From 39fa45debe3fa768d79b077833b5045e139d3a42 Mon Sep 17 00:00:00 2001
From: pommedeterresautee <pommedeterresautee@msn.com>
Date: Fri, 4 Dec 2015 15:16:58 +0100
Subject: [PATCH] Add code to demo of leaf (show imprmt in accuracy)

---
 R-package/demo/predict_leaf_indices.R | 45 ++++++++++++++++++++++-----
 1 file changed, 38 insertions(+), 7 deletions(-)

diff --git a/R-package/demo/predict_leaf_indices.R b/R-package/demo/predict_leaf_indices.R
index 110bf9602..6cde561c2 100644
--- a/R-package/demo/predict_leaf_indices.R
+++ b/R-package/demo/predict_leaf_indices.R
@@ -1,4 +1,9 @@
 require(xgboost)
+require(data.table)
+require(Matrix)
+
+set.seed(1982)
+
 # load in the agaricus dataset
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
@@ -6,16 +11,42 @@ dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
 dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
 
 param <- list(max.depth=2, eta=1, silent=1, objective='binary:logistic')
-watchlist <- list(eval = dtest, train = dtrain)
-nround = 5
+nround = 4
 
 # training the model for two rounds
-bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2, watchlist = watchlist)
-cat('start testing prediction from first n trees\n')
+bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
+
+# Model accuracy without new features
+accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
 
-### predict using first 2 tree
-pred_with_leaf = predict(bst, dtest, ntreelimit = 2, predleaf = TRUE)
-head(pred_with_leaf)
 # by default, we predict using all the trees
+
 pred_with_leaf = predict(bst, dtest, predleaf = TRUE)
 head(pred_with_leaf)
+
+create.new.tree.features <- function(model, original.features){
+  pred_with_leaf = predict(model, original.features, predleaf = TRUE)
+  cols <- list()
+  for(i in 1:length(trees)){
+    # max is not the real max but it s not important for the purpose of adding features
+    max <- max(pred_with_leaf[,i])
+    cols[[i]] <- factor(x = pred_with_leaf[,i], level = seq(to = max))
+  }
+  cBind(original.features, sparse.model.matrix( ~ ., as.data.frame(cols)))
+}
+
+# Convert previous features to one hot encoding
+new.features.train <- create.new.tree.features(bst, agaricus.train$data)
+new.features.test <- create.new.tree.features(bst, agaricus.test$data)
+
+# learning with new features
+new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
+new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
+watchlist <- list(train = new.dtrain)
+bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
+
+# Model accuracy with new features
+accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
+
+# Here the accuracy was already good and is now perfect.
+print(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!"))