我想获取最佳模型，以便稍后在笔记本中使用不同的测试批次进行预测。
可复现的示例（摘自 Optuna Github）：
import lightgbm as lgbimport numpy as npimport sklearn.datasetsimport sklearn.metricsfrom sklearn.model_selection import train_test_splitimport optuna# FYI: 目标函数可以接受额外的参数# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).def objective(trial): data, target = sklearn.datasets.load_breast_cancer(return_X_y=True) train_x, valid_x, train_y, valid_y = train_test_split(data, target, test_size=0.25) dtrain = lgb.Dataset(train_x, label=train_y) dvalid = lgb.Dataset(valid_x, label=valid_y) param = { "objective": "binary", "metric": "auc", "verbosity": -1, "boosting_type": "gbdt", "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), } # 添加修剪回调。 pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc") gbm = lgb.train( param, dtrain, valid_sets=[dvalid], verbose_eval=False, callbacks=[pruning_callback] ) preds = gbm.predict(valid_x) pred_labels = np.rint(preds) accuracy = sklearn.metrics.accuracy_score(valid_y, pred_labels) return accuracy我的理解是下面的研究将调整准确性。我希望以某种方式从研究中检索最佳模型（不仅仅是参数），而不是将其保存为 pickle 文件，我只是想在笔记本的其他地方使用该模型。
if __name__ == "__main__": study = optuna.create_study( pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize" ) study.optimize(objective, n_trials=100) print("Best trial:") trial = study.best_trial print(" Params: ") for key, value in trial.params.items(): print(" {}: {}".format(key, value))期望的输出是
best_model = ~model from above~new_target_pred = best_model.predict(new_data_test)metrics.accuracy_score(new_target_test, new__target_pred)回答：
我认为你可以使用 Study.optimize 的 callback 参数来保存最佳模型。在下面的代码示例中，回调函数检查给定的试验是否对应于最佳试验，并将模型保存为全局变量 best_booster。
best_booster = Nonegbm = Nonedef objective(trial): global gbm # ...def callback(study, trial): global best_booster if study.best_trial == trial: best_booster = gbmif __name__ == "__main__": study = optuna.create_study( pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize" ) study.optimize(objective, n_trials=100, callbacks=[callback])如果你将目标函数定义为类，你可以去掉全局变量。我创建了一个笔记本作为代码示例，请查看：https://colab.research.google.com/drive/1ssjXp74bJ8bCAbvXFOC4EIycBto_ONp_?usp=sharing
我想以某种方式从研究中检索最佳模型（不仅仅是参数），而不是将其保存为 pickle 文件
仅供参考，如果你可以对 booster 进行 pickle 处理，我认为你可以通过遵循 这个 FAQ 来简化代码。
