[英]Plotting variable importance from ensemble of models with for loop
I keep running into an error while attempting to plot variable importance from ensemble of models. 在尝试从模型集合中绘制变量重要性时,我一直遇到错误。
I have ensemble of models I've fitted and now I am trying to create multiple variable importance plots for each algorithm I've fitted. 我已经安装了多个模型,现在我正尝试为每个已安装的算法创建多个变量重要性图。 I am using
varImp()
function from caret to extract variable importance, then plot()
it. 我从插入号使用
varImp()
函数提取变量重要性,然后对其进行plot()
。 To fit ensemble of models, I am using caretEnsemble
package. 为了适合模型集合,我使用了
caretEnsemble
包。
Thank you for any help, please see the example of code below. 感谢您的帮助,请参见下面的代码示例。
# Caret ensemble is needed to produce list of models
library(caret)
library(caretEnsemble)
# Set algorithms I wish to fit
my_algorithms <- c("glmnet", "svmRadial", "rf", "nnet", "knn", "rpart")
# Define controls
my_controls <- trainControl(
method = "cv",
savePredictions = "final",
number = 3
)
# Run the models all at once with caretEnsemble
my_list_of_models <- caretEnsemble::caretList(Species ~ .,
data = iris,
trControl = my_controls,
methodList = my_algorithms)
# Subset models
list_of_algorithms <- my_list_of_models[my_algorithms]
# Create first for loop to extract variable importance via caret::varImp()
importance <- list()
for (algo in seq_along(list_of_algorithms)) {
importance[[algo]] <- varImp(list_of_algorithms[[algo]])
}
# Create second loop to go over extracted importance and plot it using plot()
importance_plots <- list()
for (imp in seq_along(importance)) {
importance_plots[[imp]] <- plot(importance[[imp]])
}
# Error occurs during the second for loop:
Error in data.frame(values = unlist(unname(x)), ind, stringsAsFactors = FALSE):arguments imply differing number of rows: 16,
I've come up with the solution to the problem above and decided to post it as my own answer. 我已经提出了上述问题的解决方案,并决定将其发布为我自己的答案。 I've written a small function to plot variable importance without relying on
caret
helper functions to create plots. 我编写了一个小函数来绘制变量的重要性,而不必依赖
caret
辅助函数来创建图。 I used dotplot
and levelplot
because caret
returns data.frame
that differs based on provided algorithm. 我使用
dotplot
和levelplot
因为caret
返回的data.frame
随提供的算法而有所不同。 It may not work on different algorithms and models that didn't fit. 它可能不适用于不合适的不同算法和模型。
# Libraries ---------------------------------------------------------------
library(caret) # To train ML algorithms
library(dplyr) # Required for %>% operators in custom function below
library(caretEnsemble) # To train multiple caret models
library(lattice) # Required for plotting, should be loaded alongside caret
library(gridExtra) # Required for plotting multiple plots
# Custom function ---------------------------------------------------------
# The function requires list of models as input and is used in for loop
plot_importance <- function(importance_list, imp, algo_names) {
importance <- importance_list[[imp]]$importance
model_title <- algo_names[[imp]]
if (ncol(importance) < 2) { # Plot dotplot if dim is ncol < 2
importance %>%
as.matrix() %>%
dotplot(main = model_title)
} else { # Plot heatmap if ncol > 2
importance %>%
as.matrix() %>%
levelplot(xlab = NULL, ylab = NULL, main = model_title, scales = list(x = list(rot = 45)))
}
}
# Tuning parameters -------------------------------------------------------
# Set algorithms I wish to fit
# Rather than using methodList as provided above, I've switched to tuneList because I need to control tuning parameters of random forest algorithm.
my_algorithms <- list(
glmnet = caretModelSpec(method = "glmnet"),
rpart = caretModelSpec(method = "rpart"),
svmRadial = caretModelSpec(method = "svmRadial"),
rf = caretModelSpec(method = "rf", importance = TRUE), # Importance is not computed for "rf" by default
nnet = caretModelSpec(method = "nnet"),
knn = caretModelSpec(method = "knn")
)
# Define controls
my_controls <- trainControl(
method = "cv",
savePredictions = "final",
number = 3
)
# Run the models all at once with caretEnsemble
my_list_of_models <- caretList(Species ~ .,
data = iris,
tuneList = my_algorithms,
trControl = my_controls
)
# Extract variable importance ---------------------------------------------
importance <- lapply(my_list_of_models, varImp)
# Plotting variable immportance -------------------------------------------
# Create second loop to go over extracted importance and plot it using plot()
importance_plots <- list()
for (imp in seq_along(importance)) {
# importance_plots[[imp]] <- plot(importance[[imp]])
importance_plots[[imp]] <- plot_importance(importance_list = importance, imp = imp, algo_names = names(my_list_of_models))
}
# Multiple plots at once
do.call("grid.arrange", c(importance_plots))
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