def test_get_new_suggestions(self):
gridSearchAlgorithm = GridSearchAlgorithm()
new_trials = gridSearchAlgorithm.get_new_suggestions(
self.study.id, self.trials, 1)
# Assert getting two trials
self.assertEqual(len(new_trials), 1)
def test_get_four_new_suggestions(self):
gridSearchAlgorithm = GridSearchAlgorithm()
new_trials = gridSearchAlgorithm.get_new_suggestions(
self.study.id, self.trials, 4)
# Assert getting two trials
self.assertEqual(len(new_trials), 4)
# 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.assertEqual(new_parameter_values_json["hidden1"], 40)
new_trial = new_trials[1]
new_parameter_values = new_trial.parameter_values
new_parameter_values_json = json.loads(new_parameter_values)
self.assertEqual(new_parameter_values_json["hidden1"], 160)
new_trial = new_trials[2]
new_parameter_values = new_trial.parameter_values
new_parameter_values_json = json.loads(new_parameter_values)
self.assertEqual(new_parameter_values_json["hidden1"], 280)
new_trial = new_trials[3]
new_parameter_values = new_trial.parameter_values
new_parameter_values_json = json.loads(new_parameter_values)
self.assertEqual(new_parameter_values_json["hidden1"], 400)
def test_get_two_new_suggestions(self):
gridSearchAlgorithm = GridSearchAlgorithm()
new_trials = gridSearchAlgorithm.get_new_suggestions(
self.study.id, self.trials, 2)
# Assert getting two trials
self.assertEqual(len(new_trials), 2)
# 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.assertEqual(new_parameter_values_json["hidden2"], "8")
self.assertEqual(new_parameter_values_json["optimizer"], "sgd")
self.assertEqual(new_parameter_values_json["batch_normalization"], "true")
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()
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 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 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): gridSearchAlgorithm = GridSearchAlgorithm() self.assertEqual(gridSearchAlgorithm.__class__, GridSearchAlgorithm)
