def test_invalid_log_format(self):
rng = np.random.RandomState(seed=7)
with self.assertRaises(TypeError):
Simulator(bandits=[("example",
MAB([0, 1], LearningPolicy.EpsilonGreedy()))],
decisions=[rng.randint(0, 2) for _ in range(10)],
rewards=[rng.randint(0, 100) for _ in range(10)],
contexts=[[rng.rand() for _ in range(5)]
for _ in range(10)],
scaler=StandardScaler(),
test_size=0.4,
batch_size=0,
is_ordered=True,
seed=7,
log_format=7)
with self.assertRaises(TypeError):
Simulator(bandits=[("example",
MAB([0, 1], LearningPolicy.EpsilonGreedy()))],
decisions=[rng.randint(0, 2) for _ in range(10)],
rewards=[rng.randint(0, 100) for _ in range(10)],
contexts=[[rng.rand() for _ in range(5)]
for _ in range(10)],
scaler=StandardScaler(),
test_size=0.4,
batch_size=0,
is_ordered=True,
seed=7,
log_format=None)
def test_simulator_hyper_parameter(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
contexts = [[random.random() for _ in range(50)] for _ in range(size)]
n_jobs = 1
hyper_parameter_tuning = []
for radius in range(6, 10):
hyper_parameter_tuning.append(
('Radius' + str(radius),
MAB([0, 1],
LearningPolicy.UCB1(1),
NeighborhoodPolicy.Radius(radius),
n_jobs=n_jobs)))
sim = Simulator(hyper_parameter_tuning,
decisions,
rewards,
contexts,
scaler=StandardScaler(),
test_size=0.5,
is_ordered=False,
batch_size=0,
seed=123456,
is_quick=True)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
def test_simulator_mixed(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
contexts = [[random.random() for _ in range(50)] for _ in range(size)]
n_jobs = 1
mixed = [('RandomRadius',
MAB([0, 1],
LearningPolicy.Random(),
NeighborhoodPolicy.Radius(10),
n_jobs=n_jobs)),
('Random', MAB([0, 1], LearningPolicy.Random(),
n_jobs=n_jobs))]
sim = Simulator(mixed,
decisions,
rewards,
contexts,
scaler=StandardScaler(),
test_size=0.5,
is_ordered=False,
batch_size=0,
seed=123456)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
def test_simulator_contextual(self):
size = 100
decisions = [random.randint(0, 2) for _ in range(size)]
rewards = [random.randint(0, 1000) for _ in range(size)]
contexts = [[random.random() for _ in range(50)] for _ in range(size)]
def binarize(decision, reward):
if decision == 0:
return reward <= 50
else:
return reward >= 220
n_jobs = 1
contextual_mabs = [('Random',
MAB([0, 1],
LearningPolicy.Random(),
NeighborhoodPolicy.Radius(10),
n_jobs=n_jobs)),
('UCB1',
MAB([0, 1],
LearningPolicy.UCB1(1),
NeighborhoodPolicy.Radius(10),
n_jobs=n_jobs)),
('ThompsonSampling',
MAB([0, 1],
LearningPolicy.ThompsonSampling(binarize),
NeighborhoodPolicy.Radius(10),
n_jobs=n_jobs)),
('EpsilonGreedy',
MAB([0, 1],
LearningPolicy.EpsilonGreedy(epsilon=.15),
NeighborhoodPolicy.Radius(10),
n_jobs=n_jobs)),
('Softmax',
MAB([0, 1],
LearningPolicy.Softmax(),
NeighborhoodPolicy.Radius(10),
n_jobs=n_jobs))]
sim = Simulator(contextual_mabs,
decisions,
rewards,
contexts,
scaler=StandardScaler(),
test_size=0.5,
is_ordered=False,
batch_size=0,
seed=123456)
sim.run()
self.assertTrue(sim.bandit_to_confusion_matrices)
self.assertTrue(sim.bandit_to_predictions)
def test_invalid_simulator_stats_scope(self):
rng = np.random.RandomState(seed=7)
decisions = np.array([rng.randint(0, 2) for _ in range(10)])
rewards = np.array([rng.randint(0, 100) for _ in range(10)])
sim = Simulator(bandits=[("example",
MAB([0, 1],
LearningPolicy.EpsilonGreedy()))],
decisions=decisions,
rewards=rewards,
contexts=[[rng.rand() for _ in range(5)]
for _ in range(10)],
scaler=StandardScaler(),
test_size=0.4,
batch_size=0,
is_ordered=True,
seed=7)
with self.assertRaises(ValueError):
sim._set_stats('validation', decisions, rewards)
def test_invalid_get_arm_stats(self):
rng = np.random.RandomState(seed=9)
decisions = np.array([rng.randint(0, 2) for _ in range(5)])
rewards = np.array([rng.randint(0, 100) for _ in range(5)])
new_rewards = np.array(['h', 'e', 'l', 'l', 'o'])
sim = Simulator(bandits=[("example",
MAB([0, 1],
LearningPolicy.EpsilonGreedy()))],
decisions=decisions,
rewards=rewards,
contexts=[[rng.rand() for _ in range(5)]
for _ in range(5)],
scaler=StandardScaler(),
test_size=0.4,
batch_size=0,
is_ordered=True,
seed=7)
with self.assertRaises(TypeError):
stats = sim.get_arm_stats(decisions, new_rewards)
def test_invalid_plot_args_metric_value(self):
rng = np.random.RandomState(seed=7)
sim = Simulator(bandits=[("example",
MAB([0, 1],
LearningPolicy.EpsilonGreedy()))],
decisions=[rng.randint(0, 2) for _ in range(10)],
rewards=[rng.randint(0, 100) for _ in range(10)],
contexts=[[rng.rand() for _ in range(5)]
for _ in range(10)],
scaler=StandardScaler(),
test_size=0.4,
batch_size=0,
is_ordered=True,
seed=7)
sim.run()
with self.assertRaises(ValueError):
sim.plot('mean')
def test_invalid_get_stats(self):
data = np.array(['h', 'e', 'l', 'l', 'o'])
with self.assertRaises(TypeError):
Simulator.get_stats(data)
mixed = [('RandomRadius', MAB([0, 1], LearningPolicy.Random(), NeighborhoodPolicy.Radius(10), n_jobs=n_jobs)),
('Random', MAB([0, 1], LearningPolicy.Random(), n_jobs=n_jobs))]
hyper_parameter_tuning = []
for radius in range(6, 10):
hyper_parameter_tuning.append(('Radius'+str(radius),
MAB([0, 1], LearningPolicy.UCB1(1), NeighborhoodPolicy.Radius(radius),
n_jobs=n_jobs)))
####################################
# Contextual Simulation
####################################
start = time()
sim = Simulator(contextual_mabs, decisions, rewards, contexts,
scaler=StandardScaler(), test_size=0.5, is_ordered=False, batch_size=0, seed=123456)
sim.run()
end = time()
runtime = (end - start) / 60
print('Complete', str(runtime) + ' minutes')
print('\n')
for mab_name, mab in sim.bandits:
print(mab_name)
# Since simulation is offline, print the bandit stats directly
print('Worst Case Scenario', sim.bandit_to_arm_to_stats_min[mab_name])
print('Average Case Scenario', sim.bandit_to_arm_to_stats_avg[mab_name])
print('Best Case Scenario:', sim.bandit_to_arm_to_stats_max[mab_name])
