78 lines
2.3 KiB
Markdown
78 lines
2.3 KiB
Markdown
## About
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This package contains a series of utilities for forecast aggregation. It is currently in _alpha_, meaning that the code hasn't been tested much.
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For an introduction to different aggregation methods, see Jaime Sevilla's [Aggregation](https://forum.effectivealtruism.org/s/hjiBqAJNKhfJFq7kf) series. For an explanation of the neyman method, see [here](https://forum.effectivealtruism.org/s/hjiBqAJNKhfJFq7kf/p/biL94PKfeHmgHY6qe).
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## Built with
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- vanilla javascript
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- [Best readme template](https://github.com/othneildrew/Best-README-Template)
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- [lerna](https://github.com/lerna/lerna)
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## Getting started
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### Installation
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```sh
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npm install @forecasting/aggregation
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```
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### Usage
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```js
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import {
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median,
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arithmeticMean,
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geometricMean,
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geometricMeanOfOdds,
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extremizedGeometricMeanOfOdds,
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neyman,
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} from "@forecasting/aggregation";
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let ps = [0.1, 0.2, 0.4, 0.5];
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console.log(ps);
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console.log(median(ps));
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console.log(arithmeticMean(ps));
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console.log(geometricMean(ps));
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console.log(geometricMeanOfOdds(ps));
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console.log(extremizedGeometricMeanOfOdds(ps, 1.5)); // 1.5 is the extremization factor
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console.log(extremizedGeometricMeanOfOdds(ps, 2.5));
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console.log(neyman(ps));
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// invalid inputs, will return -1
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let notArrayOfProbabilities0 = "Hello world!";
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console.log(arithmeticMean(notArrayOfProbabilities0)); // -1
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let notArrayOfProbabilities1 = [];
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console.log(arithmeticMean(notArrayOfProbabilities1)); // -1
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let notArrayOfProbabilities2 = ["a"];
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console.log(arithmeticMean(notArrayOfProbabilities2)); // -1
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let notArrayOfProbabilities3 = [2, 4, 5];
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console.log(arithmeticMean(notArrayOfProbabilities3)); // -1
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let notArrayOfProbabilities4 = [0.2, 4, 5];
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console.log(arithmeticMean(notArrayOfProbabilities4)); // -1
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const chosenAggregationMethod = neyman;
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const getAggregatedProbabilities = (array) => {
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let result = neyman(array);
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if (result == -1) {
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// handle case somehow; maybe throw an error, e.g.:
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// throw new Error("Invalid array of probabilities")
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} else {
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return result;
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}
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};
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```
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## Roadmap
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- [x] validate probability (must be 0<= p <=1)
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- [x] Decide on a return type if probabilities are not validated (-1? / null?)
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- [x] Write wrapper code for validation
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- [x] Validate that array.length > 0
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- [ ] add weighting? by recency?
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- [ ] filter outliers?
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