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R语言机器学习与临床预测模型69--机器学习模型解释利器:SHA

R语言机器学习与临床预测模型69--机器学习模型解释利器:SHA

作者: 科研私家菜 | 来源:发表于2022-07-13 16:29 被阅读0次

    R小盐准备介绍R语言机器学习与预测模型的学习笔记, 快来收藏关注【科研私家菜】


    01 机器学习的可解释性

    对于集成学习方法,效果虽好,但一直无法解决可解释性的问题。我们知道一个xgboost或lightgbm模型,是由N棵树组成,所以对于特定的一个样本,我们无法知道这个样本的特征值是如何影响最终结果。虽说“不管白猫黑猫,抓住耗子的就是好猫”,但在具体任务中,我们还是希望能够获得样本每个特征与其结果之间的关系,特别是针对模型误分的那些样本,如果能够从特征和结果的角度进行分析,对于提高模型效果或是分析异常样本,是非常有帮助的。但是,其可解释性相对困难。
    对于集成树模型来说,当做分类任务时,模型输出的是一个概率值。前文提到,SHAP是SHapley Additive exPlanations的缩写,即沙普利加和解释,因此SHAP实际是将输出值归因到每一个特征的shapely值上,换句话说,就是计算每一个特征的shapley值,依此来衡量特征对最终输出值的影响。

    其原理及推到公式不再赘述。。。

    02 SHAP的R语言实现

    SHAP(SHapley Additive exPlanations)

    library(tidyverse)
    library(xgboost)
    library(caret)
    library(dplyr)
    source("shap.R")
    
    bike <- read.csv("../shap-values-master/bike.csv",header = T)
    
    
    bike_2=select(bike, -days_since_2011, -cnt, -yr)
    
    bike_dmy = dummyVars(" ~ .", data = bike_2, fullRank=T)
    bike_x = predict(bike_dmy, newdata = bike_2)
    
    ## Create the xgboost model
    model_bike = xgboost(data = bike_x, 
                       nround = 10, 
                       objective="reg:linear",
                       label= bike$cnt)  
    
    
    ## Calculate shap values
    shap_result_bike = shap.score.rank(xgb_model = model_bike, 
                                  X_train =bike_x,
                                  shap_approx = F
                                  )
    
    # `shap_approx` comes from `approxcontrib` from xgboost documentation. 
    # Faster but less accurate if true. Read more: help(xgboost)
    
    ## Plot var importance based on SHAP
    var_importance(shap_result_bike, top_n=15)
    
    ## Prepare data for top N variables
    shap_long_bike = shap.prep(shap = shap_result_bike,
                               X_train = bike_x , 
                               top_n = 10
                               )
    
    ## Plot shap overall metrics
    plot.shap.summary(data_long = shap_long_bike)
    
    
    ## 
    xgb.plot.shap(data = bike_x, # input data
                  model = model_bike, # xgboost model
                  features = names(shap_result_bike$mean_shap_score[1:10]), # only top 10 var
                  n_col = 3, # layout option
                  plot_loess = T # add red line to plot
                  )
    
    

    效果如下:



    03 SHAP R语言示例

    data("iris")
    X1 = as.matrix(iris[,-5])
    mod1 = xgboost::xgboost(
      data = X1, label = iris$Species, gamma = 0, eta = 1,
      lambda = 0, nrounds = 1, verbose = FALSE)
    
    # shap.values(model, X_dataset) returns the SHAP
    # data matrix and ranked features by mean|SHAP|
    shap_values <- shap.values(xgb_model = mod1, X_train = X1)
    shap_values$mean_shap_score
    shap_values_iris <- shap_values$shap_score
    
    # shap.prep() returns the long-format SHAP data from either model or
    shap_long_iris <- shap.prep(xgb_model = mod1, X_train = X1)
    # is the same as: using given shap_contrib
    shap_long_iris <- shap.prep(shap_contrib = shap_values_iris, X_train = X1)
    
    # **SHAP summary plot**
    shap.plot.summary(shap_long_iris, scientific = TRUE)
    shap.plot.summary(shap_long_iris, x_bound  = 1.5, dilute = 10)
    
    # Alternatives options to make the same plot:
    # option 1: from the xgboost model
    shap.plot.summary.wrap1(mod1, X = as.matrix(iris[,-5]), top_n = 3)
    
    # option 2: supply a self-made SHAP values dataset
    # (e.g. sometimes as output from cross-validation)
    shap.plot.summary.wrap2(shap_score = shap_values_iris, X = X1, top_n = 3)
    

    效果如下:




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