class PluginTeachMe(Plugin):
def __init__(self, *args):
Plugin.__init__(self, *args)
self.classifier = None
self.load({})
self.curr_msg = ''
self.last_msg = ''
self.last_joke = ()
self.just_joked = False
def load(self, data):
storage_backend = MemoryBackend(data)
self.classifier = NaiveBayesClassifier(storage_backend)
def save(self):
return self.classifier.storage.data
def get_what_to_learn(self):
if self.curr_msg in ('CMB', 'cmb'):
return 'CMB'
if self.curr_msg in ('CTB', 'ctb'):
return 'CTB'
if self.curr_msg in ('TWSS', 'twss'):
return "That's what she said!"
return 'None'
def got_congratulated(self):
return self.curr_msg in ('GG', 'gg', 'GG Tofbot', 'gg Tofbot')
def did_bad_joke(self):
return self.curr_msg in ('TG', 'tg', 'TG Tofbot', 'tg Tofbot')
def handle_msg(self, msg_text, chan, nick):
just_joked = self.just_joked
self.just_joked = False
self.last_msg = self.curr_msg
self.curr_msg = msg_text.strip()
if self.got_congratulated():
if self.last_joke:
self.classifier.train(*self.last_joke)
elif self.did_bad_joke():
if self.last_joke:
self.classifier.train(self.last_joke[0], 'None')
else:
scores = self.classifier.classify(self.curr_msg.split())
joke = 'None'
if scores:
joke = scores[0][0]
if joke != 'None':
self.say(joke)
self.last_joke = (self.curr_msg.split(), joke)
else:
if not just_joked:
self.classifier.train(self.last_msg.split(),
self.get_what_to_learn())
class PluginTeachMe(Plugin):
def __init__(self, *args):
Plugin.__init__(self, *args)
self.classifier = None
self.load({})
self.curr_msg = ""
self.last_msg = ""
self.last_joke = ()
self.just_joked = False
def load(self, data):
storage_backend = MemoryBackend(data)
self.classifier = NaiveBayesClassifier(storage_backend)
def save(self):
return self.classifier.storage.data
def get_what_to_learn(self):
if self.curr_msg in ("CMB", "cmb"):
return "CMB"
if self.curr_msg in ("CTB", "ctb"):
return "CTB"
if self.curr_msg in ("TWSS", "twss"):
return "That's what she said!"
return "None"
def got_congratulated(self):
return self.curr_msg in ("GG", "gg", "GG Tofbot", "gg Tofbot")
def did_bad_joke(self):
return self.curr_msg in ("TG", "tg", "TG Tofbot", "tg Tofbot")
def handle_msg(self, msg_text, chan, nick):
just_joked = self.just_joked
self.just_joked = False
self.last_msg = self.curr_msg
self.curr_msg = msg_text.strip()
if self.got_congratulated():
if self.last_joke:
self.classifier.train(*self.last_joke)
elif self.did_bad_joke():
if self.last_joke:
self.classifier.train(self.last_joke[0], "None")
else:
scores = self.classifier.classify(self.curr_msg.split())
joke = "None"
if scores:
joke = scores[0][0]
if joke != "None":
self.say(joke)
self.last_joke = (self.curr_msg.split(), joke)
else:
if not just_joked:
self.classifier.train(self.last_msg.split(), self.get_what_to_learn())
os.path.join(ROOT_DIR, 'data', 'emails'), 0.6)
label_lookup = get_label_lookup(
os.path.join(ROOT_DIR, 'data', 'labels.txt'))
nb_classifier = NaiveBayesClassifier()
training_data = [
(label_lookup[x],
features_from_file(os.path.join(ROOT_DIR, 'data', 'emails', x)))
for x in train
]
nb_classifier.train(training_data)
true_positive = true_negative = false_positive = false_negative = 0
for filename in test:
predicted_label = nb_classifier.classify(
features_from_file(
os.path.join(ROOT_DIR, 'data', 'emails', filename)), 'spam',
'not_spam')
if predicted_label == 'spam' and label_lookup[filename] == 'spam':
true_positive += 1
if predicted_label == 'not_spam' and label_lookup[
filename] == 'not_spam':
true_negative += 1
if predicted_label == 'spam' and label_lookup[filename] == 'not_spam':
false_positive += 1
if predicted_label == 'not_spam' and label_lookup[filename] == 'spam':
false_negative += 1
print "True Positives:", true_positive
print "True Negatives:", true_negative
print "False Positives:", false_positive
print "False Negatives:", false_negative
