Shapley Additive Explanations(SHAP值)的实用性在于了解每个特征对模型预测的贡献。对于某些目标,例如以RMSE为目标函数的回归,SHAP值以标签值的本机单位表示。例如,如果估计住房成本,SHAP值可以表示为美元。如下所示,这并非所有目标函数的情况。特别是,Tweedie回归目标不会产生本机单位的SHAP值。这是一个解释问题,因为我们想知道住房成本如何受到特征的影响,以+/-美元为单位。
鉴于此,我的问题是:当使用Tweedie回归目标解释模型时,我们如何将每个单独特征的SHAP值转换为目标标签的数据空间?
我不知道是否有任何包当前实现了这样的转换。 shap作者自己发布的包中仍存在这个问题未解决。
我以下面的R实现lightgbm说明这个问题的细节:
library(tweedie)
library(lightgbm)
set.seed(123)
tweedie_variance_power <- 1.2
labels <- rtweedie(1000, mu = 1, phi = 1, power = tweedie_variance_power)
hist(labels)
feat1 <- labels + rnorm(1000) #good signal for label with some noise
feat2 <-rnorm(1000) #garbage feature
feat3 <-rnorm(1000) #garbage feature
features <- cbind(feat1, feat2, feat3)
dTrain <- lgb.Dataset(data = features,
label = labels)
params <- c(objective = 'tweedie',
tweedie_variance_power = tweedie_variance_power)
mod <- lgb.train(data = dTrain,
params = params,
nrounds = 100)
#Predictions in the native units of the labels
predsNative <- predict(mod, features, rawscore = FALSE)
#Predictions in the raw format
predsRaw <- predict(mod, features, rawscore = TRUE)
#We do not expect these values to be equal
all.equal(predsTrans, predsRaw)
"Mean relative difference: 1.503072"
#We expect values to be equal if raw scores are exponentiated
all.equal(predsTrans, exp(predsRaw))
"TRUE" #... our expectations are correct
#SHAP values
shapNative <- predict(mod, features, rawscore = FALSE, predcontrib = TRUE)
shapRaw <- predict(mod, features, rawscore = TRUE, predcontrib = TRUE )
#Are there differences between shap values when rawscore is TRUE or FALSE?
all.equal(shapNative, shapRaw)
"TRUE" #outputs are identical, that is surprising!
#So are the shap values in raw or native formats?
#To anwser this question we can sum them
#testing raw the raw case first
all.equal(rowSums(shapRaw), predsRaw)
"TRUE"
#from this we can conclude that shap values are not in native units,
#regardless of whether rawscore is TRUE or FALSE
#Test native scores just to prove point
all.equal(rowSums(shapNative), predsNative)
"Mean relative difference: 1.636892" # reaffirms that shap values are not in native units
#However, we can perform this operation on the raw shap scores
#to get the prediction in the native value
all.equal(exp(rowSums(shapRaw)), predsNative)
'TRUE'
#reversing the operations does not yield the same result
all.equal(rowSums(exp(shapRaw)), predsNative)
"Mean relative difference: 0.7662481"
#The last line is relevant because it implies
#The relationship between native predictions
#and exponentiated shap values is not linear
#So, given the point of SHAP is to understand how each
#feature impacts the prediction in its native units
#the raw shap values are not as useful as they could be
#Thus, how how would we convert
#each of these four raw shap value elements to native units,
#thus understanding their contributions to their predictions
#in currency of native units?
shapRaw[1,]
-0.15429227 0.04858757 -0.27715359 -0.48454457
原帖和编辑
我理解SHAP值是在回归时标签/响应的本机单位,SHAP值的总和近似于模型的预测。
我正在尝试从LightGBM包中提取SHAP值,使用Tweedie回归目标,但发现SHAP值不是标签的本机单位,并且它们并不相加到预测值。
看起来它们必须被指数化,这是正确的吗?
附注:我理解SHAP值矩阵的最后一列代表基础预测,并且必须添加。
可重现的例子:
library(tweedie)
library(caret)
library(lightgbm)
set.seed(123)
tweedie_variance_power <- 1.2
labels <- rtweedie(1000, mu = 1, phi = 1, power = tweedie_variance_power)
hist(labels)
feat1 <- labels + rnorm(1000) #good signal for label with some noise
feat2 <-rnorm(1000) #garbage feature
feat3 <-rnorm(1000) #garbage feature
features <- cbind(feat1, feat2, feat3)
dTrain <- lgb.Dataset(data = features,
label = labels)
params <- c(objective = 'tweedie',
tweedie_variance_power = tweedie_variance_power)
mod <- lgb.train(data = dTrain,
params = params,
nrounds = 100)
preds <- predict(mod, features)
plot(preds, labels,
main = paste('RMSE =',
RMSE(pred = preds, obs = labels)))
#shap values are summing to negative values?
shap_vals <- predict(mod, features, predcontrib = TRUE, rawscore = FALSE)
shaps_sum <- rowSums(shap_vals)
plot(shaps_sum, labels,
main = paste('RMSE =',
RMSE(pred = shaps_sum, obs = labels)))
#maybe we need to exponentiate?
shap_vals_exp <- exp(shap_vals)
shap_vals_exp_sum <- rowSums(shap_vals_exp)
#still looks a little weird, overpredicting
plot(shap_vals_exp_sum, labels,
main = paste('RMSE =',
RMSE(pred = shap_vals_exp_sum, obs = labels)))
编辑
操作顺序是先求和,然后对SHAP值进行指数运算,这将给出以本机单位为预测的结果。尽管我仍不清楚如何将特征级别的值转换为本机响应单位。
shap_vals_sum_exp <- exp(shaps_sum)
plot(shap_vals_sum_exp, labels,
main = paste('RMSE =',
RMSE(pred = shap_vals_sum_exp, obs = labels)))
predict_contrib
给了我预测值,而不是shap值。无论如何,我会仔细看看,如果确实是这样,我会提出一个问题。 - desertnaut