@arizeai/phoenix-evals/dist/src/code/createClassificationMetricEvaluator.d.ts

A library for running evaluations for AI use cases

import type { EvaluatorBase } from "../core/EvaluatorBase";
import { type ClassificationExample, type PrecisionRecallFScoreOptions, type PrecisionRecallFScoreResult } from "./classificationMetrics";
/**
 * Computes a {@link PrecisionRecallFScoreResult} for a batch of labels.
 * Injectable so callers (e.g. {@link createPrecisionRecallFScoreEvaluators})
 * can share one computed result across multiple metric evaluators instead of
 * recomputing it per evaluator.
 */
export type ClassificationMetricComputer = (example: Pick<ClassificationExample, "expected" | "output">, options: PrecisionRecallFScoreOptions) => PrecisionRecallFScoreResult;
/**
 * Internal factory shared by `createPrecisionEvaluator`, `createRecallEvaluator`,
 * and `createFBetaEvaluator` — each is a thin wrapper that only differs in
 * which result field it reads and how its metric name is built.
 */
export declare function createClassificationMetricEvaluator<RecordType extends ClassificationExample = ClassificationExample>(name: string, field: "precision" | "recall" | "fScore", options: PrecisionRecallFScoreOptions, compute?: ClassificationMetricComputer): EvaluatorBase<RecordType>;
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