python - 使用 sklearn xgboost gridsearchcv 的多个评分指标

Question

如何使用 sklearn xgboost 运行网格搜索并获取各种指标(最好是 F1 阈值)？

请参阅下面的代码...找不到我做错了什么/不理解错误..

######################### just making up a dataset here##############
from sklearn import datasets

from sklearn.metrics import precision_score, recall_score, accuracy_score,   roc_auc_score, make_scorer
from sklearn.calibration import CalibratedClassifierCV, calibration_curve
from sklearn.model_selection import train_test_split
from sklearn.grid_search import RandomizedSearchCV

import xgboost as xgb

X, y = datasets.make_classification(n_samples=100000, n_features=20,
                                    n_informative=2, n_redundant=10,
                                    random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.99,
                                                    random_state=42)

剩下的就是一堆参数，然后是随机网格搜索......如果我将“SCORING_EVALS”更改为“roc_auc”，那么它会起作用...如果我尝试执行似乎是记录在案的方法，我会收到错误？我哪里错了？

此外，我如何确保这些指标在 F1 阈值下报告!？

params = {
    'min_child_weight': [0.5, 1.0, 3.0, 5.0, 7.0, 10.0],
    'gamma': [0, 0.25, 0.5, 1.0],
    'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0],
    "max_depth": [2,4,6,10],
    "learning_rate": [0.05,0.1, 0.2, 0.3,0.4],
    "colsample_bytree":[1, .8, .5],
    "subsample": [0.8],
    'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0],
            'n_estimators': [50]
}


folds = 5
max_models = 5

scoring_evals = {'AUC': 'roc_auc', 'Accuracy': make_scorer(accuracy_score), 'Precision': make_scorer(precision_score),'Recall': make_scorer(recall_score)}


xgb_algo = xgb.XGBClassifier()
random_search = RandomizedSearchCV(xgb_algo,
                                   param_distributions=params, n_iter=max_models, 
                                   scoring= scoring_evals, n_jobs=4, cv=5, verbose=False, random_state=2018 )

random_search.fit(X_train, y_train)

我的错误是:

ValueError: scoring value should either be a callable, string or None. {'AUC': 'roc_auc', 'Accuracy': make_scorer(accuracy_score), 'Precision': make_scorer(precision_score), 'Recall': make_scorer(recall_score)} was passed

Answer 1

首先检查您正在使用的 scikit-learn 版本。如果它是 v0.19 ，那么您正在使用已弃用的模块。

你正在这样做:

from sklearn.grid_search import RandomizedSearchCV

您一定收到过如下警告:

DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. ... ... ...

grid_search 模块中的类已过时且已弃用，并且不包含您正在使用的多指标功能。

注意该警告并执行以下操作:

from sklearn.model_selection import RandomizedSearchCV

...
...
...

random_search = RandomizedSearchCV(xgb_algo,
                               param_distributions=params,  
                               n_iter=max_models, 
                               scoring= scoring_evals, n_jobs=4, cv=5,  
                               verbose=False, random_state=2018, refit=False )

现在仔细查看 refit 参数。在多指标设置中，您需要对其进行设置，以便最终模型能够与其拟合，因为模型的最佳超参数将仅根据单个指标来决定。

如果您不想要最终模型并且只想要模型在数据和不同参数上的性能，则可以将其设置为False，或者将其设置为任何key 你的评分字典中有。