def getSolution(self):
classifier = NaiveBayesClassifier()
solution = ''.join([
classifier.getClassification(jchar_CSV)
for jchar_CSV in self.jchar_CSV_list
])
return solution
def summarize_text(self, sites, articles):
if (len(articles) < 1):
return ["Not enought information about player"]
summary_methods = NaiveBayesClassifier()
return summary_methods.get_summary(sites, articles)
def NBCTest():
nbc = NaiveBayesClassifier()
sites = [
"https://www.theplayerstribune.com/doublelift-league-of-legends-everyone-else-is-trash/"
]
article_extractor = articles.ArticleExtractor('doublelift',
'league of legends', 5)
#sites = article_extractor.get_websites()
article = [article_extractor.parse_websites(site) for site in sites]
string_text = list_to_string(article)
string_text = string_text.replace("', '", ' ')
string_text = string_text.replace('", "', ' ')
summary = nbc.get_summary(string_text, 5)
print("Summary:")
for sentence in summary:
print(u'\u2022 ' + sentence.lstrip("[]1234567890',.\" "))
def main():
data = []
for verdict in ['spam', 'not_spam']:
for files in glob.glob(PATH + verdict + "/*")[:500]:
is_spam = True if verdict == 'spam' else False
with open(files, "r", encoding='utf-8', errors='ignore') as f:
for line in f:
if line.startswith("Subject:"):
subject = re.sub("^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.75)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
print("Spam" if classifier.classify("Get free laptops now!") > 0.5 else
"Not Spam")
classified = [(subject, is_spam, classifier.classify(subject))
for subject, is_spam in test_data]
count = Counter((is_spam, spam_probability > 0.5)
for _, is_spam, spam_probability in classified)
spammiest_hams, hammiest_spams = most_misclassified(classified)
print("Accuracy: ", accuracy(count))
print("Precision: ", precision(count))
print("Recall: ", recall(count))
print("\nTop 5 falsely classified as spam:\n\n", spammiest_hams)
print("\nTop 5 falsely classified as not spam:\n\n", hammiest_spams)
print("\nMost spammiest words: ", spammiest_word(classifier))
def spamFilterChecker():
print('Received the JAVA Request!')
# Get the text data from the JAVA Program.
req_data = abhishek_request.get_json()
text_to_be_classified = req_data['text_to_be_classified']
print(text_to_be_classified)
# ----------------------------------------------------------------------------
# Make a POST request to the plino Spam API.
# ----------------------------------------------------------------------------
data = []
for verdict in ['spam', 'not_spam']:
for files in glob.glob(PATH + verdict + "/*")[:500]:
is_spam = True if verdict == 'spam' else False
with open(files, "r", encoding='utf-8', errors='ignore') as f:
for line in f:
if line.startswith("Subject:"):
subject = re.sub("^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.80)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
json_response = ""
value = classifier.classify(text_to_be_classified)
if value < 0.9:
json_response = "{'email_class' : 'spam'}"
else:
json_response = "{'email_class' : 'ham'}"
print("====================================================")
print("POSSIBILITY OF HAM : ", value)
print(json_response)
print("====================================================")
return json_response
import pandas
from naive_bayes_classifier import NaiveBayesClassifier
df = pandas.read_csv("weather.csv", sep=";")
naive_bayes = NaiveBayesClassifier(df, 0.6)
naive_bayes.train_algorithm()
accuracy = naive_bayes.test_algorithm()
print("Algorithm Accuracy : " + str(accuracy) + " %")
tuple_data = ('Overcast', 'Hot', 'Normal', True)
prediction = naive_bayes.predict(tuple_data)
print("Prediction for " + str(tuple_data) + " is " + prediction)
svm_classifier = SvmClassifier()
svm_classifier.setSvm(svm)
#CONFIGURACAO DO RF
rf = RfModule()
rf_classifier = RfClassifier()
rf_classifier.setRf(rf)
#CONFIGURACAO DO RF
dt = DecisionTreeModule()
dt_classifier = DecisionTreeClassifier()
dt_classifier.setDecisionTree(dt)
#CONFIGURACAO DA NAIVEBAYES
naive_bayes = NaiveBayesModule()
naive_bayes_classifier = NaiveBayesClassifier()
naive_bayes_classifier.setNaiveBayes(naive_bayes)
#CONFIGURACAO DO LSTM
lstm = LstmModule()
lstm.setInputLength(20)
lstm.setNumberExamples(1000)
lstm_classifier = LstmClassifier()
lstm_classifier.setLstm(lstm)
#CONFIGURACAO DA REDE NEURAL
rna = RnaModule()
rna.setNumberNeuronsImputLayer(20)
rna.setActivationFunctionImputLayer("tanh")
rna.setImputDimNeurons(20)
rna.setNumberNeuronsHiddenLayer(20)
def setUp(self):
self.examples = {'university': ['''Abbottabad Public School , also commonly referred to as
APS and Railway Public School , is a private , all boys , boarding
school for , 7th to 12th grade students , located in Abbottabad ,
Pakistan .''']}
self.classifier = NaiveBayesClassifier(self.examples)
class TestClassifier(unittest.TestCase):
def setUp(self):
self.examples = {'university': ['''Abbottabad Public School , also commonly referred to as
APS and Railway Public School , is a private , all boys , boarding
school for , 7th to 12th grade students , located in Abbottabad ,
Pakistan .''']}
self.classifier = NaiveBayesClassifier(self.examples)
def test_create_vocabulary(self):
self.classifier.vocabulary.should.contain('private')
def test_vocabulary_size(self):
self.classifier.vocabulary_size.should.eql(28)
def test_subset_of_documents_with_target_value(self):
len(self.classifier.get_documents_with_target_value('university')).should.eql(1)
def test_text_of_documents(self):
documents = self.classifier.get_documents_with_target_value('university')
self.classifier.get_text(documents).should.contain('private')
def test_text_distinct_words(self):
documents = self.classifier.get_documents_with_target_value('university')
text = self.classifier.get_text(documents)
self.classifier.get_text_diff_words_count(text).should.eql(28)
def test_example_count(self):
self.classifier.get_example_count().should.eql(1)
def test_occurrences_of_word_count(self):
documents = self.classifier.get_documents_with_target_value('university')
text = self.classifier.get_text(documents)
self.classifier.occurrences_count(',', text).should.eql(7)
def test_learn(self):
self.classifier.learn()
def test_word_positions_in_doc(self):
documents = self.classifier.get_documents_with_target_value('university')
len(self.classifier.word_positions(documents[0])).should.eql(38)
def test_classify(self):
self.classifier.learn()
self.classifier.classify(self.examples['university'][0]).should.eql('university')
y_train = []
X_test = []
y_test = []
for i in range(len(train)):
y_train.append(train[i][0])
X_train.append(train[i][1:])
for i in range(len(test)):
y_test.append(test[i][0])
X_test.append(test[i][1:])
# Instantiate the Naive Bayes classifier model made from scratch
model = NaiveBayesClassifier()
# Fit the model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Print the accuracy of the model
print("NaiveBayesClassifier accuracy: {0:.3f}".format(model.accuracy(y_test, y_pred)))
# Instantiate the Gaussian Naive Bayes classifier model from Scikit-learn
time_period = "daily"
keywords = "hillary-trump"
training_required = 0
# log('Failed to load configurations; error in function loadConfig; exiting...', 'error')
# print 'FAILED to load configurations'
# check if the model dump file is missing
#if training_required == 0:
# if not os.path.exists(classifier):
# training_required = 1
# train a model if required
if training_required:
tweets = []
nb = NaiveBayesClassifier(tweets, keywords, time_period, training_data, classifier, training_required)
# create the HBase tweets table if missing
print "Checking database tables..."
try:
connection = happybase.Connection('localhost')
connection.create_table('tweets',
{'keyword': dict(max_versions=10),
'sentiment': dict(max_versions=10),
'tweet': dict(max_versions=10)
}
)
print "...OK"
except:
print "Table already exists"
# log('Table already exists; skipping creation.')
# glob.glob returns every filename that matches the wildcarded path
for fn in glob.glob(path):
is_spam = "ham" not in fn
with open(fn, "r") as file:
for line in file:
if line.startswith("Subject:"):
# remove the leading "Subject: " and keep what's left
subject = re.sub(r"^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.75)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
# triplets (subject, actual is_spam, predicted spam probability)
classified = [(subject, is_spam, classifier.classify(subject)) for subject, is_spam in test_data]
# assume that spam_probability > 0.5 corresponds to spam prediction
# and count the combinations of (actual is_spam, predicted is_spam)
counts = Counter((is_spam, spam_probability > 0.5) for _, is_spam, spam_probability in classified)
# sort by spam_probability from smallest to largest
classified.sort(key=lambda row: row[2])
# the highest predicted spam probabilities among the non-spams
spammiest_hams = filter(lambda row: not row[1], classified)[-5:]
mid_point = len(unique_data) // 2
high = unique_data[mid_point:]
low = unique_data[:mid_point]
fixed_array = []
for element in array:
if element in low:
fixed_array.append(False)
elif element in high:
fixed_array.append(True)
return fixed_array
naive_bayes_classifier_random_50_dataframe_1 = DataFrame.from_array(
[boolize_data(row) for row in dataframe.to_array()], dataframe.columns)
naive_bayes_classifier = NaiveBayesClassifier(
dataframe=naive_bayes_classifier_random_50_dataframe_1,
dependent_variable='Survived')
naive_bayes_classifier_classifications = get_classifications(
naive_bayes_classifier, testing_dataframe)
for row in naive_bayes_classifier_classifications:
print(row)
print('\n')
max_depth_5_decision_tree = DecisionTree(
dataframe=dataframe,
class_name='Survived',
features=[column for column in dataframe.columns if column != 'Survived'],
max_depth=5)
max_depth_5_decision_tree.fit()
max_depth_5_decision_tree_classifications = get_classifications(
max_depth_5_decision_tree, testing_dataframe)
import pandas as pd
from naive_bayes_classifier import NaiveBayesClassifier
if __name__ == '__main__':
df = pd.read_csv('spam.csv', encoding='ISO-8859-1')
df = df.sample(frac=1.0)
spam_data, legit_data = [], []
for _, row in df.iterrows():
if row['v1'] == 'spam':
spam_data.append(row['v2'])
else:
legit_data.append(row['v2'])
NB_classifier = NaiveBayesClassifier()
spam_train = spam_data[:int(len(spam_data) * 2 / 3)]
spam_test = spam_data[int(len(spam_data) * 2 / 3):]
legit_train = legit_data[:int(len(legit_data) * 2 / 3)]
legit_test = legit_data[int(len(legit_data) * 2 / 3):]
NB_classifier.train(spam_train, legit_train)
spam_accuracy = 0
legit_accuracy = 0
for text in spam_test:
prediction = NB_classifier.predict(text)
spam_accuracy += prediction
df = DataFrame.from_array(
[
[False, False, False],
[True, True, True],
[True, True, True],
[False, False, False],
[False, True, False],
[True, True, True],
[True, False, False],
[False, True, False],
[True, False, True],
[False, True, False]
],
columns = ['errors', 'links', 'scam']
)
naive_bayes = NaiveBayesClassifier(df, dependent_variable='scam')
print("Testing Probabilities")
assert naive_bayes.probability('scam', True) == 0.4
assert naive_bayes.probability('scam', False) == 0.6
print("passed")
print("Testing Conditional Probabilities")
assert naive_bayes.conditional_probability(('errors',True), given=('scam',True)) == 1.0
assert naive_bayes.conditional_probability(('links',False), given=('scam',True)) == 0.25
assert naive_bayes.conditional_probability(('errors',True), given=('scam',False)) == 0.16666666666666666
assert naive_bayes.conditional_probability(('links',False), given=('scam',False)) == 0.5
# glob.glob returns every filename that matches the wildcarded path
for fn in glob.glob(path):
is_spam = "ham" not in fn
with open(fn, 'r') as file:
for line in file:
if line.startswith("Subject:"):
# remove the leading "Subject: " and keep what's left
subject = re.sub(r"^Subject: ", "", line).strip()
data.append((subject, is_spam))
random.seed(0)
train_data, test_data = split_data(data, 0.75)
classifier = NaiveBayesClassifier()
classifier.train(train_data)
# triplets (subject, actual is_spam, predicted spam probability)
classified = [(subject, is_spam, classifier.classify(subject))
for subject, is_spam in test_data]
# assume that spam_probability > 0.5 corresponds to spam prediction
# and count the combinations of (actual is_spam, predicted is_spam)
counts = Counter((is_spam, spam_probability > 0.5)
for _, is_spam, spam_probability in classified)
# sort by spam_probability from smallest to largest
classified.sort(key=lambda row: row[2])
# the highest predicted spam probabilities among the non-spams
