| import { describe, expect, it } from "vitest"; |
| import type { SkynetCausalEpisode } from "../causal-valence/episode-ledger.js"; |
| import { |
| buildSkynetRuntimeTrajectorySamples, |
| encodeSkynetRuntimeTrajectoryFeatures, |
| } from "./trajectory-builder.js"; |
| import { |
| predictSkynetRuntimeObserverLabel, |
| trainSkynetRuntimeObserverModel, |
| } from "./trajectory-learner.js"; |
|
|
| function buildEpisode( |
| index: number, |
| sessionKey: string, |
| bootstrapLabel: SkynetCausalEpisode["bootstrapLabel"], |
| kind: "noop" | "edit" | "delete", |
| ): SkynetCausalEpisode { |
| const success = bootstrapLabel !== "damage"; |
| return { |
| id: `${sessionKey}-${index}`, |
| sessionKey, |
| recordedAt: index, |
| context: { |
| continuityFreshness: bootstrapLabel === "stall" ? "aging" : "fresh", |
| failureStreak: bootstrapLabel === "damage" ? 2 : bootstrapLabel === "relief" ? 1 : 0, |
| targetCount: 1, |
| validationIntensity: kind === "noop" ? 0.2 : 0.9, |
| }, |
| transition: { |
| targetPaths: ["src/app.ts"], |
| operations: [{ path: "src/app.ts", kind, isTarget: true }], |
| }, |
| outcome: { |
| status: success ? "ok" : "error", |
| failureDomain: success ? "none" : "cognitive", |
| failureClass: success ? "none" : "validation_error", |
| targetSatisfied: bootstrapLabel === "progress" || bootstrapLabel === "relief", |
| validationPassed: success, |
| continuityDelta: bootstrapLabel === "progress" || bootstrapLabel === "relief" ? 0.7 : 0.05, |
| recoveryBurden: bootstrapLabel === "damage" ? 0.8 : 0.1, |
| collateralDamage: bootstrapLabel === "damage" ? 0.5 : 0, |
| }, |
| bootstrapLabel, |
| }; |
| } |
|
|
| describe("skynet runtime trajectory learner", () => { |
| it("encodes non-empty history-aware vectors", () => { |
| const samples = buildSkynetRuntimeTrajectorySamples({ |
| episodes: [buildEpisode(1, "s", "stall", "noop"), buildEpisode(2, "s", "progress", "edit")], |
| }); |
|
|
| expect(encodeSkynetRuntimeTrajectoryFeatures(samples[0]!).length).toBeGreaterThan(10); |
| }); |
|
|
| it("predicts labels from a separable synthetic sequence dataset", () => { |
| const samples = buildSkynetRuntimeTrajectorySamples({ |
| episodes: [ |
| buildEpisode(1, "a", "stall", "noop"), |
| buildEpisode(2, "a", "progress", "edit"), |
| buildEpisode(3, "a", "stall", "noop"), |
| buildEpisode(4, "a", "progress", "edit"), |
| buildEpisode(10, "b", "damage", "delete"), |
| buildEpisode(11, "b", "stall", "noop"), |
| buildEpisode(12, "b", "damage", "delete"), |
| buildEpisode(13, "b", "stall", "noop"), |
| ], |
| }); |
| const model = trainSkynetRuntimeObserverModel(samples)!; |
| const prediction = predictSkynetRuntimeObserverLabel(model, samples[0]!); |
|
|
| expect(model.labels.length).toBeGreaterThanOrEqual(2); |
| expect(["progress", "stall", "damage"]).toContain(prediction.label); |
| }); |
| }); |
|
|
