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Generate data for machine learning benchmark for Spark
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## get the data | |
for yr in 2005 2006 2007; do | |
wget http://stat-computing.org/dataexpo/2009/$yr.csv.bz2 | |
bunzip2 $yr.csv.bz2 | |
done | |
## install R and data.table | |
echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" > /etc/apt/sources.list.d/r.list | |
apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 | |
apt-get update | |
apt-get install r-base-dev libcurl4-openssl-dev | |
R --vanilla << EOF | |
install.packages(c("data.table"), repos="http://cran.rstudio.com") | |
EOF | |
## generate standard format dataset | |
time R --vanilla --quiet << EOF | |
library(data.table) | |
set.seed(123) | |
d1a <- fread("2005.csv") | |
d1b <- fread("2006.csv") | |
d2 <- fread("2007.csv") | |
d1 <- rbind(d1a, d1b) | |
d1 <- d1[!is.na(DepDelay)] | |
d2 <- d2[!is.na(DepDelay)] | |
for (k in c("Month","DayofMonth","DayOfWeek")) { | |
d1[[k]] <- as.character(d1[[k]]) | |
d2[[k]] <- as.character(d2[[k]]) | |
} | |
d1[["dep_delayed_15min"]] <- ifelse(d1[["DepDelay"]]>=15,"Y","N") | |
d2[["dep_delayed_15min"]] <- ifelse(d2[["DepDelay"]]>=15,"Y","N") | |
cols <- c("Month", "DayofMonth", "DayOfWeek", "DepTime", "UniqueCarrier", | |
"Origin", "Dest", "Distance","dep_delayed_15min") | |
d1 <- d1[, cols, with = FALSE] | |
d2 <- d2[, cols, with = FALSE] | |
for (n in c(1e6)) { | |
write.table(d1[sample(nrow(d1),n),], file = paste0("train-",n/1e6,"m.csv"), row.names = FALSE, sep = ",") | |
} | |
write.table(d2[sample(nrow(d2),1e5),], file = "test.csv", row.names = FALSE, sep = ",") | |
EOF | |
## generate 1-hot encoded dataset | |
for SIZE in 1; do | |
time R --vanilla --quiet << EOF | |
library(data.table) | |
d1 <- fread("train-${SIZE}m.csv") | |
d2 <- fread("test.csv") | |
d <- rbind(d1,d2) | |
X <- model.matrix(dep_delayed_15min ~ ., d) | |
y <- ifelse(d[["dep_delayed_15min"]]=="Y",1,0) | |
dd <- cbind(y,X) | |
dd1 <- dd[1:nrow(d1),] | |
dd2 <- dd[(nrow(d1)+1):(nrow(d1)+nrow(d2)),] | |
write.table(dd1, "train-1hot-${SIZE}m.csv", row.names=FALSE, col.names=FALSE, sep=",") | |
write.table(dd2, "test-1hot-${SIZE}m.csv", row.names=FALSE, col.names=FALSE, sep=",") | |
EOF | |
done | |
## generate integer-encoded categoricals | |
for SIZE in 1; do | |
time R --vanilla --quiet << EOF | |
library(data.table) | |
d1 <- as.data.frame(fread("train-${SIZE}m.csv")) | |
d2 <- as.data.frame(fread("test.csv")) | |
d <- rbind(d1,d2) | |
for (k in c("Month","DayofMonth","DayOfWeek","UniqueCarrier","Origin","Dest")) { | |
d[,k] <- as.numeric(as.factor(d[,k]))-1 | |
} | |
d[["dep_delayed_15min"]] <- ifelse(d[["dep_delayed_15min"]]=="Y",1,0) | |
s <- "" | |
for (k in c("Month","DayofMonth","DayOfWeek","UniqueCarrier","Origin","Dest")) { | |
s <- paste0(s, which(names(d)==k)-1, " -> ", length(unique(d[,k])) ,", ") | |
} | |
s | |
## 0 -> 12, 1 -> 31, 2 -> 7, 4 -> 23, 5 -> 307, 6 -> 308 | |
dd1 <- d[1:nrow(d1),] | |
dd2 <- d[(nrow(d1)+1):(nrow(d1)+nrow(d2)),] | |
write.table(dd1, "train-intcateg-${SIZE}m.csv", row.names=FALSE, col.names=FALSE, sep=",") | |
write.table(dd2, "test-intcateg-${SIZE}m.csv", row.names=FALSE, col.names=FALSE, sep=",") | |
EOF | |
done | |
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