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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
//! Proposed API for the relevancy component (validation phase)
//!
//! The goal here is to allow us to validate that we can reliably detect user interests from
//! history data, without spending too much time building the API out. There's some hand-waving
//! towards how we would use this data to rank search results, but we don't need to come to a final
//! decision on that yet.
mod db;
mod error;
mod ingest;
mod interest;
mod ranker;
mod rs;
mod schema;
pub mod url_hash;
use rand_distr::{Beta, Distribution};
pub use db::RelevancyDb;
pub use error::{ApiResult, Error, RelevancyApiError, Result};
pub use interest::{Interest, InterestVector};
pub use ranker::score;
use error_support::handle_error;
uniffi::setup_scaffolding!();
#[derive(uniffi::Object)]
pub struct RelevancyStore {
db: RelevancyDb,
}
/// Top-level API for the Relevancy component
// Impl block to be exported via `UniFFI`.
#[uniffi::export]
impl RelevancyStore {
/// Construct a new RelevancyStore
///
/// This is non-blocking since databases and other resources are lazily opened.
#[uniffi::constructor]
pub fn new(db_path: String) -> Self {
Self {
db: RelevancyDb::new(db_path),
}
}
/// Close any open resources (for example databases)
///
/// Calling `close` will interrupt any in-progress queries on other threads.
pub fn close(&self) {
self.db.close()
}
/// Interrupt any current database queries
pub fn interrupt(&self) {
self.db.interrupt()
}
/// Ingest top URLs to build the user's interest vector.
///
/// Consumer should pass a list of the user's top URLs by frecency to this method. It will
/// then:
///
/// - Download the URL interest data from remote settings. Eventually this should be cached /
/// stored in the database, but for now it would be fine to download fresh data each time.
/// - Match the user's top URls against the interest data to build up their interest vector.
/// - Store the user's interest vector in the database.
///
/// This method may execute for a long time and should only be called from a worker thread.
#[handle_error(Error)]
pub fn ingest(&self, top_urls_by_frecency: Vec<String>) -> ApiResult<InterestVector> {
ingest::ensure_interest_data_populated(&self.db)?;
let interest_vec = self.classify(top_urls_by_frecency)?;
self.db
.read_write(|dao| dao.update_frecency_user_interest_vector(&interest_vec))?;
Ok(interest_vec)
}
/// Calculate metrics for the validation phase
///
/// This runs after [Self::ingest]. It takes the interest vector that ingest created and
/// calculates a set of metrics that we can report to glean.
#[handle_error(Error)]
pub fn calculate_metrics(&self) -> ApiResult<InterestMetrics> {
todo!()
}
/// Get the user's interest vector directly.
///
/// This runs after [Self::ingest]. It returns the interest vector directly so that the
/// consumer can show it in an `about:` page.
#[handle_error(Error)]
pub fn user_interest_vector(&self) -> ApiResult<InterestVector> {
self.db.read(|dao| dao.get_frecency_user_interest_vector())
}
/// Initializes probability distributions for any uninitialized items (arms) within a bandit model.
///
/// This method takes a `bandit` identifier and a list of `arms` (items) and ensures that each arm
/// in the list has an initialized probability distribution in the database. For each arm, if the
/// probability distribution does not already exist, it will be created, using Beta(1,1) as default,
/// which represents uniform distribution.
#[handle_error(Error)]
pub fn bandit_init(&self, bandit: String, arms: &[String]) -> ApiResult<()> {
self.db.read_write(|dao| {
for arm in arms {
dao.initialize_multi_armed_bandit(&bandit, arm)?;
}
Ok(())
})?;
Ok(())
}
/// Selects the optimal item (arm) to display to the user based on a multi-armed bandit model.
///
/// This method takes in a `bandit` identifier and a list of possible `arms` (items) and uses a
/// Thompson sampling approach to select the arm with the highest probability of success.
/// For each arm, it retrieves the Beta distribution parameters (alpha and beta) from the
/// database, creates a Beta distribution, and samples from it to estimate the arm's probability
/// of success. The arm with the highest sampled probability is selected and returned.
#[handle_error(Error)]
pub fn bandit_select(&self, bandit: String, arms: &[String]) -> ApiResult<String> {
// we should cache the distribution so we don't retrieve each time
let mut best_sample = f64::MIN;
let mut selected_arm = String::new();
for arm in arms {
let (alpha, beta) = self
.db
.read(|dao| dao.retrieve_bandit_arm_beta_distribution(&bandit, arm))?;
// this creates a Beta distribution for an alpha & beta pair
let beta_dist = Beta::new(alpha as f64, beta as f64)
.expect("computing betas dist unexpectedly failed");
// Sample from the Beta distribution
let sampled_prob = beta_dist.sample(&mut rand::thread_rng());
if sampled_prob > best_sample {
best_sample = sampled_prob;
selected_arm.clone_from(arm);
}
}
return Ok(selected_arm);
}
/// Updates the bandit model's arm data based on user interaction (selection or non-selection).
///
/// This method takes in a `bandit` identifier, an `arm` identifier, and a `selected` flag.
/// If `selected` is true, it updates the model to reflect a successful selection of the arm,
/// reinforcing its positive reward probability. If `selected` is false, it updates the
/// beta (failure) distribution of the arm, reflecting a lack of selection and reinforcing
/// its likelihood of a negative outcome.
#[handle_error(Error)]
pub fn bandit_update(&self, bandit: String, arm: String, selected: bool) -> ApiResult<()> {
self.db
.read_write(|dao| dao.update_bandit_arm_data(&bandit, &arm, selected))?;
Ok(())
}
}
impl RelevancyStore {
/// Download the interest data from remote settings if needed
#[handle_error(Error)]
pub fn ensure_interest_data_populated(&self) -> ApiResult<()> {
ingest::ensure_interest_data_populated(&self.db)?;
Ok(())
}
pub fn classify(&self, top_urls_by_frecency: Vec<String>) -> Result<InterestVector> {
let mut interest_vector = InterestVector::default();
for url in top_urls_by_frecency {
let interest_count = self.db.read(|dao| dao.get_url_interest_vector(&url))?;
log::trace!("classified: {url} {}", interest_count.summary());
interest_vector = interest_vector + interest_count;
}
Ok(interest_vector)
}
}
/// Interest metrics that we want to send to Glean as part of the validation process. These contain
/// the cosine similarity when comparing the user's interest against various interest vectors that
/// consumers may use.
///
/// Cosine similarly was chosen because it seems easy to calculate. This was then matched against
/// some semi-plausible real-world interest vectors that consumers might use. This is all up for
/// debate and we may decide to switch to some other metrics.
///
/// Similarity values are transformed to integers by multiplying the floating point value by 1000 and
/// rounding. This is to make them compatible with Glean's distribution metrics.
#[derive(uniffi::Record)]
pub struct InterestMetrics {
/// Similarity between the user's interest vector and an interest vector where the element for
/// the user's top interest is copied, but all other interests are set to zero. This measures
/// the highest possible similarity with consumers that used interest vectors with a single
/// interest set.
pub top_single_interest_similarity: u32,
/// The same as before, but the top 2 interests are copied. This measures the highest possible
/// similarity with consumers that used interest vectors with a two interests (note: this means
/// they would need to choose the user's top two interests and have the exact same proportion
/// between them as the user).
pub top_2interest_similarity: u32,
/// The same as before, but the top 3 interests are copied.
pub top_3interest_similarity: u32,
}
#[cfg(test)]
mod test {
use crate::url_hash::hash_url;
use super::*;
use rand::Rng;
use std::collections::HashMap;
fn make_fixture() -> Vec<(String, Interest)> {
vec![
]
}
fn expected_interest_vector() -> InterestVector {
InterestVector {
inconclusive: 1,
animals: 1,
food: 2,
..InterestVector::default()
}
}
fn setup_store(test_id: &'static str) -> RelevancyStore {
let relevancy_store =
RelevancyStore::new(format!("file:test_{test_id}_data?mode=memory&cache=shared"));
relevancy_store
.db
.read_write(|dao| {
for (url, interest) in make_fixture() {
dao.add_url_interest(hash_url(&url).unwrap(), interest)?;
}
Ok(())
})
.expect("Insert should succeed");
relevancy_store
}
#[test]
fn test_ingest() {
let relevancy_store = setup_store("ingest");
let (top_urls, _): (Vec<String>, Vec<Interest>) = make_fixture().into_iter().unzip();
assert_eq!(
relevancy_store.ingest(top_urls).unwrap(),
expected_interest_vector()
);
}
#[test]
fn test_get_user_interest_vector() {
let relevancy_store = setup_store("get_user_interest_vector");
let (top_urls, _): (Vec<String>, Vec<Interest>) = make_fixture().into_iter().unzip();
relevancy_store
.ingest(top_urls)
.expect("Ingest should succeed");
assert_eq!(
relevancy_store.user_interest_vector().unwrap(),
expected_interest_vector()
);
}
#[test]
fn test_thompson_sampling_convergence() {
let relevancy_store = setup_store("thompson_sampling_convergence");
let arms_to_ctr_map: HashMap<String, f64> = [
("wiki".to_string(), 0.1), // 10% CTR
("geolocation".to_string(), 0.3), // 30% CTR
("weather".to_string(), 0.8), // 80% CTR
]
.into_iter()
.collect();
let arm_names: Vec<String> = arms_to_ctr_map.keys().cloned().collect();
let bandit = "provider".to_string();
// initialize bandit
relevancy_store
.bandit_init(bandit.clone(), &arm_names)
.unwrap();
let mut rng = rand::thread_rng();
// Create a HashMap to map arm names to their selection counts
let mut selection_counts: HashMap<String, usize> =
arm_names.iter().map(|name| (name.clone(), 0)).collect();
// Simulate 1000 rounds of Thompson Sampling
for _ in 0..1000 {
// Use Thompson Sampling to select an arm
let selected_arm_name = relevancy_store
.bandit_select(bandit.clone(), &arm_names)
.expect("Failed to select arm");
// increase the selection count for the selected arm
*selection_counts.get_mut(&selected_arm_name).unwrap() += 1;
// get the true CTR for the selected arm
let true_ctr = &arms_to_ctr_map[&selected_arm_name];
// simulate a click or no-click based on the true CTR
let clicked = rng.gen_bool(*true_ctr);
// update beta distribution for arm based on click/no click
relevancy_store
.bandit_update(bandit.clone(), selected_arm_name, clicked)
.expect("Failed to update beta distribution for arm");
}
//retrieve arm with maximum selection count
let most_selected_arm_name = selection_counts
.iter()
.max_by_key(|(_, count)| *count)
.unwrap()
.0;
assert_eq!(
most_selected_arm_name, "weather",
"Thompson Sampling did not favor the best-performing arm"
);
}
}