def investigate(self, case_number):
data = self.scrape(links=self.base_urls, scraping_ads=True)
training_data = [(elem, "trafficking")
for elem in BackpageLogger.query.filter_by(
is_trafficking=True).all()]
training_data += [(elem, "not trafficking")
for elem in BackpageLogger.query.filter_by(
is_trafficking=False).all()]
trafficking_numbers = [
elem.phone_number for elem in BackpageLogger.query.filter_by(
is_trafficking=True).all()
]
cls = []
cls.append(algorithms.svm(training_data))
cls.append(algorithms.decision_tree(training_data))
using_naive_bayes = len(
training_data
) > 50 #totally a hack, consider getting advice / changing this??
if using_naive_bayes:
nb = algorithms.naive_bayes(training_data)
for datum in data:
if datum["phone_number"] in trafficking_numbers:
self.save([datum], case_number)
if not using_naive_bayes:
for cl in cls:
if cl.classify(algorithms.preprocess(
datum["text_body"])) == "trafficking":
self.save([datum], case_number)
else:
if nb.classify(datum["text_body"]) == 'trafficking':
self.save([datum], case_number)
time.sleep(700) # wait ~ 12 minutes (consider changing this)
self.investigate(
case_number) #this is an infinite loop, which I am okay with.
def investigate(self, case_number):
data = self.scrape(links=self.base_urls, scraping_ads=True)
training_data = [(elem, "trafficking") for elem in BackpageLogger.query.filter_by(is_trafficking=True).all()]
training_data += [
(elem, "not trafficking") for elem in BackpageLogger.query.filter_by(is_trafficking=False).all()
]
trafficking_numbers = [elem.phone_number for elem in BackpageLogger.query.filter_by(is_trafficking=True).all()]
cls = []
cls.append(algorithms.svm(training_data))
cls.append(algorithms.decision_tree(training_data))
using_naive_bayes = len(training_data) > 50 # totally a hack, consider getting advice / changing this??
if using_naive_bayes:
nb = algorithms.naive_bayes(training_data)
for datum in data:
if datum["phone_number"] in trafficking_numbers:
self.save([datum], case_number)
if not using_naive_bayes:
for cl in cls:
if cl.classify(algorithms.preprocess(datum["text_body"])) == "trafficking":
self.save([datum], case_number)
else:
if nb.classify(datum["text_body"]) == "trafficking":
self.save([datum], case_number)
time.sleep(700) # wait ~ 12 minutes (consider changing this)
self.investigate(case_number) # this is an infinite loop, which I am okay with.
def investigate(self):
data = self.scrape(self.base_urls)
training_data = [(elem, "trafficking") for elem in BackpageLogger.query.filter_by(is_trafficking=True).all()]
training_data = [(elem, "not trafficking") for elem in BackpageLogger.query.filter_by(is_trafficking=False).all()]
cls = []
cls.append(algorithms.svm(train))
cls.append(algorithms.decision_tree(train))
nb = algorithms.naive_bayes(train)
for datum in data:
if len(train) > 50: #totally a hack/rule of thumb
for cl in cls:
if cl.classify(algorithms.preprocess(datum["text_body"])) == "trafficking":
self.save_ads([datum])
else:
if nb.classify(datum["text_body"]) == 'trafficking':
self.save_ads([datum])
time.sleep(700) # wait ~ 12 minutes (consider changing this)
self.investigate() #this is an infinite loop, which I am okay with.
def test_decision_tree():
testing = [("hello there","greeting"),("later","goodbye")]
cl = algorithms.decision_tree(testing)
test = algorithms.preprocess("hello there friends")
assert cl.classify(test) == "greeting"
def test_decision_tree():
testing = [("hello there", "greeting"), ("later", "goodbye")]
cl = algorithms.decision_tree(testing)
test = algorithms.preprocess("hello there friends")
assert cl.classify(test) == "greeting"