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return multiple trials per suggestion for bayesian optimization #7

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9c36930
return multi trial per suggestion for bayesian optimization
Apr 9, 2018
0fb3344
evite iterate over bound_dict
Apr 10, 2018
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126 changes: 68 additions & 58 deletions advisor_server/suggestion/algorithm/bayesian_optimization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,6 @@ def get_random_value(self, min_value, max_value):
return random.uniform(min_value, max_value)

def get_new_suggestions(self, study_id, trials, number=1):
# TODO: Only support retuning one trial

study = Study.objects.get(id=study_id)
completed_trials = Trial.objects.filter(
Expand All @@ -136,28 +135,29 @@ def get_new_suggestions(self, study_id, trials, number=1):

random_init_trials = study_configuration_json.get("randomInitTrials", 3)

# a list: [{}, {}, ...]
params = study_configuration_json["params"]

# Use random search if it has less dataset
if len(completed_trials) < random_init_trials:
randomSearchAlgorithm = RandomSearchAlgorithm()
return_trials = randomSearchAlgorithm.get_new_suggestions(
study_id, trials)
study_id, trials, number=random_init_trials - len(completed_trials))
return return_trials

else:
return_trial = Trial.create(study.id, "BayesianOptimizationTrial")
return_trials = []
acquisition_fucntion_kappa = 5

# Example: {'x': (-4, 4), 'y': (-3, 3)}
bound_dict = {}
# iterate over dict can be dangerous, so abandon bound_dict
bounds = []

for param in params:

if param["type"] == "DOUBLE" or param["type"] == "INTEGER":
min_value = param["minValue"]
max_value = param["maxValue"]
bound_dict[param["parameterName"]] = (min_value, max_value)
bounds.append((min_value, max_value))
elif param["type"] == "DISCRETE":
feasible_points_string = param["feasiblePoints"]
feasible_points = [
Expand All @@ -167,7 +167,7 @@ def get_new_suggestions(self, study_id, trials, number=1):
feasible_points.sort()
min_value = feasible_points[0]
max_value = feasible_points[-1]
bound_dict[param["parameterName"]] = (min_value, max_value)
bounds.append((min_value, max_value))
elif param["type"] == "CATEGORICAL":
feasible_points_string = param["feasiblePoints"]
feasible_points = [
Expand All @@ -176,11 +176,8 @@ def get_new_suggestions(self, study_id, trials, number=1):
for feasible_point in feasible_points:
parameter_name = "{}_{}".format(param["parameterName"],
feasible_point)
bound_dict[parameter_name] = (0, 1)
bounds.append((0, 1))

bounds = []
for key in bound_dict.keys():
bounds.append(bound_dict[key])
bounds = np.asarray(bounds)

gp = GaussianProcessRegressor(
Expand Down Expand Up @@ -234,74 +231,87 @@ def get_new_suggestions(self, study_id, trials, number=1):
gp.fit(train_features, train_labels)

# Example: [[-3.66909025, -0.84486644], [-1.93270006, -0.95367483], [1.36095631, 0.61358525], ...], shape is [100000, 2]
# shape: [10000, n_params]
x_tries = np.random.uniform(
bounds[:, 0], bounds[:, 1], size=(100000, bounds.shape[0]))

mean, std = gp.predict(x_tries, return_std=True)
# Confidence bound criteria
# shape: [100000]
acquisition_fucntion_values = mean + acquisition_fucntion_kappa * std
x_max = x_tries[acquisition_fucntion_values.argmax()]
# Topn max ac function values
# shape: [number]
topn_indices = acquisition_fucntion_values.argsort()[-number:][::-1]
# shape: [number, n_params]
x_max_topn = x_tries[topn_indices]
max_acquision_fucntion_value = acquisition_fucntion_values.max()

# Example: [3993.864683994805, 44.15441513231316]
x_max = np.clip(x_max, bounds[:, 0], bounds[:, 1])
print("Current max acquision function choose: {}".format(x_max))
# Example: [[3993.864683994805, 44.15441513231316], ...]
# shape: [number, n_params]
x_max_topn = np.clip(x_max_topn, bounds[:, 0], bounds[:, 1])
print("Current topn max acquision function choose: {}".format(x_max_topn))

# Example: {"hidden2": 3993.864683994805, "hidden1": 44.15441513231316}
suggested_parameter_values_json = {}
# Example: [{"hidden2": 3993.864683994805, "hidden1": 44.15441513231316}, ...]
suggested_parameter_values_jsons = []

index = 0
"""
# Example: [0.1, 0.5, 0.3, 0.9]
# Example: {"learning_rate": (0.01, 0.5), "hidden1": (40, 400), "optimizer_sgd": (0, 1), "optimizer_ftrl": (0, 1)}
for key in bound_dict.keys():
parameter_values_json[key] = x_max[index]
index += 1
"""
for x_max in x_max_topn:

for param in params:
return_trial = Trial.create(study.id, "BayesianOptimizationTrial")
suggested_parameter_values_json = {}
index = 0

if param["type"] == "DOUBLE" or param["type"] == "DISCRETE":
suggested_parameter_values_json[param["parameterName"]] = x_max[
index]
index += 1
elif param["type"] == "INTEGER":
suggested_parameter_values_json[param["parameterName"]] = int(
round(x_max[index]))
index += 1
elif param["type"] == "DISCRETE":
feasible_points_string = param["feasiblePoints"]
feasible_points = [
float(value.strip())
for value in feasible_points_string.split(",")
]
feasible_points.sort()
selected_value = self.find_closest_value_in_list(
feasible_points, x_max[index])
suggested_parameter_values_json[param[
"parameterName"]] = selected_value
index += 1
elif param["type"] == "CATEGORICAL":
feasible_points_string = param["feasiblePoints"]
# Example: ["sgd", "adagrad", "adam", "ftrl"]
feasible_points = [
value.strip() for value in feasible_points_string.split(",")
]
for param in params:

# 记录这4个值中数最大的，然后取到对应的字符串
current_max = x_max[index]
suggested_parameter_value = feasible_points[0]
for feasible_point in feasible_points:
if x_max[index] > current_max:
current_max = x_max[index]
suggested_parameter_value = feasible_point
if param["type"] == "DOUBLE" or param["type"] == "DISCRETE":
suggested_parameter_values_json[param["parameterName"]] = x_max[
index]
index += 1
elif param["type"] == "INTEGER":
suggested_parameter_values_json[param["parameterName"]] = int(
round(x_max[index]))
index += 1
elif param["type"] == "DISCRETE":
feasible_points_string = param["feasiblePoints"]
feasible_points = [
float(value.strip())
for value in feasible_points_string.split(",")
]
feasible_points.sort()
selected_value = self.find_closest_value_in_list(
feasible_points, x_max[index])
suggested_parameter_values_json[param[
"parameterName"]] = selected_value
index += 1
elif param["type"] == "CATEGORICAL":
feasible_points_string = param["feasiblePoints"]
# Example: ["sgd", "adagrad", "adam", "ftrl"]
feasible_points = [
value.strip() for value in feasible_points_string.split(",")
]

# 记录这4个值中数最大的，然后取到对应的字符串
current_max = x_max[index]
suggested_parameter_value = feasible_points[0]
for feasible_point in feasible_points:
if x_max[index] > current_max:
current_max = x_max[index]
suggested_parameter_value = feasible_point
index += 1

suggested_parameter_values_json[param[
"parameterName"]] = suggested_parameter_value
suggested_parameter_values_json[param[
"parameterName"]] = suggested_parameter_value

return_trial.parameter_values = json.dumps(
suggested_parameter_values_json)
return_trial.save()
suggested_parameter_values_jsons.append(suggested_parameter_values_json)
return_trial.parameter_values = json.dumps(
suggested_parameter_values_json)
return_trial.save()
return_trials.append(return_trial)

return [return_trial]
return return_trials