In mneunhoe/dpBootstrap: What the Package Does (One Line, Title Case)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(dpBootstrap)
library(reticulate)
use_condaenv("r-reticulate")
# You need to install the private-pgm python package in the conda environment
# to get started.
mbi <- reticulate::import("mbi")
sp <- reticulate::import("scipy")
full_data <-
mbi$Dataset$load(
'/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult.csv',
'/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult-domain.json'
)
data <-
mbi$Dataset$load(
'/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult.csv',
'/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult-domain.json'
)
test_data <-
mbi$Dataset$load(
'/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult.csv',
'/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult-domain.json'
)
# Take a sample from the data
sel <- sample(nrow(data$df), size = 5000)
data$df <- data$df[sel, ]
testset <- test_data$df
testset <- testset[-sel,]
# Define a draw from a Laplace distribution
rlaplace <- function(n, b) {
rexp(n, 1 / b) - rexp(n, 1 / b)
}
Now we can define the mechanism. It works on the mbi Dataset class and returns an object of the same class.
First the unsupported case (only one-way marginals are measured).
data_mechanism <- function(data) {
rdomain <- colnames(data$df)
domain <- data$domain
total <- dim(data$df)[1]
cliques <- list()
# cliques = list(
# list('age', 'education-num'),
# list('marital-status', 'race'),
# list('sex', 'hours-per-week'),
# list('hours-per-week', 'income>50K'),
# list('education-num', 'income>50K'),
# list('native-country', 'marital-status', 'occupation')
# )
# cliques = list(
# list('age', 'education-num'),
# list('education-num', 'race'),
# list('race', 'hours-per-week'),
# list('hours-per-week', 'income>50K'),
# list('native-country', 'income>50K'),
# list('native-country', 'marital-status', 'occupation')
# )
epsilon = 1.0
epsilon_split = epsilon / (length(domain) + length(cliques))
sigma = 2.0 / epsilon_split
measurements <- list()
counter <- 1
for (col in rdomain) {
x <- data$project(col)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, list(col))
counter <- counter + 1
}
if(length(cliques) > 0){
for (cl in cliques) {
x = data$project(cl)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, cl)
counter <- counter + 1
}
}
engine = mbi$FactoredInference(domain, log=TRUE, iters=200L)
#engine = mbi$LocalInference(domain,
# log = TRUE,
# iters = 2500L,
# marginal_oracle = 'convex')
model = engine$estimate(measurements, total = total)
#synth = model$synthetic_data(rows = as.integer(total))
return(model)
}
The bootstrap mechanism should be the same as the data mechanism. With the only difference, that the data mechanism takes a dataset as input and the bootstrap mechanism takes a generative model as input.
bootstrap_mechanism <- function(P_tilde, n = 5000) {
synth = P_tilde$synthetic_data(rows = as.integer(n))
rdomain <- colnames(synth$df)
domain <- synth$domain
total <- dim(synth$df)[1]
cliques <- list()
# cliques = list(
# list('age', 'education-num'),
# list('marital-status', 'race'),
# list('sex', 'hours-per-week'),
# list('hours-per-week', 'income>50K'),
# list('education-num', 'income>50K'),
# list('native-country', 'marital-status', 'occupation')
# )
# cliques = list(
# list('age', 'education-num'),
# list('education-num', 'race'),
# list('race', 'hours-per-week'),
# list('hours-per-week', 'income>50K'),
# list('native-country', 'income>50K'),
# list('native-country', 'marital-status', 'occupation')
# )
epsilon = 1.0
epsilon_split = epsilon / (length(domain) + length(cliques))
sigma = 2.0 / epsilon_split
measurements <- list()
counter <- 1
for (col in rdomain) {
x <- data$project(col)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, list(col))
counter <- counter + 1
}
if(length(cliques) > 0){
for (cl in cliques) {
x = data$project(cl)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, cl)
counter <- counter + 1
}
}
engine = mbi$FactoredInference(domain, log=TRUE, iters=200L)
#engine = mbi$LocalInference(domain,
# log = TRUE,
# iters = 2500L,
# marginal_oracle = 'convex')
model = engine$estimate(measurements, total = total)
#synth = model$synthetic_data(rows = as.integer(total))
return(model)
}
What statistics of interest do you want to calculate? Make sure that the calculation works on the output object.
Here just some column statistics (mean and quantiles) and a simple logistic regression.
statistics_of_interest <- function(P_tilde, n = 5000) {
synth = P_tilde$synthetic_data(rows = as.integer(n))
df <- synth$df
m1 <- glm(
`income>50K` ~ age + workclass + `education-num` + `hours-per-week`,
family = binomial(link = "logit"), data = df
)
auc <- as.numeric(pROC::auc(pROC::roc(testset$`income>50K`, predict.glm(m1, newdata = testset, type = "response"), direction = "<")))
list(
mean = apply(df, 2, mean),
quantiles = apply(df, 2, quantile, c(0.1, 0.25, 0.5, 0.75, 0.9)),
logit_coefs = coef(
m1
),
auc = auc
)
}
statistics_of_interest_data <- function(x, n = 5000) {
df <- x$df
m1 <- glm(
`income>50K` ~ age + workclass + `education-num` + `hours-per-week`,
family = binomial(link = "logit"), data = df
)
auc <- as.numeric(pROC::auc(pROC::roc(testset$`income>50K`, predict.glm(m1, newdata = testset, type = "response"), direction = "<")))
list(
mean = apply(df, 2, mean),
quantiles = apply(df, 2, quantile, c(0.1, 0.25, 0.5, 0.75, 0.9)),
logit_coefs = coef(
m1
),
auc = auc
)
}
Now we can define the bootstrap loop.
bootstrap_loop <-
function(P_tilde,
bootstrap_mechanism,
statistics_of_interest,
B) {
theta_tilde_tilde_list <- vector("list", length = B)
cli::cli_progress_bar("Bootstrapping", total = B)
for (b in 1:B) {
P_tilde_tilde <- bootstrap_mechanism(P_tilde)
theta_tilde_tilde <- statistics_of_interest(P_tilde_tilde)
theta_tilde_tilde_list[[b]] <- theta_tilde_tilde
cli::cli_progress_update()
}
return(theta_tilde_tilde_list)
}
results_pgm1 <- dp_bootstrap(
x = data,
data_mechanism = data_mechanism,
bootstrap_mechanism = bootstrap_mechanism,
statistics_of_interest = statistics_of_interest,
B = 5,
bootstrap_loop
)
Finally, summarize the results.
# Statistic of interest
results_pgm1$theta_tilde$logit_coefs
results_pgm1$theta_tilde$auc
sapply(results_pgm1$theta_tilde_tilde, function(x) x$auc)
# And confidence intervals
apply(sapply(results_pgm1$theta_tilde_tilde, function(x) x$logit_coefs), 1, quantile, c(0.025, 0.975))
statistics_of_interest_data(data)$logit_coefs
statistics_of_interest_data(full_data)$logit_coefs
Now a "more" supported case with all two-way marginals of outcome and predictors.
data_mechanism <- function(data) {
rdomain <- colnames(data$df)
domain <- data$domain
total <- dim(data$df)[1]
cliques = list(
list('age', 'income>50K'),
list('workclass', 'income>50K'),
list('education-num', 'income>50K'),
list('hours-per-week', 'income>50K')
)
epsilon = 1.0
epsilon_split = epsilon / (length(domain) + length(cliques))
sigma = 2.0 / epsilon_split
measurements <- list()
counter <- 1
for (col in rdomain) {
x <- data$project(col)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, list(col))
counter <- counter + 1
}
if(length(cliques) > 0){
for (cl in cliques) {
x = data$project(cl)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, cl)
counter <- counter + 1
}
}
engine = mbi$FactoredInference(domain, log=TRUE, iters=200L)
#engine = mbi$LocalInference(domain,
# log = TRUE,
# iters = 2500L,
# marginal_oracle = 'convex')
model = engine$estimate(measurements, total = total)
#synth = model$synthetic_data(rows = as.integer(total))
return(model)
}
The bootstrap mechanism should be the same as the data mechanism. With the only difference, that the data mechanism takes a dataset as input and the bootstrap mechanism takes a generative model as input.
bootstrap_mechanism <- function(P_tilde, n = 5000) {
synth = P_tilde$synthetic_data(rows = as.integer(n))
rdomain <- colnames(synth$df)
domain <- synth$domain
total <- dim(synth$df)[1]
cliques = list(
list('age', 'income>50K'),
list('workclass', 'income>50K'),
list('education-num', 'income>50K'),
list('hours-per-week', 'income>50K')
)
epsilon = 1.0
epsilon_split = epsilon / (length(domain) + length(cliques))
sigma = 2.0 / epsilon_split
measurements <- list()
counter <- 1
for (col in rdomain) {
x <- data$project(col)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, list(col))
counter <- counter + 1
}
if(length(cliques) > 0){
for (cl in cliques) {
x = data$project(cl)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, cl)
counter <- counter + 1
}
}
engine = mbi$FactoredInference(domain, log=TRUE, iters=200L)
#engine = mbi$LocalInference(domain,
# log = TRUE,
# iters = 2500L,
# marginal_oracle = 'convex')
model = engine$estimate(measurements, total = total)
#synth = model$synthetic_data(rows = as.integer(total))
return(model)
}
results_pgm2 <- dp_bootstrap(
x = data,
data_mechanism = data_mechanism,
bootstrap_mechanism = bootstrap_mechanism,
statistics_of_interest = statistics_of_interest,
B = 5,
bootstrap_loop
)
Finally, summarize the results.
# Statistic of interest
results_pgm2$theta_tilde$logit_coefs
results_pgm2$theta_tilde$auc
sapply(results_pgm2$theta_tilde_tilde, function(x) x$auc)
# And confidence intervals
apply(sapply(results_pgm2$theta_tilde_tilde, function(x) x$logit_coefs), 1, quantile, c(0.025, 0.975))
statistics_of_interest_data(data)$logit_coefs
statistics_of_interest_data(full_data)$logit_coefs
Now a "more" supported case with all two-way marginals of outcome and predictors.
data_mechanism <- function(data) {
rdomain <- colnames(data$df)
domain <- data$domain
total <- dim(data$df)[1]
cliques = list(
list('workclass', 'age', 'income>50K'),
list('education-num', 'workclass', 'income>50K'),
list('hours-per-week', 'education-num', 'income>50K'),
list('age', 'hours-per-week', 'income>50K'),
list('workclass', 'hours-per-week', 'income>50K'),
list('education-num', 'age', 'income>50K'),
list('education-num', 'hours-per-week', 'income>50K')
)
epsilon = 1.0
epsilon_split = epsilon / (length(domain) + length(cliques))
sigma = 2.0 / epsilon_split
measurements <- list()
counter <- 1
for (col in rdomain) {
x <- data$project(col)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, list(col))
counter <- counter + 1
}
if(length(cliques) > 0){
for (cl in cliques) {
x = data$project(cl)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, cl)
counter <- counter + 1
}
}
engine = mbi$FactoredInference(domain, log=TRUE, iters=200L)
#engine = mbi$LocalInference(domain,
# log = TRUE,
# iters = 2500L,
# marginal_oracle = 'convex')
model = engine$estimate(measurements, total = total)
#synth = model$synthetic_data(rows = as.integer(total))
return(model)
}
The bootstrap mechanism should be the same as the data mechanism. With the only difference, that the data mechanism takes a dataset as input and the bootstrap mechanism takes a generative model as input.
bootstrap_mechanism <- function(P_tilde, n = 5000) {
synth = P_tilde$synthetic_data(rows = as.integer(n))
rdomain <- colnames(synth$df)
domain <- synth$domain
total <- dim(synth$df)[1]
cliques = list(
list('workclass', 'age', 'income>50K'),
list('education-num', 'workclass', 'income>50K'),
list('hours-per-week', 'education-num', 'income>50K'),
list('age', 'hours-per-week', 'income>50K'),
list('workclass', 'hours-per-week', 'income>50K'),
list('education-num', 'age', 'income>50K'),
list('education-num', 'hours-per-week', 'income>50K')
)
epsilon = 1.0
epsilon_split = epsilon / (length(domain) + length(cliques))
sigma = 2.0 / epsilon_split
measurements <- list()
counter <- 1
for (col in rdomain) {
x <- data$project(col)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, list(col))
counter <- counter + 1
}
if(length(cliques) > 0){
for (cl in cliques) {
x = data$project(cl)$datavector()
y = x + rlaplace(n = length(x), b = sigma)
I = Matrix::Diagonal(length(x))
measurements[[counter]] <- list(I, y, sigma, cl)
counter <- counter + 1
}
}
engine = mbi$FactoredInference(domain, log=TRUE, iters=200L)
#engine = mbi$LocalInference(domain,
# log = TRUE,
# iters = 2500L,
# marginal_oracle = 'convex')
model = engine$estimate(measurements, total = total)
#synth = model$synthetic_data(rows = as.integer(total))
return(model)
}
results_pgm3 <- dp_bootstrap(
x = data,
data_mechanism = data_mechanism,
bootstrap_mechanism = bootstrap_mechanism,
statistics_of_interest = statistics_of_interest,
B = 250,
bootstrap_loop
)
Finally, summarize the results.
# Statistic of interest
results_pgm2$theta_tilde$logit_coefs
results_pgm2$theta_tilde$auc
sapply(results_pgm2$theta_tilde_tilde, function(x) x$auc)
# And confidence intervals
apply(sapply(results_pgm2$theta_tilde_tilde, function(x) x$logit_coefs), 1, quantile, c(0.025, 0.975))
statistics_of_interest_data(data)$logit_coefs
statistics_of_interest_data(full_data)$logit_coefs
mneunhoe/dpBootstrap documentation built on Feb. 10, 2023, 6:31 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(dpBootstrap) library(reticulate) use_condaenv("r-reticulate")
# You need to install the private-pgm python package in the conda environment # to get started. mbi <- reticulate::import("mbi") sp <- reticulate::import("scipy") full_data <- mbi$Dataset$load( '/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult.csv', '/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult-domain.json' ) data <- mbi$Dataset$load( '/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult.csv', '/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult-domain.json' ) test_data <- mbi$Dataset$load( '/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult.csv', '/Users/marcelneunhoeffer/Desktop/github/private-pgm/data/adult-domain.json' ) # Take a sample from the data sel <- sample(nrow(data$df), size = 5000) data$df <- data$df[sel, ] testset <- test_data$df testset <- testset[-sel,] # Define a draw from a Laplace distribution rlaplace <- function(n, b) { rexp(n, 1 / b) - rexp(n, 1 / b) }
Now we can define the mechanism. It works on the mbi Dataset class and returns an object of the same class. First the unsupported case (only one-way marginals are measured).
data_mechanism <- function(data) { rdomain <- colnames(data$df) domain <- data$domain total <- dim(data$df)[1] cliques <- list() # cliques = list( # list('age', 'education-num'), # list('marital-status', 'race'), # list('sex', 'hours-per-week'), # list('hours-per-week', 'income>50K'), # list('education-num', 'income>50K'), # list('native-country', 'marital-status', 'occupation') # ) # cliques = list( # list('age', 'education-num'), # list('education-num', 'race'), # list('race', 'hours-per-week'), # list('hours-per-week', 'income>50K'), # list('native-country', 'income>50K'), # list('native-country', 'marital-status', 'occupation') # ) epsilon = 1.0 epsilon_split = epsilon / (length(domain) + length(cliques)) sigma = 2.0 / epsilon_split measurements <- list() counter <- 1 for (col in rdomain) { x <- data$project(col)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, list(col)) counter <- counter + 1 } if(length(cliques) > 0){ for (cl in cliques) { x = data$project(cl)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, cl) counter <- counter + 1 } } engine = mbi$FactoredInference(domain, log=TRUE, iters=200L) #engine = mbi$LocalInference(domain, # log = TRUE, # iters = 2500L, # marginal_oracle = 'convex') model = engine$estimate(measurements, total = total) #synth = model$synthetic_data(rows = as.integer(total)) return(model) }
The bootstrap mechanism should be the same as the data mechanism. With the only difference, that the data mechanism takes a dataset as input and the bootstrap mechanism takes a generative model as input.
bootstrap_mechanism <- function(P_tilde, n = 5000) { synth = P_tilde$synthetic_data(rows = as.integer(n)) rdomain <- colnames(synth$df) domain <- synth$domain total <- dim(synth$df)[1] cliques <- list() # cliques = list( # list('age', 'education-num'), # list('marital-status', 'race'), # list('sex', 'hours-per-week'), # list('hours-per-week', 'income>50K'), # list('education-num', 'income>50K'), # list('native-country', 'marital-status', 'occupation') # ) # cliques = list( # list('age', 'education-num'), # list('education-num', 'race'), # list('race', 'hours-per-week'), # list('hours-per-week', 'income>50K'), # list('native-country', 'income>50K'), # list('native-country', 'marital-status', 'occupation') # ) epsilon = 1.0 epsilon_split = epsilon / (length(domain) + length(cliques)) sigma = 2.0 / epsilon_split measurements <- list() counter <- 1 for (col in rdomain) { x <- data$project(col)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, list(col)) counter <- counter + 1 } if(length(cliques) > 0){ for (cl in cliques) { x = data$project(cl)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, cl) counter <- counter + 1 } } engine = mbi$FactoredInference(domain, log=TRUE, iters=200L) #engine = mbi$LocalInference(domain, # log = TRUE, # iters = 2500L, # marginal_oracle = 'convex') model = engine$estimate(measurements, total = total) #synth = model$synthetic_data(rows = as.integer(total)) return(model) }
What statistics of interest do you want to calculate? Make sure that the calculation works on the output object. Here just some column statistics (mean and quantiles) and a simple logistic regression.
statistics_of_interest <- function(P_tilde, n = 5000) { synth = P_tilde$synthetic_data(rows = as.integer(n)) df <- synth$df m1 <- glm( `income>50K` ~ age + workclass + `education-num` + `hours-per-week`, family = binomial(link = "logit"), data = df ) auc <- as.numeric(pROC::auc(pROC::roc(testset$`income>50K`, predict.glm(m1, newdata = testset, type = "response"), direction = "<"))) list( mean = apply(df, 2, mean), quantiles = apply(df, 2, quantile, c(0.1, 0.25, 0.5, 0.75, 0.9)), logit_coefs = coef( m1 ), auc = auc ) } statistics_of_interest_data <- function(x, n = 5000) { df <- x$df m1 <- glm( `income>50K` ~ age + workclass + `education-num` + `hours-per-week`, family = binomial(link = "logit"), data = df ) auc <- as.numeric(pROC::auc(pROC::roc(testset$`income>50K`, predict.glm(m1, newdata = testset, type = "response"), direction = "<"))) list( mean = apply(df, 2, mean), quantiles = apply(df, 2, quantile, c(0.1, 0.25, 0.5, 0.75, 0.9)), logit_coefs = coef( m1 ), auc = auc ) }
Now we can define the bootstrap loop.
bootstrap_loop <- function(P_tilde, bootstrap_mechanism, statistics_of_interest, B) { theta_tilde_tilde_list <- vector("list", length = B) cli::cli_progress_bar("Bootstrapping", total = B) for (b in 1:B) { P_tilde_tilde <- bootstrap_mechanism(P_tilde) theta_tilde_tilde <- statistics_of_interest(P_tilde_tilde) theta_tilde_tilde_list[[b]] <- theta_tilde_tilde cli::cli_progress_update() } return(theta_tilde_tilde_list) }
results_pgm1 <- dp_bootstrap( x = data, data_mechanism = data_mechanism, bootstrap_mechanism = bootstrap_mechanism, statistics_of_interest = statistics_of_interest, B = 5, bootstrap_loop )
Finally, summarize the results.
# Statistic of interest results_pgm1$theta_tilde$logit_coefs results_pgm1$theta_tilde$auc sapply(results_pgm1$theta_tilde_tilde, function(x) x$auc) # And confidence intervals apply(sapply(results_pgm1$theta_tilde_tilde, function(x) x$logit_coefs), 1, quantile, c(0.025, 0.975)) statistics_of_interest_data(data)$logit_coefs statistics_of_interest_data(full_data)$logit_coefs
Now a "more" supported case with all two-way marginals of outcome and predictors.
data_mechanism <- function(data) { rdomain <- colnames(data$df) domain <- data$domain total <- dim(data$df)[1] cliques = list( list('age', 'income>50K'), list('workclass', 'income>50K'), list('education-num', 'income>50K'), list('hours-per-week', 'income>50K') ) epsilon = 1.0 epsilon_split = epsilon / (length(domain) + length(cliques)) sigma = 2.0 / epsilon_split measurements <- list() counter <- 1 for (col in rdomain) { x <- data$project(col)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, list(col)) counter <- counter + 1 } if(length(cliques) > 0){ for (cl in cliques) { x = data$project(cl)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, cl) counter <- counter + 1 } } engine = mbi$FactoredInference(domain, log=TRUE, iters=200L) #engine = mbi$LocalInference(domain, # log = TRUE, # iters = 2500L, # marginal_oracle = 'convex') model = engine$estimate(measurements, total = total) #synth = model$synthetic_data(rows = as.integer(total)) return(model) }
The bootstrap mechanism should be the same as the data mechanism. With the only difference, that the data mechanism takes a dataset as input and the bootstrap mechanism takes a generative model as input.
bootstrap_mechanism <- function(P_tilde, n = 5000) { synth = P_tilde$synthetic_data(rows = as.integer(n)) rdomain <- colnames(synth$df) domain <- synth$domain total <- dim(synth$df)[1] cliques = list( list('age', 'income>50K'), list('workclass', 'income>50K'), list('education-num', 'income>50K'), list('hours-per-week', 'income>50K') ) epsilon = 1.0 epsilon_split = epsilon / (length(domain) + length(cliques)) sigma = 2.0 / epsilon_split measurements <- list() counter <- 1 for (col in rdomain) { x <- data$project(col)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, list(col)) counter <- counter + 1 } if(length(cliques) > 0){ for (cl in cliques) { x = data$project(cl)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, cl) counter <- counter + 1 } } engine = mbi$FactoredInference(domain, log=TRUE, iters=200L) #engine = mbi$LocalInference(domain, # log = TRUE, # iters = 2500L, # marginal_oracle = 'convex') model = engine$estimate(measurements, total = total) #synth = model$synthetic_data(rows = as.integer(total)) return(model) }
results_pgm2 <- dp_bootstrap( x = data, data_mechanism = data_mechanism, bootstrap_mechanism = bootstrap_mechanism, statistics_of_interest = statistics_of_interest, B = 5, bootstrap_loop )
Finally, summarize the results.
# Statistic of interest results_pgm2$theta_tilde$logit_coefs results_pgm2$theta_tilde$auc sapply(results_pgm2$theta_tilde_tilde, function(x) x$auc) # And confidence intervals apply(sapply(results_pgm2$theta_tilde_tilde, function(x) x$logit_coefs), 1, quantile, c(0.025, 0.975)) statistics_of_interest_data(data)$logit_coefs statistics_of_interest_data(full_data)$logit_coefs
Now a "more" supported case with all two-way marginals of outcome and predictors.
data_mechanism <- function(data) { rdomain <- colnames(data$df) domain <- data$domain total <- dim(data$df)[1] cliques = list( list('workclass', 'age', 'income>50K'), list('education-num', 'workclass', 'income>50K'), list('hours-per-week', 'education-num', 'income>50K'), list('age', 'hours-per-week', 'income>50K'), list('workclass', 'hours-per-week', 'income>50K'), list('education-num', 'age', 'income>50K'), list('education-num', 'hours-per-week', 'income>50K') ) epsilon = 1.0 epsilon_split = epsilon / (length(domain) + length(cliques)) sigma = 2.0 / epsilon_split measurements <- list() counter <- 1 for (col in rdomain) { x <- data$project(col)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, list(col)) counter <- counter + 1 } if(length(cliques) > 0){ for (cl in cliques) { x = data$project(cl)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, cl) counter <- counter + 1 } } engine = mbi$FactoredInference(domain, log=TRUE, iters=200L) #engine = mbi$LocalInference(domain, # log = TRUE, # iters = 2500L, # marginal_oracle = 'convex') model = engine$estimate(measurements, total = total) #synth = model$synthetic_data(rows = as.integer(total)) return(model) }
The bootstrap mechanism should be the same as the data mechanism. With the only difference, that the data mechanism takes a dataset as input and the bootstrap mechanism takes a generative model as input.
bootstrap_mechanism <- function(P_tilde, n = 5000) { synth = P_tilde$synthetic_data(rows = as.integer(n)) rdomain <- colnames(synth$df) domain <- synth$domain total <- dim(synth$df)[1] cliques = list( list('workclass', 'age', 'income>50K'), list('education-num', 'workclass', 'income>50K'), list('hours-per-week', 'education-num', 'income>50K'), list('age', 'hours-per-week', 'income>50K'), list('workclass', 'hours-per-week', 'income>50K'), list('education-num', 'age', 'income>50K'), list('education-num', 'hours-per-week', 'income>50K') ) epsilon = 1.0 epsilon_split = epsilon / (length(domain) + length(cliques)) sigma = 2.0 / epsilon_split measurements <- list() counter <- 1 for (col in rdomain) { x <- data$project(col)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, list(col)) counter <- counter + 1 } if(length(cliques) > 0){ for (cl in cliques) { x = data$project(cl)$datavector() y = x + rlaplace(n = length(x), b = sigma) I = Matrix::Diagonal(length(x)) measurements[[counter]] <- list(I, y, sigma, cl) counter <- counter + 1 } } engine = mbi$FactoredInference(domain, log=TRUE, iters=200L) #engine = mbi$LocalInference(domain, # log = TRUE, # iters = 2500L, # marginal_oracle = 'convex') model = engine$estimate(measurements, total = total) #synth = model$synthetic_data(rows = as.integer(total)) return(model) }
results_pgm3 <- dp_bootstrap( x = data, data_mechanism = data_mechanism, bootstrap_mechanism = bootstrap_mechanism, statistics_of_interest = statistics_of_interest, B = 250, bootstrap_loop )
Finally, summarize the results.
# Statistic of interest results_pgm2$theta_tilde$logit_coefs results_pgm2$theta_tilde$auc sapply(results_pgm2$theta_tilde_tilde, function(x) x$auc) # And confidence intervals apply(sapply(results_pgm2$theta_tilde_tilde, function(x) x$logit_coefs), 1, quantile, c(0.025, 0.975)) statistics_of_interest_data(data)$logit_coefs statistics_of_interest_data(full_data)$logit_coefs
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