status-im
/
sourcecred
mirror of https://github.com/status-im/sourcecred.git
2
0
Fork 0
Code Issues Projects Releases Wiki Activity

Add a test helper function for converged graphs (#1093) 

Really minor refactor, adds a `convergedPagerankGraph` helper method
which provides a converged pagerank graph. :)

Test plan: `yarn test` suffices.
This commit is contained in:
Dandelion Mané 2019-02-18 13:10:55 -07:00 committed by GitHub
parent 4adbec03c2
commit c353efff36
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 9 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
src/core/pagerankGraph.test.js
View File

@ -17,10 +17,15 @@ describe("core/pagerankGraph", () => {
const nonEmptyGraph = () => const nonEmptyGraph = () =>
new Graph().addNode(NodeAddress.fromParts(["hi"])); new Graph().addNode(NodeAddress.fromParts(["hi"]));
function examplePagerankGraph() { function examplePagerankGraph(): PagerankGraph {
const g = advancedGraph().graph1(); const g = advancedGraph().graph1();
return new PagerankGraph(g, defaultEvaluator); return new PagerankGraph(g, defaultEvaluator);
} }
async function convergedPagerankGraph(): Promise<PagerankGraph> {
const pg = examplePagerankGraph();
await pg.runPagerank({maxIterations: 100, convergenceThreshold: 1e-4});
return pg;
}
it("cannot construct PagerankGraph with empty Graph", () => { it("cannot construct PagerankGraph with empty Graph", () => {
const eg1 = new Graph(); const eg1 = new Graph();
@ -50,9 +55,7 @@ describe("core/pagerankGraph", () => {
expect(new Set(graphNodes)).toEqual(new Set(pgNodes)); expect(new Set(graphNodes)).toEqual(new Set(pgNodes));
}); });
it("node and nodes both return consistent scores", async () => { it("node and nodes both return consistent scores", async () => {
const g = advancedGraph().graph1(); const pg = await convergedPagerankGraph();
const pg = new PagerankGraph(g, defaultEvaluator);
await pg.runPagerank({maxIterations: 1, convergenceThreshold: 0.001});
for (const {node, score} of pg.nodes()) { for (const {node, score} of pg.nodes()) {
expect(score).toEqual(NullUtil.get(pg.node(node)).score); expect(score).toEqual(NullUtil.get(pg.node(node)).score);
} }
@ -289,15 +292,13 @@ describe("core/pagerankGraph", () => {
describe("to/from JSON", () => { describe("to/from JSON", () => {
it("to->fro is identity", async () => { it("to->fro is identity", async () => {
const pg = examplePagerankGraph(); const pg = await convergedPagerankGraph();
await pg.runPagerank({maxIterations: 1, convergenceThreshold: 0.01});
const pgJSON = pg.toJSON(); const pgJSON = pg.toJSON();
const pg_ = PagerankGraph.fromJSON(pgJSON); const pg_ = PagerankGraph.fromJSON(pgJSON);
expect(pg.equals(pg_)).toBe(true); expect(pg.equals(pg_)).toBe(true);
}); });
it("fro->to is identity", async () => { it("fro->to is identity", async () => {
const pg = examplePagerankGraph(); const pg = await convergedPagerankGraph();
await pg.runPagerank({maxIterations: 1, convergenceThreshold: 0.01});
const pgJSON = pg.toJSON(); const pgJSON = pg.toJSON();
const pg_ = PagerankGraph.fromJSON(pgJSON); const pg_ = PagerankGraph.fromJSON(pgJSON);
const pgJSON_ = pg_.toJSON(); const pgJSON_ = pg_.toJSON();