@arizeai/phoenix-evals/dist/esm/code/createPrecisionRecallFScoreEvaluators.js

A library for running evaluations for AI use cases

import { computePrecisionRecallFScore, formatBetaForMetricName, getAverageMetricNameSuffix, } from "./classificationMetrics.js";
import { createClassificationMetricEvaluator } from "./createClassificationMetricEvaluator.js";
/**
 * Wraps `computePrecisionRecallFScore` so repeated calls with the same
 * `example` object (by reference) reuse the first computed result, instead
 * of recomputing the full confusion matrix once per evaluator.
 */
function createCachedComputer() {
    const cache = new WeakMap();
    return (example, options) => {
        const cached = cache.get(example);
        if (cached) {
            return cached;
        }
        const result = computePrecisionRecallFScore(example, options);
        cache.set(example, result);
        return result;
    };
}
/**
 * Creates matching precision, recall, and F-beta evaluators from a single set
 * of options, so all three are computed with the same `average`, `beta`,
 * `positiveLabel`, and `zeroDivision` settings. When the same `expected`/
 * `output` example object is passed to all three evaluators, the underlying
 * confusion matrix is only computed once and shared across them.
 *
 * @example
 * ```typescript
 * const { precision, recall, fScore } = createPrecisionRecallFScoreEvaluators({
 *   average: "weighted",
 * });
 * ```
 */
export function createPrecisionRecallFScoreEvaluators(options = {}) {
    const { beta = 1 } = options;
    const suffix = getAverageMetricNameSuffix(options);
    const compute = createCachedComputer();
    return {
        precision: createClassificationMetricEvaluator(`precision${suffix}`, "precision", options, compute),
        recall: createClassificationMetricEvaluator(`recall${suffix}`, "recall", options, compute),
        fScore: createClassificationMetricEvaluator(`${formatBetaForMetricName(beta)}${suffix}`, "fScore", options, compute),
    };
}
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