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我编写了以下自定义评估函数以与 xgboost 一起使用，以优化 F1。不幸的是，它在使用 xgboost 运行时会返回异常。

评价函数如下：

def F1_eval(preds, labels):

    t = np.arange(0, 1, 0.005)
    f = np.repeat(0, 200)
    Results = np.vstack([t, f]).T

    P = sum(labels == 1)

    for i in range(200):
        m = (preds >= Results[i, 0])
        TP = sum(labels[m] == 1)
        FP = sum(labels[m] == 0)

        if (FP + TP) > 0:
            Precision = TP/(FP + TP)

        Recall = TP/P

        if (Precision + Recall >0) :
            F1 = 2 * Precision * Recall / (Precision + Recall)                
        else:                
            F1 = 0

        Results[i, 1] = F1

    return(max(Results[:, 1]))

下面我提供了一个可重现的示例以及错误消息：

    from sklearn import datasets

    Wine = datasets.load_wine()

    X_wine = Wine.data
    y_wine = Wine.target

    y_wine[y_wine == 2] = 1

    X_wine_train, X_wine_test, y_wine_train, y_wine_test = train_test_split(X_wine, y_wine, test_size = 0.2)

    clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', \
                      booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, \
                      subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)

    clf_wine.fit(X_wine_train, y_wine_train,\
    eval_set=[(X_wine_train, y_wine_train), (X_wine_test, y_wine_test)], eval_metric=F1_eval, early_stopping_rounds=10, verbose=True)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-453-452852658dd8> in <module>()
     12 clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic',                   booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0,                   subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)
     13 
---> 14 clf_wine.fit(X_wine_train, y_wine_train,eval_set=[(X_wine_train, y_wine_train), (X_wine_test, y_wine_test)], eval_metric=F1_eval, early_stopping_rounds=10, verbose=True)
     15 

C:\ProgramData\Anaconda3\lib\site-packages\xgboost\sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set)
    519                               early_stopping_rounds=early_stopping_rounds,
    520                               evals_result=evals_result, obj=obj, feval=feval,
--> 521                               verbose_eval=verbose, xgb_model=None)
    522 
    523         self.objective = xgb_options["objective"]

C:\ProgramData\Anaconda3\lib\site-packages\xgboost\training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)
    202                            evals=evals,
    203                            obj=obj, feval=feval,
--> 204                            xgb_model=xgb_model, callbacks=callbacks)
    205 
    206 

C:\ProgramData\Anaconda3\lib\site-packages\xgboost\training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
     82         # check evaluation result.
     83         if len(evals) != 0:
---> 84             bst_eval_set = bst.eval_set(evals, i, feval)
     85             if isinstance(bst_eval_set, STRING_TYPES):
     86                 msg = bst_eval_set

C:\ProgramData\Anaconda3\lib\site-packages\xgboost\core.py in eval_set(self, evals, iteration, feval)
    957         if feval is not None:
    958             for dmat, evname in evals:
--> 959                 feval_ret = feval(self.predict(dmat), dmat)
    960                 if isinstance(feval_ret, list):
    961                     for name, val in feval_ret:

<ipython-input-383-dfb8d5181b18> in F1_eval(preds, labels)
     11 
     12 
---> 13         P = sum(labels == 1)
     14 
     15 

TypeError: 'bool' object is not iterable

我不明白为什么该功能不起作用。我已经按照这里的例子：https ://github.com/dmlc/xgboost/blob/master/demo/guide-python/custom_objective.py

我想了解我错在哪里。

4

1 回答 1

11

这样做时sum(labels == 1)，Python 将标签 == 1 评估为一个Boolean对象，因此您得到TypeError: 'bool' object is not iterable

该函数sum需要一个可迭代对象，如列表。这是您的错误的示例：

In[32]: sum(True)
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-32-6eb8f80b7f2e>", line 1, in <module>
    sum(True)
TypeError: 'bool' object is not iterable

如果你想使用 scikit-learn 的 f1_score，你可以实现以下总结：

from sklearn.metrics import f1_score
import numpy as np

def f1_eval(y_pred, dtrain):
    y_true = dtrain.get_label()
    err = 1-f1_score(y_true, np.round(y_pred))
    return 'f1_err', err

总结的参数是list（预测的）和DMatrix，它返回一个字符串，浮点数

# Setting your classifier
clf_wine = xgb.XGBClassifier(max_depth=6, learning_rate=0.1,silent=False, objective='binary:logistic', \
                      booster='gbtree', n_jobs=8, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, \
                      subsample=0.8, colsample_bytree=0.8, colsample_bylevel=1, reg_alpha=0, reg_lambda=1)

# When you fit, add eval_metric=f1_eval
# Please don't forget to insert all the .fit arguments required
clf_wine.fit(eval_metric=f1_eval)

在这里您可以看到如何实现自定义目标函数和自定义评估指标的示例

包含以下代码的示例：

# user defined evaluation function, return a pair metric_name, result
# NOTE: when you do customized loss function, the default prediction value is margin
# this may make builtin evaluation metric not function properly
# for example, we are doing logistic loss, the prediction is score before logistic transformation
# the builtin evaluation error assumes input is after logistic transformation
# Take this in mind when you use the customization, and maybe you need write customized evaluation function
def evalerror(preds, dtrain):
    labels = dtrain.get_label()
    # return a pair metric_name, result
    # since preds are margin(before logistic transformation, cutoff at 0)
    return 'error', float(sum(labels != (preds > 0.0))) / len(labels)

它指定评估函数作为参数（预测，dtrain） dtrain 是类型DMatrix并返回一个字符串，浮点数是度量和错误的名称。

添加工作 python 代码示例

import numpy as np

def _F1_eval(preds, labels):
    t = np.arange(0, 1, 0.005)
    f = np.repeat(0, 200)
    results = np.vstack([t, f]).T
    # assuming labels only containing 0's and 1's
    n_pos_examples = sum(labels)
    if n_pos_examples == 0:
        raise ValueError("labels not containing positive examples")

    for i in range(200):
        pred_indexes = (preds >= results[i, 0])
        TP = sum(labels[pred_indexes])
        FP = len(labels[pred_indexes]) - TP
        precision = 0
        recall = TP / n_pos_examples

        if (FP + TP) > 0:
            precision = TP / (FP + TP)

        if (precision + recall > 0):
            F1 = 2 * precision * recall / (precision + recall)
        else:
            F1 = 0
        results[i, 1] = F1
    return (max(results[:, 1]))

if __name__ == '__main__':
    labels = np.random.binomial(1, 0.75, 100)
    preds = np.random.random_sample(100)
    print(_F1_eval(preds, labels))

如果你想实现 _F1_eval 专门为 xgboost 评估方法工作，请添加：

def F1_eval(preds, dtrain):
    res = _F1_eval(preds, dtrain.get_label())
    return 'f1_err', 1-res
于 2018-07-30T06:55:30.657 回答