class tweeter_markov_tests(unittest.TestCase):
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def setUp(self, _):
self.corpus = path.join(path.dirname(__file__), 'data', 'tweets.txt')
self.configfile = path.join(path.dirname(__file__), '..', 'bots.yaml')
self.tm = TwitterMarkov('example_screen_name', [self.corpus], config=self.configfile,
dry_run=True, learn=False)
self.tm.log.setLevel(100)
def testCheckModels(self):
for m in self.tm.models.values():
self.assertIsInstance(m, markovify.text.NewlineText)
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovCompose(self, *_):
response = self.tm.compose(tries=150, max_overlap_ratio=2, max_overlap_total=100)
assert isinstance(response, basestring)
assert len(response) < 140
@mock.patch.object(tweepy.API, 'mentions_timeline', return_value=fake_timeline())
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovReply(self, *_):
r = self.tm.reply_all(tries=75, max_overlap_ratio=2, max_overlap_total=100)
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovRecentlyTweeted(self, _):
recents = self.tm.recently_tweeted
assert recents[0] == TIMELINE[0]['text']
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovCheckTweet(self, _):
assert self.tm.check_tweet('') is False
assert self.tm.check_tweet('badword') is False
assert self.tm.check_tweet('Lorem ipsum dolor sit amet') is False
assert self.tm.check_tweet('Lorem ipsum dolor sit amet!') is False
assert self.tm.check_tweet('Lorem ipsum dolor set namet') is False
assert self.tm.check_tweet('Random Text that should work totally') is True
assert self.tm.check_tweet('@reply Random Text') is True
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovLearn(self, _):
tmp = path.join(path.dirname(__file__), 'data', 'tmp.txt')
self.tm.learn_parent(corpus=tmp)
try:
with open(tmp) as f:
result = f.read()
assert TIMELINE[0]['text'] in result
assert TIMELINE[1]['text'] in result
assert TIMELINE[2]['text'] in result
finally:
os.remove(tmp)
class tweeter_markov_tests(unittest.TestCase):
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def setUp(self, _):
self.corpus = path.join(path.dirname(__file__), 'data', 'tweets.txt')
self.configfile = path.join(path.dirname(__file__), '..', 'bots.yaml')
self.tm = TwitterMarkov('example_screen_name', [self.corpus], config=self.configfile,
dry_run=True, learn=False)
self.tm.log.setLevel(100)
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovCompose(self, *_):
response = self.tm.compose(tries=150, max_overlap_ratio=2, max_overlap_total=100)
assert isinstance(response, basestring)
assert len(response) < 140
@mock.patch.object(tweepy.API, 'mentions_timeline', return_value=fake_timeline())
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovReply(self, *_):
r = self.tm.reply_all(tries=75, max_overlap_ratio=2, max_overlap_total=100)
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovRecentlyTweeted(self, _):
recents = self.tm.recently_tweeted
assert recents[0] == TIMELINE[0]['text']
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovCheckTweet(self, _):
assert self.tm.check_tweet('') is False
assert self.tm.check_tweet('badword') is False
assert self.tm.check_tweet('Lorem ipsum dolor sit amet') is False
assert self.tm.check_tweet('Lorem ipsum dolor sit amet!') is False
assert self.tm.check_tweet('Lorem ipsum dolor set namet') is False
assert self.tm.check_tweet('Random Text that should work totally') is True
assert self.tm.check_tweet('@reply Random Text') is True
@mock.patch.object(tweepy.API, 'user_timeline', return_value=fake_timeline())
def testTwitterMarkovLearn(self, _):
tmp = path.join(path.dirname(__file__), 'data', 'tmp.txt')
self.tm.learn_parent(corpus=tmp)
try:
with open(tmp) as f:
result = f.read()
assert TIMELINE[0]['text'] in result
assert TIMELINE[1]['text'] in result
assert TIMELINE[2]['text'] in result
finally:
os.remove(tmp)
length = None
propoganda = (random.random() <= 0.4)
if not propoganda:
#state_size = coherence(5)
length = 92
try:
model = learn(state_size, propoganda)
except IOError as e:
exit(0)
#tm.models = tm._setup_models(tm.corpora, state_size)
print 'composing....\n'
tweet = tm.compose(model=model, max_len=length)
tries = 1
print 'checking...\n'
while not check(tweet):
if (tries % 10) == 0:
state_size = coherence(state_size - 1) #coherence(state_size)
try:
model = learn(state_size, propoganda)
except IOError as e:
exit(0)
#tm.models = tm._setup_models(tm.corpora, state_size)
tweet = tm.compose(model=model, max_len=length)
length = None
propoganda = (random.random() <= 0.4)
if not propoganda:
#state_size = coherence(5)
length = 92
try:
model = learn(state_size, propoganda)
except IOError as e:
exit(0)
#tm.models = tm._setup_models(tm.corpora, state_size)
print 'composing....\n'
tweet = tm.compose(model = model, max_len=length)
tries = 1
print 'checking...\n'
while not check(tweet):
if (tries % 10) == 0:
state_size = coherence(state_size -1) #coherence(state_size)
try:
model = learn(state_size, propoganda)
except IOError as e:
exit(0)
#tm.models = tm._setup_models(tm.corpora, state_size)
tweet = tm.compose(model = model, max_len=length)