def test_transaction_resume_1(self):
sim = LambdaSimulation(2, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
working_learner = ModuloLearner()
broken_learner = BrokenLearner()
benchmark = Benchmark([sim])
#the second time the broken_factory() shouldn't ever be used for learning or choosing
#because it already worked the first time and we are "resuming" benchmark from transaction.log
try:
first_result = benchmark.evaluate(
[working_learner], "coba/tests/.temp/transactions.log")
second_result = benchmark.evaluate(
[broken_learner], "coba/tests/.temp/transactions.log")
actual_learners = second_result.learners.to_tuples()
actual_simulations = second_result.simulations.to_tuples()
actual_interactions = second_result.interactions.to_tuples()
expected_learners = [(0, "Modulo(p=0)", "Modulo", '0')]
expected_simulations = [(0, "LambdaSimulation", "None", "None",
'"LambdaSimulation"')]
expected_interactions = [(0, 0, 1, 0), (0, 0, 2, 1)]
except Exception as e:
raise
finally:
if Path('coba/tests/.temp/transactions.log').exists():
Path('coba/tests/.temp/transactions.log').unlink()
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_interactions, expected_interactions)
def test_wrapped_not_picklable_learner_with_reduce(self):
sim1 = LambdaSimulation(5, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
learner = WrappedLearner(NotPicklableLearnerWithReduce())
benchmark = Benchmark([sim1], shuffle=[1, 4])
benchmark.evaluate([learner])
def test_not_picklable_learner(self):
sim1 = LambdaSimulation(5, lambda t: t, lambda t: [0,1,2], lambda c,a: a)
learner = NotPicklableLearner()
benchmark = Benchmark([sim1], batch_sizes=[2], ignore_raise=False, seeds=[1,4])
with self.assertRaises(Exception) as cm:
benchmark.evaluate([learner])
self.assertTrue("Learners are required to be picklable" in str(cm.exception))
def test_wrapped_not_picklable_learner_sans_reduce(self):
sim1 = LambdaSimulation(5, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
learner = WrappedLearner(NotPicklableLearner())
benchmark = Benchmark([sim1])
CobaConfig.Logger = BasicLogger(MemorySink())
benchmark.evaluate([learner])
self.assertEqual(1, len(CobaConfig.Logger.sink.items))
self.assertIn("pickle", CobaConfig.Logger.sink.items[0])
def test_transaction_resume_1(self):
sim = LambdaSimulation(5, lambda t: t, lambda t: [0,1,2], lambda c,a: a)
working_learner = ModuloLearner()
broken_learner = BrokenLearner()
benchmark = Benchmark([sim], batch_count=1)
#the second time the broken_factory() shouldn't ever be used for learning or choosing
#because it already worked the first time and we are "resuming" benchmark from transaction.log
try:
first_results = benchmark.evaluate([working_learner], "coba/tests/.temp/transactions.log")
second_results = benchmark.evaluate([broken_learner], "coba/tests/.temp/transactions.log")
actual_learners,actual_simulations,actual_batches = second_results.to_tuples()
expected_learners = [(0,"0","0")]
expected_simulations = [(0, '0', ['{"Batch":[None, 1, None]}'], 5, 1, 1, 3)]
expected_batches = [(0, 0, [5], [mean([0,1,2,0,1])])]
finally:
if Path('coba/tests/.temp/transactions.log').exists(): Path('coba/tests/.temp/transactions.log').unlink()
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_batches, expected_batches)
def test_seeds(self):
sim1 = LambdaSimulation(5, lambda t: t, lambda t: [0,1,2], lambda c,a: a)
learner = ModuloLearner()
benchmark = Benchmark([sim1], batch_sizes=[2], ignore_raise=False, seeds=[1,4])
actual_learners,actual_simulations,actual_batches = benchmark.evaluate([learner]).to_tuples()
expected_learners = [(0,"0","0")]
expected_simulations = [(0, '0', ['{"Shuffle":1}', '{"Batch":[None, None, [2]]}'], 2, 1, 1, 3), (1, '0', ['{"Shuffle":4}', '{"Batch":[None, None, [2]]}'], 2, 1, 1, 3)]
expected_batches = [(0, 0, [2], [mean([1,0])]), (1, 0, [2], [mean([2,0])])]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_batches, expected_batches)
def test_take(self):
sim1 = LambdaSimulation(5, lambda t: t, lambda t: [0,1,2], lambda c,a: a)
sim2 = LambdaSimulation(4, lambda t: t, lambda t: [3,4,5], lambda c,a: a)
learner = ModuloLearner()
benchmark = Benchmark([sim1,sim2], batch_count=1, take=5, ignore_raise=False)
actual_learners,actual_simulations,actual_batches = benchmark.evaluate([learner]).to_tuples()
expected_learners = [(0,"0","0")]
expected_simulations = [(0, '0', ['{"Take":5}', '{"Batch":[None, 1, None]}'], 5, 1, 1, 3), (1, '1', ['{"Take":5}', '{"Batch":[None, 1, None]}'], 0, 0, 0, 0)]
expected_batches = [(0, 0, [5], [mean([0,1,2,0,1])])]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_batches, expected_batches)
def test_learners(self):
sim = LambdaSimulation(5, lambda t: t, lambda t: [0,1,2], lambda c,a: a)
learner1 = ModuloLearner("0") #type: ignore
learner2 = ModuloLearner("1") #type: ignore
benchmark = Benchmark([sim], batch_count=1, ignore_raise=False)
actual_results = benchmark.evaluate([learner1, learner2])
actual_learners,actual_simulations,actual_batches = actual_results.to_tuples()
expected_learners = [(0,"0","0"), (1,"1","1")]
expected_simulations = [(0, '0', ['{"Batch":[None, 1, None]}'], 5, 1, 1, 3)]
expected_batches = [(0, 0, [5], [mean([0,1,2,0,1])]), (0, 1, [5], [mean([0,1,2,0,1])]) ]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_batches, expected_batches)
def test_sources(self):
sim1 = LambdaSimulation(2, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
learner = ModuloLearner()
benchmark = Benchmark([sim1])
result = benchmark.evaluate([learner])
actual_learners = result.learners.to_tuples()
actual_simulations = result.simulations.to_tuples()
actual_interactions = result.interactions.to_tuples()
expected_learners = [(0, "Modulo(p=0)", "Modulo", '0')]
expected_simulations = [(0, "LambdaSimulation", "None", "None",
'"LambdaSimulation"')]
expected_interactions = [(0, 0, 1, 0), (0, 0, 2, 1)]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_interactions, expected_interactions)
def test_learners(self):
sim = LambdaSimulation(2, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
learner1 = ModuloLearner("0") #type: ignore
learner2 = ModuloLearner("1") #type: ignore
benchmark = Benchmark([sim])
actual_result = benchmark.evaluate([learner1, learner2])
actual_learners = actual_result._learners.to_tuples()
actual_simulations = actual_result._simulations.to_tuples()
actual_interactions = actual_result._interactions.to_tuples()
expected_learners = [(0, "Modulo(p=0)", "Modulo", '0'),
(1, "Modulo(p=1)", "Modulo", '1')]
expected_simulations = [(0, "LambdaSimulation", "None", "None",
'"LambdaSimulation"')]
expected_interactions = [(0, 0, 1, 0), (0, 0, 2, 1), (0, 1, 1, 0),
(0, 1, 2, 1)]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_interactions, expected_interactions)
def test_eval_seeds(self):
sim1 = LambdaSimulation(3, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
learner = RandomLearner()
benchmark = Benchmark([sim1], shuffle=[1, 4])
result = benchmark.evaluate([learner], seed=1)
actual_learners = result.learners.to_tuples()
actual_simulations = result.simulations.to_tuples()
actual_interactions = result.interactions.to_tuples()
expected_learners = [(0, "random", "random")]
expected_simulations = [(0, "LambdaSimulation", "1", "None",
'"LambdaSimulation",{"Shuffle":1}'),
(1, "LambdaSimulation", "4", "None",
'"LambdaSimulation",{"Shuffle":4}')]
expected_interactions = [(0, 0, 1, 0), (0, 0, 2, 2), (0, 0, 3, 1),
(1, 0, 1, 0), (1, 0, 2, 2), (1, 0, 3, 1)]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_interactions, expected_interactions)
def test_ignore_raise(self):
log_sink = MemorySink()
CobaConfig.Logger = IndentLogger(log_sink)
sim1 = LambdaSimulation(2, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
sim2 = LambdaSimulation(3, lambda i: i, lambda i, c: [3, 4, 5],
lambda i, c, a: cast(float, a))
learners = [ModuloLearner(), BrokenLearner()]
benchmark = Benchmark([sim1, sim2])
result = benchmark.evaluate(learners)
actual_learners = result.learners.to_tuples()
actual_simulations = result.simulations.to_tuples()
actual_interactions = result.interactions.to_tuples()
expected_learners = [(0, "Modulo(p=0)", "Modulo", '0'),
(1, "Broken", "Broken", float('nan'))]
expected_simulations = [
(0, "LambdaSimulation", "None", "None", '"LambdaSimulation"'),
(1, "LambdaSimulation", "None", "None", '"LambdaSimulation"')
]
expected_interactions = [(0, 0, 1, 0), (0, 0, 2, 1), (1, 0, 1, 3),
(1, 0, 2, 4), (1, 0, 3, 5)]
self.assertEqual(
2,
sum([
int("Unexpected exception:" in item) for item in log_sink.items
]))
self.assertCountEqual(actual_learners[0], expected_learners[0])
self.assertCountEqual(actual_learners[1][:3], expected_learners[1][:3])
self.assertTrue(math.isnan(expected_learners[1][3]))
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_interactions, expected_interactions)
def test_take(self):
sim1 = LambdaSimulation(5, lambda i: i, lambda i, c: [0, 1, 2],
lambda i, c, a: cast(float, a))
sim2 = LambdaSimulation(2, lambda i: i, lambda i, c: [3, 4, 5],
lambda i, c, a: cast(float, a))
learner = ModuloLearner()
benchmark = Benchmark([sim1, sim2], take=3)
result = benchmark.evaluate([learner])
actual_learners = result.learners.to_tuples()
actual_simulations = result.simulations.to_tuples()
actual_interactions = result.interactions.to_tuples()
expected_learners = [(0, "Modulo(p=0)", "Modulo", '0')]
expected_simulations = [(0, "LambdaSimulation", "None", "3",
'"LambdaSimulation",{"Take":3}'),
(1, "LambdaSimulation", "None", "3",
'"LambdaSimulation",{"Take":3}')]
expected_interactions = [(0, 0, 1, 0), (0, 0, 2, 1), (0, 0, 3, 2)]
self.assertCountEqual(actual_learners, expected_learners)
self.assertCountEqual(actual_simulations, expected_simulations)
self.assertCountEqual(actual_interactions, expected_interactions)
#LambdaSimulation(2000, no_contexts, actions, random_rewards_2, seed=10), #not CB since reward is independent of context
#LambdaSimulation(2000, no_contexts, actions, random_rewards_3, seed=10), #not CB since reward is independent of context
LambdaSimulation(2000,
contexts,
actions,
linear_plus_random_rewards_1,
seed=10),
LambdaSimulation(2000,
contexts,
actions,
linear_plus_random_rewards_2,
seed=10),
LambdaSimulation(2000, contexts, actions, polynomial_reward_1,
seed=10),
]
#define a benchmark: this benchmark replays the simulation 15 times
benchmark = Benchmark(simulations, batch_size=1, seeds=list(range(5)))
#create the learner factories
learner_factories = [
RandomLearner(seed=10),
EpsilonLearner(epsilon=0.025, seed=10),
UcbTunedLearner(seed=10),
VowpalLearner(epsilon=0.025, seed=10),
VowpalLearner(epsilon=0.025, is_adf=False, seed=10),
VowpalLearner(bag=5, seed=10),
]
benchmark.evaluate(learner_factories).standard_plot()
#First, we define the learners that we want to test
learners = [
RandomLearner(),
EpsilonBanditLearner(epsilon=0.025),
VowpalLearner(
epsilon=.1), #This learner requires that VowpalWabbit be installed
]
#Then we define the simulations that we want to test our learners on
simulations = [
ValidationSimulation(300,
context_features=True,
action_features=False,
seed=1000)
]
#And also define a collection of seeds used to shuffle our simulations
seeds = [0, 1, 2, 3]
#We then create our benchmark using our simulations and seeds
benchmark = Benchmark(simulations, shuffle=seeds)
#Finally we evaluate our learners on our benchmark (the results will be saved in `result_file`).
result = benchmark.evaluate(learners)
#After evaluating can create a quick summary plot to get a sense of how the learners performed
result.plot_learners()
#We can also create a plot examining how one specific learner did across each shuffle of our simulation
result.plot_shuffles(learner_pattern="vw")