linting

R-package/demo/leaf_stability.R

@@ -4,15 +4,87 @@
 
 library(lightgbm)
 
-# Second, we load our data
+# define helper functions for creating plots
+
+# output of `RColorBrewer::brewer.pal(10, "RdYlGn")`, hardcooded here to avoid a dependency
+.diverging_palette <- c(
+  "#A50026", "#D73027", "#F46D43", "#FDAE61", "#FEE08B"
+  , "#D9EF8B", "#A6D96A", "#66BD63", "#1A9850", "#006837"
+)
+
+.prediction_depth_plot <- function(df) {
+  plot(
+    x = df$X
+    , y = df$Y
+    , type = "p"
+    , main = "Prediction Depth"
+    , xlab = "Leaf Bin"
+    , ylab = "Prediction Probability"
+    , pch = 19L
+    , col = .diverging_palette[df$binned + 1L]
+  )
+  legend(
+    "topright"
+    , title = "bin"
+    , legend = sort(unique(df$binned))
+    , pch = 19L
+    , col = .diverging_palette[sort(unique(df$binned + 1L))]
+    , cex = 0.7
+  )
+}
+
+.prediction_depth_spread_plot <- function(df) {
+  plot(
+    x = df$binned
+    , xlim = c(0L, 9L)
+    , y = df$Z
+    , type = "p"
+    , main = "Prediction Depth Spread"
+    , xlab = "Leaf Bin"
+    , ylab = "Logloss"
+    , pch = 19L
+    , col = .diverging_palette[df$binned + 1L]
+  )
+  legend(
+    "topright"
+    , title = "bin"
+    , legend = sort(unique(df$binned))
+    , pch = 19L
+    , col = .diverging_palette[sort(unique(df$binned + 1L))]
+    , cex = 0.7
+  )
+}
+
+.depth_density_plot <- function(df) {
+  plot(
+    x = density(df$Y)
+    , xlim = c(min(df$Y), max(df$Y))
+    , type = "p"
+    , main = "Depth Density"
+    , xlab = "Prediction Probability"
+    , ylab = "Bin Density"
+    , pch = 19L
+    , col = .diverging_palette[df$binned + 1L]
+  )
+  legend(
+    "topright"
+    , title = "bin"
+    , legend = sort(unique(df$binned))
+    , pch = 19L
+    , col = .diverging_palette[sort(unique(df$binned + 1L))]
+    , cex = 0.7
+  )
+}
+
+# load some data
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
 
-# Third, we setup parameters and we train a model
+# setup parameters and we train a model
 params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
 model <- lgb.train(
@@ -64,105 +136,9 @@ table(new_data$binned)
 # We can plot the binned content
 # On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss
 # On the third plot, it is smooth!
-.diverging_palette <- c(
-  "#A50026", "#D73027", "#F46D43", "#FDAE61", "#FEE08B"
-  , "#D9EF8B", "#A6D96A", "#66BD63", "#1A9850", "#006837"
-)
-
-.prediction_depth_plot <- function(df){
-  plot(
-    x = df$X
-    , y = df$Y
-    , type = "p"
-    , main = "Prediction Depth"
-    , xlab = "Leaf Bin"
-    , ylab = "Prediction Probability"
-    , pch = 19
-    , col = .diverging_palette[df$binned + 1]
-  )
-  legend(
-    "topright"
-    , title = "bin"
-    , legend = sort(unique(df$binned))
-    , pch = 19
-    , col = .diverging_palette[sort(unique(df$binned + 1))]
-    , cex = 0.7
-  )
-}
-
-
 .prediction_depth_plot(df = new_data)
-
-ggplot(
-    data = new_data
-    , mapping = aes(x = X, y = Y, color = binned)
-) + geom_point() +
-  theme_bw() +
-  labs(title = "Prediction Depth", x = "Leaf Bin", y = "Prediction Probability")
-
-
-.prediction_depth_spread_plot <- function(df){
-  plot(
-    x = df$binned
-    , xlim = c(0, 9)
-    , y = df$Z
-    , type = "p"
-    , main = "Prediction Depth Spread"
-    , xlab = "Leaf Bin"
-    , ylab = "Logloss"
-    , pch = 19
-    , col = .diverging_palette[df$binned + 1]
-  )
-  legend(
-    "topright"
-    , title = "bin"
-    , legend = sort(unique(df$binned))
-    , pch = 19
-    , col = .diverging_palette[sort(unique(df$binned + 1))]
-    , cex = 0.7
-  )
-}
-
-
 .prediction_depth_spread_plot(df = new_data)
-ggplot(
-    data = new_data
-    , mapping = aes(x = binned, y = Z, fill = binned, group = binned)
-) + geom_boxplot() +
-  theme_bw() +
-  labs(title = "Prediction Depth Spread", x = "Leaf Bin", y = "Logloss")
-
-.depth_density_plot <- function(df){
-  plot(
-    x = density(df$Y)
-    , xlim = c(min(df$Y), max(df$Y))
-    , type = "p"
-    , main = "Depth Density"
-    , xlab = "Prediction Probability"
-    , ylab = "Bin Density"
-    , pch = 19
-    , col = .diverging_palette[df$binned + 1]
-  )
-  legend(
-    "topright"
-    , title = "bin"
-    , legend = sort(unique(df$binned))
-    , pch = 19
-    , pt.cex = 0.1
-    , col = .diverging_palette[sort(unique(df$binned + 1))]
-    , cex = 0.7
-  )
-}
-
-
 .depth_density_plot(df = new_data)
-ggplot(
-    data = new_data
-    , mapping = aes(x = Y, y = ..count.., fill = binned)
-) + geom_density(position = "fill") +
-  theme_bw() +
-  labs(title = "Depth Density", x = "Prediction Probability", y = "Bin Density")
-
 
 # Now, let's show with other parameters
 model2 <- lgb.train(
@@ -213,29 +189,8 @@ table(new_data2$binned)
 # real thus it is not an issue
 # However, if the rules were not true, the loss would explode.
 .prediction_depth_plot(df = new_data2)
-ggplot(
-    data = new_data2
-    , mapping = aes(x = X, y = Y, color = binned)
-) + geom_point() +
-  theme_bw() +
-  labs(title = "Prediction Depth", x = "Leaf Bin", y = "Prediction Probability")
-
 .prediction_depth_spread_plot(df = new_data2)
-ggplot(
-    data = new_data2
-    , mapping = aes(x = binned, y = Z, fill = binned, group = binned)
-) + geom_boxplot() +
-  theme_bw() +
-  labs(title = "Prediction Depth Spread", x = "Leaf Bin", y = "Logloss")
-
 .depth_density_plot(df = new_data2)
-ggplot(
-    data = new_data2
-    , mapping = aes(x = Y, y = ..count.., fill = binned)
-) + geom_density(position = "fill") +
-  theme_bw() +
-  labs(title = "Depth Density", x = "Prediction Probability", y = "Bin Density")
-
 
 # Now, try with very severe overfitting
 model3 <- lgb.train(
@@ -285,19 +240,6 @@ table(new_data3$binned)
 # are real thus it is not an issue.
 # However, if the rules were not true, the loss would explode. See the sudden spikes?
 .depth_density_plot(df = new_data3)
-ggplot(
-    data = new_data3
-    , mapping = aes(x = Y, y = ..count.., fill = binned)
-) +
-  geom_density(position = "fill") +
-  theme_bw() +
-  labs(title = "Depth Density", x = "Prediction Probability", y = "Bin Density")
 
 # Compare with our second model, the difference is severe. This is smooth.
 .depth_density_plot(df = new_data2)
-ggplot(
-    data = new_data2
-    , mapping = aes(x = Y, y = ..count.., fill = binned)
-) + geom_density(position = "fill") +
-  theme_bw() +
-  labs(title = "Depth Density", x = "Prediction Probability", y = "Bin Density")