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April 19, 2022 02:00
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source("manifesto_functions.R") | |
###################### | |
### Naive Escape Rates | |
MakeBetaGraph(10, 100, xlab = "Rate", ylab = "Strength", | |
tlab = "January Escape Rates", | |
slab = "Mean and Highest Density Interval", xadj = .001) | |
################################### | |
### Coding Up Bayesian Escape Rates | |
### Step 1: SIMULATE DATA ### | |
# First call takes ~ 4 mins and saves to disc | |
group_count <- 500 | |
record_count <- 50000 | |
dt <- GetLargeVulnGroups(TRUE,group_count) | |
# Extract samples as years | |
dt_year_one <- dt[sample(nrow(dt), record_count, replace = FALSE), ] | |
dt_year_two <- dt[sample(nrow(dt), record_count, replace = FALSE), ] | |
dt_year_three <- dt[sample(nrow(dt), record_count, replace = FALSE), ] | |
### Step 2: GET TIDY DATA ### | |
year_one_group <- GetEscapeGroups(dt_year_one, group_count) | |
year_two_group <- GetEscapeGroups(dt_year_two, group_count) | |
year_three_group <- GetEscapeGroups(dt_year_three, group_count) | |
# Put years into one data frame | |
group_vals <- AddEscapeDF(year_one_group, year_two_group) | |
group_vals <- AddEscapeDF(group_vals, year_three_group) | |
### Step 3: EMPIRICAL BAYES ENRICHMENT ### | |
# Get Basic Average | |
group_vals <- group_vals %>% | |
mutate(escape_avg = prod_vuln / total_vulns) | |
# Get Long Run Groups That started in Q1 | |
vgroups <- group_vals %>% | |
filter(total_weeks >= 40) | |
# Get the Maximum Likelihood Estimation for Priors | |
mle_vals <- GetBurnMLE(vgroups$prod_vuln, vgroups$dev_vuln ) | |
# Use MLE's to set alpha and beta prior vars | |
alpha <- mle_vals[[1]] | |
beta <- mle_vals[[2]] | |
# Beta average per group | |
group_vals <- group_vals %>% | |
mutate(emp_bayes_avg = | |
(prod_vuln + alpha) / (total_vulns + alpha + beta)) | |
# Posterior update for each group | |
group_vals <- group_vals %>% | |
mutate(alpha_update = alpha + prod_vuln, | |
beta_update = beta + dev_vuln) | |
# Confidence intervals for each group | |
group_vals <- group_vals %>% | |
mutate(low_ci = qbeta(.025, alpha_update, beta_update), | |
high_ci = qbeta(.975, alpha_update, beta_update)) | |
### Step 4: VISUALIZE DATA ### | |
MakeBetaGraph(alpha, beta,"Empirical Bayes 'Prior' Escape Rate","Strength", | |
paste0("Escape Rates Derived From: ",sum(vgroups$total_vulns), | |
" Vulnerabilities"), | |
paste0("Across ", nrow(vgroups), | |
" groups supporting externally facing and audited services"), | |
xadj = .001) | |
# Get sample data for graph | |
short_year <- group_vals %>% filter(total_weeks <= 25) %>% head(25) | |
full_year <- group_vals %>% filter(total_weeks == max(total_weeks)) %>% | |
head(20) | |
# Merge sample data into one df | |
example_df <- bind_rows(short_year, full_year) | |
# Make graph that makes empirical bayes impact apparent | |
MakeProportionChart(df_val = example_df, kpis=c(.05,.1,.2)) | |
#################################### | |
### Escape Rates In 10 Lines Of Code | |
# Raw data | |
group_vals <- tibble(group = 1:100, | |
prod_vuln = sample(10:70,100, replace = TRUE), | |
dev_vuln = sample(70:300,100, replace = TRUE), | |
total_vulns = prod_vuln + dev_vuln) | |
# Groups with over 300 vulns | |
vgroups <- group_vals %>% filter(total_vulns > 300) | |
# Get the Maximum Likelihood Estimation | |
mle_vals <- GetBurnMLE(vgroups$prod_vuln, vgroups$dev_vuln ) | |
# Use MLE's to set alpha and beta vars | |
alpha <- mle_vals[[1]] | |
beta <- mle_vals[[2]] | |
# Bayesian Average Update All Groups | |
group_vals <- group_vals %>% | |
mutate(emp_bayes_avg = | |
(prod_vuln + alpha) / (total_vulns + alpha + beta)) | |
# Posterior Update | |
group_vals <- group_vals %>% | |
mutate(alpha_update = alpha + prod_vuln, | |
beta_update = beta + dev_vuln) | |
# Credible Intervals | |
group_vals <- group_vals %>% | |
mutate(low_ci = qbeta(.025, alpha_update, beta_update), | |
high_ci = qbeta(.975, alpha_update, beta_update)) | |
# Basic Average | |
group_vals <- group_vals %>% | |
mutate(escape_avg = prod_vuln / total_vulns) | |
# Final Chart | |
MakeProportionChart(df_val = group_vals) |
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