def test_bayesian_save_reload(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice('a', [1, 2], default=1)
hps.Choice('b', [3, 4], default=3)
hps.Choice('c', [5, 6], default=5)
hps.Choice('d', [7, 8], default=7)
hps.Choice('e', [9, 0], default=9)
oracle = bo_module.BayesianOptimizationOracle(objective=kt.Objective(
'score', 'max'),
max_trials=20,
hyperparameters=hps)
oracle._set_project_dir(tmp_dir, 'untitled')
for _ in range(3):
trial = oracle.create_trial('tuner_id')
oracle.update_trial(trial.trial_id, {'score': 1.})
oracle.end_trial(trial.trial_id, "COMPLETED")
oracle.save()
oracle = bo_module.BayesianOptimizationOracle(objective=kt.Objective(
'score', 'max'),
max_trials=20,
hyperparameters=hps)
oracle._set_project_dir(tmp_dir, 'untitled')
oracle.reload()
for trial_id in range(3):
trial = oracle.create_trial('tuner_id')
oracle.update_trial(trial.trial_id, {'score': 1.})
oracle.end_trial(trial.trial_id, "COMPLETED")
assert len(oracle.trials) == 6
def test_bayesian_save_reload(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice("a", [1, 2], default=1)
hps.Choice("b", [3, 4], default=3)
hps.Choice("c", [5, 6], default=5)
hps.Choice("d", [7, 8], default=7)
hps.Choice("e", [9, 0], default=9)
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", "max"), max_trials=20, hyperparameters=hps
)
oracle._set_project_dir(tmp_dir, "untitled")
for _ in range(3):
trial = oracle.create_trial("tuner_id")
oracle.update_trial(trial.trial_id, {"score": 1.0})
oracle.end_trial(trial.trial_id, "COMPLETED")
oracle.save()
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", "max"), max_trials=20, hyperparameters=hps
)
oracle._set_project_dir(tmp_dir, "untitled")
oracle.reload()
for trial_id in range(3):
trial = oracle.create_trial("tuner_id")
oracle.update_trial(trial.trial_id, {"score": 1.0})
oracle.end_trial(trial.trial_id, "COMPLETED")
assert len(oracle.trials) == 6
def test_bayesian_oracle_maximize(tmp_dir):
hps = hp_module.HyperParameters()
hps.Int("a", -100, 100)
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", direction="max"),
max_trials=20,
hyperparameters=hps,
num_initial_points=2,
)
oracle._set_project_dir(tmp_dir, "untitled")
# Make examples with high 'a' and high score.
for i in range(5):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values["a"] = 10 * i
trial.score = i
trial.status = "COMPLETED"
oracle.trials[trial.trial_id] = trial
# Make examples with low 'a' and low score
for i in range(5):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values["a"] = -10 * i
trial.score = -i
trial.status = "COMPLETED"
oracle.trials[trial.trial_id] = trial
trial = oracle.create_trial("tuner0")
assert trial.status == "RUNNING"
# Assert that the oracle suggests hps it thinks will maximize.
assert trial.hyperparameters.get("a") > 0
def test_hyperparameters_added(tmp_dir):
hps = hp_module.HyperParameters()
hps.Int("a", -100, 100)
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", direction="max"),
max_trials=20,
hyperparameters=hps,
num_initial_points=2,
)
oracle._set_project_dir(tmp_dir, "untitled")
# Populate initial trials.
for i in range(10):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values["a"] = 10 * i
trial.score = i
trial.status = "COMPLETED"
oracle.trials[trial.trial_id] = trial
# Update the space.
new_hps = hp_module.HyperParameters()
new_hps.Float("b", 3.2, 6.4, step=0.2, default=3.6)
new_hps.Boolean("c", default=True)
oracle.update_space(new_hps)
# Make a new trial, it should have b set.
trial = oracle.create_trial("tuner0")
assert trial.status == "RUNNING"
assert "b" in trial.hyperparameters.values
assert "c" in trial.hyperparameters.values
def test_bayesian_oracle_maximize(tmp_dir):
hps = hp_module.HyperParameters()
hps.Int('a', -100, 100)
oracle = bo_module.BayesianOptimizationOracle(objective=kt.Objective(
'score', direction='max'),
max_trials=20,
hyperparameters=hps,
num_initial_points=2)
oracle._set_project_dir(tmp_dir, 'untitled')
# Make examples with high 'a' and high score.
for i in range(5):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values['a'] = 10 * i
trial.score = i
trial.status = 'COMPLETED'
oracle.trials[trial.trial_id] = trial
# Make examples with low 'a' and low score
for i in range(5):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values['a'] = -10 * i
trial.score = -i
trial.status = 'COMPLETED'
oracle.trials[trial.trial_id] = trial
trial = oracle.create_trial('tuner0')
assert trial.status == 'RUNNING'
# Assert that the oracle suggests hps it thinks will maximize.
assert trial.hyperparameters.get('a') > 0
def test_hyperparameters_added(tmp_dir):
hps = hp_module.HyperParameters()
hps.Int('a', -100, 100)
oracle = bo_module.BayesianOptimizationOracle(objective=kt.Objective(
'score', direction='max'),
max_trials=20,
hyperparameters=hps,
num_initial_points=2)
oracle._set_project_dir(tmp_dir, 'untitled')
# Populate initial trials.
for i in range(10):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values['a'] = 10 * i
trial.score = i
trial.status = 'COMPLETED'
oracle.trials[trial.trial_id] = trial
# Update the space.
new_hps = hp_module.HyperParameters()
new_hps.Float('b', 3.2, 6.4, step=0.2, default=3.6)
new_hps.Boolean('c', default=True)
oracle.update_space(new_hps)
# Make a new trial, it should have b set.
trial = oracle.create_trial('tuner0')
assert trial.status == 'RUNNING'
assert 'b' in trial.hyperparameters.values
assert 'c' in trial.hyperparameters.values
def test_save_before_result(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1),
hp_module.Int('b', 3, 10, default=3),
hp_module.Float('c', 0, 1, 0.1, default=0),
hp_module.Fixed('d', 7),
hp_module.Choice('e', [9, 0], default=9)]
oracle = bo_module.BayesianOptimizationOracle()
oracle.populate_space(str(1), hp_list)
oracle.save(os.path.join(tmp_dir, 'temp_oracle'))
oracle.result(str(1), 0)
def test_bayesian_oracle_with_zero_y(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1),
hp_module.Int('b', 3, 10, default=3),
hp_module.Float('c', 0, 1, 0.1, default=0),
hp_module.Fixed('d', 7),
hp_module.Choice('e', [9, 0], default=9)]
oracle = bo_module.BayesianOptimizationOracle()
for i in range(100):
oracle.populate_space(str(i), hp_list)
oracle.result(str(i), 0)
def test_bayesian_save_reload(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1),
hp_module.Choice('b', [3, 4], default=3),
hp_module.Choice('c', [5, 6], default=5),
hp_module.Choice('d', [7, 8], default=7),
hp_module.Choice('e', [9, 0], default=9)]
oracle = bo_module.BayesianOptimizationOracle()
for trial_id in range(3):
oracle.populate_space('0_' + str(trial_id), hp_list)
for trial_id in range(2):
oracle.result('0_' + str(trial_id), trial_id)
fname = os.path.join(tmp_dir, 'oracle')
oracle.save(fname)
oracle = bo_module.BayesianOptimizationOracle()
oracle.reload(fname)
for trial_id in range(20):
hp = oracle.populate_space('1_' + str(trial_id), hp_list)
assert hp['status'] == 'RUN'
def test_save_before_result(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice('a', [1, 2], default=1)
hps.Int('b', 3, 10, default=3)
hps.Float('c', 0, 1, 0.1, default=0)
hps.Fixed('d', 7)
hps.Choice('e', [9, 0], default=9)
oracle = bo_module.BayesianOptimizationOracle(
objective='score', max_trials=10, hyperparameters=hps)
oracle._set_project_dir(tmp_dir, 'untitled')
oracle._populate_space(str(1))
oracle.save()
def test_save_before_result(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice("a", [1, 2], default=1)
hps.Int("b", 3, 10, default=3)
hps.Float("c", 0, 1, 0.1, default=0)
hps.Fixed("d", 7)
hps.Choice("e", [9, 0], default=9)
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", "max"), max_trials=10, hyperparameters=hps
)
oracle._set_project_dir(tmp_dir, "untitled")
oracle.populate_space(str(1))
oracle.save()
def test_bayesian_dynamic_space(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1)]
oracle = bo_module.BayesianOptimizationOracle()
for i in range(10):
oracle.populate_space(str(i), hp_list)
oracle.result(str(i), i)
hp_list.append(hp_module.Int('b', 3, 10, default=3))
assert 'b' in oracle.populate_space('1_0', hp_list)['values']
hp_list.append(hp_module.Float('c', 0, 1, 0.1, default=0))
assert 'c' in oracle.populate_space('1_1', hp_list)['values']
hp_list.append(hp_module.Fixed('d', 7))
assert 'd' in oracle.populate_space('1_2', hp_list)['values']
hp_list.append(hp_module.Choice('e', [9, 0], default=9))
assert 'e' in oracle.populate_space('1_3', hp_list)['values']
def test_bayesian_oracle_with_zero_y(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice('a', [1, 2], default=1)
hps.Int('b', 3, 10, default=3)
hps.Float('c', 0, 1, 0.1, default=0)
hps.Fixed('d', 7)
hps.Choice('e', [9, 0], default=9)
oracle = bo_module.BayesianOptimizationOracle(
objective='score', max_trials=20, hyperparameters=hps)
oracle._set_project_dir(tmp_dir, 'untitled')
for i in range(5):
trial = oracle.create_trial(str(i))
oracle.update_trial(trial.trial_id, {'score': 0})
oracle.end_trial(trial.trial_id, "COMPLETED")
def test_bayesian_dynamic_space(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice('a', [1, 2], default=1)
oracle = bo_module.BayesianOptimizationOracle(
objective='val_acc', max_trials=20)
oracle._set_project_dir(tmp_dir, 'untitled')
oracle.hyperparameters = hps
for i in range(10):
oracle._populate_space(str(i))
hps.Int('b', 3, 10, default=3)
assert 'b' in oracle._populate_space('1_0')['values']
hps.Float('c', 0, 1, 0.1, default=0)
assert 'c' in oracle._populate_space('1_1')['values']
hps.Fixed('d', 7)
assert 'd' in oracle._populate_space('1_2')['values']
hps.Choice('e', [9, 0], default=9)
assert 'e' in oracle._populate_space('1_3')['values']
def test_bayesian_dynamic_space(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice("a", [1, 2], default=1)
oracle = bo_module.BayesianOptimizationOracle(
objective="val_acc", max_trials=20, num_initial_points=10
)
oracle._set_project_dir(tmp_dir, "untitled")
oracle.hyperparameters = hps
for i in range(10):
oracle.populate_space(str(i))
hps.Int("b", 3, 10, default=3)
assert "b" in oracle.populate_space("1_0")["values"]
hps.Float("c", 0, 1, 0.1, default=0)
assert "c" in oracle.populate_space("1_1")["values"]
hps.Fixed("d", 7)
assert "d" in oracle.populate_space("1_2")["values"]
hps.Choice("e", [9, 0], default=9)
assert "e" in oracle.populate_space("1_3")["values"]
def test_bayesian_oracle_with_zero_y(tmp_dir):
hps = hp_module.HyperParameters()
hps.Choice("a", [1, 2], default=1)
hps.Int("b", 3, 10, default=3)
hps.Float("c", 0, 1, 0.1, default=0)
hps.Fixed("d", 7)
hps.Choice("e", [9, 0], default=9)
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", "max"),
max_trials=20,
num_initial_points=2,
hyperparameters=hps,
)
oracle._set_project_dir(tmp_dir, "untitled")
for i in range(5):
trial = oracle.create_trial(str(i))
oracle.update_trial(trial.trial_id, {"score": 0})
oracle.end_trial(trial.trial_id, "COMPLETED")
def test_distributed_optimization(tmp_dir):
hps = hp_module.HyperParameters()
hps.Int('a', 0, 10)
hps.Float('b', -1, 1, step=0.1)
hps.Float('c', 1e-5, 1e-2, sampling='log')
def evaluate(hp):
# Minimum at a=4, b=1, c=1e-3 with score=-1
return abs(hp['a'] -
4) - hp['b'] + 0.1 * abs(3 + math.log(hp['c'], 10))
oracle = bo_module.BayesianOptimizationOracle(objective=kt.Objective(
'score', 'min'),
hyperparameters=hps,
max_trials=60)
oracle._set_project_dir(tmp_dir, 'untitled')
tuners = 4
for _ in range(10):
trials = []
for i in range(tuners):
trial = oracle.create_trial('tuner_' + str(i))
trials.append(trial)
for trial in trials:
oracle.update_trial(trial.trial_id,
{'score': evaluate(trial.hyperparameters)})
for trial in trials:
oracle.end_trial(trial.trial_id, 'COMPLETED')
atol, rtol = 1e-1, 1e-1
best_trial = oracle.get_best_trials()[0]
best_hps = best_trial.hyperparameters
# The minimum is not always found but it is always close.
assert best_trial.score < -0.8, best_hps.values
assert np.isclose(best_hps['a'], 4, atol=atol, rtol=rtol)
assert np.isclose(best_hps['b'], 1, atol=atol, rtol=rtol)
# For log-scale param, just check that the order of magnitude is correct.
log_best_c = math.log(best_hps['c'], 10)
assert log_best_c > -4 and log_best_c < -2
def test_step_respected(tmp_dir):
hps = hp_module.HyperParameters()
hps.Float('c', 0, 10, step=3)
oracle = bo_module.BayesianOptimizationOracle(objective=kt.Objective(
'score', direction='max'),
max_trials=20,
hyperparameters=hps,
num_initial_points=2)
oracle._set_project_dir(tmp_dir, 'untitled')
# Populate initial trials.
for i in range(10):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values['c'] = 3.
trial.score = i
trial.status = 'COMPLETED'
oracle.trials[trial.trial_id] = trial
trial = oracle.create_trial('tuner0')
# Check that oracle respects the `step` param.
assert trial.hyperparameters.get('c') in {0, 3, 6, 9}
def test_step_respected(tmp_dir):
hps = hp_module.HyperParameters()
hps.Float("c", 0, 10, step=3)
oracle = bo_module.BayesianOptimizationOracle(
objective=kt.Objective("score", direction="max"),
max_trials=20,
hyperparameters=hps,
num_initial_points=2,
)
oracle._set_project_dir(tmp_dir, "untitled")
# Populate initial trials.
for i in range(10):
trial = trial_module.Trial(hyperparameters=hps.copy())
trial.hyperparameters.values["c"] = 3.0
trial.score = i
trial.status = "COMPLETED"
oracle.trials[trial.trial_id] = trial
trial = oracle.create_trial("tuner0")
# Check that oracle respects the `step` param.
assert trial.hyperparameters.get("c") in {0, 3, 6, 9}