Jyotisha/frontend/tests/rectification-distinguish-contract.test.ts

import assert from "node:assert/strict";
import test from "node:test";

import { applyProbeOutcome } from "../src/lib/rectification-agentic/core/apply-probe-outcome.ts";
import { buildInferenceState } from "../src/lib/rectification-agentic/core/build-state.ts";
import { distinguishContractErrors } from "../src/lib/rectification-agentic/core/distinguish-contract.ts";
import { probeFromEngine } from "../src/lib/rectification-agentic/core/probes-from-engine.ts";
import { parseDiscriminatingEventProbes } from "../src/lib/rectification-agentic/v9/refinement-packet.ts";

test("CI forbids distinguish probes with empty mapping or non-positive gain", () => {
  assert.deepEqual(distinguishContractErrors({
    role: "distinguish",
    information_gain: 0,
    candidate_ids: ["05:00", "05:20"],
    expected_outcomes: [
      { answer_class: "yes", supports: ["05:00"], conflicts: ["05:20"] },
      { answer_class: "no", supports: ["05:20"], conflicts: ["05:00"] },
    ],
  }), ["distinguish_non_positive_information_gain"]);
  assert.deepEqual(distinguishContractErrors({
    role: "distinguish",
    information_gain: 0.4,
    candidate_ids: [],
    expected_outcomes: [
      { answer_class: "yes", supports: [], conflicts: [] },
      { answer_class: "no", supports: [], conflicts: [] },
    ],
  }), ["distinguish_empty_candidate_ids"]);
  assert.deepEqual(distinguishContractErrors({
    role: "distinguish",
    information_gain: 0.4,
    candidate_ids: ["05:00", "05:20"],
    expected_outcomes: [],
  }), ["distinguish_empty_expected_outcomes"]);
});

test("receipt parser drops invalid distinguish probes and known_event_quality", () => {
  const parsed = parseDiscriminatingEventProbes([
    {
      year: 2016,
      year_label: "2016 年前后",
      domain: "education",
      event_family: "学业变化",
      source: "known_event_quality",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "clarification only",
      role: "distinguish",
      information_gain: 0,
    },
    {
      year: 2018,
      year_label: "2018 年前后",
      domain: "career",
      event_family: "职责变化",
      source: "dasha_activation",
      tracks: ["vimshottari", "narayana"],
      tracks_agree: true,
      unique_minute_claim: false,
      user_meaning: "engine locked year and family",
      role: "distinguish",
      information_gain: 0.4,
      candidate_ids: ["05:00", "05:20"],
      expected_outcomes: [
        { answer_class: "yes", supports: ["05:00"], conflicts: ["05:20"] },
        { answer_class: "no", supports: ["05:20"], conflicts: ["05:00"] },
      ],
    },
  ]);
  assert.equal(parsed.length, 1);
  assert.equal(parsed[0]?.domain, "career");
  assert.equal(parsed[0]?.source, "dasha_activation");
  assert.ok((parsed[0]?.information_gain ?? 0) > 0);
});

test("randomized hidden mutated answers change posterior only when mapped", () => {
  const probe = probeFromEngine({
    year: 2018,
    year_label: "2018 年前后",
    domain: "career",
    event_family: "职责变化",
    source: "dasha_activation",
    tracks: ["vimshottari", "narayana"],
    tracks_agree: true,
    unique_minute_claim: false,
    user_meaning: "engine locked year and family",
    role: "distinguish",
    information_gain: 0.4,
    semantic_key: "career.2018",
    candidate_split_hash: "set:career:2018",
    candidate_ids: ["05:00", "05:20"],
    expected_outcomes: [
      { answer_class: "yes", supports: ["05:00"], conflicts: ["05:20"] },
      { answer_class: "no", supports: ["05:20"], conflicts: ["05:00"] },
      { answer_class: "unsure", supports: [], conflicts: [] },
    ],
  });
  assert.ok(probe);
  const scores = { "05:00": 10, "05:20": 10 };
  const yes = applyProbeOutcome(scores, probe, "yes");
  assert.equal(yes.kind, "informative");
  assert.notDeepEqual(yes.scores, scores);
  assert.ok(yes.deltas["05:00"] !== 0);
  const mutated = applyProbeOutcome(scores, probe, "unsure");
  assert.equal(mutated.kind, "low_information");
  assert.deepEqual(mutated.scores, scores);
  const state = buildInferenceState({
    range_start: "05:00",
    range_end: "05:20",
    candidates: [
      { id: "05:00", time: "05:00", relative_support: 10 },
      { id: "05:20", time: "05:20", relative_support: 10 },
    ],
    events: [
      { id: "e1", domain: "education", year: 2016, precision: "month" },
      { id: "e2", domain: "career", year: 2018, precision: "year" },
      { id: "e3", domain: "family", year: 2020, precision: "year" },
    ],
    probes: [probe],
    answered_probes: [{
      probe_id: probe.id,
      semantic_key: probe.semantic_key,
      candidate_split_hash: probe.candidate_split_hash,
      answer_class: "unsure",
      classified_from: "choice",
    }],
  });
  assert.equal(state.last_inference_round?.kind, "low_information");
  assert.deepEqual(state.last_inference_round?.scores_before, state.last_inference_round?.scores_after);
  assert.equal(
    state.rounds.filter((item) => item.kind === "informative").length,
    0,
  );
});