Experiment

This time

x^2+y^2+z^2

Let's optimize the minimization problem.

Definition of objective function

First, define the objective function.

#Set objective function(This time x^2+y^2+z^2)
def objective(trial):
    #Set parameters to optimize
    param = {
        'x': trial.suggest_int('x', -100, 100),
        'y': trial.suggest_int('y', -100, 100),
        'z': trial.suggest_int('z', -100, 100)
    }
    #Returns the evaluation value(It is designed to be minimized by default)
    return param['x'] ** 2 + param['y'] ** 2 + param['z'] ** 2

Optimization execution

First, create a study object and then perform optimization. You can set the number of searches with n_trials, which is an argument of optimze ().

#study object creation
study = optuna.create_study()
#Optimization execution
study.optimize(objective, n_trials=500)

When executed, the following display will be displayed. (Excerpt)

[I 2019-12-01 23:01:21,564] Finished trial#381 resulted in value: 121.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:21,705] Finished trial#382 resulted in value: 56.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:21,866] Finished trial#383 resulted in value: 88.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:22,012] Finished trial#384 resulted in value: 104.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:22,170] Finished trial#385 resulted in value: 426.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:22,361] Finished trial#386 resulted in value: 5249.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:22,523] Finished trial#387 resulted in value: 165.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.
[I 2019-12-01 23:01:22,684] Finished trial#388 resulted in value: 84.0. Current best value is 4.0 with parameters: {'x': 0, 'y': 0, 'z': 2}.

If you want to see the optimized parameters, add the following:

print(study.best_params)

If you want to check the optimized objective function value, add the following.

print(study.best_value)

Also, if you want to see each trial, you can get the information from study.trials. You can display the number of trials, parameters, and objective function values with the following code.

for i in study.trials:
    print(i.number, i.params, i.value)

code

I will put the code used this time.

# -*- coding: utf-8 -*-
import optuna
import matplotlib.pyplot as plt

#Set objective function(This time x^2+y^2+z^2)
def objective(trial):
    #Set parameters to optimize
    param = {
        'x': trial.suggest_int('x', -100, 100),
        'y': trial.suggest_int('y', -100, 100),
        'z': trial.suggest_int('z', -100, 100)
    }
    #Returns the evaluation value(It is designed to be minimized by default)
    return param['x'] ** 2 + param['y'] ** 2 + param['z'] ** 2

if __name__ == '__main__':
    #study object creation
    study = optuna.create_study()
    #Optimization execution
    study.optimize(objective, n_trials=500)

    epoches = []    #For storing the number of trials
    values = []    # best_For storing value
    best = 100000    #Store the maximum value appropriately
    #do the best update
    for i in study.trials:
        if best > i.value:
            best = i.value
        epoches.append(i.number+1)
        values.append(best)
    
    #Graph settings, etc.
    plt.plot(epoches, values, color="red")
    plt.title("optuna")
    plt.xlabel("trial")
    plt.ylabel("value")
    plt.show()

result

The figure of the result of this experiment is as follows. Since the value of best_value was 3.0, the true optimal solution was not reached, but it was confirmed that it was converging at an early stage.