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get_data = function(num_obs){ | |
x1 = runif(num_obs,0,3) | |
x2 = runif(num_obs,0,3) | |
t = rbinom(num_obs,1,.5) | |
noise_fee = rnorm(num_obs,0,1) | |
noise_costs = rnorm(num_obs,0,1) | |
#fee = log(x1*1.718+1)*(t) + noise_fee | |
#cost = .5*x2*(t) + noise_costs | |
fee = x1*(t) + noise_fee | |
cost = x2*(t) + noise_costs | |
cost = (t) + noise_costs | |
data = data.frame(fee,cost,x1,x2,t) | |
return(data) | |
} | |
training_data = get_data(100000) | |
testing_data = get_data(100000) | |
library(randomForest) | |
reg_fee = randomForest(fee ~ x1+x2+t, data = training_data) | |
reg_cost = randomForest(cost ~ x1+x2+t, data = training_data) | |
get_counterfactuals = function(model, data){ | |
data_1 = data | |
data_1$t = 1 | |
data_0 = data | |
data_0$t = 0 | |
preds_1 = predict(model, data_1) | |
preds_0 = predict(model, data_0) | |
counterfactuals = cbind(preds_0, preds_1) | |
return(counterfactuals) | |
} | |
counters_fee = get_counterfactuals(reg_fee,testing_data) | |
counters_cost = get_counterfactuals(reg_cost,testing_data) | |
erupt = function(given_tmts, assigned_tmts, response){ | |
print(sum(given_tmts == assigned_tmts)) | |
return ( colMeans(response[which(given_tmts == assigned_tmts),]) ) | |
} | |
weighted_preds = mapply(function(x,y){x*y},x=list(counters_fee, counters_cost), y=c(1,-1), SIMPLIFY = FALSE) | |
get_erupts = function(weights, preds, tmts, response){ | |
print(weights) | |
weighted_preds1 = mapply(function(x,y){x*y} ,x=preds , y=weights, SIMPLIFY = FALSE) | |
best_tmt = apply(weighted_preds1[[1]]+weighted_preds1[[2]], 1, which.max)-1 | |
print(table(best_tmt)) | |
return( erupt(tmts, best_tmt, response )) | |
} | |
get_erupts_1 = function(weights, preds){ | |
print(weights) | |
weighted_preds1 = mapply(function(x,y){x*y} ,x=preds , y=weights, SIMPLIFY = FALSE) | |
best_tmt = apply(weighted_preds1[[1]]+weighted_preds1[[2]], 1, which.max)-1 | |
fees = (sum(preds[[1]][which(best_tmt == 0),1])+sum(preds[[1]][which(best_tmt == 1),2]))/nrow(preds[[1]]) | |
costs = (sum(preds[[2]][which(best_tmt == 0),1])+sum(preds[[2]][which(best_tmt == 1),2]))/nrow(preds[[1]]) | |
return(c(fees,costs)) | |
} | |
get_true = function(weights, preds, true_values){ | |
print(weights) | |
weighted_preds1 = mapply(function(x,y){x*y} ,x=preds , y=weights, SIMPLIFY = FALSE) | |
best_tmt = apply(weighted_preds1[[1]]+weighted_preds1[[2]], 1, which.max)-1 | |
fees = (sum(true_values[[1]][which(best_tmt == 0),1])+sum(true_values[[1]][which(best_tmt == 1),2]))/nrow(preds[[1]]) | |
costs = (sum(true_values[[2]][which(best_tmt == 0),1])+sum(true_values[[2]][which(best_tmt == 1),2]))/nrow(preds[[1]]) | |
return(c(fees,costs)) | |
} | |
true_values = list(cbind(rep(0,100000),testing_data[,'x1']), cbind(rep(0,100000),rep(1,100000))) | |
weights = rbind(c(0,0),expand.grid(c(seq(0,20,.25),10000), -1)) | |
estimated_erupts = apply(weights,1,function(x) get_erupts(x, list(counters_fee, counters_cost), testing_data[,'t'], testing_data[,c('fee','cost')])) | |
estimated_erupts = t(estimated_erupts) | |
estimated_erupts = data.frame(estimated_erupts) | |
estimated_erupts[,'estimate'] = 'erupt' | |
over_estimated_erupts = apply(weights,1,function(x)( get_true(x, list(counters_fee, counters_cost),list(counters_fee, counters_cost)) ) ) | |
over_estimated_erupts = t(over_estimated_erupts) | |
over_estimated_erupts = data.frame(over_estimated_erupts) | |
over_estimated_erupts[,'estimate'] = 'model' | |
colnames(over_estimated_erupts) = c('fee','cost','estimate') | |
true_estimated_erupts = apply(weights,1,function(x)( get_true(x, list(counters_fee, counters_cost), true_values ) )) | |
true_estimated_erupts = t(true_estimated_erupts) | |
colnames(true_estimated_erupts) = c('fee','cost') | |
true_estimated_erupts = data.frame(true_estimated_erupts) | |
true_estimated_erupts[,'estimate'] = 'truth' | |
library(ggplot2) | |
graph_data = rbind(over_estimated_erupts,estimated_erupts,true_estimated_erupts) | |
ggplot(graph_data, aes(x = cost, y = fee, group = as.factor(estimate), colour = as.factor(estimate)))+geom_point() + geom_line() | |
What would this look like in a programming language? Maybe Python?
probably pretty similar considering I still use the parenthesis after return statements in python :)
It might cor-erupt the code
Well thanks for the sudo code, neat stuff!
haha nice Matt, that took me too long
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What would this look like in a programming language? Maybe Python?