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我正在创建一些 SHAP 分数图，用于可视化我使用 xgboost 创建的模型。我使用了运行良好的 SHAPforxgboost 包，现在我想在我正在编写的文本文档中使用这些数字（尤其是来自 shap.plot.summary() 的那个）。但是，x 轴和 y 轴上的标签/标题的字体大小非常小，我想知道是否有一种方法可以使它们变得更大且更具可读性。

我使用了非常相似的设置，如此处所示；https://www.rdocumentation.org/packages/SHAPforxgboost/versions/0.0.2：

library("SHAPforxgboost")
y_var <-  "diffcwv"
dataX <- dataXY_df[,-..y_var]
# hyperparameter tuning results
param_dart <- list(objective = "reg:linear",  # For regression
                   nrounds = 366,
                   eta = 0.018,
                   max_depth = 10,
                   gamma = 0.009,
                   subsample = 0.98,
                   colsample_bytree = 0.86)

mod <- xgboost::xgboost(data = as.matrix(dataX), label = as.matrix(dataXY_df[[y_var]]), 
                       xgb_param = param_dart, nrounds = param_dart$nrounds,
                       verbose = FALSE, nthread = parallel::detectCores() - 2,
                       early_stopping_rounds = 8)

# To return the SHAP values and ranked features by mean|SHAP|
shap_values <- shap.values(xgb_model = mod, X_train = dataX)
# The ranked features by mean |SHAP|
shap_values$mean_shap_score

# To prepare the long-format data:
shap_long <- shap.prep(xgb_model = mod, X_train = dataX)
# is the same as: using given shap_contrib
shap_long <- shap.prep(shap_contrib = shap_values$shap_score, X_train = dataX)
# (Notice that there will be a data.table warning from `melt.data.table` due to `dayint` coerced from integer to double)

# **SHAP summary plot**
shap.plot.summary(shap_long)

shap.plot.summary() 的输出是：像这样的东西

更具体地说，我有兴趣增加 y 轴上每个描述符的字体大小

4

2 回答 2

3

因此，由于 cbo 在大多数情况下发布了足够的答案，我无法编辑 y 轴上标签的大小（即 0.629、0.219、0.029）。我发现最好的解决方案是使用该功能

shap.plot.summary <- edit(shap.plot.summary)

编辑 ggplot 设置。对于任何好奇的人，我发现与情节相关的 ggplot 设置是：

theme(axis.line.y = element_blank(),
      axis.ticks.y = element_blank(), legend.position = "bottom",
      legend.title = element_text(size = 25),
      legend.text = element_text(size = 25), 
      axis.title.x = element_text(size = 25),
      axis.text.y = element_text(size = 40),
      axis.text.x.bottom = element_text(size  = 20))
于 2020-03-02T09:14:29.807 回答
2

看看这里的代码，因为它是用 ggplot 制作的，你应该能够覆盖默认的标签大小参数。

使用shap.plot.summary.wrap2函数的示例：

library("SHAPforxgboost")
library("ggplot2")

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$mean_shap_score
#> Petal.Length  Petal.Width Sepal.Length  Sepal.Width 
#>   0.62935975   0.21664035   0.02910357   0.00000000
shap_values_iris <- shap_values$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_values_iris, X1, top_n = 3) +
        ggplot2::theme(axis.text.y = element_text(size = 20))

于 2020-02-27T15:00:19.927 回答