def test_init_learner_default(self):
l = VowpalArgsLearner(vw=VowpalMediatorMocked())
expected_args = [
"--cb_explore_adf", "--epsilon 0.05", "--interactions ax",
"--interactions axx", "--ignore_linear x", "--random_seed 1",
"--quiet"
]
self.assertEqual((" ".join(expected_args), 4),
l._vw._init_learner_calls[0])
def test_predict_cb_explore(self):
vw = VowpalMediatorMocked([0.25, 0.75])
p = VowpalArgsLearner("--cb_explore 2",
vw).predict(None, ['yes', 'no'])[0]
self.assertIsInstance(vw._predict_calls[0], VowpalEaxmpleMock)
self.assertEqual({'x': None}, vw._predict_calls[0].ns)
self.assertEqual(None, vw._predict_calls[0].label)
self.assertEqual([.25, .75], p)
def test_params(self):
learners = VowpalArgsLearner(vw=VowpalMediatorMocked())
expected_args = [
"--cb_explore_adf",
"--epsilon 0.05",
"--interactions ax",
"--interactions axx",
"--ignore_linear x",
"--random_seed 1",
]
self.assertEqual(learners.params['family'], "vw")
self.assertEqual(learners.params["args"], " ".join(expected_args))
def test_cb_explore_action_change(self):
learner = VowpalArgsLearner("--cb_explore 3", VowpalMediatorMocked())
learner.predict(None, [1, 2, 3])
with self.assertRaises(Exception) as e:
learner.predict(None, [4, 5, 6])
self.assertTrue("`adf`" in str(e.exception))
def test_predict_cb_adf(self):
vw = VowpalMediatorMocked([.25, .75])
p = VowpalArgsLearner("--cb_adf", vw).predict(None, ['yes', 'no'])[0]
self.assertEqual(2, len(vw._predict_calls[0]))
self.assertEqual({'x': None}, vw._predict_calls[0][0].ns[0])
self.assertEqual({'a': 'yes'}, vw._predict_calls[0][0].ns[1])
self.assertEqual(None, vw._predict_calls[0][0].label)
self.assertEqual({'x': None}, vw._predict_calls[0][1].ns[0])
self.assertEqual({'a': 'no'}, vw._predict_calls[0][1].ns[1])
self.assertEqual(None, vw._predict_calls[0][1].label)
self.assertEqual([1, 0], p)
def test_learn_cb(self):
vw = VowpalMediatorMocked()
learner = VowpalArgsLearner("--cb_explore", vw)
learner.predict(None, ['yes', 'no'])
learner.learn(None, 'no', .5, 0.2, ['yes', 'no'])
self.assertIsInstance(vw._learn_calls[0], VowpalEaxmpleMock)
self.assertEqual({'x': None}, vw._learn_calls[0].ns)
self.assertEqual("2:-0.5:0.2", vw._learn_calls[0].label)
def test_learn_cb_adf(self):
vw = VowpalMediatorMocked()
learner = VowpalArgsLearner("--cb_explore_adf", vw)
learner.predict(None, ['yes', 'no'])
learner.learn(None, 'yes', 1, 0.2, ['yes', 'no'])
self.assertEqual(2, len(vw._learn_calls[0]))
self.assertEqual({'x': None}, vw._learn_calls[0][0].ns[0])
self.assertEqual({'a': 'yes'}, vw._learn_calls[0][0].ns[1])
self.assertEqual("1:-1:0.2", vw._learn_calls[0][0].label)
self.assertEqual({'x': None}, vw._learn_calls[0][1].ns[0])
self.assertEqual({'a': 'no'}, vw._learn_calls[0][1].ns[1])
self.assertEqual(None, vw._learn_calls[0][1].label)
def test_flatten_tuples(self):
vw = VowpalMediatorMocked()
learner = VowpalArgsLearner("--cb_explore", vw)
learner.predict([(0, 0, 1)], ['yes', 'no'])
learner.learn({'l': (0, 0, 1), 'j': 1}, 'no', .5, 0.2, ['yes', 'no'])
self.assertIsInstance(vw._learn_calls[0], VowpalEaxmpleMock)
self.assertEqual({'x': [0, 0, 1]}, vw._predict_calls[0].ns)
self.assertEqual(None, vw._predict_calls[0].label)
self.assertEqual({'x': {
'l_0': 0,
'l_1': 0,
'l_2': 1,
'j': 1
}}, vw._learn_calls[0].ns)
self.assertEqual("2:-0.5:0.2", vw._learn_calls[0].label)
def test_picklable(self):
pickle.dumps(VowpalArgsLearner())
def test_cb_adf_learning(self):
learner = VowpalArgsLearner()
n_actions = 3
n_features = 10
n_examples = 2000
rng = CobaRandom(11111)
contexts = [rng.randoms(n_features) for _ in range(n_examples)]
pre_learn_rewards = []
for context in contexts[:int(.9 * n_examples)]:
actions = [rng.randoms(n_features) for _ in range(n_actions)]
rewards = [
sum([a * c for a, c in zip(action, context)])
for action in actions
]
rewards = [int(r == max(rewards)) for r in rewards]
pre_learn_rewards.append(
rng.choice(rewards,
learner.predict(context, actions)[0]))
for context in contexts[:int(.9 * n_examples)]:
actions = [rng.randoms(n_features) for _ in range(n_actions)]
rewards = [
sum([a * c for a, c in zip(action, context)])
for action in actions
]
rewards = [int(r == max(rewards)) for r in rewards]
probs, info = learner.predict(context, actions)
choice = rng.choice(list(range(3)), probs)
learner.learn(context, actions[choice], rewards[choice],
probs[choice], info)
post_learn_rewards = []
for context in contexts[int(.9 * n_examples):]:
actions = [rng.randoms(n_features) for _ in range(n_actions)]
rewards = [
sum([a * c for a, c in zip(action, context)])
for action in actions
]
rewards = [int(r == max(rewards)) for r in rewards]
post_learn_rewards.append(
rng.choice(rewards,
learner.predict(context, actions)[0]))
average_pre_learn_reward = sum(pre_learn_rewards) / len(
pre_learn_rewards)
average_post_learn_reward = sum(post_learn_rewards) / len(
post_learn_rewards)
self.assertAlmostEqual(.33, average_pre_learn_reward, places=2)
self.assertAlmostEqual(.78, average_post_learn_reward, places=2)
def test_cb_explore_action_count_mismatch(self):
with self.assertRaises(CobaException):
VowpalArgsLearner("--cb_explore 20",
VowpalMediatorMocked()).predict(
None, ['yes', 'no'])
def test_cb_explore_no_predict_for_inference(self):
with self.assertRaises(CobaException):
VowpalArgsLearner("--cb_explore", VowpalMediatorMocked()).learn(
None, 1, .2, .2, None)
def test_predict_cb(self):
vw = VowpalMediatorMocked(2)
p = VowpalArgsLearner("--cb 2", vw).predict(None, ['yes', 'no'])[0]
self.assertEqual([0, 1], p)
def test_init_learner_cb_action_infer(self):
vw = VowpalMediatorMocked()
VowpalArgsLearner("--cb", vw).predict(None, ['yes', 'no'])
self.assertEqual(("--cb 2", 4), vw._init_learner_calls[0])
def test_init_learner_cb_adf(self):
l = VowpalArgsLearner("--cb_adf", VowpalMediatorMocked())
self.assertEqual(("--cb_adf", 4), l._vw._init_learner_calls[0])
def test_init_no_cb_term(self):
with self.assertRaises(CobaException):
VowpalArgsLearner('--epsilon .1')
