Fix CSP eval and analytics violations #5008
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,107 @@ | ||
| import { | ||
| AggregationMethod, | ||
| DistributionQuantileComponent, | ||
| Quantile, | ||
| QuestionType, | ||
| QuestionWithNumericForecasts, | ||
| } from "@/types/question"; | ||
| import { getSliderNumericForecastDataset } from "@/utils/forecasts/dataset"; | ||
| import { getSliderDistributionFromQuantiles } from "@/utils/forecasts/switch_forecast_type"; | ||
| import { computeQuartilesFromCDF } from "@/utils/math"; | ||
|
|
||
| function makeQuestion(): QuestionWithNumericForecasts { | ||
| return { | ||
| id: 1, | ||
| title: "Test question", | ||
| description: "", | ||
| created_at: "", | ||
| updated_at: "", | ||
| scheduled_close_time: "", | ||
| scheduled_resolve_time: "", | ||
| type: QuestionType.Numeric, | ||
| scaling: { | ||
| range_min: 0, | ||
| range_max: 100, | ||
| zero_point: null, | ||
| }, | ||
| open_lower_bound: false, | ||
| open_upper_bound: false, | ||
| inbound_outcome_count: 200, | ||
| resolution: null, | ||
| include_bots_in_aggregates: false, | ||
| question_weight: 1, | ||
| default_score_type: "peer", | ||
| default_aggregation_method: AggregationMethod.recency_weighted, | ||
| fine_print: null, | ||
| resolution_criteria: null, | ||
| label: "", | ||
| unit: "", | ||
| author_username: "", | ||
| post_id: 1, | ||
| aggregations: { | ||
| recency_weighted: { history: [], latest: undefined }, | ||
| unweighted: { history: [], latest: undefined }, | ||
| single_aggregation: { history: [], latest: undefined }, | ||
| metaculus_prediction: { history: [], latest: undefined }, | ||
| }, | ||
| my_forecasts: undefined, | ||
| my_forecast: undefined, | ||
| }; | ||
| } | ||
|
|
||
| function makeQuantiles(q1: number, q2: number, q3: number) { | ||
| return [ | ||
| { quantile: Quantile.lower, value: 0, isDirty: false }, | ||
| { quantile: Quantile.q1, value: q1, isDirty: false }, | ||
| { quantile: Quantile.q2, value: q2, isDirty: false }, | ||
| { quantile: Quantile.q3, value: q3, isDirty: false }, | ||
| { quantile: Quantile.upper, value: 0, isDirty: false }, | ||
| ] satisfies DistributionQuantileComponent; | ||
| } | ||
|
|
||
| function convertToSliderQuartiles(q1: number, q2: number, q3: number) { | ||
| const question = makeQuestion(); | ||
| const slider = getSliderDistributionFromQuantiles( | ||
| makeQuantiles(q1, q2, q3), | ||
| question | ||
| ); | ||
|
|
||
| expect(slider).toHaveLength(1); | ||
| const component = slider[0]; | ||
| expect(component).toBeDefined(); | ||
| if (!component) { | ||
| throw new Error("Expected slider component"); | ||
| } | ||
| expect(component.left).toBeLessThan(component.center); | ||
| expect(component.center).toBeLessThan(component.right); | ||
|
|
||
| const { cdf } = getSliderNumericForecastDataset(slider, question); | ||
| return computeQuartilesFromCDF(cdf); | ||
| } | ||
|
|
||
| describe("getSliderDistributionFromQuantiles", () => { | ||
| it.each([ | ||
| { label: "balanced", q1: 25, q2: 50, q3: 75, maxError: 0.05 }, | ||
| { label: "narrow cluster", q1: 25, q2: 30, q3: 35, maxError: 0.05 }, | ||
| { label: "moderately left-skewed", q1: 15, q2: 30, q3: 70, maxError: 0.1 }, | ||
| { label: "very left-skewed", q1: 10, q2: 20, q3: 80, maxError: 0.25 }, | ||
| { label: "very wide uncertainty", q1: 5, q2: 50, q3: 95, maxError: 0.25 }, | ||
| ])( | ||
| "returns a valid slider distribution near the input quartiles for $label", | ||
| ({ q1, q2, q3, maxError }) => { | ||
| const quartiles = convertToSliderQuartiles(q1, q2, q3); | ||
|
|
||
| expect(quartiles.lower25).toBeLessThan(quartiles.median); | ||
| expect(quartiles.median).toBeLessThan(quartiles.upper75); | ||
| expect(Math.abs(quartiles.lower25 - q1 / 100)).toBeLessThanOrEqual( | ||
| maxError | ||
| ); | ||
| expect(Math.abs(quartiles.median - q2 / 100)).toBeLessThanOrEqual( | ||
| maxError | ||
| ); | ||
| expect(Math.abs(quartiles.upper75 - q3 / 100)).toBeLessThanOrEqual( | ||
| maxError | ||
| ); | ||
| } | ||
| ); | ||
| }); |
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Could we add an open-bound case here and tighten the search before merging? On a both-open 0–100 question with quartiles 5/65/95, this converts back to 14.14/52.20/99.43. The old optimizer came back at 6.86/60.66/99.21, so switching input modes can now move a valid forecast quite a lot. This feels risky for a core forecasting interaction.