当与 SKlearn RandomseachCV 一起使用时，XGboost 给出 objective 函数错误

Question

我正在尝试运行使用 sklearn 运行dom 搜索 CV 函数对 XGBoost 的 select 超参数进行回归任务的运行dom 搜索。这是我的代码：

#search space
params_xgboost = {
 "learning_rate"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30],
 "max_depth"        : [ 3, 4, 5, 6, 8, 10, 12, 15],
 "min_child_weight" : [ 1, 3, 5, 7 ],
 "gamma"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],
 "colsample_bytree" : [ 0.3, 0.4, 0.5 , 0.7],
 'n_estimators'     : [5, 10, 15, 20, 25, 30, 35],
 'objective': 'reg:squarederror' 
}

model = XGBRegressor()

random_search = RandomizedSearchCV(estimator = model, 
                      param_distributions = params_xgboost, 
                      n_iter = 100, 
                      cv = 5, 
                      verbose=1, 
                      random_state=42,
                      scoring = 'neg_mean_squared_error', 
                      n_jobs = -1)

#params glare proba
random_search.fit(X_transform, Y['dgp'])

我真的很难理解为什么会出现以下错误

Unknown objective function: `u`

XGBoostError: [16:46:53] /Users/runner/miniforge3/conda-bld/xgboost_1607604592557/work/src/objective/objective.cc:26: Unknown objective function: `u`
Objective candidate: survival:aft
Objective candidate: binary:hinge
Objective candidate: multi:softmax
Objective candidate: multi:softprob
Objective candidate: rank:pairwise
Objective candidate: rank:ndcg
Objective candidate: rank:map
Objective candidate: reg:squarederror
Objective candidate: reg:squaredlogerror
Objective candidate: reg:logistic
Objective candidate: reg:pseudohubererror
Objective candidate: binary:logistic
Objective candidate: binary:logitraw
Objective candidate: reg:linear
Objective candidate: count:poisson
Objective candidate: survival:cox
Objective candidate: reg:gamma
Objective candidate: reg:tweedie

Stack trace:
  [bt] (0) 1   libxgboost.dylib                    0x00000001210ad23e dmlc::LogMessageFatal::~LogMessageFatal() + 110
  [bt] (1) 2   libxgboost.dylib                    0x00000001211a4bd7 xgboost::ObjFunction::Create(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > const&, xgboost::GenericParameter const*) + 759
  [bt] (2) 3   libxgboost.dylib                    0x0000000121168d06 xgboost::LearnerConfiguration::ConfigureObjective(xgboost::LearnerTrainParam const&, std::__1::vector<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > >, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> > > > >*) + 1926
  [bt] (3) 4   libxgboost.dylib                    0x000000012115de1f xgboost::LearnerConfiguration::Configure() + 1247
  [bt] (4) 5   libxgboost.dylib                    0x000000012115e2e7 xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr<xgboost::DMatrix>) + 119
  [bt] (5) 6   libxgboost.dylib                    0x00000001210b1e5c XGBoosterUpdateOneIter + 156
  [bt] (6) 7   libffi.7.dylib                      0x0000000107c40ead ffi_call_unix64 + 85
  [bt] (7) 8   ???                                 0x00007ffee8691a00 0x0 + 140732797622784

我运行在单独的分类任务上使用相同的代码并且使用 multi:softmax objective 它工作正常所以我不确定为什么在上述情况下我会收到此错误.

Answer 1

objective='reg:squarederror' 是默认值，因此您可以安全地忽略它：

XGBRegressor?

Init signature: XGBRegressor(objective='reg:squarederror', **kwargs)
Docstring:     
Implementation of the scikit-learn API for XGBoost regression.

如果你想明确指定它，你总是可以这样做：

XGBRegressor(objective='reg:squarederror'...)

注意以及关于 **kwargs for sklearn API (docs) 的注释：

**kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.

Answer 2

我同意@SergeyBushmanov 的观点，但要澄清这里的错误：您没有提供目标列表，只是字符串 'reg:squarederror'。但是由于字符串在 python 中是可迭代的，随机搜索将其视为 16 个要尝试的目标：'r'、'e'、'g' 等（第一个尝试了是 'u'，因为你的错误抱怨）。在它周围添加列表括号应该可以；但同样，对于您实际上对搜索不感兴趣的事情，只需在估算器中指定它们，就像@Sergey 的回答一样。

当与 SKlearn RandomseachCV 一起使用时，XGboost 给出 objective 函数错误

XGboost gives objective function error when used with SKlearn RandomseachCV

machine-learning

python-3.x

scikit-learn

xgboost