python-3.6 - 用于Xgboost的基于F1的自定义评估功能-Python API

Question

为了优化F1，我编写了以下与xgboost结合使用的自定义评估函数。不幸的是，使用xgboost运行时，它返回异常。

评估功能如下：

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，我提供了一个可复制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无法正常工作。我在这里遵循了以下示例： https://github.com/dmlc/xgboost/blob/master/demo/guide-python/custom_objective.py

我想了解我在哪里犯错。

Answer 1

在执行sum(labels == 1)时，Python将标签== 1评估为Boolean对象，因此得到TypeError: 'bool' object is not iterable

函数sum需要一个可迭代的对象，例如列表。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

如果要使用scikit-learn的f1_score，可以实现以下包装：

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（预测的） list和 DMatrix，它返回一个字符串float

# 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)

Here您可以看到有关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)

它指定一个评估函数作为参数（预测，dtrain）获取dtrain的类型为 DMatrix，并返回一个字符串，即float，它是度量标准和错误的名称。



添加有效的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))

如果要实现_F1_eval以专门用于xgboost评估方法，请添加以下内容：

def F1_eval(preds, dtrain):
    res = _F1_eval(preds, dtrain.get_label())
    return 'f1_err', 1-res