def test_simple_run(self):
means = [0.3, 0.5, 0.7]
arms = [BernoulliArm(mean) for mean in means]
thres_bandit = ThresholdingBandit(arms=arms, theta=0.5, eps=0)
thres_bandit.reset()
assert thres_bandit.regret(MaxCorrectAnswers(answers=[0, 1, 1])) == 0
assert thres_bandit.regret(AllCorrect(answers=[0, 1, 0])) == 1
def test_simple_run(self):
means = [0, 0.5, 0.7, 1]
arms = [BernoulliArm(mean) for mean in means]
learner = EpsGreedy(arm_num=len(arms))
learner.reset()
# Pull each arm once during the initial steps
for time in range(1, len(arms) + 1):
assert learner.actions(
Context()).SerializeToString() == text_format.Parse(
"""
arm_pulls <
arm <
id: {arm_id}
>
times: 1
>
""".format(arm_id=time - 1), Actions()).SerializeToString()
learner.update(
text_format.Parse(
"""
arm_feedbacks <
arm <
id: {arm_id}
>
rewards: 0
>
""".format(arm_id=time - 1), Feedback()))
def test_simple_run(self):
means = [0, 1]
arms = [BernoulliArm(mean) for mean in means]
ordinary_bandit = OrdinaryBandit(arms)
ordinary_bandit.reset()
# pull arm 0 for 100 times
ordinary_bandit.feed([(0, 100)])
assert ordinary_bandit.regret(MaxReward()) == 100
assert ordinary_bandit.regret(BestArmId(best_arm=1)) == 0
def test_simple_run(self):
means = [0, 0.5, 0.7, 1]
arms = [BernoulliArm(mean) for mean in means]
learner = EpsGreedy(arm_num=len(arms), horizon=10)
learner.reset()
for arm_id in range(len(arms)):
assert learner.actions() == [(arm_id, 1)]
learner.update(([np.array([0])], ))
def test_simple_run(self):
means = [0.3, 0.5, 0.7]
arms = [BernoulliArm(mean) for mean in means]
ordinary_bandit = OrdinaryBandit(arms)
eps_greedy_learner = EpsGreedy(arm_num=3, horizon=10)
single_player = SinglePlayerProtocol(bandit=ordinary_bandit,
learners=[eps_greedy_learner])
temp_file = tempfile.NamedTemporaryFile()
single_player.play(trials=3, output_filename=temp_file.name)
with open(temp_file.name, 'r') as f:
# check number of records is 3
lines = f.readlines()
assert len(lines) == 3
def test_simple_run(self):
means = [0.3, 0.5, 0.7]
arms = [BernoulliArm(mean) for mean in means]
ordinary_bandit = MultiArmedBandit(arms)
eps_greedy_learner = EpsGreedy(arm_num=3)
single_player = SinglePlayerProtocol(bandit=ordinary_bandit,
learners=[eps_greedy_learner])
temp_file = tempfile.NamedTemporaryFile()
single_player.play(3, temp_file.name, horizon=10)
with open(temp_file.name, 'rb') as f:
# check number of records is 3
trials = parse_trials_from_bytes(f.read())
assert len(trials) == 3
def test_simple_run(self):
means = [0, 1]
arms = [BernoulliArm(mean) for mean in means]
ordinary_bandit = MultiArmedBandit(arms)
ordinary_bandit.reset()
# Pull arm 0 for 100 times
actions = text_format.Parse(
"""
arm_pulls {
arm {
id: 0
}
times: 100
}
""", Actions())
ordinary_bandit.feed(actions)
assert ordinary_bandit.regret(MaximizeTotalRewards()) == 100
arm = Arm()
arm.id = 1
assert ordinary_bandit.regret(IdentifyBestArm(best_arm=arm)) == 0