def test_complete_and_get_new_suggestions(self):
tpeAlgorithm = TpeAlgorithm()
new_trials = tpeAlgorithm.get_new_suggestions(self.study.id, [], 1)
new_trials[0].status = "Completed"
new_trials[0].objective_value = 0.6
new_trials[0].save()
new_trials = tpeAlgorithm.get_new_suggestions(self.study.id, [], 1)
new_trials[0].status = "Completed"
new_trials[0].objective_value = 0.7
new_trials[0].save()
new_trials = tpeAlgorithm.get_new_suggestions(self.study.id, [], 3)
# Assert getting two trials
self.assertEqual(len(new_trials), 3)
# Assert getting the trials
new_trial = new_trials[0]
new_parameter_values = new_trial.parameter_values
new_parameter_values_json = json.loads(new_parameter_values)
self.assertTrue(
0.99 >= new_parameter_values_json["l1_normalization"] >= 0.01)
self.assertTrue(
0.5 >= new_parameter_values_json["learning_rate"] >= 0.01)
self.assertTrue(
new_parameter_values_json["hidden2"] in [8, 16, 32, 64])
self.assertTrue(new_parameter_values_json["optimizer"] in
["sgd", "adagrad", "adam", "ftrl"])
def test_get_multiple_new_suggestions(self):
tpeAlgorithm = TpeAlgorithm()
# Assert getting one trial
new_trials = tpeAlgorithm.get_new_suggestions(self.study.id, number=1)
self.assertEqual(len(new_trials), 1)
# Assert getting multiple trials
new_trials = tpeAlgorithm.get_new_suggestions(self.study.id, number=10)
self.assertEqual(len(new_trials), 10)
def test_get_new_suggestions(self):
tpeAlgorithm = TpeAlgorithm()
new_trials = tpeAlgorithm.get_new_suggestions(
self.study.id, number=1)
new_trial = new_trials[0]
new_parameter_values_json = json.loads(new_trial.parameter_values)
#self.assertTrue(0.99 >= new_parameter_values_json["l1_normalization"] >= 0.01)
#self.assertTrue(0.5 >= new_parameter_values_json["learning_rate"] >= 0.01)
self.assertTrue(new_parameter_values_json["l1_normalization"] >= 0.01)
self.assertTrue(new_parameter_values_json["learning_rate"] >= 0.01)
def test_get_new_suggestions(self):
tpeAlgorithm = TpeAlgorithm()
new_trials = tpeAlgorithm.get_new_suggestions(self.study.id, number=1)
new_trial = new_trials[0]
new_parameter_values_json = json.loads(new_trial.parameter_values)
self.assertTrue(
0.99 >= new_parameter_values_json["l1_normalization"] >= 0.01)
self.assertTrue(
0.5 >= new_parameter_values_json["learning_rate"] >= 0.01)
self.assertTrue(
new_parameter_values_json["hidden2"] in [8, 16, 32, 64])
self.assertTrue(new_parameter_values_json["optimizer"] in
["sgd", "adagrad", "adam", "ftrl"])
def v1_study_suggestions(request, study_name):
# Create the trial
if request.method == "POST":
data = json.loads(request.body)
trials_number = 1
# TODO: Use the trial name to create trial object
trial_name = "Trial"
if "trials_number" in data:
trials_number = data["trials_number"]
if "trial_name" in data:
trial_name = data["trial_name"]
study = Study.objects.get(name=study_name)
trials = Trial.objects.filter(study_name=study_name)
trials = [trial for trial in trials]
if study.algorithm == "RandomSearch":
algorithm = RandomSearchAlgorithm()
elif study.algorithm == "GridSearch":
algorithm = GridSearchAlgorithm()
elif study.algorithm == "BayesianOptimization":
algorithm = BayesianOptimization()
elif study.algorithm == "TPE":
algorithm = TpeAlgorithm()
elif study.algorithm == "HyperoptRandomSearch":
algorithm = HyperoptRandomSearchAlgorithm
elif study.algorithm == "SimulateAnneal":
algorithm = SimulateAnnealAlgorithm()
elif study.algorithm == "QuasiRandomSearch":
algorithm = QuasiRandomSearchAlgorithm()
elif study.algorithm == "ChocolateRandomSearch":
algorithm = ChocolateRandomSearchAlgorithm()
elif study.algorithm == "ChocolateGridSearch":
algorithm = ChocolateGridSearchAlgorithm()
elif study.algorithm == "ChocolateBayes":
algorithm = ChocolateBayesAlgorithm()
elif study.algorithm == "CMAES":
algorithm = CmaesAlgorithm()
elif study.algorithm == "MOCMAES":
algorithm = MocmaesAlgorithm()
elif study.algorithm == "SkoptBayesianOptimization":
algorithm = SkoptBayesianOptimization()
else:
return JsonResponse(
{"error": "Unknown algorithm: {}".format(study.algorithm)})
new_trials = algorithm.get_new_suggestions(study.name, trials,
trials_number)
return JsonResponse(
{"data": [trial.to_json() for trial in new_trials]})
else:
return JsonResponse({"error": "Unsupported http method"})
def v1_study_suggestions(request, study_id):
# Create the trial
if request.method == "POST":
data = json.loads(request.body)
trials_number = 1
trial_name = "Trial"
if "trials_number" in data:
trials_number = data["trials_number"]
if "trial_name" in data:
trial_name = data["trial_name"]
study = Study.objects.get(id=study_id)
trials = Trial.objects.filter(study_id=study_id)
trials = [trial for trial in trials]
if study.algorithm == "RandomSearchAlgorithm":
algorithm = RandomSearchAlgorithm()
elif study.algorithm == "GridSearchAlgorithm":
algorithm = GridSearchAlgorithm()
elif study.algorithm == "BayesianOptimization":
algorithm = BayesianOptimization()
elif study.algorithm == "TpeAlgorithm":
algorithm = TpeAlgorithm()
elif study.algorithm == "SimulateAnnealAlgorithm":
algorithm = TpeAlgorithm()
else:
return JsonResponse(
{"error": "Unknown algorithm: {}".format(study.algorithm)})
new_trials = algorithm.get_new_suggestions(study.id, trials,
trials_number)
return JsonResponse(
{"data": [trial.to_json() for trial in new_trials]})
else:
return JsonResponse({"error": "Unsupported http method"})
def get_suggestions(self, study_name, trials_number=1):
study = Study.objects.get(name=study_name)
trials = Trial.objects.filter(study_name=study_name)
if study.algorithm == "RandomSearch":
algorithm = RandomSearchAlgorithm()
elif study.algorithm == "GridSearch":
algorithm = GridSearchAlgorithm()
elif study.algorithm == "BayesianOptimization":
algorithm = BayesianOptimization()
elif study.algorithm == "TPE":
algorithm = TpeAlgorithm()
elif study.algorithm == "HyperoptRandomSearch":
algorithm = HyperoptRandomSearchAlgorithm
elif study.algorithm == "SimulateAnneal":
algorithm = SimulateAnnealAlgorithm()
# elif study.algorithm == "QuasiRandomSearch":
# algorithm = QuasiRandomSearchAlgorithm()
# elif study.algorithm == "ChocolateRandomSearch":
# algorithm = ChocolateRandomSearchAlgorithm()
# elif study.algorithm == "ChocolateGridSearch":
# algorithm = ChocolateGridSearchAlgorithm()
# elif study.algorithm == "ChocolateBayes":
# algorithm = ChocolateBayesAlgorithm()
# elif study.algorithm == "CMAES":
# algorithm = CmaesAlgorithm()
# elif study.algorithm == "MOCMAES":
# algorithm = MocmaesAlgorithm()
elif study.algorithm == "SkoptBayesianOptimization":
algorithm = SkoptBayesianOptimization()
else:
raise ValueError('Error, Unknown algorithm: {}'.format(
study.algorithm))
new_trials = algorithm.get_new_suggestions(study.name, trials,
trials_number)
return new_trials
def test_init(self):
instance = TpeAlgorithm()
self.assertTrue(isinstance(instance, AbstractSuggestionAlgorithm))
self.assertEqual(instance.__class__, TpeAlgorithm)