = [(list(movie_reviews.words(fileid)), category)
for category in movie_reviews.categories()
for fileid in movie_reviews.fileids(category)]
featuresets = [(document_features(d), c) for (d, c) in documents]
x_Neo = classifier.classify(gender_features('Neo'))
print('x_Neo: ', x_Neo)
x_Trinity = classifier.classify(gender_features('Trinity'))
print('x_Trinity: ', x_Trinity)
# 系统评估分类器
ratio = nltk.classify.accuracy(classifier, X_test)
print('准确率： ', ratio)
# 最有效特征
classifier.show_most_informative_features(5)
from nltk.classify import apply_features
train_set = apply_features(gender_features, names[500:])
print('train_set: ', train_set)
print('X_train: ', X_train)
# 2.选择正确的特征
# 特征提取器过拟合性别特征
print('\n特征提取器过拟合性别特征:')
def gender_features2(name):
features = {}
features['firstletter'] = name[0].lower()
features['lastletter'] = name[-1].lower()
for letter in 'abcdefghijklmnopqrstuvwxyz':
features['count(%s)' % letter] = name.lower().count(letter)
features['has(%s)' % letter] = (letter in name.lower())
def classifyTestData(filename, classifier): labelTestData = buildLabelData(filename) test_set = apply_features(charNgramfeatureDict, labelTestData) accuracy = nltk.classify.accuracy(classifier, test_set) return accuracy
import nltk
# nltk.download()
from nltk.corpus import names
import random
def gender_features(word):
return {'last_letter': word[-1]}
names = ([(name, 'male') for name in names.words('male.txt')] +
[(name, 'female') for name in names.words('female.txt')])
# print(names)
random.shuffle(names)
featureset = [(gender_features(n), g) for (n, g) in names]
# print(featureset)
train_set, test_set = featureset[500:], featureset[:500]
classifier = nltk.NaiveBayesClassifier.train(train_set)
# print(classifier)
# print(classifier.classify(gender_features("Neo")))
# print(nltk.classify.accuracy(classifier, test_set))
# classifier.show_most_informative_features(5)
from nltk.classify import apply_features
train_set = apply_features(gender_features(), featureset[500:])
text += sh.cell(i, 8).value
if sh.cell(i, 9).value:
text += sh.cell(i, 9).value
if not text or type(text) is float:
continue
else:
text = ''.join(ch for ch in text if ch not in exclude)
if category:
categorized.append((text, category))
else:
uncategorized.append((text, category))
# Shuffle the categorized documents so that the order in which they are in the excel file does not affect the results
random.shuffle(categorized)
all_words = []
for d in categorized:
all_words += d[0].split()
# Take the 2000 most relevant words
all_words = nltk.FreqDist(w.lower() for w in all_words if (not w in stopwords.words('dutch') and len(w) > 1))
word_features = all_words.keys()[:2000]
# Take the 500 first registers for testing and the rest for training
test_set = apply_features(document_features, categorized[:500])
train_set = apply_features(document_features, categorized[500:])
# Train the classifier
classifier = nltk.NaiveBayesClassifier.train(train_set)
print (nltk.classify.accuracy(classifier, test_set) * 100)
print(classifier.classify(gender_features('Neo')))
print(classifier.classify(gender_features('Trinity')))
print("-" * 40)
print(nltk.classify.accuracy(classifier, test_set))
print("-" * 40)
classifier.show_most_informative_features(5)
print("-" * 40)
def gender_features(word):
return {'last_letter': word[-1], 'length': len(word), 'first_letter': word[0]}
from nltk.classify import apply_features
train_set = apply_features(gender_features, labeled_names[500:])
test_set = apply_features(gender_features, labeled_names[:500])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classifier, test_set))
print("-" * 40)
print("""
----------------------------------------------------------------------
1.2 Choosing The Right Features
----------------------------------------------------------------------
""")
def gender_features2(name):
features = {}
def Naive_Bayes_classify(input_df,
feature_column,
label_column,
process_text=False,
test_size=.2,
random_state=1,
feature_fct=None,
most_informative_features=10,
show_info=True,
return_fscore=True):
df = input_df.copy()
if process_text:
df[feature_column] = df[feature_column].progress_apply(process_sow)
else:
df[feature_column] = df[feature_column].apply(process_sow_quick)
features = list(df[feature_column])
labels = list(df[label_column])
data_set = [(f, labels[index]) for index, f in enumerate(features)]
if feature_fct:
get_features = feature_fct
else:
def get_features(text):
features = {}
tokens = text.split(' ')
features["sow length"] = len(tokens)
fd = nltk.FreqDist(tokens)
most_common = fd.most_common(3)
top = most_common[0][0]
sec = most_common[1][0]
thr = most_common[2][0]
features["1st word"] = top
features["2nd word"] = sec
features["3rd word"] = thr
return features
X_train, X_test = train_test_split(data_set,
test_size=test_size,
random_state=random_state)
train_set = apply_features(get_features, X_train)
test_set = apply_features(get_features, X_test)
classifier = nltk.NaiveBayesClassifier.train(train_set)
y_pred = []
y_test = []
for i in range(len(list(test_set))):
y_pred.append(classifier.classify(test_set[i][0]))
y_test.append(test_set[i][1])
f, cf = single_label_f_score(y_gold=y_test, y_pred=y_pred)
if show_info:
print('f-score:', f)
print('label wise f-score', cf)
print(
classifier.show_most_informative_features(
most_informative_features))
conf_mat = confusion_matrix(y_test, y_pred)
fig, ax = plt.subplots(figsize=(4, 4))
labels = list(set(labels))
sns.heatmap(conf_mat,
annot=True,
cmap="Blues",
fmt='d',
xticklabels=labels,
yticklabels=labels)
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.title("NaiveBayes CONFUSION MATRIX", size=16)
if return_fscore:
return f, cf
for fileid in movie_reviews.fileids(category)]
random.shuffle(documents)
all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())
word_features = list(all_words)[:2000]
def document_features(document):
document_words = set(document)
features = {}
for word in word_features:
features["contains({})".format(word)] = (word in document_words)
return features
def document_features2(document):
words_counter = Counter(document)
features = {}
for word in word_features:
features["count({})".format(word)] = words_counter[word]
return features
length = len(documents)
test_size = int(0.2 * length)
train_set = apply_features(document_features2, documents[test_size:])
test_set = apply_features(document_features2, documents[:test_size])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(5)
word_features = all_words.keys()[:2000]
# Get the top synsets in the document from the top 2000 words
synset_features = synsets(word_features)
def document_features(document):
document_words = set(document)
document_synsets = synsets(document_words)
for word in document_words:
document_synsets.update(str(s) for s in wordnet.synsets(word))
features = dict()
# for word in word_features:
# features['contains({})'.format(word)] = (word in document_words)
for synset in synset_features:
features[synset] = (synset in document_synsets)
return features
train_set, test_set = apply_features(document_features, documents[100:]), apply_features(document_features, documents[:100])
print 'training classifier'
classifier = nltk.NaiveBayesClassifier.train(train_set)
print nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(10)
def trainNaivebayes(trainFile): labelTrainData = buildLabelData(trainFile) train_set = apply_features(charNgramfeatureDict, labelTrainData) classifier = nltk.NaiveBayesClassifier.train(train_set) return classifier
def _getPredictedAndLabeled(self, documents):
from nltk.classify import apply_features
documents = list(documents)
testSet = apply_features(Classifier.extractFeatures, list(documents))
return (array(self.classifier.batch_classify([fs for (fs,l) in testSet])), array([l for (fs,l) in documents]))
# 'is_female'
print nltk.classify.accuracy(classifier, test_set)
# 0.758
classifier.show_most_informative_features(5)
# Most Informative Features
# last_letter = 'a' female : male = 38.3 : 1.0
# last_letter = 'k' male : female = 31.4 : 1.0
# last_letter = 'f' male : female = 15.3 : 1.0
# last_letter = 'p' male : female = 10.6 : 1.0
# last_letter = 'w' male : female = 10.6 : 1.0
from nltk.classify import apply_features
train_set = apply_features(g_f, names[500:])
test_set = apply_features(g_f, names[:500])
classifier = nltk.NaiveBayesClassifier.train(train_set)
print classifier.classify(g_f('Neo'))
# 'is_male'
print classifier.classify(g_f('Trinity'))
# 'is_female'
print nltk.classify.accuracy(classifier, test_set)
# 0.758
classifier.show_most_informative_features(5)
# Most Informative Features
# last_letter = 'a' female : male = 38.3 : 1.0
# last_letter = 'k' male : female = 31.4 : 1.0
# last_letter = 'f' male : female = 15.3 : 1.0
def trainClassifier(self, documents):
from nltk.classify import apply_features
trainSet = apply_features(Classifier.extractFeatures, list(documents))
self.classifier = self.nltkClassifier.train(trainSet)
labeled_names = ([(name, 'male') for name in names.words(
'male.txt')] + [(name, 'female') for name in names.words('female.txt')])
# 打乱顺序
random.shuffle(labeled_names)
featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]
train_set, test_set = featuresets[500:], featuresets[:500]
classfier = nltk.NaiveBayesClassifier.train(train_set)
print(nltk.classify.accuracy(classfier, test_set))
train_names = labeled_names[1500:]
devtest_names = labeled_names[500:1000]
test_names = labeled_names[:500]
train_set1 = apply_features(gender_features2, train_names)
devtest_set = apply_features(gender_features2, devtest_names)
test_set1 = apply_features(gender_features2, test_names)
classfier1 = nltk.NaiveBayesClassifier.train(train_set1)
print(nltk.classify.accuracy(classfier1, devtest_set))
error = []
for (name, tag) in devtest_names:
guess = classfier1.classify(gender_features2(name))
if guess != tag:
error.append((tag, guess, name))
for (tag, guess, name) in sorted(error):
print(
'correct = {:<8} guess = {:<8s} name={:<30}'.format(tag, guess, name))
features = {}
features["first_letter"] = name[0].lower()
features["last_letter"] = name[-1].lower()
for letter in 'abcdefghijklmnopqrstuvwxyz':
features["count({})".format(letter)] = name.lower().count(letter)
features["has({})".format(letter)] = (letter in name.lower())
return features
labeled_names = ([(name, 'male') for name in names.words('male.txt')] +
[(name, 'female') for name in names.words('female.txt')])
random.shuffle(labeled_names)
# Just last letter gave acc of 0.752
# featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]
train_set = apply_features(gender_features, labeled_names[500:])
test_set = apply_features(gender_features, labeled_names[:500])
classifier = nltk.NaiveBayesClassifier.train(train_set)
# print(classifier.classify(gender_features('Neo')))
# print(classifier.classify(gender_features('Trinity')))
# print(classifier.show_most_informative_features(5))
train_names = labeled_names[1500:]
devtest_names = labeled_names[500:1500]
test_names = labeled_names[:500]
# def gender_features(word):
# return {'suffix1': word[-1:], 'suffix2': word[-2:]}
train_set = [(gender_features(n), gender) for (n, gender) in train_names]
devtest_set = [(gender_features(n), gender) for (n, gender) in devtest_names]
