我想可视化我放入混淆矩阵的数据。是否有一个函数我可以简单地放置混淆矩阵并将其可视化（绘制它）？
示例我想做的事情（Matrix$nnet 只是一个包含分类结果的表）：
Confusion$nnet <- confusionMatrix(Matrix$nnet)
plot(Confusion$nnet)
我的 Confusion$nnet$table 看起来像这样：
prediction (I would also like to get rid of this string, any help?)
1 2
1 42 6
2 8 28
您可以只使用 r 中的 rect 功能来布局混淆矩阵。在这里，我们将创建一个函数，允许用户传入由 caret 包创建的 cm 对象以产生视觉效果。
让我们首先创建一个评估数据集，如插入符号演示中所做的那样：
# construct the evaluation dataset
set.seed(144)
true_class <- factor(sample(paste0("Class", 1:2), size = 1000, prob = c(.2, .8), replace = TRUE))
true_class <- sort(true_class)
class1_probs <- rbeta(sum(true_class == "Class1"), 4, 1)
class2_probs <- rbeta(sum(true_class == "Class2"), 1, 2.5)
test_set <- data.frame(obs = true_class,Class1 = c(class1_probs, class2_probs))
test_set$Class2 <- 1 - test_set$Class1
test_set$pred <- factor(ifelse(test_set$Class1 >= .5, "Class1", "Class2"))
现在让我们使用插入符号来计算混淆矩阵：
# calculate the confusion matrix
cm <- confusionMatrix(data = test_set$pred, reference = test_set$obs)
现在我们创建一个函数，根据需要对矩形进行布局，以更具视觉吸引力的方式展示混淆矩阵：
draw_confusion_matrix <- function(cm) {
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX', cex.main=2)
# create the matrix
rect(150, 430, 240, 370, col='#3F97D0')
text(195, 435, 'Class1', cex=1.2)
rect(250, 430, 340, 370, col='#F7AD50')
text(295, 435, 'Class2', cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col='#F7AD50')
rect(250, 305, 340, 365, col='#3F97D0')
text(140, 400, 'Class1', cex=1.2, srt=90)
text(140, 335, 'Class2', cex=1.2, srt=90)
# add in the cm results
res <- as.numeric(cm$table)
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}
最后，传入我们在使用插入符号创建混淆矩阵时计算的cm 对象：
draw_confusion_matrix(cm)
结果如下：
你可以使用内置的fourfoldplot. 例如，
ctable <- as.table(matrix(c(42, 6, 8, 28), nrow = 2, byrow = TRUE))
fourfoldplot(ctable, color = c("#CC6666", "#99CC99"),
conf.level = 0, margin = 1, main = "Confusion Matrix")
您可以使用plus的函数conf_mat()在几行中获得一个非常好的结果。yardstickautoplot()
另外，您仍然可以使用基本ggplot的 sintax 来修复样式。
library(yardstick)
library(ggplot2)
# The confusion matrix from a single assessment set (i.e. fold)
cm <- conf_mat(truth_predicted, obs, pred)
autoplot(cm, type = "heatmap") +
scale_fill_gradient(low="#D6EAF8",high = "#2E86C1")
作为进一步自定义的示例，ggplot您还可以使用 sintax 添加回图例：
+ theme(legend.position = "right")
更改图例的名称也很容易：+ labs(fill="legend_name")
数据示例：
set.seed(123)
truth_predicted <- data.frame(
obs = sample(0:1,100, replace = T),
pred = sample(0:1,100, replace = T)
)
truth_predicted$obs <- as.factor(truth_predicted$obs)
truth_predicted$pred <- as.factor(truth_predicted$pred)
我真的很喜欢来自@Cybernetic 的漂亮的混淆矩阵可视化，并进行了两次调整以希望进一步改进它。
1）我用类的实际值替换了 Class1 和 Class2。2) 我将橙色和蓝色替换为基于百分位数生成红色（未命中）和绿色（命中）的函数。这个想法是快速查看问题/成功在哪里以及它们的大小。
截图和代码：
draw_confusion_matrix <- function(cm) {
total <- sum(cm$table)
res <- as.numeric(cm$table)
# Generate color gradients. Palettes come from RColorBrewer.
greenPalette <- c("#F7FCF5","#E5F5E0","#C7E9C0","#A1D99B","#74C476","#41AB5D","#238B45","#006D2C","#00441B")
redPalette <- c("#FFF5F0","#FEE0D2","#FCBBA1","#FC9272","#FB6A4A","#EF3B2C","#CB181D","#A50F15","#67000D")
getColor <- function (greenOrRed = "green", amount = 0) {
if (amount == 0)
return("#FFFFFF")
palette <- greenPalette
if (greenOrRed == "red")
palette <- redPalette
colorRampPalette(palette)(100)[10 + ceiling(90 * amount / total)]
}
# set the basic layout
layout(matrix(c(1,1,2)))
par(mar=c(2,2,2,2))
plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
title('CONFUSION MATRIX', cex.main=2)
# create the matrix
classes = colnames(cm$table)
rect(150, 430, 240, 370, col=getColor("green", res[1]))
text(195, 435, classes[1], cex=1.2)
rect(250, 430, 340, 370, col=getColor("red", res[3]))
text(295, 435, classes[2], cex=1.2)
text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
text(245, 450, 'Actual', cex=1.3, font=2)
rect(150, 305, 240, 365, col=getColor("red", res[2]))
rect(250, 305, 340, 365, col=getColor("green", res[4]))
text(140, 400, classes[1], cex=1.2, srt=90)
text(140, 335, classes[2], cex=1.2, srt=90)
# add in the cm results
text(195, 400, res[1], cex=1.6, font=2, col='white')
text(195, 335, res[2], cex=1.6, font=2, col='white')
text(295, 400, res[3], cex=1.6, font=2, col='white')
text(295, 335, res[4], cex=1.6, font=2, col='white')
# add in the specifics
plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)
# add in the accuracy information
text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}
这里有一个简单ggplot2的想法，可以根据需要进行更改，我正在使用来自此链接的数据：
#data
confusionMatrix(iris$Species, sample(iris$Species))
newPrior <- c(.05, .8, .15)
names(newPrior) <- levels(iris$Species)
cm <-confusionMatrix(iris$Species, sample(iris$Species))
现在 cm 是一个混淆矩阵对象，可以取出一些对问题有用的东西：
# extract the confusion matrix values as data.frame
cm_d <- as.data.frame(cm$table)
# confusion matrix statistics as data.frame
cm_st <-data.frame(cm$overall)
# round the values
cm_st$cm.overall <- round(cm_st$cm.overall,2)
# here we also have the rounded percentage values
cm_p <- as.data.frame(prop.table(cm$table))
cm_d$Perc <- round(cm_p$Freq*100,2)
现在我们准备绘制：
library(ggplot2) # to plot
library(gridExtra) # to put more
library(grid) # plot together
# plotting the matrix
cm_d_p <- ggplot(data = cm_d, aes(x = Prediction , y = Reference, fill = Freq))+
geom_tile() +
geom_text(aes(label = paste("",Freq,",",Perc,"%")), color = 'red', size = 8) +
theme_light() +
guides(fill=FALSE)
# plotting the stats
cm_st_p <- tableGrob(cm_st)
# all together
grid.arrange(cm_d_p, cm_st_p,nrow = 1, ncol = 2,
top=textGrob("Confusion Matrix and Statistics",gp=gpar(fontsize=25,font=1)))
我知道这已经很晚了，但我一直在寻找自己的解决方案。除了这篇文章之外，还要处理上面的一些先前的答案。使用ggplot2package 和 basetable函数，我制作了这个简单的函数来绘制一个颜色漂亮的混淆矩阵：
conf_matrix <- function(df.true, df.pred, title = "", true.lab ="True Class", pred.lab ="Predicted Class",
high.col = 'red', low.col = 'white') {
#convert input vector to factors, and ensure they have the same levels
df.true <- as.factor(df.true)
df.pred <- factor(df.pred, levels = levels(df.true))
#generate confusion matrix, and confusion matrix as a pecentage of each true class (to be used for color)
df.cm <- table(True = df.true, Pred = df.pred)
df.cm.col <- df.cm / rowSums(df.cm)
#convert confusion matrices to tables, and binding them together
df.table <- reshape2::melt(df.cm)
df.table.col <- reshape2::melt(df.cm.col)
df.table <- left_join(df.table, df.table.col, by =c("True", "Pred"))
#calculate accuracy and class accuracy
acc.vector <- c(diag(df.cm)) / c(rowSums(df.cm))
class.acc <- data.frame(Pred = "Class Acc.", True = names(acc.vector), value = acc.vector)
acc <- sum(diag(df.cm)) / sum(df.cm)
#plot
ggplot() +
geom_tile(aes(x=Pred, y=True, fill=value.y),
data=df.table, size=0.2, color=grey(0.5)) +
geom_tile(aes(x=Pred, y=True),
data=df.table[df.table$True==df.table$Pred, ], size=1, color="black", fill = 'transparent') +
scale_x_discrete(position = "top", limits = c(levels(df.table$Pred), "Class Acc.")) +
scale_y_discrete(limits = rev(unique(levels(df.table$Pred)))) +
labs(x=pred.lab, y=true.lab, fill=NULL,
title= paste0(title, "\nAccuracy ", round(100*acc, 1), "%")) +
geom_text(aes(x=Pred, y=True, label=value.x),
data=df.table, size=4, colour="black") +
geom_text(data = class.acc, aes(Pred, True, label = paste0(round(100*value), "%"))) +
scale_fill_gradient(low=low.col, high=high.col, labels = scales::percent,
limits = c(0,1), breaks = c(0,0.5,1)) +
guides(size=F) +
theme_bw() +
theme(panel.border = element_blank(), legend.position = "bottom",
axis.text = element_text(color='black'), axis.ticks = element_blank(),
panel.grid = element_blank(), axis.text.x.top = element_text(angle = 30, vjust = 0, hjust = 0)) +
coord_fixed()
}
您可以复制并粘贴该函数，并将其保存到您的全局环境中。
这是一个例子：
mydata <- data.frame(true = c("a", "b", "c", "a", "b", "c", "a", "b", "c"),
predicted = c("a", "a", "c", "c", "a", "c", "a", "b", "c"))
conf_matrix(mydata$true, mydata$predicted, title = "Conf. Matrix Example")
cvms还有plot_confusion_matrix()一些花里胡哨的东西：
# Create targets and predictions data frame
data <- data.frame(
"target" = c("A", "B", "A", "B", "A", "B", "A", "B",
"A", "B", "A", "B", "A", "B", "A", "A"),
"prediction" = c("B", "B", "A", "A", "A", "B", "B", "B",
"B", "B", "A", "B", "A", "A", "A", "A"),
stringsAsFactors = FALSE
)
# Evaluate predictions and create confusion matrix
eval <- evaluate(
data = data,
target_col = "target",
prediction_cols = "prediction",
type = "binomial"
)
eval
> # A tibble: 1 x 19
> `Balanced Accuracy` Accuracy F1 Sensitivity Specificity `Pos Pred Value` `Neg Pred Value` AUC `Lower CI`
> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
> 1 0.690 0.688 0.667 0.714 0.667 0.625 0.75 0.690 0.447
> # … with 10 more variables: Upper CI <dbl>, Kappa <dbl>, MCC <dbl>, Detection Rate <dbl>,
> # Detection Prevalence <dbl>, Prevalence <dbl>, Predictions <list>, ROC <named list>, Confusion Matrix <list>,
> # Process <list>
# Plot confusion matrix
# Either supply confusion matrix tibble directly
plot_confusion_matrix(eval[["Confusion Matrix"]][[1]])
# Or plot first confusion matrix in evaluate() output
plot_confusion_matrix(eval)
输出是一个 ggplot 对象。
library(caret)
library(yardstick)
library(ggplot2)
plsFit <- train(
y ~ .,
data = trainData
)
plsClasses <- predict(plsFit, newdata = testdata)
truth_predicted<-data.frame(
obs = testdata$y,
pred = plsClasses
)
cm <- conf_mat(truth_predicted, obs, pred)
autoplot(cm, type = "heatmap") +
scale_fill_gradient(low="#D6EAF8",high = "#2E86C1")