我可以采用 optuna 函数的最佳参数和最佳模型并将该模型直接应用到我的笔记本中吗？

Question

我建立了 optuna 的一个功能来为我的数据找出 GBM 和 xgboost 的最佳模型，但我想知道是否可以采用最佳模型并将其直接应用到我的笔记本中（提取最佳模型作为对象以便稍后重用） 这是我的目标函数：

import lightgbm as lgb 
import optuna
import sklearn.metrics
from xgboost import XGBRegressor
from optuna.integration import XGBoostPruningCallback
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
best_booster = None
gbm = None
def objective(trial,random_state=22,n_jobs=1,early_stopping_rounds=50):
    
    regrosser_name = trial.suggest_categorical("regressor", ["XGBoost", "lightgbm"])
    train_x, valid_x, train_y, valid_y = train_test_split(X_train, y_train, test_size=0.25)
    dtrain = lgb.Dataset(train_x, label=train_y)
    # Step 2. Setup values for the hyperparameters:
    if regrosser_name == 'XGBoost':
        params = {
        "verbosity": 0,  # 0 (silent) - 3 (debug)
        "objective": "reg:squarederror",
        "n_estimators": 10000,
        "max_depth": trial.suggest_int("max_depth", 4, 12),
        "learning_rate": trial.suggest_loguniform("learning_rate", 0.005, 0.05),
        "colsample_bytree": trial.suggest_loguniform("colsample_bytree", 0.2, 0.6),
        "subsample": trial.suggest_loguniform("subsample", 0.4, 0.8),
        "alpha": trial.suggest_loguniform("alpha", 0.01, 10.0),
        "lambda": trial.suggest_loguniform("lambda", 1e-8, 10.0),
        "gamma": trial.suggest_loguniform("lambda", 1e-8, 10.0),
        "min_child_weight": trial.suggest_loguniform("min_child_weight", 10, 1000),
        "seed": random_state,
        "n_jobs": n_jobs,
        }
        model = XGBRegressor(**params)
        model.fit(train_x, train_y)
        y_pred = model.predict(X_val)
        accuracy_rf = sklearn.metrics.mean_absolute_error(valid_y, y_pred)
        return accuracy_rf
    
        print(rf_max_depth)
        print(rf_n_estimators)
        
    else:
        param = {
        "objective": "binary",
        "metric": "binary_logloss",
        "verbosity": -1,
        "boosting_type": "gbdt",
        "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True),
        "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True),
        "num_leaves": trial.suggest_int("num_leaves", 2, 256),
        "feature_fraction": trial.suggest_float("feature_fraction", 0.4, 1.0),
        "bagging_fraction": trial.suggest_float("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),
        }
        gbm = lgb.train(param, dtrain)
        preds_gbm = gbm.predict(valid_x)
        pred_labels_gbm = np.rint(preds_gbm)
        accuracy_gbm = sklearn.metrics.mean_absolute_error(valid_y, pred_labels_gbm)
        return accuracy_gbm

这是我尝试解决这个问题的方法：

def callback(study, trial):
    global best_booster
    if study.best_trial == trial:
        best_booster = gbm
if __name__ == "__main__":
    study = optuna.create_study(direction="maximize")
    study.optimize(objective, n_trials=100, callbacks=[callback])

我认为它是关于导入一些东西，如果我的 optuna 函数有任何提示，请说明

Answer 1

如果我正确理解您的问题，那么是的，这就是模型的目的。

就像将保存的模型带到笔记本上一样，馈送与您过去训练的结构相同的结构的数据，并且应该达到其目的。或在管道中使用它。

甚至可以使用与NP阵列相同结构的1行。例如，我的模型预测是否应批准贷款。

例如，银行客户希望贷款并提交他的信息。银行官员在系统中输入此信息。该系统将这些信息转换为单个NP阵列，其结构与用于训练模型的数据集相同。

然后，系统将使用该模型来预测是否应批准贷款。

我将Optuna XGB模型保存为JSON，例如

My_model.get_booster（）。save_model（f'{savepath} my_model.json'）