Background
- I've built an Extreme Gradient Boosting (XGB) model using R
- I've used the model object to score my test set
- But, I'm unable to use the model object to score my deployment set
Load R Libraries
library(xgboost)
library(Matrix)
Create Dummy Data
### Training Set ###
train1 <- c("5032","1","66","139","0","9500","12","0")
train2 <-c("5031","1","61","34","5078","5100","12","2")
train3 <-c("5030","0","72","161","2540","4000","11","2")
train4 <-c("5029","1","68","0","6456","10750","12","4")
train5 <-c("5028","1","59","86","0","10000","12","0")
train6 <-c("5027","0","49","42","1756","4500","12","2")
train7 <-c("5026","0","61","14","0","2500","12","0")
train8 <-c("5025","0","44","153","0","9000","12","0")
train9 <-c("5024","1","79","61","0","5000","12","0")
train10 <-c("5023","1","46","139","2121","5600","6","3")
train <- rbind.data.frame(train1, train2, train3, train4, train5,
train6, train7, train8, train9, train10)
names(train) <- c("customer_id","target","v1","v2","v3","v4","v5","v6")
for(i in 1:ncol(train)) {
train[,i] <- as.character(train[,i])
}
for(i in 1:ncol(train)) {
train[,i] <- as.integer(train[,i])
}
### Testing Set ###
test1 <- c("5021","0","55","64","2891","5000","12","4")
test2 <-c("5020","1","57","49","167","3000","12","2")
test3 <-c("5019","1","54","55","4352","9000","12","4")
test4 <-c("5018","0","70","8","2701","5000","12","3")
test5 <-c("5017","0","64","59","52","3000","12","2")
test6 <-c("5016","1","57","73","0","4000","12","0")
test7 <-c("5015","0","46","28","1187","6000","12","3")
test8 <-c("5014","1","57","38","740","4500","12","2")
test9 <-c("5013","1","54","159","0","3300","11","0")
test10 <-c("5012","0","48","19","690","6500","11","2")
test <- rbind.data.frame(test1, test2, test3, test4, test5,
test6, test7, test8, test9, test10)
names(test) <- c("customer_id","target","v1","v2","v3","v4","v5","v6")
for(i in 1:ncol(test)) {
test[,i] <- as.character(test[,i])
}
for(i in 1:ncol(test)) {
test[,i] <- as.integer(test[,i])
}
### Deployment Set ###
deploy1 <- c("5011","58","5","7897","12000","12","4")
deploy2 <- c("5010","60","161","1601","7500","12","2")
deploy3 <- c("5009","40","59","0","5000","12","0")
deploy4 <- c("5008","57","80","0","3500","12","0")
deploy5 <- c("5007","50","70","1056","3000","12","2")
deploy6 <- c("5006","65","6","1010","9000","12","3")
deploy7 <- c("5005","65","17","1978","4500","12","2")
deploy8 <- c("5004","80","103","0","10000","12","0")
deploy9 <- c("5003","52","11","2569","3500","12","2")
deploy10 <- c("5002","54","81","1905","4000","12","4")
deploy <- rbind.data.frame(deploy1, deploy2, deploy3, deploy4, deploy5,
deploy6, deploy7, deploy8, deploy9, deploy10)
names(deploy) <- c("customer_id","v1","v2","v3","v4","v5","v6")
for(i in 1:ncol(deploy)) {
deploy[,i] <- as.character(deploy[,i])
}
for(i in 1:ncol(deploy)) {
deploy[,i] <- as.integer(deploy[,i])
}
Convert to matrix
# Remove customer Id
train_A <- train %>% select(-customer_id)
test_A <- test %>% select(-customer_id)
# Covert training set into sparse-matrix
train_sparse_matrix<- sparse.model.matrix(target ~.-1, data = train_A)
test_sparse_matrix<- sparse.model.matrix(target ~.-1, data = test_A)
# Create target vector
train_target <- as.vector(train_A$target)
test_target <- as.vector(test_A$target)
# Convert training set to dmatrix (preferred for xgboost)
train_dmatrix <- xgboost::xgb.DMatrix(data=train_sparse_matrix, label=train_target)
test_dmatrix <- xgboost::xgb.DMatrix(data=test_sparse_matrix, label=test_target)
Train Model
hn_xgb <- xgboost(tar_flag ~ .,
data = train_dmatrix,
max_depth = 6,
eta = 0.3,
num_parallel_tree = 1,
nthread = 2,
nround = 100,
metrics = 'error',
objective = 'binary:logistic')
Score Test Set
predict(hn_xgb, test_dmatrix)
Score Deployment Set
Deployment set has no target variable because the target has not occcured i.e. it is the very thing the deployed scores will try to predict.
### Convert to matrix ###
# Remove customer Id
deploy_A <- deploy %>% select(-customer_id)
# Covert deployment set into sparse-matrix
deploy_sparse_matrix<- sparse.model.matrix(data = deploy_A) ## Error !!!
Returns the following error:
Since I failed to create a sparse-matrix, the next step to create DMatrix doesn't work ...
# Convert training set to dmatrix (preferred for xgboost)
deploy_dmatrix <- xgboost::xgb.DMatrix(data=deploy_sparse_matrix)
Which means I cannot score my deployment set ...
Question
- How can I convert my deployment set to sparse-matrix or DMatrix?
- Can you recommend any easier step to score my deployment set?
