根据@knb 的建议,我编写了一个函数来从 SMOreg 模型中提取权重,并返回一个小标题,其中一列用于特征名称,一列用于特征权重,行排列在权重的绝对值之后。
请注意,此功能仅适用于 SMOreg 分类器,因为其他分类器的输出在布局方面略有不同。但是,我认为该功能可以很容易地适应其他分类器。
library(stringr)
library(tidyverse)
extract_weights_from_SMOreg <- function(model) {
oldw <- getOption("warn")
options(warn = -1)
raw_output <- capture.output(model)
trimmed_output <- raw_output[-c(1:3,(length(raw_output) - 4): length(raw_output))]
df <- data_frame(features_name = vector(length = length(trimmed_output) + 1, "character"),
features_weight = vector(length = length(trimmed_output) + 1, "numeric"))
for (line in 1:length(trimmed_output)) {
string_as_vector <- trimmed_output[line] %>%
str_split(string = ., pattern = " ") %>%
unlist(.)
numeric_element <- trimmed_output[line] %>%
str_split(string = ., pattern = " ") %>%
unlist(.) %>%
as.numeric(.)
position_mul <- string_as_vector[is.na(numeric_element)] %>%
str_detect(string = ., pattern = "[*]") %>%
which(.)
numeric_element <- numeric_element %>%
`[`(., c(1:position_mul))
text_element <- string_as_vector[is.na(numeric_element)]
there_is_plus <- string_as_vector[is.na(numeric_element)] %>%
str_detect(string = ., pattern = "[+]") %>%
sum(.)
if (there_is_plus) { sign_is <- "+"} else { sign_is <- "-"}
feature_weight <- numeric_element[!is.na(numeric_element)]
if (sign_is == "-") {df[line, "features_weight"] <- feature_weight * -1} else {df[line, "features_weight"] <- numeric_element[!(is.na(numeric_element))]}
df[line, "features_name"] <- paste(text_element[(position_mul + 1): length(text_element)], collapse = " ")
}
intercept_line <- raw_output[length(raw_output) - 4]
there_is_plus_intercept <- intercept_line %>%
str_detect(string = ., pattern = "[+]") %>%
sum(.)
if (there_is_plus_intercept) { intercept_sign_is <- "+"} else { intercept_sign_is <- "-"}
numeric_intercept <- intercept_line %>%
str_split(string = ., pattern = " ") %>%
unlist(.) %>%
as.numeric(.) %>%
`[`(., length(.))
df[nrow(df), "features_name"] <- "intercept"
if (intercept_sign_is == "-") {df[nrow(df), "features_weight"] <- numeric_intercept * -1} else {df[nrow(df), "features_weight"] <- numeric_intercept}
options(warn = oldw)
df <- df %>%
arrange(desc(abs(features_weight)))
return(df)
}
这是一个模型的示例
library(RWeka)
data("mtcars")
SMOreg_classifier <- make_Weka_classifier("weka/classifiers/functions/SMOreg")
mpg_model_weights <- extract_weights_from_SMOreg(SMOreg_classifier(data = mtcars, mpg ~ .))
mpg_model_weights