ggplot2混淆矩阵条件填充

Question

我希望混淆矩阵有两个不同的尺度。对于真正的正面/负面，我希望绿色代表高，红色代表低。对于错误的结果，我希望颜色反转。这可能需要两个传说？这可能解释起来很麻烦，但我觉得倒过来的颜色更直观。您的真实结果应该很高（绿色代表良好），而您的负面结果应该很低（在这种情况下绿色代表良好）。

如果除了我的建议之外还有其他建议或最佳实践，我愿意接受其他选择。

library(caret)
library(tidyverse)
data("GermanCredit")
cm <- confusionMatrix(GermanCredit$Class, sample(GermanCredit$Class))

cm$table %>%
  data.frame() %>% 
  mutate(Prediction = factor(Prediction, levels = c("Good", "Bad"))) %>%
  group_by(Reference) %>% 
  mutate(total = sum(Freq)) %>% 
  ungroup() %>% 
  ggplot(aes(Reference, Prediction, fill = Freq)) +
  geom_tile() +
  geom_text(aes(label = Freq), size = 8) +
  scale_fill_gradient(low = "#ea4434", high = "#badb33") +
  scale_x_discrete(position = "top") +
  geom_tile(color = "black", fill = "black", alpha = 0)

我根据一些 cmets 添加了我的问题：我希望不相关的对角线为白色，红色（低）和绿色（高）比例仅适用于真正的正/负结果。例如，下面的图部分实现了这一点，除了我将低点设置为白色......我希望低点为红色，而白色方块保持白色。

library(reprex)
library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
library(tidyverse)
data("GermanCredit")
cm <- confusionMatrix(GermanCredit$Class, sample(GermanCredit$Class))

cm$table %>%
  data.frame() %>% 
  mutate(Prediction = factor(Prediction, levels = c("Good", "Bad"))) %>%
  group_by(Reference) %>% 
  mutate(
    total = sum(Freq),
    frac_fill = if_else(Prediction == Reference, Freq / total, 0),
    frac = Freq / total * frac_fill
  ) %>%
  ggplot(aes(Prediction, Reference, fill = frac_fill)) +
  geom_tile() +
  geom_text(aes(label = str_c(Freq, ", ", round(frac * 100), "%")), size = 8) +
  scale_fill_gradient(low = "white", high = "#badb33") +
  scale_x_discrete(position = "top") +
  geom_tile(color = "black", fill = "black", alpha = 0)

Answer 1

好的，这是一个尝试，它基本上对非对角线图块使用反向填充值。我将轴倒置为我认为通常描绘混淆矩阵的方式。我想这表明该模型在预测Good 方面比Bad 更好，但我认为它在混淆时掩盖了实际混淆的内容。像这样呈现时，传说也不是最有用的，我什至可能会忽略它。

library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
library(tidyverse)
data("GermanCredit")
cm <- confusionMatrix(GermanCredit$Class, sample(GermanCredit$Class))

cm$table %>%
  data.frame() %>% 
  mutate(Prediction = factor(Prediction, levels = c("Good", "Bad"))) %>%
  group_by(Reference) %>% 
  mutate(
    total = sum(Freq),
    frac = Freq / total,
    frac_fill = if_else(Prediction == Reference, Freq / total, 1 - (Freq / total))
    ) %>%
  ggplot(aes(Prediction, Reference, fill = frac_fill)) +
  geom_tile() +
  geom_text(aes(label = str_c(Freq, ", ", round(frac * 100), "%")), size = 8) +
  scale_fill_gradient(low = "#ea4434", high = "#badb33") +
  scale_x_discrete(position = "top") +
  geom_tile(color = "black", fill = "black", alpha = 0)

由reprex package (v0.2.0) 于 2018 年 7 月 9 日创建。

Answer 2

如果我的理解正确，您需要一个发散的比例，这需要将比例设为负数和正数。我创建了一个变量frac_directed，如果预测为“坏”，它就是frac_fill 的负数，尽管这可能与您正在寻找的相反。然后用scale_fill_gradient2代替scale_fill_gradient，可以设置一个中点（默认0，这里合适）和一个中间填充值。

library(caret)
library(tidyverse)
data("GermanCredit")
cm <- confusionMatrix(GermanCredit$Class, sample(GermanCredit$Class))

cm$table %>%
  data.frame() %>% 
  mutate(Prediction = factor(Prediction, levels = c("Good", "Bad"))) %>%
  group_by(Reference) %>% 
  mutate(
    total = sum(Freq),
    frac_fill = if_else(Prediction == Reference, Freq / total, 0),
    frac = Freq / total * frac_fill
  ) %>%
  mutate(frac_directed = if_else(Prediction == "Bad", frac_fill * -1, frac_fill)) %>%
  ggplot(aes(Prediction, Reference, fill = frac_directed)) +
  geom_tile(color = "black") +
  geom_text(aes(label = str_c(Freq, ", ", round(frac * 100), "%")), size = 8) +
  scale_fill_gradient2(low = "red", mid = "white", high = "#badb33") +
  scale_x_discrete(position = "top")

由reprex package (v0.2.0) 于 2018 年 7 月 9 日创建。