transform=transforms.ToTensor()),batch_size=BATCHSIZE,shuffle=True,)returntrain_loader,valid_loaderdefobjective(trial):# Generate the model.model=define_model(trial).to(DEVICE)# Generate the optimizers.optimizer_name=trial.suggest_categorical("optimizer",["Adam","RMSprop","SGD"])lr=trial.suggest_...
1) Study:整个优化过程基于一个目标函数,即研究需要一个可以优化的函数。 2) Trial:优化函数的单次执行称为trial。因此,这项研究(Study)是一系列试验(trial)。 将使用 Optuna 的数据集 在本文中,我们将使用来自ASHRAE – Great Energy Predictor III的数据集,这是一项 Kaggle 竞赛,用于预测建筑物消耗的能源量。...
optuna.trial.Trial.suggest_categorical() 用于类别参数 optuna.trial.Trial.suggest_int() 用于整形参数 optuna.trial.Trial.suggest_float() 用于浮点型参数 通过可选的step与log参数,我们可以对整形或者浮点型参数进行离散化或者取对数操作。 这里的step比较好理解,对于整型就是步长,对于float就是离散化程度(分箱)...
logging.ERROR) def objective(trial): iris = load_iris() data,target = iris["data"],iris["target"] x_train, x_valid, y_train, y_valid = train_test_split(data, target) classes = list(set(target)) clf = MLPClassifier( hidden_layer_sizes=tuple( [trial.suggest_int("n_units_l{}"...
trial.report(intermediate_value, step=epoch) if trial.should_prune(): raise optuna.TrialPruned() 注意:trial.report不支持多目标优化 defobjective(trial):# Generate the model.model=define_model(trial).to(DEVICE)# Generate the optimizers.optimizer_name=trial.suggest_categorical("optimizer",["Adam",...
上面我们定义了一个objective,在它内部,模型的均方误差被作为返回值,具体使用时可以用优化指标作为返回。中间还使用trial.suggest_*设置了参数空间和随机分布。支持的方法有: suggest_categorical(name, choices):从枚举值中随机取一个值 suggest_uniform(name, low, high):随机一个小数,范围为[low, high)。
# pip install optunahub smac import optuna import optunahub from optuna.distributions import FloatDistribution def objective(trial: optuna.Trial) -> float: x = trial.suggest_float("x", -10, 10) y = trial.suggest_float("y", -10, 10) return x**2 + y**2 smac_mod = optunahub.load_...
'num_leaves': trial.suggest_int('num_leaves',2,256), 'learning_rate': trial.suggest_float('learning_rate',1e-8,1.0, log=True), 'min_data_in_leaf': trial.suggest_int('min_data_in_leaf',2,256), 'max_depth': trial.suggest_int('max_depth',2,256), ...
alpha = trial.suggest_float("alpha", 0.0, 1.0) clf = SGDClassifier(alpha=alpha) for step in range(n_train_iter): clf.partial_fit(X_train, y_train, classes=classes) if step > n_train_iter//2: intermediate_value = clf.score(X_valid, y_valid) ...
import optunafromsklearn.ensemble import RandomForestClassifierfromsklearn.model_selection import cross_val_score# 定义目标函数def objective(trial): n_estimators = trial.suggest_int('n_estimators', 10, 100) max_depth = trial.suggest_categorical('max_depth', [None, 10, 20, 30]) ...