Question

我有这个数据，我试图绘制。下面我列出了前500个观察中的dput，但是如果这还不够，我已经在dropbox上传了.csv文件。

我正在尝试做两件事。

首先将配色方案更改为更美观的配色方案。我查看了scale_colour_manual和scale_colour_brewer命令，但没有任何工作，我是否正确认为colour = factor(group_id)是否覆盖了这些命令？

plot <- combo_data %>%
  ggplot(aes(ID)) + 
  geom_line(aes(y = value, colour = factor(group_id))) +
  facet_wrap("type", scales = "free") +
  labs(title = "data", y = "y score", x = "x result") +
  theme_bw(base_size = 11, base_family = "") +
  theme(aspect.ratio = 1) + theme(legend.position="none")

plot

其次，我认为以不同的尺寸和颜色绘制最佳线条可能会很有趣。我对最佳线的意思是在测试数据上在迭代100处得分最高的线。然后在测试线和火车线上将此线显示为黑色。我不确定从哪里开始。

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    0.9450798, 0.9452847, 0.9455464, 0.9458335, 0.9460275, 0.9462473, 
    0.9464572, 0.9466602, 0.9469266, 0.9471858, 0.9473951, 0.9476667, 
    0.9479168, 0.9481527, 0.9483371, 0.9485883, 0.9488048, 0.949047, 
    0.9492654, 0.9494981, 0.9497255), ID = c(1L, 2L, 3L, 4L, 
    5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
    18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
    30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 
    42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 
    54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 
    66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 
    78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 
    90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 
    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
    15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
    27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 
    39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 
    51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 
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    87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 
    99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
    12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
    24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 
    36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 
    48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 
    60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 
    72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 
    84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 
    96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
    9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
    21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 
    33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 
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    57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 
    69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 
    81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 
    93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 
    5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
    18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
    30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 
    42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 
    54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 
    66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 
    78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 
    90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L)), .Names = c("type", 
"group_id", "value", "ID"), row.names = c(NA, -500L), class = c("tbl_df", 
"tbl", "data.frame"))

编辑：xgbmodel.cv$evaluation_log。

structure(list(iter = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20), train_auc_mean = c(0.8783153, 
0.8953862, 0.900536, 0.9030217, 0.9049995, 0.9066914, 0.9080874, 
0.9088161, 0.9100754, 0.9108984, 0.912043, 0.9128935, 0.9137224, 
0.91465, 0.9154111, 0.9162045, 0.916924, 0.9176951, 0.9183826, 
0.9189338), train_auc_std = c(0.00717009945607681, 0.00249586501237372, 
0.00269250771585954, 0.00254956443534881, 0.00232702583784572, 
0.00181327638266765, 0.00133006362257542, 0.0013852951634466, 
0.00113504547930466, 0.00130808304018898, 0.00111638568601384, 
0.00112030141031364, 0.000975854825194455, 0.00115169779023825, 
0.00108698007799259, 0.00111895515998081, 0.00100959803891298, 
0.000955721345432234, 0.000954352681180355, 0.000932746782367693
), test_auc_mean = c(0.8679278, 0.8843016, 0.8896035, 0.8919551, 
0.8938367, 0.8955483, 0.8964231, 0.8969212, 0.8978634, 0.8986166, 
0.899632, 0.9003129, 0.9007414, 0.9013951, 0.9017771, 0.9022693, 
0.9027743, 0.9030824, 0.9035715, 0.9037952), test_auc_std = c(0.00695311168901748, 
0.00404615660101717, 0.00577139448054154, 0.0057856306907767, 
0.00606116769360227, 0.00624799443101764, 0.00538974117096598, 
0.00540648223525145, 0.00556229789206102, 0.00540702262249037, 
0.00515849813414524, 0.00518186297484329, 0.00513731438008862, 
0.00526564910433983, 0.0053733205273774, 0.00549749955981443, 
0.00561555858396273, 0.0057395095469953, 0.00560829002906322, 
0.00561293322246843)), .Names = c("iter", "train_auc_mean", "train_auc_std", 
"test_auc_mean", "test_auc_std"), class = c("data.table", "data.frame"
), row.names = c(NA, -20L), .internal.selfref = <pointer: 0x00000000047e0788>)

Answer 1

我要做的是：绘制所有具有相同颜色和低alpha（甚至低于0.1的曲线）的曲线，并绘制不同颜色的最佳曲线（我在本例中只选了一个随机曲线）

library(tidyverse)
combo_data %>%
  ggplot(aes(ID)) + 
  geom_path(aes(y = value, group = group_id), color = "black", alpha =0.1) +
  facet_wrap("type", scales = "free") +
  geom_path(data = combo_data %>%
              filter(group_id == 12), aes(y = value), color = "orange")

我不认为你可以为400个团队做得更好（除非使用交互式图（例如plotly），这样可以在两个方面中挑出每一行）。

这是方法：

combo_data %>%
  ggplot(aes(ID)) + 
  geom_path(aes(y = value, group = group_id), color = "black", alpha =0.1) +
  geom_path(aes(y = value, frame = group_id), color = "orange") +
  facet_wrap("type", scales = "free") +
  labs(title = "data", y = "y score", x = "x result") +
  theme_bw(base_size = 11, base_family = "") +
  theme(aspect.ratio = 1) + theme(legend.position="none") ->p

library(plotly)
ggplotly(p)

在这里你可以看到一个可以播放的滑块，它会在动画中逐个显示所有的线条。

EDIT1：顺便提一下，我建议检查那些在测试集中开始下降的ROC曲线。 ROC曲线不应下降（根据定义）。 根据评论，这些不是ROC曲线。从我的回答中删除了对ROC的提及。

EDIT2：如果你想要一个传奇：

combo_data %>%
  mutate(what = factor(ifelse(group_id == 12,
                                 "the_one",
                                 "all_others"))) %>% #create  grouping variable
  ggplot(aes(ID)) + 
  geom_path(aes(y = value, group = group_id, color = what, alpha = what)) + #color by grouping variable and alpha by it as well
  facet_wrap("type", scales = "free") +
  scale_color_manual(values = c("grey50", "orange")) + #I like this color combo
  scale_alpha_manual(values = c(0.05, 1)) +
  guides(alpha = guide_legend(override.aes = list(alpha = 1))) #so the legend is meaningful

获取具有最高测试值的曲线首先找到值：

combo_data %>%
  filter(type == "Test") %>%
  summarise(max = max(value)) %>%
  pull(max) -> best_val

然后用它来标记组：

combo_data %>%
  group_by(group_id) %>%
  mutate(what = factor(ifelse(any(value == best_val),
                              "the_one",
                              "all_others"))) %>%
  ggplot(aes(ID)) + 
  geom_path(aes(y = value, group = group_id, color = what, alpha = what)) +
  facet_wrap("type", scales = "free") +
  scale_color_manual(values = c("grey50", "orange")) +
  scale_alpha_manual(values = c(0.05, 1)) +
  guides(alpha = guide_legend(override.aes = list(alpha = 1)))

400多个线图的ggplot颜色相互重叠

1 个答案: