def test_default_generation_strategy(self) -> None:
"""Test that Sobol+GPEI is used if no GenerationStrategy is provided."""
ax_client = AxClient()
ax_client.create_experiment(
parameters=[ # pyre-fixme[6]: expected union that should include
{"name": "x1", "type": "range", "bounds": [-5.0, 10.0]},
{"name": "x2", "type": "range", "bounds": [0.0, 15.0]},
],
objective_name="branin",
minimize=True,
)
self.assertEqual(
[s.model for s in not_none(ax_client.generation_strategy)._steps],
[Models.SOBOL, Models.GPEI],
)
with self.assertRaisesRegex(ValueError, ".* no trials."):
ax_client.get_optimization_trace(objective_optimum=branin.fmin)
for i in range(6):
parameterization, trial_index = ax_client.get_next_trial()
x1, x2 = parameterization.get("x1"), parameterization.get("x2")
ax_client.complete_trial(
trial_index,
raw_data={
"branin": (
checked_cast(
float,
branin(checked_cast(float, x1), checked_cast(float, x2)),
),
0.0,
)
},
sample_size=i,
)
if i < 5:
with self.assertRaisesRegex(ValueError, "Could not obtain contour"):
ax_client.get_contour_plot(param_x="x1", param_y="x2")
ax_client.get_optimization_trace(objective_optimum=branin.fmin)
ax_client.get_contour_plot()
self.assertIn("x1", ax_client.get_trials_data_frame())
self.assertIn("x2", ax_client.get_trials_data_frame())
self.assertIn("branin", ax_client.get_trials_data_frame())
self.assertEqual(len(ax_client.get_trials_data_frame()), 6)
# Test that Sobol is chosen when all parameters are choice.
ax_client = AxClient()
ax_client.create_experiment(
parameters=[ # pyre-fixme[6]: expected union that should include
{"name": "x1", "type": "choice", "values": [1, 2, 3]},
{"name": "x2", "type": "choice", "values": [1, 2, 3]},
]
)
self.assertEqual(
[s.model for s in not_none(ax_client.generation_strategy)._steps],
[Models.SOBOL],
)
self.assertEqual(ax_client.get_recommended_max_parallelism(), [(-1, -1)])
self.assertTrue(ax_client.get_trials_data_frame().empty)
def test_default_generation_strategy_continuous(self, _a, _b, _c,
_d) -> None:
"""Test that Sobol+GPEI is used if no GenerationStrategy is provided."""
ax_client = AxClient()
ax_client.create_experiment(
parameters=[ # pyre-fixme[6]: expected union that should include
{
"name": "x",
"type": "range",
"bounds": [-5.0, 10.0]
},
{
"name": "y",
"type": "range",
"bounds": [0.0, 15.0]
},
],
objective_name="a",
minimize=True,
)
self.assertEqual(
[s.model for s in not_none(ax_client.generation_strategy)._steps],
[Models.SOBOL, Models.GPEI],
)
with self.assertRaisesRegex(ValueError, ".* no trials"):
ax_client.get_optimization_trace(objective_optimum=branin.fmin)
for i in range(6):
parameterization, trial_index = ax_client.get_next_trial()
x, y = parameterization.get("x"), parameterization.get("y")
ax_client.complete_trial(
trial_index,
raw_data={
"a": (
checked_cast(
float,
branin(checked_cast(float, x),
checked_cast(float, y)),
),
0.0,
)
},
sample_size=i,
)
self.assertEqual(ax_client.generation_strategy.model._model_key,
"GPEI")
ax_client.get_optimization_trace(objective_optimum=branin.fmin)
ax_client.get_contour_plot()
ax_client.get_feature_importances()
trials_df = ax_client.get_trials_data_frame()
self.assertIn("x", trials_df)
self.assertIn("y", trials_df)
self.assertIn("a", trials_df)
self.assertEqual(len(trials_df), 6)
def automatic_hyperparameter_search(drug_id,
num_trails=50,
num_splits=3,
num_epochs=50,
data_folder='../example_data/'):
data_file_name = f'{data_folder}/{drug_id}.pickle'
with open(data_file_name, 'rb') as f:
data = pickle.load(f)
if 'hyperparameter_search' in data.keys():
return
ax_client = AxClient()
ax_client.create_experiment(name=f"drug_id_{drug_id}",
parameters=[
{
"name": "lr",
"value_type": 'float',
"type": "range",
"bounds": [1e-5, 1e0],
"log_scale": True
},
{
"name":
"l2_regularization_coefficient",
"value_type": 'float',
"type": "range",
"bounds": [1e-5, 1e0],
"log_scale": True
},
{
"name": "num_layers",
"value_type": 'int',
"type": "range",
"bounds": [1, 5]
},
{
"name": "num_neurons",
"value_type": 'int',
"type": "range",
"bounds": [10, 100]
},
],
objective_name="score",
minimize=False)
for i in range(num_trails):
parameters, trial_index = ax_client.get_next_trial()
ax_client.complete_trial(trial_index=trial_index,
raw_data=evaluate(parameters, data,
num_splits, num_epochs))
best_parameters, values = ax_client.get_best_parameters()
trace = ax_client.get_optimization_trace()
data['hyperparameter_search'] = {}
data['hyperparameter_search']['score'] = values[0]['score']
data['hyperparameter_search']['neural_net_config'] = best_parameters
with open(data_file_name, 'wb') as f:
pickle.dump(data, f)
