Merge pull request #114 from mayer79/ice-plot-facets

Ice plot facets

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.1.1
-Date: 2023-12-06 07:21:05 UTC
-SHA: 7ee87f42cc26b33f15e8944e7019d22f58c81350
+Version: 1.1.2
+Date: 2024-02-03 15:31:07 UTC
+SHA: 9eebdab2e583bf97d51dff9e7783df2b87751097

DESCRIPTION

@@ -18,13 +18,13 @@ Depends:
     R (>= 3.2.0)
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Imports: 
     ggplot2,
     stats,
     utils
 Suggests: 
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
-URL: https://github.com/mayer79/hstats
+URL: https://github.com/mayer79/hstats, https://mayer79.github.io/hstats/
 BugReports: https://github.com/mayer79/hstats/issues

NEWS.md

@@ -1,5 +1,9 @@
 # hstats 1.1.2
 
+## ICE plots
+
+- The ICE plot of a multioutput model without BY variable will now be using facets (instead of color). Use `swap_dim = TRUE` for the old behavior.
+
 ## API
 
 - {mlr3}: Non-probabilistic classification now works.

R/ice.R

@@ -258,11 +258,8 @@ plot.ice <- function(x, center = FALSE, alpha = 0.2,
   data <- poor_man_stack(data, to_stack = pred_names)
 
   # Distinguish all possible cases
-  grp <- if (is.null(by_names) && K > 1L) "varying_" else by_names[1L]   # can be NULL
-  wrp <- if (!is.null(by_names) && K > 1L) "varying_" 
-  if (length(by_names) == 2L) {
-    wrp <- by_names[2L]
-  }
+  grp <- if (!is.null(by_names)) by_names[1L]
+  wrp <- if (K > 1L) "varying_" else if (length(by_names) == 2L) by_names[2L]
   if (swap_dim) {
     tmp <- grp
     grp <- wrp

README.md

@@ -275,7 +275,7 @@ ice(fit, v = "Petal.Length", X = iris, BY = "Petal.Width") |>
 
 ### LightGBM
 
-Note: Versions from 4.0.0 upwards to not anymore require passing `reshape = TRUE` to the prediction function.
+Note: Versions < 4.0.0 require passing `reshape = TRUE` to the prediction function.
 
 ```r
 library(lightgbm)
@@ -294,30 +294,22 @@ fit <- lgb.train(
   nrounds = 1000
 )
 
-# Check that predictions require reshape = TRUE to be a matrix
-predict(fit, head(X_train, 2), reshape = TRUE)
-#           [,1]         [,2]         [,3]
-# [1,] 0.9999997 2.918695e-07 2.858720e-14
-# [2,] 0.9999999 1.038470e-07 7.337221e-10
-
 # mlogloss: 9.331699e-05
-average_loss(fit, X = X_valid, y = y_valid, loss = "mlogloss", reshape = TRUE)
+average_loss(fit, X = X_valid, y = y_valid, loss = "mlogloss")
 
-perm_importance(
-  fit, X = X_valid, y = y_valid, loss = "mlogloss", reshape = TRUE, m_rep = 100
-)
+perm_importance(fit, X = X_valid, y = y_valid, loss = "mlogloss", m_rep = 100)
 # Permutation importance regarding mlogloss
 # Petal.Length  Petal.Width  Sepal.Width Sepal.Length 
 #  2.624241332  1.011168660  0.082477177  0.009757393
 
-partial_dep(fit, v = "Petal.Length", X = X_train, reshape = TRUE) |> 
+partial_dep(fit, v = "Petal.Length", X = X_train) |> 
   plot(show_points = FALSE)
 
-ice(fit, v = "Petal.Length", X = X_train, reshape = TRUE) |> 
-  plot(swap_dim = TRUE, alpha = 0.05)
+ice(fit, v = "Petal.Length", X = X_train) |> 
+  plot(alpha = 0.05)
 
 # Interaction statistics, including three-way stats
-(H <- hstats(fit, X = X_train, reshape = TRUE, threeway_m = 4))  
+(H <- hstats(fit, X = X_train, threeway_m = 4))  
 # 0.3010446 0.4167927 0.1623982
 
 plot(H, ncol = 1)
@@ -327,7 +319,7 @@ plot(H, ncol = 1)
 
 ### XGBoost
 
-Also here, mind the `reshape = TRUE` sent to the prediction function.
+Mind the `reshape = TRUE` sent to the prediction function.
 
 ```r
 library(xgboost)
@@ -379,7 +371,7 @@ plot(H, normalize = FALSE, squared = FALSE, facet_scales = "free_y", ncol = 1)
 
 ### Non-probabilistic classification
 
-When predictions are factor levels, {hstats} uses internal one-hot-encoding.
+When predictions are factor levels, {hstats} uses internal one-hot-encoding. Usually, probabilistic classification makes more sense though.
 
 ```r
 library(ranger)

backlog/benchmark.R

@@ -44,7 +44,7 @@ fit <- xgb.train(
 )
 
 # Interpret via {hstats}
-average_loss(fit, X = X_valid, y = y_valid)  # 0.0247 MSE -> 0.157 RMSE
+average_loss(fit, X = X_valid, y = y_valid)  # 0.023 MSE
 
 perm_importance(fit, X = X_valid, y = y_valid) |> 
   plot()
@@ -116,11 +116,11 @@ bench::mark(
   min_iterations = 3
 )
 
-# expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time
-# iml           1.58s    1.58s     0.631   209.4MB    2.73      3    13      4.76s
-# dalex      566.21ms 586.91ms     1.72     34.6MB    0.572     3     1      1.75s
-# flashlight 587.03ms 613.15ms     1.63     27.1MB    1.63      3     3      1.84s
-# hstats     353.78ms 360.57ms     2.79     27.2MB    0         3     0      1.08s
+#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time
+# 1 iml           1.72s    1.75s     0.574   210.6MB    1.34      3     7      5.23s <NULL>
+# 2 dalex      744.82ms 760.02ms     1.31     35.2MB    0.877     3     2      2.28s <NULL>
+# 3 flashlight    1.29s    1.35s     0.742      63MB    0.990     3     4      4.04s <NULL>
+# 4 hstats     407.26ms 412.31ms     2.43     26.5MB    0         3     0      1.23s <NULL>
 
 # Partial dependence (cont)
 v <- "tot_lvg_area"
@@ -132,12 +132,12 @@ bench::mark(
   check = FALSE,
   min_iterations = 3
 )
-# expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time
-# iml           1.11s    1.13s     0.887   376.3MB     3.84     3    13      3.38s
-# dalex      782.13ms 783.08ms     1.24    192.8MB     2.90     3     7      2.41s
-# flashlight 367.73ms  372.5ms     2.68     67.9MB     2.68     3     3      1.12s
-# hstats     220.88ms  222.5ms     4.50     14.2MB     0        3     0   666.33ms
-
+#   expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time
+# 1 iml           1.14s    1.16s     0.861   376.7MB    3.73      3    13      3.48s <NULL>
+# 2 dalex      653.24ms 654.51ms     1.35    192.8MB    2.24      3     5      2.23s <NULL>
+# 3 flashlight 352.34ms 361.79ms     2.72     66.7MB    0.906     3     1       1.1s <NULL>
+# 4 hstats     239.03ms 242.79ms     4.04     14.2MB    1.35      3     1   743.43ms <NULL>
+  
 # Partial dependence (discrete)
 v <- "structure_quality"
 bench::mark(
@@ -148,11 +148,11 @@ bench::mark(
   check = FALSE,
   min_iterations = 3
 )
-# expression      min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time
-# iml            90ms     96ms     10.6    13.29MB     7.06     3     2      283ms
-# dalex       170.6ms  174.4ms      5.73   20.55MB     2.87     2     1      349ms
-# flashlight   40.8ms   43.8ms     23.1     6.36MB     2.10    11     1      476ms
-# hstats       23.5ms   24.4ms     40.6     1.53MB     2.14    19     1      468ms
+# expression        min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time
+# 1 iml         100.6ms  103.6ms      9.46   13.34MB     0        5     0      529ms <NULL>
+# 2 dalex       172.4ms  177.9ms      5.62   20.55MB     2.81     2     1      356ms <NULL>
+# 3 flashlight   43.5ms   45.5ms     21.9     6.36MB     2.19    10     1      457ms <NULL>
+# 4 hstats       25.3ms   25.8ms     37.9     1.54MB     2.10    18     1      475ms <NULL>
 
 # H-Stats -> we use a subset of 500 rows
 X_v500 <- X_valid[1:500, ]
@@ -167,17 +167,17 @@ system.time(  # 135s for all combinations of latitude
   iml_pairwise <- Interaction$new(mod500, grid.size = 500, feature = "latitude")
 )
 
-# flashlight: 14s total, doing only one pairwise calculation, otherwise would take 63s
-system.time(  # 12s
+# flashlight: 13s total, doing only one pairwise calculation, otherwise would take 63s
+system.time(  # 11.5s
   fl_overall <- light_interaction(fl500, v = x, grid_size = Inf, n_max = Inf)
 )
-system.time(  # 2s
+system.time(  # 2.4s
   fl_pairwise <- light_interaction(
     fl500, v = coord, grid_size = Inf, n_max = Inf, pairwise = TRUE
   )
 )
 
-# hstats: 3.4s total
+# hstats: 3.5s total
 system.time({
   H <- hstats(fit, v = x, X = X_v500, n_max = Inf)
   hstats_overall <- h2_overall(H, squared = FALSE, zero = FALSE)
@@ -193,26 +193,26 @@ system.time(
 # Overall statistics correspond exactly
 iml_overall$results |> filter(.interaction > 1e-6)
 #     .feature .interaction
-# 1:  latitude    0.2458269
-# 2: longitude    0.2458269
+# 1:  latitude    0.2791144
+# 2: longitude    0.2791144
 
 fl_overall$data |> subset(value > 0, select = c(variable, value))
 #   variable  value
-# 1 latitude  0.246
-# 2 longitude 0.246
+# 1 latitude  0.279
+# 2 longitude 0.279
 
 hstats_overall
 # longitude  latitude 
-# 0.2458269 0.2458269 
+# 0.2791144 0.2791144 
 
 # Pairwise results match as well
 iml_pairwise$results |> filter(.interaction > 1e-6)
 #              .feature .interaction
-# 1: longitude:latitude    0.3942526
+# 1: longitude:latitude    0.4339574
 
 fl_pairwise$data |> subset(value > 0, select = c(variable, value))
-# latitude:longitude 0.394
+# latitude:longitude 0.434
 
 hstats_pairwise
 # latitude:longitude 
-# 0.3942526 
+# 0.4339574

cran-comments.md

@@ -1,17 +1,21 @@
-# hstats 1.1.1
+# hstats 1.1.2
 
-Hello CRAN
+Hello CRAN team
 
-This is a small release with a bugfix and some speed improvements.
+This is a small release with two convenient API improvements.
 
-## Local checks: 1 NOTE
+## Local checks: 0 errors, 0 warnings, 0 notes
 
-- Skipping checking math rendering: package 'V8' unavailable
-
-## Rhub: 2 NOTES
+## Rhub: 3 NOTES (sounding harmless)
 
-- Skipping checking HTML validation: no command 'tidy' found
-- Skipping checking math rendering: package 'V8' unavailable
+- checking HTML version of manual ... NOTE
+  Skipping checking math rendering: package 'V8' unavailable
+- checking for non-standard things in the check directory ... NOTE
+  Found the following files/directories:
+  ''NULL''
+- checking for detritus in the temp directory ... NOTE
+  Found the following files/directories:
+  'lastMiKTeXException'
 
 ## Winbuilder