library(reticulate)
library(rfSLAM)
pd <- import("pandas")
df <- pd$read_pickle("old/data/time_constant/data_nsub1000nint10.pkl")
df$ev <- as.factor(df$event)
df$rt <- df$t1 - df$t0
rf.c.ppt <- rfsrc(ev ~ cov1 + cov2, data = df, ntree=100, risk.time=df$rt, bootstrap  = "by.user")
ntree    <- 500
samp     <- matrix(1, dim(df)[1], ntree)
samp[,1] <- samp[,1] * 10
rf.c.ppt <- rfsrc(ev ~ cov1 + cov2, data = df, ntree=100, risk.time=df$rt, bootstrap  = "by.user")
rf.c.ppt <- rfsrc(ev ~ cov1 + cov2, data = df, ntree=100, risk.time=df$rt, bootstrap  = "by.user", samp=samp)
new.pred <- predict.rfsrc(rf.c.ppt, newdata = df, membership = TRUE)
