simplify simulation 02.
This commit is contained in:
parent
27a3849d5d
commit
2cd447c327
@ -31,7 +31,7 @@ source("simulation_base.R")
|
||||
|
||||
## one way to do it is by adding correlation to x.obs and y that isn't in w.
|
||||
## in other words, the model is missing an important feature of x.obs that's related to y.
|
||||
simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, accuracy_imbalance_difference=0.3){
|
||||
simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.025, prediction_accuracy=0.73, y_bias=-0.8){
|
||||
set.seed(seed)
|
||||
# make w and y dependent
|
||||
z <- rbinom(N, 1, 0.5)
|
||||
@ -49,107 +49,59 @@ simulate_data <- function(N, m, B0, Bxy, Bzx, Bzy, seed, y_explained_variance=0.
|
||||
df <- df[, x.obs := x]
|
||||
}
|
||||
|
||||
## px <- mean(x)
|
||||
## accuracy_imbalance_ratio <- (prediction_accuracy + accuracy_imbalance_difference/2) / (prediction_accuracy - accuracy_imbalance_difference/2)
|
||||
## probablity of an error is correlated with y
|
||||
p.correct <- plogis(y_bias*scale(y) + qlogis(prediction_accuracy))
|
||||
|
||||
## # this works because of conditional probability
|
||||
## accuracy_x0 <- prediction_accuracy / (px*(accuracy_imbalance_ratio) + (1-px))
|
||||
## accuracy_x1 <- accuracy_imbalance_ratio * accuracy_x0
|
||||
acc.x0 <- p.correct[df[,x==0]]
|
||||
acc.x1 <- p.correct[df[,x==1]]
|
||||
|
||||
## x0 <- df[x==0]$x
|
||||
## x1 <- df[x==1]$x
|
||||
## nx1 <- nrow(df[x==1])
|
||||
## nx0 <- nrow(df[x==0])
|
||||
df[x==0,w:=rlogis(.N,qlogis(1-acc.x0))]
|
||||
df[x==1,w:=rlogis(.N,qlogis(acc.x1))]
|
||||
|
||||
## yx0 <- df[x==0]$y
|
||||
## yx1 <- df[x==1]$y
|
||||
df[,w_pred := as.integer(w>0.5)]
|
||||
|
||||
# tranform yz0.1 into a logistic distribution with mean accuracy_z0
|
||||
## acc.x0 <- plogis(0.5*scale(yx0) + qlogis(accuracy_x0))
|
||||
## acc.x1 <- plogis(1.5*scale(yx1) + qlogis(accuracy_x1))
|
||||
|
||||
## w0x0 <- (1-x0)**2 + (-1)**(1-x0) * acc.x0
|
||||
## w0x1 <- (1-x1)**2 + (-1)**(1-x1) * acc.x1
|
||||
pz <- mean(z)
|
||||
accuracy_imbalance_ratio <- (prediction_accuracy + accuracy_imbalance_difference/2) / (prediction_accuracy - accuracy_imbalance_difference/2)
|
||||
|
||||
# this works because of conditional probability
|
||||
accuracy_z0 <- prediction_accuracy / (pz*(accuracy_imbalance_ratio) + (1-pz))
|
||||
accuracy_z1 <- accuracy_imbalance_ratio * accuracy_z0
|
||||
|
||||
z0x0 <- df[(z==0) & (x==0)]$x
|
||||
z0x1 <- df[(z==0) & (x==1)]$x
|
||||
z1x0 <- df[(z==1) & (x==0)]$x
|
||||
z1x1 <- df[(z==1) & (x==1)]$x
|
||||
|
||||
yz0x0 <- df[(z==0) & (x==0)]$y
|
||||
yz0x1 <- df[(z==0) & (x==1)]$y
|
||||
yz1x0 <- df[(z==1) & (x==0)]$y
|
||||
yz1x1 <- df[(z==1) & (x==1)]$y
|
||||
|
||||
nz0x0 <- nrow(df[(z==0) & (x==0)])
|
||||
nz0x1 <- nrow(df[(z==0) & (x==1)])
|
||||
nz1x0 <- nrow(df[(z==1) & (x==0)])
|
||||
nz1x1 <- nrow(df[(z==1) & (x==1)])
|
||||
|
||||
yz1 <- df[z==1]$y
|
||||
yz1 <- df[z==1]$y
|
||||
|
||||
# tranform yz0.1 into a logistic distribution with mean accuracy_z0
|
||||
acc.z0x0 <- plogis(0.5*scale(yz0x0) + qlogis(accuracy_z0))
|
||||
acc.z0x1 <- plogis(0.5*scale(yz0x1) + qlogis(accuracy_z0))
|
||||
acc.z1x0 <- plogis(1.5*scale(yz1x0) + qlogis(accuracy_z1))
|
||||
acc.z1x1 <- plogis(1.5*scale(yz1x1) + qlogis(accuracy_z1))
|
||||
|
||||
w0z0x0 <- (1-z0x0)**2 + (-1)**(1-z0x0) * acc.z0x0
|
||||
w0z0x1 <- (1-z0x1)**2 + (-1)**(1-z0x1) * acc.z0x1
|
||||
w0z1x0 <- (1-z1x0)**2 + (-1)**(1-z1x0) * acc.z1x0
|
||||
w0z1x1 <- (1-z1x1)**2 + (-1)**(1-z1x1) * acc.z1x1
|
||||
|
||||
##perrorz0 <- w0z0*(pyz0)
|
||||
##perrorz1 <- w0z1*(pyz1)
|
||||
|
||||
w0z0x0.noisy.odds <- rlogis(nz0x0,qlogis(w0z0x0))
|
||||
w0z0x1.noisy.odds <- rlogis(nz0x1,qlogis(w0z0x1))
|
||||
w0z1x0.noisy.odds <- rlogis(nz1x0,qlogis(w0z1x0))
|
||||
w0z1x1.noisy.odds <- rlogis(nz1x1,qlogis(w0z1x1))
|
||||
|
||||
df[(z==0)&(x==0),w:=plogis(w0z0x0.noisy.odds)]
|
||||
df[(z==0)&(x==1),w:=plogis(w0z0x1.noisy.odds)]
|
||||
df[(z==1)&(x==0),w:=plogis(w0z1x0.noisy.odds)]
|
||||
df[(z==1)&(x==1),w:=plogis(w0z1x1.noisy.odds)]
|
||||
|
||||
df[,w_pred:=as.integer(w > 0.5)]
|
||||
print(mean(df[z==0]$x == df[z==0]$w_pred))
|
||||
print(mean(df[z==1]$x == df[z==1]$w_pred))
|
||||
print(mean(df$w_pred == df$x))
|
||||
print(mean(df[y>=0]$w_pred == df[y>=0]$x))
|
||||
print(mean(df[y<=0]$w_pred == df[y<=0]$x))
|
||||
|
||||
return(df)
|
||||
}
|
||||
|
||||
parser <- arg_parser("Simulate data and fit corrected models")
|
||||
parser <- add_argument(parser, "--N", default=1400, help="number of observations of w")
|
||||
parser <- add_argument(parser, "--N", default=1000, help="number of observations of w")
|
||||
parser <- add_argument(parser, "--m", default=500, help="m the number of ground truth observations")
|
||||
parser <- add_argument(parser, "--seed", default=50, help='seed for the rng')
|
||||
parser <- add_argument(parser, "--seed", default=51, help='seed for the rng')
|
||||
parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
|
||||
parser <- add_argument(parser, "--y_explained_variance", help='what proportion of the variance of y can be explained?', default=0.01)
|
||||
parser <- add_argument(parser, "--prediction_accuracy", help='how accurate is the predictive model?', default=0.73)
|
||||
parser <- add_argument(parser, "--accuracy_imbalance_difference", help='how much more accurate is the predictive model for one class than the other?', default=0.3)
|
||||
parser <- add_argument(parser, "--Bzx", help='Effect of z on x', default=0.3)
|
||||
parser <- add_argument(parser, "--Bzy", help='Effect of z on y', default=-0.3)
|
||||
|
||||
parser <- add_argument(parser, "--Bxy", help='Effect of z on y', default=0.3)
|
||||
parser <- add_argument(parser, "--proxy_formula", help='formula for the proxy variable', default="w_pred~x*y")
|
||||
parser <- add_argument(parser, "--y_bias", help='coefficient of y on the probability a classification is correct', default=-0.75)
|
||||
|
||||
args <- parse_args(parser)
|
||||
|
||||
B0 <- 0
|
||||
Bxy <- 0.3
|
||||
Bxy <- args$Bxy
|
||||
Bzy <- args$Bzy
|
||||
Bzx <- args$Bzx
|
||||
|
||||
if(args$m < args$N){
|
||||
df <- simulate_data(args$N, args$m, B0, Bxy, args$Bzx, Bzy, args$seed, args$y_explained_variance, args$prediction_accuracy, args$accuracy_imbalance_difference)
|
||||
|
||||
result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=args$Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, error='')
|
||||
df <- simulate_data(args$N, args$m, B0, Bxy, Bzx, Bzy, args$seed, args$y_explained_variance, args$prediction_accuracy, y_bias=args$y_bias)
|
||||
|
||||
outline <- run_simulation(df, result, outcome_formula=y~x+z, proxy_formula=w_pred~x+z+y+x:y, truth_formula=x~z)
|
||||
## df.pc <- df[,.(x,y,z,w_pred)]
|
||||
## # df.pc <- df.pc[,err:=x-w_pred]
|
||||
## pc.df <- pc(suffStat=list(C=cor(df.pc),n=nrow(df.pc)),indepTest=gaussCItest,labels=names(df.pc),alpha=0.05)
|
||||
## plot(pc.df)
|
||||
|
||||
result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, Bzx=args$Bzx, 'Bzy'=Bzy, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'y_bias'=args$y_bias,error='')
|
||||
|
||||
outline <- run_simulation(df, result, outcome_formula=y~x+z, proxy_formula=as.formula(args$proxy_formula), truth_formula=x~z)
|
||||
|
||||
outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
|
||||
if(file.exists(args$outfile)){
|
||||
|
||||
Loading…
Reference in New Issue
Block a user