@reaatech/classifier-evals-metrics

Status: Pre-1.0 — APIs may change in minor versions. Pin to a specific version in production.

Confusion matrix calculation, comprehensive classification metrics, model comparison with statistical significance, and evaluation run construction. Built on the @reaatech/classifier-evals core types.

Installation

npm install @reaatech/classifier-evals-metrics
# or
pnpm add @reaatech/classifier-evals-metrics

Feature Overview

• Confusion matrix — multi-class matrix with per-class precision, recall, F1, and support
• 14 classification metrics — accuracy, macro/micro/weighted precision/recall/F1, Matthews Correlation Coefficient (Gorodkin 2004), Cohen’s Kappa
• Matrix normalization — row-wise (recall), column-wise (precision), or overall normalization
• Top misclassifications — identify the most common class confusions
• Model comparison — accuracy and F1 comparison with McNemar’s test p-values, Cohen’s d effect size, and per-class F1 diffs
• Evaluation run construction — assemble a complete EvalRun from samples with metadata, metrics, judge results, and gate results
• Visualization data — generate chart-ready data for confusion matrix heatmaps, per-class bar charts, and cluster maps
• Dual ESM/CJS output — works with import and require

Quick Start

import {
  buildConfusionMatrix,
  calculateAllMetrics,
  compareModels,
} from "@reaatech/classifier-evals-metrics";
 
const samples = [
  { text: "Reset password", label: "account", predicted_label: "account", confidence: 0.95 },
  { text: "Cancel sub", label: "billing", predicted_label: "account", confidence: 0.72 },
  { text: "Where is order", label: "order", predicted_label: "order", confidence: 0.88 },
];
 
// Build confusion matrix
const cm = buildConfusionMatrix(samples);
console.log(cm.labels);     // ["account", "billing", "order"]
console.log(cm.per_class);  // Per-class precision, recall, F1, support
 
// Calculate all metrics at once
const metrics = calculateAllMetrics(samples);
console.log(`Accuracy: ${(metrics.accuracy * 100).toFixed(1)}%`);
console.log(`Macro F1: ${(metrics.f1_macro * 100).toFixed(1)}%`);
console.log(`MCC: ${metrics.matthews_correlation.toFixed(3)}`);
 
// Compare two models
const comparison = compareModels(baselineSamples, candidateSamples, "baseline", "candidate");
console.log(`Accuracy diff: ${comparison.accuracy_difference.toFixed(3)}`);

API Reference

Confusion Matrix

buildConfusionMatrix(samples: ClassificationResult[]): ConfusionMatrix

Builds a multi-class confusion matrix from classification results. The union of all unique labels (both true and predicted) forms the label set. Returns { labels, matrix, per_class }.

const cm = buildConfusionMatrix(samples);
// cm.matrix[trueLabel][predictedLabel] = count
// cm.per_class[i] = { label, true_positives, false_positives, false_negatives, true_negatives,
//                      precision, recall, f1, support }

normalizeConfusionMatrix(cm: ConfusionMatrix, mode?: 'true' | 'pred' | 'all'): number[][]

Normalizes the matrix by row (true — shows recall per class), column (pred — shows precision per class), or overall (shows proportion of total).

const rowNormalized = normalizeConfusionMatrix(cm, "true");
// rowNormalized[i][j] = proportion of true class i predicted as class j

getDiagonal(cm: ConfusionMatrix): number[]

Returns the diagonal of the confusion matrix (correct predictions per class).

getTotalCorrect(cm: ConfusionMatrix): number

Total correct predictions across all classes.

getTotalSamples(cm: ConfusionMatrix): number

Total samples in the matrix.

getErrorRates(cm: ConfusionMatrix): Record<string, number>

Error rate per class (1 - recall).

getTopMisclassifications(cm: ConfusionMatrix, topN?: number): Misclassification[]

Returns the most common misclassifications, sorted by count descending.

const top = getTopMisclassifications(cm, 5);
// [{ trueLabel: "billing", predictedLabel: "account", count: 42 }, ...]

formatConfusionMatrix(cm: ConfusionMatrix): string

Returns a human-readable formatted string of the confusion matrix.

Classification Metrics

calculateAccuracy(samples: ClassificationResult[]): number

Simple accuracy: #correct / #total.

calculateAllMetrics(samples: ClassificationResult[]): ClassificationMetrics

Calculates all 14 metrics in a single pass. Returns:

Individual metric functions are also exported: calculatePrecisionMacro, calculateRecallMacro, calculateF1Macro, calculatePrecisionMicro, calculateRecallMicro, calculateF1Micro, calculatePrecisionWeighted, calculateRecallWeighted, calculateF1Weighted, calculateMCC, calculateCohensKappa.

Model Comparison

compareModels(baseline: ClassificationResult[], candidate: ClassificationResult[], baselineName?: string, candidateName?: string): ModelComparison

Compares two sets of predictions with statistical significance tests.

const comp = compareModels(baselineSamples, candidateSamples);
// {
//   baseline_accuracy, candidate_accuracy, accuracy_difference,
//   p_value: McNemar's test, is_significant: p < 0.05,
//   effect_size: Cohen's d,
//   per_class_comparison: [{ label, baseline_f1, candidate_f1, difference, improved }]
// }

summarizeComparison(comparison: ModelComparison): string

Returns a human-readable summary string of the comparison.

comparePersistedEvalRuns(baseline: EvalRun, candidate: EvalRun): ModelComparison

Compares two EvalRun objects from their persisted metrics.

Evaluation Run Construction

createEvalRunFromSamples(options: EvalRunBuildOptions): EvalRun

Builds a complete EvalRun from raw samples, computing the confusion matrix, all metrics, and distribution metadata.

const evalRun = createEvalRunFromSamples({
  datasetPath: "./test-set.csv",
  datasetName: "production-test",
  samples,
  judgedResults,
  judgeCost: 12.34,
  gateResults,
  metadata: { model: "v2", commit: "abc123" },
});

The returned EvalRun includes distribution_metrics in its metadata: unknown_rate, label_cardinality, and prediction_cardinality.

Visualization Data

generateHeatmapData(cm: ConfusionMatrix): { x, y, z }[]

Generates x, y, z tuples for heatmap charting libraries.

generatePerClassBarData(cm: ConfusionMatrix): { labels, precision, recall, f1 }

Generates arrays suitable for grouped bar charts.

generateClusterMapData(cm: ConfusionMatrix): { labels, matrix, similarity }

Generates normalized data for cluster heatmap visualization.

Usage Pattern

import { buildConfusionMatrix, calculateAllMetrics } from "@reaatech/classifier-evals-metrics";
import { loadDataset } from "@reaatech/classifier-evals-dataset";
 
const dataset = await loadDataset("./data/classifications.csv");
const cm = buildConfusionMatrix(dataset.samples);
const metrics = calculateAllMetrics(dataset.samples);
 
console.log(`F1: ${metrics.f1_macro.toFixed(3)}, MCC: ${metrics.matthews_correlation.toFixed(3)}`);

Related Packages

• @reaatech/classifier-evals — Core types and schemas
• @reaatech/classifier-evals-dataset — Dataset loading and validation

License

MIT