Merge pull request #113 from ModelOriented/add_blogpost

add blogpost

.Rbuildignore

@@ -6,7 +6,7 @@
 ^cran-comments\.md$
 ^.*\.Rproj$
 ^\.Rproj\.user$
-^compare_with_python.R$
+^\backlog$
 ^CRAN-SUBMISSION$
 ^_pkgdown\.yml$
 ^docs$

backlog/2023-11-11 Permutation-SHAP.R

@@ -0,0 +1,72 @@
+library(kernelshap)
+library(ranger)
+
+# Simulation 1: iris
+differences <- numeric(4)
+
+for (depth in 1:4) {
+  fit <- ranger(
+    Sepal.Length ~ Petal.Width + Petal.Length + Species, 
+    mtry = 3,
+    data = iris, 
+    max.depth = depth,
+    seed = 1
+  )
+  ps <- permshap(fit, iris[3:5], bg_X = iris)
+  ks <- kernelshap(fit, iris[3:5], bg_X = iris)
+  differences[depth] <- mean(abs(ks$S - ps$S))
+}
+differences
+ps
+ks
+
+# larger data, more features
+library(xgboost)
+library(shapviz)
+
+colnames(miami) <- tolower(colnames(miami))
+miami$log_ocean <- log(miami$ocean_dist)
+x <- c("log_ocean", "tot_lvg_area", "lnd_sqfoot", "structure_quality", "age", "month_sold")
+
+# Train/valid split
+set.seed(1)
+ix <- sample(nrow(miami), 0.8 * nrow(miami))
+
+y_train <- log(miami$sale_prc[ix])
+y_valid <- log(miami$sale_prc[-ix])
+X_train <- data.matrix(miami[ix, x])
+X_valid <- data.matrix(miami[-ix, x])
+
+dtrain <- xgb.DMatrix(X_train, label = y_train)
+dvalid <- xgb.DMatrix(X_valid, label = y_valid)
+
+# Fit via early stopping (depth 1 to 3)
+differences <- numeric(3)
+
+for (i in 1:3) {
+  fit <- xgb.train(
+    params = list(learning_rate = 0.15, objective = "reg:squarederror", max_depth = i),
+    data = dtrain,
+    watchlist = list(valid = dvalid),
+    early_stopping_rounds = 20,
+    nrounds = 1000,
+    callbacks = list(cb.print.evaluation(period = 100))
+  )
+  ps <- permshap(fit, X = head(X_valid, 500), bg_X = head(X_valid, 500))
+  ks <- kernelshap(fit, X = head(X_valid, 500), bg_X = head(X_valid, 500))
+  differences[i] <- mean(abs(ks$S - ps$S))
+}
+differences
+# 2.904010e-09 5.158383e-09 6.586577e-04
+
+ps
+# SHAP values of first observations:
+# log_ocean tot_lvg_area lnd_sqfoot structure_quality        age  month_sold
+# 0.2224359   0.04941044  0.1266136         0.1360166 0.01036866 0.005557032
+# 0.3674484   0.01045079  0.1192187         0.1180312 0.01426247 0.005465283
+
+ks
+# SHAP values of first observations:
+# log_ocean tot_lvg_area lnd_sqfoot structure_quality        age  month_sold
+# 0.2245202  0.049520308  0.1266020         0.1349770 0.01142703 0.003355770
+# 0.3697167  0.009575195  0.1198201         0.1168738 0.01544061 0.003450425