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r - 使用 xgboost 对 Tweedie 回归建模

转载 作者:行者123 更新时间:2023-12-02 02:10:57 25 4
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我正在尝试使用 xgboost 制作花花呢模型,但收到一条晦涩的错误消息。

这是一个可重现的示例:

准备数据:

library(xgboost)
library(dplyr)

set.seed(123)
xx <- rpois(5000, 0.02)
xx[xx>0] <- rgamma(sum(xx>0), 50)

yy <- matrix(rnorm(15000), 5000,3, dimnames = list(1:5000, c("a", "b", "c")))

train_test <- sample(c(0,1), 5000, replace = T)

准备 xgboost,这里重要的是:objective = 'reg:tweedie'eval_metric = "tweedie-nloglik"tweedie_variance_power = 1.2:

dtrain <- xgb.DMatrix(
data = yy %>% subset(train_test == 0),
label = xx %>% subset(train_test == 0)
)

dtest <- xgb.DMatrix(
data = yy %>% subset(train_test == 1),
label = xx %>% subset(train_test == 1)
)

watchlist <- list(eval = dtest, train = dtrain)

param <- list(max.depth = 2,
eta = 0.3,
nthread = 1,
silent = 1,
objective = 'reg:tweedie',
eval_metric = "tweedie-nloglik",
tweedie_variance_power = 1.2)

最后调用 xgboost:

resBoost <- xgb.train(params = param, data=dtrain, nrounds = 20, watchlist=watchlist)

给出了这个晦涩的错误消息:

Error in xgb.iter.update(bst$handle, dtrain, iteration - 1, obj) :
[17:59:18] amalgamation/../src/metric/elementwise_metric.cc:168: Check failed: param != nullptr tweedie-nloglik must be in formattweedie-nloglik@rho

Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/R/site-library/xgboost/libs/xgboost.so(dmlc::StackTrace[abi:cxx11]()+0x1bc) [0x7f1f0ce742ac]
[bt] (1) /usr/local/lib/R/site-library/xgboost/libs/xgboost.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28) [0x7f1f0ce74e88]
[bt] (2) /usr/local/lib/R/site-library/xgboost/libs/xgboost.so(xgboost::metric::EvalTweedieNLogLik::EvalTweedieNLogLik(char const*)+0x1eb) [0x7f1f0cea00db]
[bt] (3) /usr/local/lib/R/site-library/xgboost/libs/xgboost.so(+0x68ef1) [0x7f1f0ce78ef1]
[bt] (4) /usr/local/lib/R/site-library/xgboost/libs/xgboost.so(xgboost::Metric::Create(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)+0x263) [0x7f1f0ce7ede3]
[bt] (5) /usr/local/lib/R/site-library/xgboost/libs/xgboost.so(xgboost::LearnerImpl::Configure(std::vector<std::pair

问题似乎与参数eval_metric = "tweedie-nloglik"有关,因为如果我将eval_metric更改为logloss,它就会通过:

param$eval_metric <- "logloss"
resBoost <- xgb.train(params = param, data=dtrain, nrounds = 20, watchlist=watchlist)
[1] eval-logloss:0.634391 train-logloss:0.849734
[2] eval-logloss:0.634391 train-logloss:0.849734
...

知道如何使用 eval_metric = "tweedie-nloglik" 参数吗,因为它似乎最适合我的上下文?谢谢

最佳答案

TL;DR:感谢 Frans Rodenburg 评论:use eval_metric="tweedie-nloglik@1.2

我正在研究 tweedie eval 的实现(我什至不知道 tweedie 是什么)和 following link 中的 logloss eval

花花公子:

struct EvalTweedieNLogLik: public EvalEWiseBase<EvalTweedieNLogLik> {
explicit EvalTweedieNLogLik(const char* param) {
CHECK(param != nullptr)
<< "tweedie-nloglik must be in format tweedie-nloglik@rho";
rho_ = atof(param);
CHECK(rho_ < 2 && rho_ >= 1)
<< "tweedie variance power must be in interval [1, 2)";
std::ostringstream os;
os << "tweedie-nloglik@" << rho_;
name_ = os.str();
}
const char *Name() const override {
return name_.c_str();
}
inline bst_float EvalRow(bst_float y, bst_float p) const {
bst_float a = y * std::exp((1 - rho_) * std::log(p)) / (1 - rho_);
bst_float b = std::exp((2 - rho_) * std::log(p)) / (2 - rho_);
return -a + b;
}
protected:
std::string name_;
bst_float rho_;
};

对数损失:

struct EvalLogLoss : public EvalEWiseBase<EvalLogLoss> {
const char *Name() const override {
return "logloss";
}
inline bst_float EvalRow(bst_float y, bst_float py) const {
const bst_float eps = 1e-16f;
const bst_float pneg = 1.0f - py;
if (py < eps) {
return -y * std::log(eps) - (1.0f - y) * std::log(1.0f - eps);
} else if (pneg < eps) {
return -y * std::log(1.0f - eps) - (1.0f - y) * std::log(eps);
} else {
return -y * std::log(py) - (1.0f - y) * std::log(pneg);
}
}
};

看起来EvalTweedieNLogLik应该获得一个名为param的参数。看起来就像你得到了那些确切的行:

CHECK(param != nullptr)
<< "tweedie-nloglik must be in format tweedie-nloglik@rho";

当我将它与 EvalLogLoss 进行比较时,相关性差异在于它不需要变量,这就是它工作的原因。

感谢@Frans Rodenburg 评论,我一直在搜索并阅读如何使用它的示例 here .

使用eval_metric="tweedie-nloglik@1.2

当我第一次从 xgboost 文档中阅读这些行时,我也犯了错误:

tweedie-nloglik: negative log-likelihood for Tweedie regression (at a specified value of the tweedie_variance_power parameter)

它可能只与 python 相关。

关于r - 使用 xgboost 对 Tweedie 回归建模,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/51525175/

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