[R-package] factor out {ggplot2} (#3224)

* more changes

* factor out ggplot2

* update CI

* remove library()

* linting

* reduce NOTEs on Windows

.ci/test_r_package.sh

@@ -148,7 +148,7 @@ if grep -q -R "WARNING" "$LOG_FILE_NAME"; then
     exit -1
 fi
 
-ALLOWED_CHECK_NOTES=3
+ALLOWED_CHECK_NOTES=2
 NUM_CHECK_NOTES=$(
     cat ${LOG_FILE_NAME} \
         | grep -e '^Status: .* NOTE.*' \

.ci/test_r_package_windows.ps1

@@ -157,7 +157,7 @@ if ($env:COMPILER -ne "MSVC") {
   $note_str = Get-Content -Path "${LOG_FILE_NAME}" | Select-String -Pattern '.*Status.* NOTE' | Out-String ; Check-Output $?
   $relevant_line = $note_str -match '(\d+) NOTE'
   $NUM_CHECK_NOTES = $matches[1]
-  $ALLOWED_CHECK_NOTES = 3
+  $ALLOWED_CHECK_NOTES = 2
   if ([int]$NUM_CHECK_NOTES -gt $ALLOWED_CHECK_NOTES) {
       Write-Output "Found ${NUM_CHECK_NOTES} NOTEs from R CMD check. Only ${ALLOWED_CHECK_NOTES} are allowed"
       Check-Output $False

R-package/DESCRIPTION

@@ -25,7 +25,6 @@ BugReports: https://github.com/Microsoft/LightGBM/issues
 NeedsCompilation: yes
 Biarch: false
 Suggests:
-    ggplot2 (>= 1.0.1),
     processx,
     testthat
 Depends:

R-package/demo/leaf_stability.R

@@ -2,19 +2,89 @@
 # Obviously, we are in a controlled environment, without issues (real rules).
 # Do not do this in a real scenario.
 
-# First, we load our libraries
 library(lightgbm)
-library(ggplot2)
 
-# 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(
@@ -59,33 +129,16 @@ new_data$binned <- .bincode(
     , include.lowest = TRUE
 )
 new_data$binned[is.na(new_data$binned)] <- 0L
-new_data$binned <- as.factor(new_data$binned)
 
 # We can check the binned content
 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!
-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")
-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")
-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")
-
+.prediction_depth_plot(df = new_data)
+.prediction_depth_spread_plot(df = new_data)
+.depth_density_plot(df = new_data)
 
 # Now, let's show with other parameters
 model2 <- lgb.train(
@@ -126,7 +179,6 @@ new_data2$binned <- .bincode(
     , include.lowest = TRUE
 )
 new_data2$binned[is.na(new_data2$binned)] <- 0L
-new_data2$binned <- as.factor(new_data2$binned)
 
 # We can check the binned content
 table(new_data2$binned)
@@ -136,25 +188,9 @@ table(new_data2$binned)
 # On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules are
 # real thus it is not an issue
 # However, if the rules were not true, the loss would explode.
-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")
-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")
-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")
-
+.prediction_depth_plot(df = new_data2)
+.prediction_depth_spread_plot(df = new_data2)
+.depth_density_plot(df = new_data2)
 
 # Now, try with very severe overfitting
 model3 <- lgb.train(
@@ -195,7 +231,6 @@ new_data3$binned <- .bincode(
     , include.lowest = TRUE
 )
 new_data3$binned[is.na(new_data3$binned)] <- 0L
-new_data3$binned <- as.factor(new_data3$binned)
 
 # We can check the binned content
 table(new_data3$binned)
@@ -204,18 +239,7 @@ table(new_data3$binned)
 # On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules
 # are real thus it is not an issue.
 # However, if the rules were not true, the loss would explode. See the sudden spikes?
-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")
+.depth_density_plot(df = new_data3)
 
 # Compare with our second model, the difference is severe. This is smooth.
-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")
+.depth_density_plot(df = new_data2)