436 lines
15 KiB
Rust
436 lines
15 KiB
Rust
use crate::{BridgeInfo, BridgeInfoType};
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use lox_library::proto::{level_up::LEVEL_INTERVAL, trust_promotion::UNTRUSTED_INTERVAL};
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use nalgebra::DVector;
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use statrs::distribution::{Continuous, MultivariateNormal, Normal};
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use std::{
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cmp::min,
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collections::{BTreeMap, HashSet},
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};
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/// Provides a function for predicting which countries block this bridge
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pub trait Analyzer {
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/// Evaluate open-entry bridge. Returns true if blocked, false otherwise.
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fn stage_one(
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&self,
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confidence: f64,
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bridge_ips: &[u32],
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bridge_ips_today: u32,
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negative_reports: &[u32],
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negative_reports_today: u32,
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) -> bool;
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/// Evaluate invite-only bridge without positive reports. Return true if
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/// blocked, false otherwise.
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fn stage_two(
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&self,
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confidence: f64,
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bridge_ips: &[u32],
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bridge_ips_today: u32,
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negative_reports: &[u32],
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negative_reports_today: u32,
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) -> bool;
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/// Evaluate invite-only bridge with positive reports. Return true if
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/// blocked, false otherwise.
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fn stage_three(
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&self,
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confidence: f64,
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bridge_ips: &[u32],
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bridge_ips_today: u32,
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negative_reports: &[u32],
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negative_reports_today: u32,
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positive_reports: &[u32],
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positive_reports_today: u32,
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) -> bool;
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}
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/// Accepts an analyzer, information about a bridge, and a confidence value.
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/// Returns a set of country codes where the bridge is believed to be blocked.
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pub fn blocked_in(
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analyzer: &dyn Analyzer,
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bridge_info: &BridgeInfo,
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confidence: f64,
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date: u32,
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) -> HashSet<String> {
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// TODO: Re-evaluate past days if we have backdated reports
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let mut blocked_in = HashSet::<String>::new();
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let today = date;
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let age = today - bridge_info.first_seen;
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for (country, info) in &bridge_info.info_by_country {
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if info.blocked {
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// Assume bridges never become unblocked
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blocked_in.insert(country.to_string());
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} else {
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// Get today's values
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let new_map_binding = BTreeMap::<BridgeInfoType, u32>::new();
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// TODO: Evaluate on yesterday if we don't have data for today?
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let today_info = match info.info_by_day.get(&today) {
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Some(v) => v,
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None => &new_map_binding,
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};
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let bridge_ips_today = match today_info.get(&BridgeInfoType::BridgeIps) {
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Some(&v) => v,
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None => 0,
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};
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let negative_reports_today = match today_info.get(&BridgeInfoType::NegativeReports) {
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Some(&v) => v,
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None => 0,
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};
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let positive_reports_today = match today_info.get(&BridgeInfoType::PositiveReports) {
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Some(&v) => v,
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None => 0,
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};
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let num_days = min(age, UNTRUSTED_INTERVAL);
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// Get time series for last num_days
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let mut bridge_ips = vec![0; num_days as usize];
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let mut negative_reports = vec![0; num_days as usize];
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let mut positive_reports = vec![0; num_days as usize];
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for i in 0..num_days {
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let date = today - num_days + i - 1;
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let new_map_binding = BTreeMap::<BridgeInfoType, u32>::new();
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let day_info = match info.info_by_day.get(&date) {
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Some(v) => v,
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None => &new_map_binding,
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};
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bridge_ips[i as usize] = match day_info.get(&BridgeInfoType::BridgeIps) {
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Some(&v) => v,
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None => 0,
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};
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negative_reports[i as usize] = match day_info.get(&BridgeInfoType::NegativeReports)
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{
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Some(&v) => v,
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None => 0,
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};
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positive_reports[i as usize] = match day_info.get(&BridgeInfoType::PositiveReports)
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{
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Some(&v) => v,
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None => 0,
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};
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}
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// Evaluate using appropriate stage based on age of the bridge
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if age < UNTRUSTED_INTERVAL {
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// open-entry bridge
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if analyzer.stage_one(
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confidence,
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&bridge_ips,
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bridge_ips_today,
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&negative_reports,
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negative_reports_today,
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) {
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blocked_in.insert(country.to_string());
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}
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} else if age
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< UNTRUSTED_INTERVAL + LEVEL_INTERVAL[1] + LEVEL_INTERVAL[2] + UNTRUSTED_INTERVAL
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{
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// invite-only bridge without 30+ days of historical data on
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// positive reports
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if analyzer.stage_two(
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confidence,
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&bridge_ips,
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bridge_ips_today,
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&negative_reports,
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negative_reports_today,
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) {
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blocked_in.insert(country.to_string());
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}
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} else {
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// invite-only bridge that has been up long enough that it
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// might have 30+ days of historical data on positive reports
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if analyzer.stage_three(
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confidence,
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&bridge_ips,
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bridge_ips_today,
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&negative_reports,
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negative_reports_today,
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&positive_reports,
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positive_reports_today,
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) {
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blocked_in.insert(country.to_string());
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}
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}
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}
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}
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blocked_in
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}
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// Analyzer implementations
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/// Dummy example that never thinks bridges are blocked
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pub struct ExampleAnalyzer {}
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impl Analyzer for ExampleAnalyzer {
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fn stage_one(
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&self,
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_confidence: f64,
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_bridge_ips: &[u32],
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_bridge_ips_today: u32,
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_negative_reports: &[u32],
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_negative_reports_today: u32,
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) -> bool {
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false
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}
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fn stage_two(
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&self,
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_confidence: f64,
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_bridge_ips: &[u32],
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_bridge_ips_today: u32,
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_negative_reports: &[u32],
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_negative_reports_today: u32,
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) -> bool {
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false
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}
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fn stage_three(
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&self,
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_confidence: f64,
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_bridge_ips: &[u32],
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_bridge_ips_today: u32,
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_negative_reports: &[u32],
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_negative_reports_today: u32,
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_positive_reports: &[u32],
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_positive_reports_today: u32,
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) -> bool {
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false
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}
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}
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/// Model data as multivariate normal distribution
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pub struct NormalAnalyzer {
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max_threshold: u32,
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scaling_factor: f64,
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}
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impl NormalAnalyzer {
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pub fn new(max_threshold: u32, scaling_factor: f64) -> Self {
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Self {
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max_threshold,
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scaling_factor,
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}
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}
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// Returns the mean vector, vector of individual standard deviations, and
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// covariance matrix
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fn stats(data: &[&[u32]]) -> (Vec<f64>, Vec<f64>, Vec<f64>) {
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let n = data.len();
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// Compute mean and standard deviation vectors
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let (mean_vec, sd_vec) = {
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let mut mean_vec = Vec::<f64>::new();
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let mut sd_vec = Vec::<f64>::new();
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for var in data {
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// Compute mean
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let mut sum = 0.0;
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for count in *var {
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sum += *count as f64;
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}
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let mean = sum / var.len() as f64;
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// Compute standard deviation
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let mut sum = 0.0;
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for count in *var {
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sum += (*count as f64 - mean).powi(2);
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}
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let sd = (sum / var.len() as f64).sqrt();
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mean_vec.push(mean);
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sd_vec.push(sd);
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}
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(mean_vec, sd_vec)
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};
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// Compute covariance matrix
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let cov_mat = {
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let mut cov_mat = Vec::<f64>::new();
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// We don't need to recompute Syx, but we currently do
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for i in 0..n {
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for j in 0..n {
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cov_mat.push({
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let var1 = data[i];
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let var1_mean = mean_vec[i];
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let var2 = data[j];
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let var2_mean = mean_vec[j];
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assert_eq!(var1.len(), var2.len());
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let mut sum = 0.0;
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for index in 0..var1.len() {
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sum +=
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(var1[index] as f64 - var1_mean) * (var2[index] as f64 - var2_mean);
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}
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sum / (var1.len() - 1) as f64
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});
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}
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}
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cov_mat
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};
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(mean_vec, sd_vec, cov_mat)
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}
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}
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impl Analyzer for NormalAnalyzer {
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/// Evaluate open-entry bridge based on only today's data
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fn stage_one(
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&self,
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_confidence: f64,
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_bridge_ips: &[u32],
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bridge_ips_today: u32,
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_negative_reports: &[u32],
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negative_reports_today: u32,
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) -> bool {
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negative_reports_today > self.max_threshold
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|| f64::from(negative_reports_today) > self.scaling_factor * f64::from(bridge_ips_today)
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}
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/// Evaluate invite-only bridge based on last 30 days
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fn stage_two(
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&self,
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confidence: f64,
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bridge_ips: &[u32],
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bridge_ips_today: u32,
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negative_reports: &[u32],
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negative_reports_today: u32,
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) -> bool {
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assert!(bridge_ips.len() >= UNTRUSTED_INTERVAL as usize);
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assert_eq!(bridge_ips.len(), negative_reports.len());
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let alpha = 1.0 - confidence;
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let (mean_vec, sd_vec, cov_mat) = Self::stats(&[bridge_ips, negative_reports]);
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let negative_reports_mean = mean_vec[1];
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let bridge_ips_sd = sd_vec[0];
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let negative_reports_sd = sd_vec[1];
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// Artificially create data for alternative hypothesis
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let num_days = bridge_ips.len() as usize;
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let mut bridge_ips_blocked = vec![0; num_days];
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let mut negative_reports_blocked = vec![0; num_days];
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let bridge_ips_deviation = (2.0 * bridge_ips_sd).round() as u32;
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for i in 0..num_days {
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// Suppose bridge stats will go down by 2 SDs
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bridge_ips_blocked[i] = if bridge_ips_deviation > bridge_ips[i] {
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0
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} else {
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bridge_ips[i] - bridge_ips_deviation
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};
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// Suppose negative reports will go up by 2 SDs
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negative_reports_blocked[i] =
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negative_reports[i] + (2.0 * negative_reports_sd).round() as u32;
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}
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let (mean_vec_blocked, _sd_vec_blocked, cov_mat_blocked) =
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Self::stats(&[&bridge_ips_blocked, &negative_reports_blocked]);
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let mvn = MultivariateNormal::new(mean_vec, cov_mat).unwrap();
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let pdf = mvn.pdf(&DVector::from_vec(vec![
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bridge_ips_today as f64,
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negative_reports_today as f64,
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]));
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let mvn_blocked = MultivariateNormal::new(mean_vec_blocked, cov_mat_blocked).unwrap();
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let pdf_blocked = mvn_blocked.pdf(&DVector::from_vec(vec![
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bridge_ips_today as f64,
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negative_reports_today as f64,
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]));
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// Also model negative reports in isolation
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let nr_normal = Normal::new(negative_reports_mean, negative_reports_sd).unwrap();
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let nr_pdf = nr_normal.pdf(negative_reports_today as f64);
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let nr_normal_blocked = Normal::new(
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negative_reports_mean + 2.0 * negative_reports_sd,
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negative_reports_sd,
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)
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.unwrap();
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let nr_pdf_blocked = nr_normal_blocked.pdf(negative_reports_today as f64);
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(pdf / pdf_blocked).ln() < alpha || (nr_pdf / nr_pdf_blocked).ln() < alpha
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}
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/// Evaluate invite-only bridge with lv3+ users submitting positive reports
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fn stage_three(
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&self,
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confidence: f64,
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bridge_ips: &[u32],
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bridge_ips_today: u32,
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negative_reports: &[u32],
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negative_reports_today: u32,
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positive_reports: &[u32],
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positive_reports_today: u32,
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) -> bool {
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assert!(bridge_ips.len() >= UNTRUSTED_INTERVAL as usize);
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assert_eq!(bridge_ips.len(), negative_reports.len());
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assert_eq!(bridge_ips.len(), positive_reports.len());
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let alpha = 1.0 - confidence;
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let (mean_vec, sd_vec, cov_mat) =
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Self::stats(&[bridge_ips, negative_reports, positive_reports]);
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let negative_reports_mean = mean_vec[1];
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let bridge_ips_sd = sd_vec[0];
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let negative_reports_sd = sd_vec[1];
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let positive_reports_sd = sd_vec[2];
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// Artificially create data for alternative hypothesis
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let num_days = bridge_ips.len() as usize;
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let mut bridge_ips_blocked = vec![0; num_days];
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let mut negative_reports_blocked = vec![0; num_days];
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let mut positive_reports_blocked = vec![0; num_days];
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let bridge_ips_deviation = (2.0 * bridge_ips_sd).round() as u32;
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let positive_reports_deviation = (2.0 * positive_reports_sd).round() as u32;
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for i in 0..num_days {
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// Suppose positive reports will go down by 2 SDs
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positive_reports_blocked[i] = if positive_reports_deviation > positive_reports[i] {
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0
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} else {
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positive_reports[i] - positive_reports_deviation
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};
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// Suppose bridge stats will go down by 2 SDs
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bridge_ips_blocked[i] = if bridge_ips_deviation > bridge_ips[i] {
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0
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} else {
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bridge_ips[i] - bridge_ips_deviation
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};
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// Suppose each user who would have submitted a positive report but
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// didn't submits a negative report instead.
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negative_reports_blocked[i] =
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negative_reports[i] + positive_reports[i] - positive_reports_blocked[i];
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}
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let (mean_vec_blocked, _sd_vec_blocked, cov_mat_blocked) = Self::stats(&[
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&bridge_ips_blocked,
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&negative_reports_blocked,
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&positive_reports_blocked,
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]);
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let mvn = MultivariateNormal::new(mean_vec, cov_mat).unwrap();
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let pdf = mvn.pdf(&DVector::from_vec(vec![
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bridge_ips_today as f64,
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negative_reports_today as f64,
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positive_reports_today as f64,
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]));
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let mvn_blocked = MultivariateNormal::new(mean_vec_blocked, cov_mat_blocked).unwrap();
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let pdf_blocked = mvn_blocked.pdf(&DVector::from_vec(vec![
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bridge_ips_today as f64,
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negative_reports_today as f64,
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positive_reports_today as f64,
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]));
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// Also model negative reports in isolation
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let nr_normal = Normal::new(negative_reports_mean, negative_reports_sd).unwrap();
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let nr_pdf = nr_normal.pdf(negative_reports_today as f64);
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// Note we do NOT make this a function of positive signals
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let nr_normal_blocked = Normal::new(
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negative_reports_mean + 2.0 * negative_reports_sd,
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negative_reports_sd,
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)
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.unwrap();
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let nr_pdf_blocked = nr_normal_blocked.pdf(negative_reports_today as f64);
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(pdf / pdf_blocked).ln() < alpha || (nr_pdf / nr_pdf_blocked).ln() < alpha
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}
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}
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