def train(self, training_corpus):
assert isinstance(training_corpus, (list, tuple))
assert isinstance(training_corpus[0], dict)
featureset = [(twit_features(i["text"]), i["polarity"])
for i in training_corpus
if i["denied"] == 0]
self.classifier = NaiveBayesClassifier.train(featureset)
def get_sentiment_data(query, training_set):
train = []
with open('training/' + training_set + '/training.txt') as f:
for line in f:
temp = line.split('\t')
#print temp
train.append((get_features(temp[1]), temp[0]))
clf = NaiveBayesClassifier.train(train)
tweets = grab_tweets(query)
print "HERE"
classified = {}
for tweet in tweets:
if tweet.created_at in classified.keys():
classified[tweet.created_at] = classified[tweet.created_at] + [clf.classify(get_features(tweet.text))]
else:
classified[tweet.created_at] = [clf.classify(get_features(tweet.text))]
print classified
returndata = {}
for key in classified:
#numpos = sum([1 if v=='pos' else 0 for v in classified[key]])
#returndata[key] = (numpos, len(classified[key]) - numpos) #tuple of positive, negative
# percent:
returndata[key] = float(sum([1 if v == '1' else 0 for v in classified[key]]))/len(classified[key])
#returndata[key] = math.ceil(float(sum([1 if v == '1' else 0 for v in classified[key]]))/len(classified[key])*100)/100.0
print returndata
return returndata
def nltk_model():
"""Fits the (non-parametric) naive Bayes classifier from nltk on the names
dataset."""
# each elt of all_names will be a (name, gender) tuple
all_names = list()
with open(MALE_FILE, "r") as f:
for line in f:
all_names.append((line.rstrip(), "male")) # rstrip removes trailing whitespace
with open(FEMALE_FILE, "r") as g:
for line in g:
all_names.append((line.rstrip(), "female"))
# assert stmts can be useful for debugging etc
assert len(all_names) == 7944
# shuffle all_names in place
random.shuffle(all_names)
# features are ({'feature_type': feature_value}, gender) tuples
features = [(nltk_featurize(name), gender) for name, gender in all_names]
split_pt = int(TRAIN_PCT * len(features))
train_set, test_set = features[:split_pt], features[split_pt:]
nb = NaiveBayesClassifier.train(train_set)
print "accuracy = {0} %".format(int(100 * nltk.classify.accuracy(nb, test_set)))
nb.show_most_informative_features(10)
def test_raw_mail(org_email):
features_test = {}
wordtokens_test = [word_limit.lemmatize(key.lower()) for key in
word_tokenize(org_email)]
for key in wordtokens_test:
if key not in stpwords:
features_test[key] = True
return features_test
#Extracting the features(Tonenized, stemmed and non-stopwords emails) from all the emails
feature_sets = [(raw_mail(n), g) for (n,g) in mail_shuffle]
#Splitting the test and training data sets from the whole email set features
size_feature = int(len(feature_sets) * 0.10)
train_set, test_set = feature_sets[size_feature:], feature_sets[:size_feature]
classifier = NaiveBayesClassifier.train(train_set)
#print (test_set[1:5])
#Printing the accuracy of the machine
print ('accuracy of the machine: ', (classify.accuracy(classifier,test_set))*100)
#Printing the top 50 features
classifier.show_most_informative_features(50)
#Printing the spam and ham labels
print ('labels:',classifier.labels())
#Classification of user entered email
while(True):
featset = raw_mail(input("Enter text to classify: "))
print (classifier.classify(featset))
def __init__(self, chatbot, **kwargs):
super().__init__(chatbot, **kwargs)
from nltk import NaiveBayesClassifier
self.positive = kwargs.get('positive', [
'what time is it',
'hey what time is it',
'do you have the time',
'do you know the time',
'do you know what time it is',
'what is the time'
])
self.negative = kwargs.get('negative', [
'it is time to go to sleep',
'what is your favorite color',
'i had a great time',
'thyme is my favorite herb',
'do you have time to look at my essay',
'how do you have the time to do all this'
'what is it'
])
labeled_data = (
[(name, 0) for name in self.negative] +
[(name, 1) for name in self.positive]
)
train_set = [
(self.time_question_features(text), n) for (text, n) in labeled_data
]
self.classifier = NaiveBayesClassifier.train(train_set)
def check_classifier(feature_extractor, **kwargs):
'''
Train the classifier on the training spam and ham, then check its accuracy
on the test data, and show the classifier's most informative features.
'''
# Make training and testing sets of (features, label) data
train_set, test_spam, test_ham = \
make_train_test_sets(feature_extractor, **kwargs)
#===============================================
# ADD YOUR CODE HERE
# Train the classifier on the training set (train_set)
# classifier = /your code/
# Test accuracy on test spam emails (test_spam) and test ham emails(test_ham)
# spam_accuracy = /your code/
# Test accuracy on test ham emails (test_spam) and test ham emails(test_ham)
# ham_accuracy = /your code/
#===============================================
classifier = NaiveBayesClassifier.train(train_set)
spam_accuracy = nltk.classify.accuracy(classifier, test_spam)
ham_accuracy = nltk.classify.accuracy(classifier, test_ham)
# How accurate is the classifier on the test sets?
print ('Test Spam accuracy: {0:.2f}%'
.format(100 * spam_accuracy))
print ('Test Ham accuracy: {0:.2f}%'
.format(100 * ham_accuracy))
# Show the top 20 informative features
print classifier.show_most_informative_features(20)
def train(self):
"""
"""
catalog = getToolByName(self, "portal_catalog")
presentNouns = dict()
trainingData = []
allNouns = catalog.uniqueValuesFor("noun_terms")
for item in allNouns:
presentNouns.setdefault(item, 0)
subjectIndex = catalog._catalog.getIndex("Subject")
nounTermsIndex = catalog._catalog.getIndex("noun_terms")
# The internal catalog ids of the objects
# that have noun terms in the catalog
nounTermIndexIds = IISet(nounTermsIndex._unindex.keys())
# The internal catalog ids of the objects
# that have subjects in the catalog
subjectIndexIds = IISet(subjectIndex._unindex.keys())
commonIds = intersection(subjectIndexIds, nounTermIndexIds)
for cid in commonIds:
nounPresence = presentNouns.copy()
nouns = nounTermsIndex._unindex[cid]
tags = subjectIndex._unindex[cid]
for noun in nouns:
nounPresence[noun] = 1
for tag in tags:
trainingData.append((nounPresence, tag))
if trainingData:
self.classifier = NaiveBayesClassifier.train(trainingData)
def train_nltk(data, labels):
'''
Returns a trained nltk.NaiveBayesClassifier
Inputs
---------
data -- np.array of tuples
'''
# For now, shuffle, since for now assuming that only the post language itself is all that's needed for offensive measure, though in the future, 2 anti-something users may actually not be offended by one another if they are both negative about something
kf = cv.KFold(n=len(data), n_folds=N_FOLDS, shuffle=True)
best_model = None
max_acc = float('-inf')
for k, (train_index, test_index) in enumerate(kf):
X_train, Y_train = data[train_index], labels[train_index]
X_test, Y_test = data[test_index], labels[test_index]
features_train = bulk_extract_features(X_train)
features_test = bulk_extract_features(X_test)
train_set = zip(features_train, Y_train)
test_set = zip(features_test, Y_test)
model = nbc.train(train_set)
acc = nltk.classify.accuracy(model, test_set)
print str(acc)
if acc > max_acc:
max_acc = acc
best_model = model
best_model.show_most_informative_features(30)
return best_model
def __init_naive_bayes( self ):
"""
Create and trains the NaiveBayes Classifier
"""
try:
# corpus_no = abs(int(raw_input('Enter the number (1-3) of corpus: ')))
# while corpus_no == 0 or corpus_no > 3:
# corpus_no = abs(int(raw_input('Please the number of corpus from 1 to 2:' )))
corpus = 'corpus2'#+str(corpus_no)
path = os.path.join('corpora/',corpus)
spam_path = os.path.join(path,'spam')
ham_path = os.path.join(path,'ham')
spam_dir = os.listdir(spam_path)
ham_dir = os.listdir(ham_path)
train_spam_filelist = [os.path.join(spam_path, f) for f in spam_dir]
train_ham_filelist = [os.path.join(ham_path, f) for f in ham_dir]
spam_size = len(train_spam_filelist)
ham_size = len(train_ham_filelist)
train_spam_set = self.__make_featured_set(train_spam_filelist,'spam')
train_ham_set = self.__make_featured_set(train_ham_filelist,'ham')
train_set = train_spam_set + train_ham_set
self.classifier = NaiveBayesClassifier.train( train_set )
except:
raise Exception( "Unexpected error in SpamFilter: __spamFilter:",sys.exc_info()[0].__name__,\
os.path.basename( sys.exc_info()[2].tb_frame.f_code.co_filename ),\
sys.exc_info()[2].tb_lineno, \
sys.exc_info()[1].message )
def get_matrix(spam_set, ham_set, num_folds): ''' Generate different matrix by taking the average of K Fold data ''' total_precision = total_recall = F1 = spam_accuracy = ham_accuracy = 0 for train_set, test_spam_set, test_ham_set in utils.get_kfold_data(spam_set, ham_set, num_folds): classifier = NaiveBayesClassifier.train(train_set) spam_len = len(test_spam_set) ham_len = len(test_ham_set) true_positive = false_positive = true_negative = false_negative = 0 for test in test_spam_set: features = test[0] predicted_label = classifier.classify(features) if predicted_label == 0: true_positive += 1 else: false_negative += 1 for test in test_ham_set: features = test[0] predicted_label = classifier.classify(features) if predicted_label == 1: true_negative += 1 else: false_positive += 1 precision = true_positive / float(true_positive + false_positive) recall = true_positive / float(true_positive + false_negative) F1 += (2 * precision * recall) / (precision + recall) spam_accuracy += true_positive / float(true_positive + false_negative) ham_accuracy += true_negative / float(true_negative + false_positive) total_precision += precision total_recall += recall return total_precision/num_folds, total_recall/num_folds, F1/num_folds, spam_accuracy*100/num_folds, ham_accuracy*100/num_folds
def __init__(self, **kwargs):
super(TimeLogicAdapter, self).__init__(**kwargs)
from nltk import NaiveBayesClassifier
self.positive = [
'what time is it',
'do you know the time',
'do you know what time it is',
'what is the time'
]
self.negative = [
'it is time to go to sleep',
'what is your favorite color',
'i had a great time',
'what is'
]
labeled_data = (
[(name, 0) for name in self.negative] +
[(name, 1) for name in self.positive]
)
# train_set = apply_features(self.time_question_features, training_data)
train_set = [(self.time_question_features(n), text) for (n, text) in labeled_data]
self.classifier = NaiveBayesClassifier.train(train_set)
def buildclassifiers(featureslist, SAMPLE_PROPORTION, n): classnames = ['Naive Bayes', 'Logistic Regression', 'Linear SCV'] allclassifiers = [] for name in classnames: for i in range(n): random.shuffle(featureslist) train_set, test_set = buildsets(featureslist, SAMPLE_PROPORTION) if name == 'Naive Bayes': spamclassifier = NaiveBayesClassifier.train(train_set) if name == 'Logistic Regression': spamclassifier = SklearnClassifier(LogisticRegression()) spamclassifier.train(train_set) if name == 'Linear SCV': spamclassifier = SklearnClassifier(LinearSVC(C=0.01)) spamclassifier.train(train_set) perfmeasures_i = evaluate(train_set, test_set, spamclassifier, name) if i == 0: perfmeasures_n = perfmeasures_i else: perfmeasures_n = map(add, perfmeasures_n, perfmeasures_i) # Store last classifier built per model allclassifiers.append(spamclassifier) # Print performance measures per classifier printperformance(name, perfmeasures_n, n) return allclassifiers
def train_classifiers(self):
for word in self.senses:
train_set = []
for senseId in self.senses[word]:
for lsa_vector in self.senses[word][senseId]:
train_set.append([dict(lsa_vector), senseId])
self.classifiers[word] = NaiveBayesClassifier.train(train_set)
def category_by_movie():
from nltk.corpus import movie_reviews as mr
from nltk import FreqDist
from nltk import NaiveBayesClassifier
from nltk import classify
from nltk.corpus import names
from nltk.classify import apply_features
import random
documents = [(list(mr.words(f)), c) for c in mr.categories() for f in
mr.fileids(c)]
random.shuffle(documents)
all_words = FreqDist(w.lower() for w in mr.words())
word_features = all_words.keys()[:2000]
def document_features(document):
document_words = set(document)
features = {}
for word in word_features:
features['contains(%s)' % word] = (word in document_words)
return features
#print document_features(mr.words('pos/cv957_8737.txt'))
#print documents[0]
features = [(document_features(d), c) for (d, c) in documents]
train_set, test_set = features[100:], features[:100]
classifier = NaiveBayesClassifier.train(train_set)
print classify.accuracy(classifier, train_set)
def training(features, method, proportion_training): training_set = features[:int(proportion_training*len(features))] # we take 2/3 for training and 1/3 for testing testing_set = features[int(proportion_training*len(features)):] if method == 'NaiveBayes': classifier = NaiveBayesClassifier.train(training_set) return training_set, testing_set, classifier
def train(self, foldPercent=.8):
features = self.buildFeatures()
foldIndex = int(foldPercent * len(features))
self.setTrain = features[:foldIndex]
self.setTest = features[foldIndex:]
self.classifier = nbc.train(self.setTrain)
def train(features, samples_proportion):
train_size = int(len(features) * samples_proportion)
train_set, test_set = features[:train_size], features[train_size:]
print ('Training set size = ' + str(len(train_set)) + ' emails')
print ('Test set size = ' + str(len(test_set)) + ' emails')
train_set_tuple = tuple(train_set)
classifier = NaiveBayesClassifier.train(train_set_tuple)
return train_set, test_set, classifier
def textClass():
#dbFile = open("samp.txt")
dbFile = open("all.txt")
reviews = list() #each list element is a list of words in the review
ratings = list() #ratings given
usefulness = list() #review classification
tot_recs = 0
len_tot = 0
mlen = 0
#parse the file and create the list to be passed to the NBClassifiers
while tot_recs < 150000:#True:
if tot_recs % 1000 == 0:
print "num records:", tot_recs
tot_recs += 1
raw_rec = readRec(dbFile)
if len(raw_rec) == 0:
break
review_text = [word.strip(punctuation) for word in raw_rec["text"]]
rate_val = str( raw_rec["score"][0] )
prs_rec = parse4ftrs(raw_rec)
len_tot += prs_rec["length"]
if prs_rec["length"] > mlen:
mlen = prs_rec["length"]
use_val = str( prs_rec["class"] )
#print use_val, rate_val
#word feature dictionary
wfd = word_feats(review_text)
ratings.append( ( wfd , rate_val) )
usefulness.append( ( wfd, use_val) )
dbFile.close()
print "avg length:", len_tot/tot_recs
print "max len:", mlen
#select a cutoff for test v training
#nrecs = len(ratings)
nrecs = tot_recs
rate_cl = NaiveBayesClassifier.train(ratings)
use_cl = NaiveBayesClassifier.train(usefulness)
return rate_cl, use_cl
def evaluate_classifier(train_set, test_spam, test_ham):
""" Using NaiveBayesClassifier.train() method from NLTK to train the train_set (spam + ham),
then classifier is used to evaluate the accuracy of test Spam, Ham. Finally, the most informative
features are showed.
"""
classifier = NaiveBayesClassifier.train(train_set)
print ("Test Spam accuracy: {0:.2f} %".format(100 * nltk.classify.accuracy(classifier, test_spam)))
print ("Test Ham accuracy: {0:.2f} %".format(100 * nltk.classify.accuracy(classifier, test_ham)))
print classifier.show_most_informative_features(20)
def train(features, samples_proportion):
train_size = int(len(features) * samples_proportion)
# initialise the training and test sets
train_set, test_set = features[:train_size], features[train_size:]
print ('Training set size = ' + str(len(train_set)) + ' emails')
print ('Test set size = ' + str(len(test_set)) + ' emails')
# train the classifier
classifier = NaiveBayesClassifier.train(train_set)
return train_set, test_set, classifier
def buildClassifier(hamDir, spamDir): spamEmails = [] hamEmails = [] allEmails = [] features = [] # Using glob instead of os.listdir to ignore hidden files for email in glob.glob(spamDir + "/*"): f = open(email) spamEmails.append(f.read()) f.close() for email in glob.glob(hamDir + "/*"): f = open(email) hamEmails.append(f.read()) f.close() for email in spamEmails: allEmails.append((email, 'spam')) for email in hamEmails: allEmails.append((email, 'ham')) # Shuffle to get the accuracy of the 70:30 ratio. Otherwise, if no check were to be done, would not need to shuffle. random.shuffle(allEmails) # Make a list of feature per email for (email, label) in allEmails: features.append((emailFeatures(email), label)) # 70:30 ratio for training:testing print "Using a 70:30 ratio for training:testing, the accuracy is as follows: " totalSize = int(len(features) * 0.7) trainingEmails, testingEmails = features[:totalSize], features[totalSize:] print "training size: %d; testing size: %d" %(len(trainingEmails), len(testingEmails)) classifier = NaiveBayesClassifier.train(trainingEmails) print classify.accuracy(classifier, testingEmails) print "Now creating and saving a full size classifier made up of %d emails..." %len(features) classifier = NaiveBayesClassifier.train(features) saveClassifier(classifier, "full-classifier.pickle")
def __init__(self,classifierType):
titles = []
bodies = []
invalids = []
drivers = []
fromFields = []
toFields = []
ctitles = []
cbodies = []
cdrivers = []
dirname = os.path.dirname(__file__)
with open(os.path.join(dirname,'sfIsGood.csv'), 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
i = -1
for row in spamreader:
i += 1
if (i > 0):
titles.append(row[0])
bodies.append(row[3])
fromFields.append(row[6])
toFields.append(row[7])
invalids.append(row[6] == 'invalid')
drivers.append(row[10])
if not row[6] == 'invalid':
ctitles.append(row[0])
cbodies.append(row[3])
cdrivers.append(row[10])
words = []
if classifierType == 'driver':
for i in range(len(ctitles)):
words += nltk.word_tokenize(ctitles[i])
words += nltk.word_tokenize(cbodies[i])
documents = [((nltk.word_tokenize(ctitles[i]) +
nltk.word_tokenize(cbodies[i]))
, cdrivers[i]) for i in range(len(ctitles))]
random.shuffle(documents)
elif classifierType == 'invalid':
for i in range(len(titles)):
words += nltk.word_tokenize(titles[i])
words += nltk.word_tokenize(bodies[i])
documents = [((nltk.word_tokenize(titles[i]) +
nltk.word_tokenize(bodies[i]))
, str(invalids[i])) for i in range(len(ctitles))]
random.shuffle(documents)
all_words = nltk.FreqDist(w.lower() for w in words)
self.word_features = all_words.keys()[:500]
self.training_set = [(self.document_features(d), c) for (d,c) in documents]
self.classifier = NaiveBayesClassifier.train(self.training_set)
def naives_classifier(self, training_set, dev_set, log=0):
classifier = NaiveBayesClassifier.train(training_set)
accuracy = classify.accuracy(classifier, dev_set)
print('Naive Bayes accuracy dev percent: ', (accuracy * 100))
if log == 1:
classifier.show_most_informative_features(20)
return classifier
def user_name_classify(user_name, classifier):
"""Infer a gender for a User given any name, using a Naive Bayes classifier
"""
names = [(name, 'male') for name in names.words('male.txt')] + [(name, 'female') for name in names.words('female.txt')]
features = [(name, gender) for (name, gender) in names]
training_set = features[500:]
test_set = features[:500]
classifier = NaiveBayesClassifier.train(training_set)
return classifier.classify(user_name)
def classify(text, sender=None, subject=None):
training_set = load_training_set()
classifier = NaiveBayesClassifier.train(training_set)
test_data = bag_of_words(extract_bigrams(text))
if sender is not None:
test_data[sender] = True
if subject is not None:
test_data[subject] = True
classified = classifier.prob_classify(test_data)
pprint({categories[sample]: classified.prob(sample) for sample in classified.samples()})
return categories[classified.max()]
def train(self, data):
self.result_string = self._represent(data)
self.labels = defaultdict(int)
result_string_len = len(self.result_string)
self.labels = FreqDist(self.result_string)
train = []
for start in range(0, len(self.result_string) - self.n_w, self.n_w - 1):
window = self.result_string[start:start + self.n_w]
x_key = self.result_string[start + self.n_w]
train.append(self._gen_feature(window, x_key))
self.classifier = NaiveBayesClassifier.train(train)
def train(positiveFile='positive.csv', negativeFile='negative.csv', nOccurrences=25, trainProportion=0.9):
files = [positiveFile, negativeFile]
tweetfeats = []
masterfeats = {}
for fn in files:
f = open(fn, 'r')
theclass = "pos"
if fn == negativeFile:
theclass = "neg"
sep = '\t'
fin = csv.reader(f, delimiter = sep)
for line in fin:
text = line[1]
if (len(line) != 9):
print(text)
# break up into tokens removing all non-word chars
feat = featurify(text)
for f in feat:
if f in masterfeats:
masterfeats[f] += 1
else:
masterfeats[f] = 0
if len(feat) > 0:
tweetfeats.append((feat, theclass))
mfn = masterfeats.copy()
for f in masterfeats:
if masterfeats[f] < nOccurrences:
del mfn[f]
masterfeats = mfn
f = open("features.lst", "w")
f.write('\n'.join(list(masterfeats.keys())))
f.close()
print "Number of Features = %i" % len(masterfeats)
train_cut = int(len(tweetfeats) * trainProportion)
random.shuffle(tweetfeats)
trainfeats = tweetfeats[:train_cut]
testfeats = tweetfeats[train_cut:]
print "Training sentiment classifier..."
sys.stdout.flush()
classifier = NaiveBayesClassifier.train(trainfeats)
print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
classifier.show_most_informative_features()
sys.stdout.flush()
# SAVE the classifier & features
f = open("classifier.pickle", 'w')
pickle.dump(classifier, f)
f.close()
f = open("features.pickle", 'w')
pickle.dump(masterfeats, f)
f.close()
def cross_validate():
training_set = load_training_set()
random.shuffle(training_set)
average = 0
cv = KFold(len(training_set), n_folds=10, indices=True, shuffle=False, random_state=None)
for traincv, evalcv in cv:
classifier = NaiveBayesClassifier.train(training_set[traincv[0]:traincv[len(traincv) - 1]])
acc = accuracy(classifier, training_set[evalcv[0]:evalcv[len(evalcv) - 1]])
print 'Range: ', evalcv[0], 'to', evalcv[len(evalcv) - 1]
print 'Accuracy: %4.2f' % acc
average += acc
print 'Average accuracy: %4.2f' % (average / 10)
def buildRevClassifier(self, features, normalize, validity):
revs = self.values()
random.shuffle(revs)
featureSets = [(features(rev), rev.reviewer) for rev in self.values()]
#limit = {'5':0, '4':0, '3':0, '2':0, '1':0}
#for feature, rank in featureSets:
# if limit[rank] > normalize:
# featureSets.remove((feature, rank))
# limit[rank] += 1
return NaiveBayesClassifier.train(featureSets)
pos_features = []
for words in pos_reviews:
pos_features.append((bag_of_words(words), 'pos'))
# negative reviews feature set
neg_features = []
for words in neg_reviews:
neg_features.append((bag_of_words(words), 'neg'))
shuffle(pos_features)
shuffle(neg_features)
test_feature_set = pos_features[:200] + neg_features[:200]
train_feature_set = pos_features[200:] + neg_features[200:]
classifier = NBC.train(train_feature_set)
accuracy = classify.accuracy(classifier, test_feature_set)
print(accuracy)
#f = open('unigram_classifier.pickle', 'wb')
#pickle.dump(classifier, f)
#f.close()
while (1):
custom_review = input(
"Enter a custom movie review (Press ENTER key to exit):\n")
if (len(custom_review) < 1):
break
custom_review_tokens = word_tokenize(custom_review)
custom_feature_set = bag_of_words(custom_review_tokens)
print(classifier.classify(custom_feature_set))
def nbtrain(train_set):
classifier = NaiveBayesClassifier.train(train_set)
return classifier
ts = ts[:2]
#print ts
#feat_set=dict(feat_set)
training_data = zip(tl, ts)
#training_data=dict(training_data)
#training_data, test_set = feat_set[:700],feat_set[700:]
vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
feature_set = [
({i: (i in word_tokenize(sentence.lower()))
for i in vocabulary}, tag) for sentence, tag in training_data
]
classifier = nbc.train(feature_set)
#for classifying a new sentence
test_sentence = tl[1]
featurized_test_sentence = {
i: (i in word_tokenize(test_sentence.lower()))
for i in vocabulary
}
print "test_sent:", test_sentence
print "tag:", classifier.classify(featurized_test_sentence)
#print nltk.classify.accuracy(classifier,test_set)
feature[u_word] = (u_word in doc)
return feature
extract = extract_words(['admir', 'med', 'pesso'])
#print(extract)
# Retorna todas as palavras do documento, verifica se as palavras passada por parametro tem no documento e informe ao final sua classe(alegria ou medo)
dataset_train = apply_features(extract_words, words_stemmer_train)
dataset_test = apply_features(extract_words, words_stemmer_test)
#print(dataset)
# FAZENDO O MODELO COM NAIVE BAYES
# constroi uma tabela de probabilidade
classifier = NaiveBayesClassifier.train(dataset_train)
#print(classifier.labels())
#print(classifier.show_most_informative_features())
#print(accuracy(classifier, dataset_test))
errors = []
for feature, target in dataset_test:
result = classifier.classify(feature)
if result != target:
errors.append((target, result, feature))
for (target, result, feature) in errors:
print(target, result, feature)
# usando a matrix de confução para saber como está os dados em relação de erros e acertos
y_test = []
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
positive_dataset = [
(tweet_dict, "Positive") # creating the dictionary
for tweet_dict in positive_tokens_for_model
]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset # total dataset, includes positives and negatives
random.shuffle(dataset) # shuffling it
train_data = dataset[:7000] # train data consists of %70 of dataset
test_data = dataset[7000:] # test data consists of %30 of dataset
classifier = NaiveBayesClassifier.train(
train_data) # classifying with Naive Bayes
print("Accuracy is:", classify.accuracy(classifier,
test_data)) # accuracy of testing
print(classifier.show_most_informative_features(
20)) # most informative 20 words of dataset
custom_tokens = remove_noise(word_tokenize(data)) # using our data
print(classifier.classify(dict([token, True] for token in custom_tokens)))
print(custom_tokens)
unique_words = set(custom_tokens)
freq_list = []
for words in unique_words:
freq_list.append([custom_tokens.count(words), words])
def train(self, train_set):
self.classifier = NaiveBayesClassifier.train(train_set)
return self.classifier
import nltk
from nltk import NaiveBayesClassifier
from nltk.corpus import names
import random
names = ([(name, 'male') for name in names.words('male.txt')] +
[(name, 'female') for name in names.words('female.txt')])
random.shuffle(names)
print(names[:3])
feature_sets = [(gender_features(n), g) for (n, g) in names]
print(feature_sets[:3])
train_set, test_set = feature_sets[500:], feature_sets[:500]
classifier = NaiveBayesClassifier.train(train_set)
print(classifier.classify(gender_features('Neo')))
print(classifier.classify(gender_features('Trinity')))
print(nltk.classify.accuracy(classifier, test_set))
print(classifier.show_most_informative_features(5))
from nltk.classify import apply_features
train_set = apply_features(gender_features, names[500:])
test_set = apply_features(gender_features, names[:500])
"""
素性抽出関数を改善する
訓練データに偏った素性になってしまう -> 過学習
"""
from collections import OrderedDict
def get_classifier():
train_set = get_trains_set()
return NaiveBayesClassifier.train(train_set)
features_data = np.array(sentences)
features_data_test = np.array(testSentences)
k_fold = KFold(n_splits=10, random_state=1992, shuffle=True)
word_features = None
accuracy_scores = []
accuracy_data_scores = []
for train_set, test_set in k_fold.split(features_data):
word_features = get_word_features(
get_words_in_sentences(features_data[train_set].tolist()))
train_features = apply_features(extract_features,
features_data[train_set].tolist())
test_features = apply_features(extract_features,
features_data[test_set].tolist())
classifier = NaiveBayesClassifier.train(train_features)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
testdata_features = apply_features(extract_features,
features_data_test.tolist())
refdatasets = collections.defaultdict(set)
testdatasets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_features):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
for i, (feats, label) in enumerate(testdata_features):
def train(labeled_featuresets, estimator=ELEProbDist):
label_probdist = estimator(label_freqdist)
feature_probdist = {}
return NaiveBayesClassifier(label_probdist, feature_probdist)
def evaluate_model(dataset, train_percentage=0.9):
feature_set = [(get_features(i), label) for (i, label) in dataset]
count = int(len(feature_set) * train_percentage)
train_set, test_set = feature_set[:count], feature_set[count:]
classifier = NaiveBayesClassifier.train(train_set)
return nltk.classify.accuracy(classifier, test_set)
def train_test_model(self):
'''
This functions is an entirely self contained, trained Naive Bayes Model for text sentiment analysis with a 75.467% accuracy
Importing more positive and negative classified tweets could be used to improve the model.
The results are stored in the self.trained_model variable for the DataTransform class
'''
print('Preprocessing classified tweets for model.')
from nltk.corpus import twitter_samples
import random
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
positive_df = pd.DataFrame(positive_tweets).rename(columns={0: 'text'})
negative_df = pd.DataFrame(negative_tweets).rename(columns={0: 'text'})
dict_samp = {}
positive_dict = []
positive = []
negative=[]
negative_dict = []
datatransform_positive = DataTransform()
datatransform_positive.set_df(positive_df)
datatransform_positive.clean_text('text','token_text')
for i in range(len(datatransform_positive.output_df.index)):
for j in range(len(datatransform_positive.output_df['token_text'][i])):
dict_samp.update({datatransform_positive.output_df['token_text'][i][j]: True})
positive_dict.append(dict_samp)
dict_samp = {}
for w in positive_dict:
positive.append((w, 'Positive'))
datatransform_negative = DataTransform()
datatransform_negative.set_df(negative_df)
datatransform_negative.clean_text('text','token_text')
for i in range(len(datatransform_negative.output_df.index)):
for j in range(len(datatransform_negative.output_df['token_text'][i])):
dict_samp.update({datatransform_negative.output_df['token_text'][i][j]: True})
negative_dict.append(dict_samp)
dict_samp = {}
for w in negative_dict:
negative.append((w, 'Negative'))
dataset = positive+negative
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
self.trained_model = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(self.trained_model, test_data))
return
def trainModel(self, train_data, test_data):
return NaiveBayesClassifier.train(train_data)
all_words += tweet[0]
freq = fd(all_words)
common = freq.most_common(200)
features = [i[0] for i in common]
def get_feature_dict(words):
current_features = {}
words_set = set(words)
for w in features:
current_features[w] = w in words_set
return current_features
training_data = [(get_feature_dict(tweet), sentiment)
for tweet, sentiment in clean_words_train]
testing_data = [(get_feature_dict(tweet)) for tweet in clean_words_test]
print(training_data)
print(testing_data[0])
classifier = nb.train(training_data)
output = []
# for tweet_words in testing_data:
# print("--------------------------------")
# print(tweet_words)
output = [classifier.classify(tweet_words) for tweet_words in testing_data]
print(output)
np.savetxt("predictions_twitter_sentimental.csv",
output,
fmt="%s",
delimiter=" ")
def train_test_evaluation():
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
print('Total number of positive_tweets are : ', len(positive_tweets))
print('Total number of negative_tweets are : ', len(negative_tweets))
print('-------------------------')
print('one smaple of positive_tweets : ', positive_tweets[0])
print('one smaple of negative_tweets : ', negative_tweets[0])
print('-------------------------\n\n')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
print('Total number of positive_tweet_tokens are : ',
len(positive_tweet_tokens))
print('Total number of negative_tweet_tokens are : ',
len(negative_tweet_tokens))
print('-------------------------')
print('one smaple of positive_tweet_tokens : ', positive_tweet_tokens[0])
print('one smaple of negative_tweet_tokens : ', negative_tweet_tokens[0])
print('-------------------------\n\n')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
# all_pos_words = get_all_words(positive_cleaned_tokens_list)
# freq_dist_pos = FreqDist(all_pos_words)
# print('Most Frequent Items in Positive Tweets',freq_dist_pos.most_common(10))
#
# all_neg_words = get_all_words(negative_cleaned_tokens_list)
# freq_dist_neg = FreqDist(all_neg_words)
# print('Most Frequent Items in negative Tweets',freq_dist_neg.most_common(10))
# print('-------------------------')
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:9000]
test_data = dataset[9000:]
print('Length of Train Data is : ', len(train_data))
print(' A sample of Traing Data : ', train_data[0])
print('-------------------------')
print('Length of Test Data is : ', len(train_data))
print(' A sample of Test Data : ', test_data[0])
print('-------------------------')
classifier = NaiveBayesClassifier.train(train_data)
print("\n\n Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
f = open('tweeter_trained_cls.pickle', 'wb')
pickle.dump(classifier, f)
f.close()
return classifier
def predict():
import nltk
nltk.download('twitter_samples')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import twitter_samples, stopwords
from nltk.tag import pos_tag
from nltk.tokenize import word_tokenize
from nltk import FreqDist, classify, NaiveBayesClassifier
import re, string, random
import pickle
def remove_noise(tweet_tokens, stop_words=()):
cleaned_tokens = []
for token, tag in pos_tag(tweet_tokens):
token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|'\
'(?:%[0-9a-fA-F][0-9a-fA-F]))+','', token)
token = re.sub("(@[A-Za-z0-9_]+)", "", token)
if tag.startswith("NN"):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
lemmatizer = WordNetLemmatizer()
token = lemmatizer.lemmatize(token, pos)
if len(token
) > 0 and token not in string.punctuation and token.lower(
) not in stop_words:
cleaned_tokens.append(token.lower())
return cleaned_tokens
def get_all_words(cleaned_tokens_list):
for tokens in cleaned_tokens_list:
for token in tokens:
yield token
def get_tweets_for_model(cleaned_tokens_list):
for tweet_tokens in cleaned_tokens_list:
yield dict([token, True] for token in tweet_tokens)
if __name__ == "__main__":
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized(
'positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized(
'negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(
remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(
remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
custom_tweet = ""
if request.method == 'POST':
custom_tweet = request.form['text']
custom_tokens = remove_noise(word_tokenize(custom_tweet))
NB_Cls = classifier.classify(
dict([token, True] for token in custom_tokens))
print(custom_tweet, NB_Cls)
pickle.dump(NB_Cls, open('sentimental_101.pkl', 'wb'))
return render_template('results.html', result=NB_Cls)
word_features = list(set(all_words))[:2000]
def find_features(wordList):
words = set(wordList)
features = {}
for w in word_features:
features[w] = (w in words)
return features
training_set = []
for wordList, category in documents:
training_set.append((find_features(wordList), category))
classifier = NaiveBayesClassifier.train(training_set)
while True:
choose = 0
print("Opinion List")
print("============")
if (len(opinionList) > 0):
for index, opinion in enumerate(opinionList):
print(str(index + 1) + ". " + opinion)
else:
print("No opinion inserted")
print("Opinion Analysis")
print("1. Insert Opinion")
class NBClassifier(TransformerMixin):
"""Naive Bayes classifier for part-of-text classification.
The classifier creates a wrapper around NLTK NaiveBayesClassifier
and implements `transform` and `fit_transform` methods suitable for
pipeline integration.
:param label_probdist:
P(label), the probability distribution over labels.
It is expressed as a ``ProbDistI`` whose samples are labels.
I.e., P(label) = ``label_probdist.prob(label)``.
:param feature_probdist:
P(fname=fval|label), the probability distribution for feature values, given labels.
It is expressed as a dictionary whose keys are ``(label, fname)``
pairs and whose values are ``ProbDistI`` objects over feature values.
I.e., P(fname=fval|label) = ``feature_probdist[label,fname].prob(fval)``.
If a given ``(label,fname)`` is not a key in ``feature_probdist``,
then it is assumed that the corresponding P(fname=fval|label)
is 0 for all values of ``fval``.
"""
def __init__(self,
label_probdist=None,
feature_probdist=None,
estimator=ELEProbDist):
self._estimator = estimator
# in case arguments are specified (ie. when restoring the classifier)
if all([label_probdist, feature_probdist]):
self._classifier = NaiveBayesClassifier(
label_probdist=label_probdist,
feature_probdist=feature_probdist,
)
else:
self._classifier = None
@property
def features(self):
if self._classifier is None:
return None
return self._classifier.most_informative_features()
# noinspection PyPep8Naming, PyUnusedLocal
def fit(self, X: typing.Iterable, y=None, **fit_params): # pylint: disable=invalid-name,unused-argument
"""Fits the classifier to the given data set.
:param X: Iterable, output of FeatureExtractor
The X is expected to be an iterable of tuples (tagged_word, feature_set, label),
where feature set is a dictionary of evaluated features.
The format of X matches the output of `FeatureExtractor`.
:param y: redundant (included to preserve base class method definition)
"""
# NLTK classifier expects stacked featuresets for the training,
# so we need to reduce the dimenstionality
labeled_featuresets = list()
for entry in X:
labeled_featuresets.extend([
(featureset, feature_label)
for _, featureset, feature_label in entry
])
# initialize the NLTK classifier
self._classifier = NaiveBayesClassifier.train(
labeled_featuresets, estimator=self._estimator)
return self
# noinspection PyPep8Naming, PyUnusedLocal
def transform(self, X): # pylint: disable=invalid-name,unused-argument
"""Auxiliary function to be used in pipeline."""
return self
# noinspection PyPep8Naming
def evaluate(
self,
X: typing.Iterable, # pylint: disable=invalid-name
y: typing.Iterable,
sample,
n=3):
"""Perform evaluation of the classifier instance.
:param X: Iterable, test data
Same shape as for `fit` and `fit_predict` methods
:param y: Iterable, of labels
:param sample:
one of labels to get the prediction for (for example,
if labels are ['class_A', 'class_B', 'class_C'], the sample
could be 'class_A'.
:param n: int, number of candidates to output
"""
# noinspection PyTypeChecker,PyTypeChecker
if len(X) != len(y):
raise ValueError("`X` and `y` must be of the same length.")
candidate_arr = self.fit_predict(X, n=n, sample=sample)
correctly_predicted = 0
for candidates, label in zip(candidate_arr, y):
pred = self._valid_candidates(candidates, label)
correctly_predicted += int(pred)
# return the accuracy score
# noinspection PyTypeChecker
return precision(total=len(y), correct=correctly_predicted)
# noinspection PyPep8Naming
def fit_predict(self, X: typing.Iterable, y=None, **fit_params): # pylint: disable=invalid-name,unused-argument
"""Makes prediction about the given data.
:param X: Iterable, prediction data
The prediction data is expected to be of type List[(name_tuple, feature_set [,feature,label)]
where feature_set corresponds to the output of FeatureExtractor and feature labels (if provided)
should be None (will be ignored anyway).
:param y: redundant (included to preserve bace class method definition)
:param fit_params: kwargs, fit parameters
n: number of candidates to output
sample: one of labels to get the prediction for (for example,
if labels are ['class_A', 'class_B', 'class_C'], the sample
could be 'class_A'.
"""
# get fit parameters
n = fit_params.get('n', 3)
sample = fit_params.get('sample', None)
# do not allow sample to be `None` (wouldn't be possible to sort
# the candidates in a logical way)
if sample is None:
raise ValueError("`fit_parameter` `sample` was not specified."
" This is not allowed in `fit_predict` method")
if not all([hasattr(var, '__len__') for var in [X, y or []]]):
raise TypeError("`X` and `y` must implement `__len__` method")
# noinspection PyTypeChecker
predictions = [None] * len(X)
for i, x in enumerate(X):
candidate_pred = [None] * len(x)
for j, candidate in enumerate(x):
if len(candidate) == 3:
# feature label was provided as part of X set (usual case), ignore it
name_tuple, features, _ = candidate
else:
name_tuple, features = candidate
candidate_pred[j] = (name_tuple,
self.predict(features, sample=sample))
sorted_pred = sorted(candidate_pred,
key=lambda t: t[1],
reverse=True)
predictions[i] = sorted_pred[:n]
return np.array(predictions)
def predict(self, features: dict, sample=None) -> typing.Any:
"""Make predictions based on given features.
:param features: dict, features to be used for prediction
Dictionary of (feature_key, feature_value)
:param sample:
one of labels to get the prediction for (for example,
if labels are ['class_A', 'class_B', 'class_C'], the sample
could be 'class_A'.
:returns: Union[float, dict]
If `sample` is specified, returns P(sample|features),
ie the probability of `sample` given features,
where `sample` is one of labels.
Otherwise returns dict of (label: max_prob) for all
known labels.
"""
if self._classifier is None:
raise ValueError("Unable to make predictions. "
"Classifier has not been trained yet!")
prob_dist = self._classifier.prob_classify(features)
# sort by the probability
if sample is not None:
probs = prob_dist.prob(sample)
else:
probs = {s: prob_dist.prob(s) for s in self._classifier.labels()}
return probs
def show_most_informative_features(self):
if self._classifier is None:
return
self._classifier.show_most_informative_features()
def export(self, export_dir=None, export_name=None) -> str:
"""Exports timestamped pickled classifier to the given directory.
:returns: path to the timestamped .checkpoint file
"""
export_dir = export_dir or 'export/'
export_name = export_name or 'classifier'
if export_name.endswith('.checkpoint'):
export_name = ".".join(export_name.split('.')[:-1])
time_stamp = str(datetime.datetime.now().timestamp())
# create export directory
os.makedirs(export_dir, exist_ok=True)
time_stamped_fname = ".".join([export_name, time_stamp, 'checkpoint'])
time_stamped_fpath = os.path.join(export_dir, time_stamped_fname)
# pickle and export the classifier
with open(time_stamped_fpath, 'wb') as exp_file:
pickle.dump(self, exp_file)
return time_stamped_fname
@staticmethod
def restore(checkpoint) -> "NBClassifier":
"""Restores the classifier from a checkpoint file.
:param checkpoint: path to directory or specific checkpoint
If path to directory provided, the newest checkpoint
is restored.
"""
def _restore_checkpoint(fp):
with open(fp, 'rb') as checkpoint_file:
# load the exported classifier
return pickle.load(checkpoint_file)
if os.path.isdir(checkpoint):
checkpoint_dir = checkpoint
checkpoints = [
os.path.join(checkpoint_dir, f) for f in os.listdir(checkpoint)
if f.endswith('.checkpoint')
]
# find the latest
if not checkpoints:
raise ValueError(
"No checkpoints were found in `{}`.".format(checkpoint))
latest_checkpoint = sorted(checkpoints)[-1]
clf = _restore_checkpoint(latest_checkpoint)
else:
clf = _restore_checkpoint(checkpoint)
return clf
@staticmethod
def _valid_candidates(candidates: typing.Iterable, label):
"""Check whether the correct label is among candidates."""
for candidate, _ in candidates:
# FIXME: a bug here, NLTK lets weird things like '**' go through -> causes crash
candidate_name, _ = candidate
try:
if re.search(candidate_name, label, flags=re.IGNORECASE):
return True
except:
return False
return False
def __init__(self, feat_sets):
self.train_set = feat_sets[:9500]
self.test_set = feat_sets[9500:]
self.Multinomial_classifier = SklearnClassifier(MultinomialNB())
self.bernoulli_classifier = SklearnClassifier(BernoulliNB())
self.naivebayes_classifier = NaiveBayesClassifier.train(self.train_set)
def sentim(self, data):
stop_words = ['the', 'an', 'the', 'i', 'a', 'and', 'to'] #, 'none'] #, 'heartworm', ' distemper/parvo'] #stopwords.words('english')
path_csv = '../data/csv/tf_idf_adoptable_csv.csv'
df = read_df_csv(path_csv)
X_negative = df["description"] #data
corpus_dirty = []
for doc in range(len(X_negative)):
str_corpus = str(X_negative[doc])
corpus_dirty.append(str_corpus)
negative_documents = []
for doc in range(len(X_negative)):
record = X_negative[doc]
record = (record.lower())
replaced = record.replace(", '...'", "").replace("...", '').replace('\d+', '')
remove_digits = str.maketrans('', '', digits)
replaced = replaced.translate(remove_digits)
clean = replaced.replace(", '...'", "").replace("...", '')
negative_documents.append(clean)
# print(documents)
# # # 2. Create a set of tokenized documents.
negative_descriptions = [word_tokenize(content) for content in negative_documents]
negative_cleaned_tokens_list = []
for tokens in negative_descriptions:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_neg_words = get_all_words(negative_cleaned_tokens_list)
freq_dist_neg = FreqDist(all_neg_words)
print("most common ADOPTABLE words: ", freq_dist_neg.most_common(10))
##################################################################
##################################################################
##################################################################
path_csv = '../data/csv/tf_idf_adopted_csv.csv'
df = read_df_csv(path_csv)
X_positive = df["description"] #data
corpus_dirty = []
for doc in range(len(X_positive)):
str_corpus = str(X_positive[doc])
corpus_dirty.append(str_corpus)
positive_documents = []
for doc in range(len(X_positive)):
record = X_positive[doc]
record = (record.lower())
replaced = record.replace(", '...'", "").replace("...", '').replace('\d+', '')
remove_digits = str.maketrans('', '', digits)
replaced = replaced.translate(remove_digits)
clean = replaced.replace(", '...'", "").replace("...", '')
positive_documents.append(clean)
# print(documents)
# # # 2. Create a set of tokenized documents.
positive_descriptions = [word_tokenize(content) for content in positive_documents]
# print("\n\nPositive Descriptions Tokenized: ", positive_descriptions)
# ['dora', 'female', 'shep', 'mix', 'brindle', 'dhpp', 'kc', '//', 'no', 'puppy', 'hi', 'cathleen', ',', 'she', 'is', 'doing', 'great', 'and', 'really', 'starting'], ['meet', 'nova', '!', 'now', 'that', 'she', 'is', 'done', 'raising', 'her', 'pups', 'she', 'is', 'looking', 'for', 'a', 'home', 'of', 'her', 'own', 'where']]
positive_cleaned_tokens_list = []
for tokens in positive_descriptions:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
# save_documents = open("pickled_algos/all_pos_words.pickle","wb")
# pickle.dump(positive_cleaned_tokens_list, save_documents)
# save_documents.close()
freq_dist_pos = FreqDist(all_pos_words)
print("most common ADOPTED words: ", freq_dist_pos.most_common(10))
##################################################################
##################################################################
##################################################################
positive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
# positive_tokens_for_model = all_pos_words.pickle
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
positive_dataset = [(description_dict, "Positive")
for description_dict in positive_tokens_for_model]
negative_dataset = [(description_dict, "Negative")
for description_dict in negative_tokens_for_model]
# print("positive_dataset: ", positive_dataset)
# print("negative_dataset: ", negative_dataset)
dataset = positive_dataset + negative_dataset
seventy_percent_of_data = int(len(dataset) * .7)
thirty_percent_of_data = int(len(dataset) * .3)
# print(thirty_percent_of_data) #361
random.shuffle(dataset) #to avoid bias
train_data = dataset[:seventy_percent_of_data]
test_data = dataset[thirty_percent_of_data:]
classifier = NaiveBayesClassifier.train(train_data)
# classifier = MultinomialNB.fit(train_data)
save_classifier = open("naivebayes_pet.pickle","wb")
pickle.dump(classifier, save_classifier)
save_classifier.close()
print("%%%%%%%%%%%%%%%%%%%Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
# from nltk.corpus import twitter_samples
# print("&&&&&&&&&&&&&&&&&&&&&&&&&")
# print(twitter_samples)
data = str(data)
punc = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
for ele in data:
if ele in punc:
data = data.replace(ele, "")
data = data.split()
# print("tokenized data: ", data)
#breakdown parts of speech
parts_of_speech = []
parts_of_speech.append(nltk.pos_tag(data))
print("parts of speech tagging: ", parts_of_speech)
#lemmatized data:
stop_words = [] #left here in case I want to add words in the future
cleaned_tokens = []
for token, tag in nltk.pos_tag(data):
if tag.startswith("NN"):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
lemmatizer = WordNetLemmatizer()
token = lemmatizer.lemmatize(token, pos)
if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:
cleaned_tokens.append(token.lower())
custom_tokens = remove_noise(word_tokenize(str(data)))
print(str(data), classifier.classify(dict([token, True] for token in custom_tokens)))
sentiment_result = [classifier.classify(dict([token, True] for token in custom_tokens))]
print("sentiment_result: ", type(sentiment_result), sentiment_result)
data = sentiment_result
return data
#print(positive)
sad_token = get_tweets_for_model(negative)
joy_token = get_tweets_for_model(positive)
negative_dataset = [(tweet_dict, "negative") for tweet_dict in sad_token]
positive_dataset = [(tweet_dict, "positive") for tweet_dict in joy_token]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:900]
test_data = dataset[900:]
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
# Connect to MariaDB Platform
try:
conn = mariadb.connect(
user="******", #- enter your username
#password="******" - enter your password
database="tcsproject" # - enter your database name
)
except mariadb.Error as e:
print(f"Error connecting to MariaDB Platform: {e}")
sys.exit(1)
for word in features:
if word not in labelled_features:
labelled_features[word.lower()] = label_count
labelled_features[word.lower()][label] += features[word]
print "Currently at %d distinct tokens and %d papers" % (
len(labelled_features), samplecount)
label_probdist = get_label_probdist(labelled_features)
feature_probdist = get_feature_probdist(labelled_features)
classifier = NaiveBayesClassifier(label_probdist, feature_probdist)
for samplefile in test_samples:
features = {}
p = PaperParser()
p.parsePaper(samplefile)
for sentence in p.extractRawSentences():
tokens = nltk.word_tokenize(sentence)
for word in tokens:
features[word] = True
dirname = os.path.basename(os.path.dirname(samplefile))
label = labels[dirname]
def train(all_features, ratio):
train_size = int(len(all_features) * ratio)
train_set, test_set = all_features[:train_size], all_features[train_size:]
clf = NaiveBayesClassifier.train(train_set)
return train_set, test_set, clf
print("Dictionary with Positive class : ", positiveReviewDataset[7])
print("Dictionary with Negative class : ", negativeReviewDataset[7])
#print("tagged neg :",negative_dataset[0])
dataset = positiveReviewDataset + negativeReviewDataset
print("Dataset[0] :", dataset[0])
print("Dataset length", len(dataset))
random.shuffle(dataset)
trainData = dataset[:7000]
testData = dataset[7000:]
trainedModel = NaiveBayesClassifier.train(trainData)
print("Accuracy of the model : ", classify.accuracy(trainedModel, testData))
review = "This is a bad product."
reviewTokens = noiseRemoval(word_tokenize(review))
# Test print
print(review, " : ",
trainedModel.classify(dict([token, True] for token in reviewTokens)))
#Text = "j@nittha"
#Text = re.sub("@", "a", Text)
#print(Text)
def train_topic_classifier(self, train_set):
classifier = NaiveBayesClassifier.train(train_set)
return classifier
print("Also see: Hindu Marriage Act")
elif resultc != -1 or y == "Christian":
f1 = open("Christian.txt")
f2 = open("christian01.txt")
l1 = f1.read()
arr = sent_tokenize(l1)
l2 = f2.read()
arr2 = word_tokenize(l2)
for i in range(0, len(arr)):
li1.append(tuple((arr[i], arr2[i])))
f1.close()
f2.close()
print("Also see: Indian Divorce Act")
mycase = sys.argv[3]
#mycase=input("enter your case ")
c1 = 0
c2 = 0
model = NaiveBayesClassifier(li1)
#model=nltk.NaiveBayesClassifier.train(li1)
#print(model.classify(mycase))
case = sent_tokenize(mycase)
print(mycase)
for i in range(0, len(case)):
temp = model.classify(case[i])
if temp == "0":
c1 = c1 + 1
else:
c2 = c2 + 1
print("Probability of winning case", (c1 / (c1 + c2)) * 100)
def sentim_twitter(self, data):
'''heavily borrowed from https://www.digitalocean.com/community/tutorials/how-to-perform-sentiment-analysis-in-python-3-using-the-natural-language-toolkit-nltk
to show functioning model'''
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:700]
test_data = dataset[700:]
classifier = NaiveBayesClassifier.train(train_data)
print("twitter data **********************************")
print("%%%%%%%%%%%%%%%%%%% Twitter Accuracy is:", classify.accuracy(classifier, test_data))
print("twitter data **********************************")
print(classifier.show_most_informative_features(10))
# data = (data)
# custom_tweet = str(data)
print("twitter data **********************************")
print("twitter data **********************************")
print("is this reading data correctly???: ", type(str(data)))
custom_tweet = str(data)
# this gives negative
custom_tokens = remove_noise(word_tokenize(custom_tweet))
print("twitter data **********************************")
print(custom_tweet, classifier.classify(dict([token, True] for token in custom_tokens)))
twitter = classifier.classify(dict([token, True] for token in custom_tokens))
return twitter
def train_model(self, data):
self.model = NaiveBayesClassifier.train(data)
def train(self, corpus, selected_feats):
train_set = self.parse_corpus(corpus)
print('Train set:', len(train_set))
# # unigram
self.unigrams = Counter([
word for chat, win, duration, extra in train_set for word in chat
])
self.common_unigrams = [
unigram for unigram, value in self.unigrams.items() if value > 1
]
# print(len(self.unigrams), len(self.common_unigrams))
# # bigram
self.bigrams = Counter([
' '.join((word, chat[i + 1]))
for chat, win, duration, extra in train_set
for i, word in enumerate(chat[:-1])
])
self.common_bigrams = [
bigram for bigram, value in self.bigrams.items() if value > 1
]
# print(len(self.bigrams), len(self.common_bigrams))
# # trigram
self.trigrams = Counter([
' '.join((word, chat[i + 1], chat[i + 2]))
for chat, win, duration, extra in train_set
for i, word in enumerate(chat[:-2])
])
self.common_trigrams = [
trigram for trigram, value in self.trigrams.items() if value > 1
]
# print(len(self.trigrams), len(self.common_trigrams))
# # fourgram
self.fourgrams = Counter([
' '.join((word, chat[i + 1], chat[i + 2], chat[i + 3]))
for chat, win, duration, extra in train_set
for i, word in enumerate(chat[:-3])
])
self.common_fourgrams = [
fourgram for fourgram, value in self.fourgrams.items() if value > 1
]
# print(len(self.fourgrams), len(self.common_fourgrams))
# # fivegram
self.fivegrams = Counter([
' '.join(
(word, chat[i + 1], chat[i + 2], chat[i + 3], chat[i + 4]))
for chat, win, duration, extra in train_set
for i, word in enumerate(chat[:-4])
])
self.common_fivegrams = [
fivegram for fivegram, value in self.fivegrams.items() if value > 1
]
# print(len(self.fivegrams), len(self.common_fivegrams))
###### WP30 PLOT #######
# wp30s = [len(chat) // (duration / 1800) for chat,win,duration,extra in train_set]
# n, bins, patches = plt.hist(wp30s, 100,alpha=0.75)
# plt.show()
# self.doclen = Counter([len(chat) for chat,win,duration in train_set])
###### CHATTER PLOT ######
# data = []
# for chat, win, duration,extra in w8m8.iterate(train_set, out='Training'):
# nchars = [0,0,0,0,0]
# for player, message in extra:
# nchars[player] += len(message)
# avg = sum(nchars) / 5
# data.append(max(nchars) / avg)
# n, bins, patches = plt.hist(data, 1000,alpha=0.75)
# plt.show()
t = []
for chat, win, duration, extra in w8m8.iterate(train_set,
out='Training'):
features = self.get_features(chat, duration, extra, selected_feats)
t.append((features, win))
self.classifier = NaiveBayesClassifier.train(t)
self.classifier.show_most_informative_features(20)
def main():
print('Building model...')
print('Gathering training data...')
# set nltk twitter samples as list of strings
pos_sample_tweets = twitter_samples.strings('positive_tweets.json')
neg_sample_tweets = twitter_samples.strings('negative_tweets.json')
#### UPDATE HERE: Option to add your own tweet samples
#### Remove the empty list, uncomment and update filepaths below
pos_custom_tweets = [] ## helpers.import_csv('positive_tweets.csv')
neg_custom_tweets = [] ## helpers.import_csv('negative_tweets.csv')
# combine nltk twitter samples and custom tweets
positive_tweets = pos_sample_tweets + pos_custom_tweets
negative_tweets = neg_sample_tweets + neg_custom_tweets
# tokenize tweets
positive_tweet_tokens = [casual_tokenize(i) for i in positive_tweets]
negative_tweet_tokens = [casual_tokenize(i) for i in negative_tweets]
# set cleaned tokens lists
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
stop_words = stopwords.words('english')
# get cleaned positive tokens
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(
helpers.remove_noise(tokens, stop_words))
# get cleaned negative tokens
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(
helpers.remove_noise(tokens, stop_words))
# convert tokens into iterable word lists
all_pos_words = helpers.get_all_words(positive_cleaned_tokens_list)
all_neg_words = helpers.get_all_words(negative_cleaned_tokens_list)
# get frequency distribution of word lists
freq_dist_pos = FreqDist(all_pos_words)
freq_dist_neg = FreqDist(all_neg_words)
# print top 10 positive and negative words
print('Top 10 positive and negative words:')
print(freq_dist_pos.most_common(10))
print(freq_dist_neg.most_common(10))
# convert tokens to a dictionary for modelling
positive_tokens_for_model = helpers.get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = helpers.get_tweets_for_model(
negative_cleaned_tokens_list)
# assign a label to positive tokens
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
# assign a label to negative tokens
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
# set dataset and randomize to train model
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
# split the data into a 70:30 ratio among 10K tweets
train_data = dataset[:7000]
test_data = dataset[7000:]
# train a Naive Bayes model
classifier = NaiveBayesClassifier.train(train_data)
# print model accuracy
print("Model accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
print('Model complete!\n')
return classifier
def category_by_pos():
from nltk.corpus import brown
from nltk import FreqDist
from nltk import DecisionTreeClassifier
from nltk import NaiveBayesClassifier
from nltk import classify
suffix_fdist = FreqDist()
for word in brown.words():
word = word.lower()
suffix_fdist.inc(word[-1:])
suffix_fdist.inc(word[-2:])
suffix_fdist.inc(word[-3:])
common_suffixes = suffix_fdist.keys()[:100]
# print common_suffixes
def pos_features(word):
features = {}
for suffix in common_suffixes:
features['endswith(%s)' % suffix] = word.lower().endswith(suffix)
return features
tagged_words = brown.tagged_words(categories='news')
featuresets = [(pos_features(n), g) for (n, g) in tagged_words]
size = int(len(featuresets) * 0.1)
train_set, test_set = featuresets[size:], featuresets[:size]
# classifier = DecisionTreeClassifier.train(train_set)
# print 'Decision Tree %f' % classify.accuracy(classifier, test_set)
classifier = NaiveBayesClassifier.train(train_set)
print 'NaiveBay %f' % classify.accuracy(classifier, test_set)