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#[cfg(test)]
mod fast_rp_test {
use raphtory::{
algorithms::embeddings::fast_rp::fast_rp, db::api::mutation::AdditionOps, prelude::*,
test_storage,
};
use std::collections::HashMap;
#[test]
fn simple_fast_rp_test() {
let graph = Graph::new();
let edges = vec![
(1, 2, 1),
(1, 3, 1),
(2, 3, 1),
(4, 5, 1),
(4, 6, 1),
(4, 7, 1),
(5, 6, 1),
(5, 7, 1),
(6, 7, 1),
(6, 8, 1),
];
for (src, dst, ts) in edges {
graph.add_edge(ts, src, dst, NO_PROPS, None).unwrap();
}
let baseline: HashMap<String, Vec<f64>> = HashMap::from([
(
String::from("5"),
vec![
0.0,
1.9620916355920008,
-1.6817928305074292,
-1.6817928305074292,
0.2802988050845715,
-0.2802988050845715,
0.2802988050845715,
1.4014940254228576,
-0.2802988050845715,
0.0,
0.0,
-1.6817928305074292,
0.2802988050845715,
0.2802988050845715,
-0.2802988050845715,
1.121195220338286,
],
),
(
String::from("1"),
vec![
1.6817928305074292,
0.4204482076268573,
-0.4204482076268573,
0.0,
0.0,
2.1022410381342866,
0.4204482076268573,
0.4204482076268573,
2.1022410381342866,
-0.8408964152537146,
0.0,
1.6817928305074292,
0.0,
-1.6817928305074292,
0.0,
-0.8408964152537146,
],
),
(
String::from("4"),
vec![
-1.4014940254228576,
0.560597610169143,
1.121195220338286,
-0.2802988050845715,
0.2802988050845715,
-0.2802988050845715,
0.2802988050845715,
0.0,
-1.6817928305074292,
0.0,
0.0,
-0.2802988050845715,
0.2802988050845715,
0.2802988050845715,
-0.2802988050845715,
-1.6817928305074292,
],
),
(
String::from("6"),
vec![
-0.21022410381342865,
0.6306723114402859,
-1.6817928305074292,
-1.4715687266940005,
1.6817928305074292,
-1.6817928305074292,
0.0,
-1.4715687266940005,
-0.21022410381342865,
0.0,
0.0,
-0.4204482076268573,
1.6817928305074292,
0.21022410381342865,
-0.21022410381342865,
-0.21022410381342865,
],
),
(
String::from("7"),
vec![
1.4014940254228576,
1.9620916355920008,
-0.2802988050845715,
1.121195220338286,
0.2802988050845715,
-0.2802988050845715,
1.6817928305074292,
0.0,
-0.2802988050845715,
0.0,
0.0,
-0.2802988050845715,
0.2802988050845715,
1.6817928305074292,
-1.6817928305074292,
-1.6817928305074292,
],
),
(
String::from("2"),
vec![
0.4204482076268573,
1.6817928305074292,
-1.6817928305074292,
0.0,
0.0,
0.8408964152537146,
1.6817928305074292,
1.6817928305074292,
2.1022410381342866,
-2.1022410381342866,
0.0,
0.4204482076268573,
0.0,
-0.4204482076268573,
0.0,
-2.1022410381342866,
],
),
(
String::from("8"),
vec![
-1.6817928305074292,
1.6817928305074292,
-0.8408964152537146,
0.8408964152537146,
0.8408964152537146,
-0.8408964152537146,
-1.6817928305074292,
-0.8408964152537146,
0.0,
0.0,
0.0,
-1.6817928305074292,
0.8408964152537146,
0.0,
0.0,
0.0,
],
),
(
String::from("3"),
vec![
0.4204482076268573,
0.4204482076268573,
-0.4204482076268573,
0.0,
0.0,
2.1022410381342866,
0.4204482076268573,
0.4204482076268573,
0.8408964152537146,
-2.1022410381342866,
0.0,
0.4204482076268573,
0.0,
-0.4204482076268573,
0.0,
-2.1022410381342866,
],
),
]);
test_storage!(&graph, |graph| {
let results = fast_rp(graph, 16, 1.0, vec![1.0, 1.0], Some(42), None)
.to_hashmap(|value| value.embedding_state);
assert_eq!(results, baseline);
});
}
// NOTE(Wyatt): the simple fast_rp test is more of a validation of idempotency than correctness (although the results are expected)
// This test-- in progress-- is going to validate that the algorithm preserves the pairwise topological distances
/*
use crate::io::csv_loader::CsvLoader;
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
fn print_samples(map: &HashMap<String, Vec<f64>>, n: usize) {
let mut count = 0;
for (key, value) in map {
println!("Key: {}, Value: {:#?}", key, value);
count += 1;
if count >= n {
break;
}
}
}
fn top_k_neighbors(
data: &HashMap<String, Vec<f64>>,
k: usize,
) -> HashMap<String, Vec<String>> {
let mut neighbors: HashMap<String, Vec<String>> = HashMap::new();
// Iterate over each ID to find its top K neighbors
for (id, vector) in data {
// Collect distances to all other IDs
let mut distances: Vec<(&String, f64)> = Vec::new();
for (other_id, other_vector) in data {
if id == other_id {
continue; // Skip self
}
// Compute Euclidean distance
let distance = euclidean_distance(vector, other_vector);
distances.push((other_id, distance));
}
// Sort the distances in ascending order
distances.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
// Collect top K neighbor IDs
let top_k: Vec<String> = distances
.iter()
.take(k)
.map(|(other_id, _)| (*other_id).clone())
.collect();
// Insert into the neighbors map
neighbors.insert(id.clone(), top_k);
}
neighbors
}
fn euclidean_distance(a: &Vec<f64>, b: &Vec<f64>) -> f64 {
assert_eq!(a.len(), b.len(), "Vectors must be of the same length");
a.iter()
.zip(b.iter())
.map(|(&x, &y)| (x - y).powi(2))
.sum::<f64>()
.sqrt()
}
#[test]
fn big_fast_rp_test() {
let mut d = PathBuf::from(env!("CARGO_MANIFEST_DIR"));
d.push("resources/test");
let loader = CsvLoader::new(d.join("test.csv")).set_delimiter(",");
let graph = Graph::new();
#[derive(Deserialize, Serialize, Debug)]
struct CsvEdge {
src: u64,
dst: u64,
}
loader
.load_into_graph(&graph, |e: CsvEdge, g| {
g.add_edge(1, e.src, e.dst, NO_PROPS, None).unwrap();
g.add_edge(1, e.dst, e.src, NO_PROPS, None).unwrap();
})
.unwrap();
test_storage!(&graph, |graph| {
let results = fast_rp(
graph,
32,
1.0,
vec![1.0, 1.0, 0.5],
Some(42),
None,
).get_all_with_names();
// println!("Result: {:#?}", results);
print_samples(&results, 10);
});
}
*/
}