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Python:如何从opgbm研究中获取最佳模型?

optuna lightgbm machine-learning python

0

HarriS · 技术社区 · 6 年前

我想得到最好的模型,以便以后在笔记本中使用,以预测使用不同的测试批次。

可复制示例(摘自Ophub):

import lightgbm as lgb
import numpy as np
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split

import optuna


# FYI: Objective functions can take additional arguments
# (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),
    }

    # Add a callback for pruning.
    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,下面的研究将调整的准确性。我想以某种方式从研究中检索到最好的模型(不仅仅是参数),而不将其保存为pickle,我只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)

0 回复 | 直到 6 年前

1

2

Toshihiko Yanase 6 年前

我想你可以用 callback 的论点 Study.optimize 保存最佳模型。在下面的代码示例中,回调将检查给定的试用是否对应于最佳试用,并将模型保存为全局变量 best_booster .

best_booster = None
gbm = None

def objective(trial):
    global gbm
    # ...

def callback(study, trial):
    global best_booster
    if study.best_trial == trial:
        best_booster = gbm

if __name__ == "__main__":
    study = optuna.create_study(
        pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize"
    )
    study.optimize(objective, n_trials=100, callbacks=[callback])

如果将目标函数定义为类,则可以删除全局变量。我创建了一个笔记本作为代码示例。请看一下: 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),而不将其保存为pickle

仅供参考,如果你能对助推器进行pickle处理,我认为你可以通过以下方法简化代码 this FAQ .

2

avinashpancham 6 年前

简短的补充@Toshihiko Yanase的回答,因为条件 study.best_trial==trial 对我来说从来都不是真的。甚至在两个(冻结的)试验对象具有相同内容的情况下也是这样,因此这很可能是Optuna中的一个bug。将条件更改为 study.best_trial.number==trial.number 帮我解决问题。

另外,如果您不希望在Python中使用globals,那么可以使用study和trial用户属性

def objective(trial):
    gmb = ...
    trial.set_user_attr(key="best_booster", value=gbm)

def callback(study, trial):
    if study.best_trial.number == trial.number:
        study.set_user_attr(key="best_booster", value=trial.user_attrs["best_booster"])


if __name__ == "__main__":
    study = optuna.create_study(
        pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize"
    )
    study.optimize(objective, n_trials=100, callbacks=[callback])
    best_model=study.user_attrs["best_booster"]