Jesse_Chen
127 lines
3.7 KiB
TypeScript
127 lines
3.7 KiB
TypeScript
import assert from "node:assert/strict";
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import test from "node:test";
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import {
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dynamicChoiceScoreInput,
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dynamicDifferenceInput,
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} from "../src/lib/birth-time-dynamic-engine-input.ts";
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import { dynamicCase } from "./birth-time-dynamic-persistence-fixture.ts";
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function matchingCandidateModel(activation = 0) {
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return {
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version: "birth-time-choice-scoring-v2",
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opportunity_model_version: "birth-time-opportunity-model-v4",
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historical_event_fingerprint: "fixture-event-fingerprint",
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range: { start_time: "05:02", end_time: "05:03" },
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windows: [{
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activations: { "05:02": activation, "05:03": 1 },
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fact_selection_priority: 1,
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fact_priority_version: "birth-time-question-fact-priority-v1",
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event_fact_selection_priority: 0,
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event_fact_priority_version: "birth-time-question-event-fact-priority-v1",
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}],
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};
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}
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function narrowedCase(startTime = "05:02", endTime = "05:03") {
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const stored = dynamicCase();
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return {
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...stored,
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eventContext: {
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birthDate: "1993-04-17",
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lat: 31.23,
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lon: 121.47,
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tz: 8,
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},
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choiceEvidence: [{
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questionId: "prior-question",
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opportunityId: "prior-opportunity",
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partitionId: "prior-partition",
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dimensionCode: "relocation_change",
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candidateScores: {
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"05:00": 0,
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"05:01": 0.25,
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"05:02": 0.5,
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"05:03": 0.75,
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"05:04": 1,
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},
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informationGain: 0.4,
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}],
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candidateModel: {
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version: "birth-time-choice-scoring-v2",
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range: { start_time: "05:00", end_time: "05:04" },
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},
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dynamicTurnState: {
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...stored.dynamicTurnState,
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progress: {
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...stored.dynamicTurnState.progress,
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currentRange: { startTime, endTime },
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},
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},
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};
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}
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test("narrowed scoring projects prior evidence onto the current candidate range", () => {
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const input = dynamicChoiceScoreInput(narrowedCase());
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assert.deepEqual(input.evidence[0]?.candidateScores, {
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"05:02": 0.5,
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"05:03": 0.75,
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});
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});
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test("narrowed question generation rebuilds a candidate model for the current range", () => {
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const input = dynamicDifferenceInput(narrowedCase());
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assert.deepEqual(input.evidence[0]?.candidateScores, {
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"05:02": 0.5,
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"05:03": 0.75,
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});
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assert.equal(input.candidateModel, null);
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});
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test("matching candidate models remain reusable", () => {
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const stored = narrowedCase();
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const matchingModel = matchingCandidateModel();
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const input = dynamicDifferenceInput({ ...stored, candidateModel: matchingModel });
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assert.equal(input.candidateModel, matchingModel);
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});
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test("legacy v2 candidate models are rebuilt for fact-driven question selection", () => {
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const stored = narrowedCase();
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const matchingModel = {
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...matchingCandidateModel(),
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opportunity_model_version: "birth-time-opportunity-model-v2",
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};
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const input = dynamicDifferenceInput({ ...stored, candidateModel: matchingModel });
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assert.equal(input.candidateModel, null);
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});
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test("persisted candidate models with legacy negative activations are rebuilt", () => {
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const stored = narrowedCase();
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const input = dynamicDifferenceInput({
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...stored,
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candidateModel: matchingCandidateModel(-0.2),
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});
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assert.equal(input.candidateModel, null);
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});
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test("evidence projection preserves cross-midnight candidate chronology", () => {
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const stored = narrowedCase("23:59", "00:00");
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const input = dynamicChoiceScoreInput({
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...stored,
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choiceEvidence: [{
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...stored.choiceEvidence[0],
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candidateScores: { "23:58": 0, "23:59": 0.25, "00:00": 0.5, "00:01": 0.75 },
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}],
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});
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assert.deepEqual(input.evidence[0]?.candidateScores, {
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"23:59": 0.25,
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"00:00": 0.5,
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});
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});
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