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我正在使用 Optuna 来优化一些目标函数。我想创建“包装”标准 Optuna 代码的自定义类。

例如，这是我的课（它仍在进行中！）：

class Optimizer(object):
    
    def __init__(self, param_dict, model, train_x, valid_x, train_y, valid_y):
        self.model = model
        self.param_dict = param_dict
        self.train_x, self.valid_x, self.train_y, self.valid_y = train_x, valid_x, train_y, valid_y
        
    def optimization_function(self, trial):
        self.dtrain = lgb.Dataset(self.train_x, label=self.train_y)
        gbm = lgb.train(param, dtrain)
        
        preds = gbm.predict(self.valid_x)
        pred_labels = np.rint(preds)
        accuracy = sklearn.metrics.accuracy_score(self.valid_y, pred_labels)
        return accuracy
    
    
    def optimize(self, direction, n_trials):
        study = optuna.create_study(direction = direction)
        study.optimize(self.optimization_function, n_trials = n_trials)    
        return study.best_trial

我试图在这个类中包装 optuna 优化的所有“逻辑”，而不是每次都编写如下代码（来自文档）：

import optuna


class Objective(object):
    def __init__(self, min_x, max_x):
        # Hold this implementation specific arguments as the fields of the class.
        self.min_x = min_x
        self.max_x = max_x

    def __call__(self, trial):
        # Calculate an objective value by using the extra arguments.
        x = trial.suggest_float("x", self.min_x, self.max_x)
        return (x - 2) ** 2


# Execute an optimization by using an `Objective` instance.
study = optuna.create_study()
study.optimize(Objective(-100, 100), n_trials=100)

我想让我的代码“模块化”并将所有内容合并到一个类中。我的最终目标是根据函数中给定的输入模型设置优化函数的不同“模板” __init__。

所以，回到主要问题，我想从param字典之外传递。基本上我希望能够从我的班级之外声明它并在__init__函数中传递我的字典。

但是，Optuna 代码中常用的范围和分布取决于trial对象，因此我无法执行以下操作：

my_dict = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'verbosity': -1,
    'boosting_type': 'gbdt',
     # HERE I HAVE A DEPENDENCY FROM trial.suggest_loguniform, I can't declare the dictionary outside the objective function
    'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
    'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
    'num_leaves': trial.suggest_int('num_leaves', 2, 256),
    'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0),
    'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0),
    'bagging_freq': trial.suggest_int('bagging_freq', 1, 7),
    'min_child_samples': trial.suggest_int('min_child_samples', 5, 100),
} 
my_optimizer = Optimizer(my_dict, ..., ..., ..., ......)
best_result = my_optimizer.optimize('maximize', 100)

是否有任何解决方法或解决方案可以通过这本字典？

4

1 回答 1

0

怎么样，创建一个详细的 dict 传递给类，然后将其重构为试用建议类型。

代码

class Optimizer(object):    
    def __init__(self, param_dict):
        self.param_dic = param_dict

        self.objective = self.param_dic.get('objective', None)
        self.metric = self.param_dic.get('metric', None)

    def optimization_function(self, trial):
        suggested_param = {}  # param storage

        # int
        int_param = self.param_dic['param'].get('int', None)
        if int_param is not None:     
            for k, v in int_param.items():
                suggested = trial.suggest_int(k, v['low'], v['high'])
                suggested_param.update({k: suggested})

        # log
        loguniform_param = self.param_dic['param'].get('loguniform', None)
        if loguniform_param is not None:
            for k, v in loguniform_param.items():
                suggested = trial.suggest_loguniform(k, v['low'], v['high'])
                suggested_param.update({k: suggested})

        a = suggested_param.get('a', None)
        b = suggested_param.get('b', None)
        c = suggested_param.get('c', None)

        return a + b + 1.5*c


    def optimize(self, direction, n_trials):
        study = optuna.create_study(direction = direction)
        study.optimize(self.optimization_function, n_trials = n_trials)
        return study.best_trial


my_dict = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'param': {
        'int': {
            'a': {
                'low': 0,
                'high': 20
            },
            'b': {
                'low': 0,
                'high': 10
            }
        },
        'loguniform': {
            'c': {
                'low': 1e-8,
                'high': 10.0
            }
        }
    }
}

my_optimizer = Optimizer(my_dict)
best_result = my_optimizer.optimize('maximize', 100)
print(f'best param: {best_result.params}')
print(f'best value: {best_result.values}')
于 2021-10-23T10:48:37.207 回答