Function perClassAccuracy

tfjs-vis/src/util/math.ts:281–326

(
    labels: Tensor1D, predictions: Tensor1D,
    numClasses?: number)

 * @doc {heading: 'Metrics', namespace: 'metrics'}
 */
export async function perClassAccuracy(
    labels: Tensor1D, predictions: Tensor1D,
    numClasses?: number): Promise<Array<{accuracy: number, count: number}>> {
  assert(labels.rank === 1, 'labels must be a 1D tensor');
  assert(predictions.rank === 1, 'predictions must be a 1D tensor');
  assert(
      labels.size === predictions.size,
      'labels and predictions must be the same length');

  if (numClasses == null) {
    const maximumTensor = tidy(() => {
      return maximum(labels.max(), predictions.max());
    });
    const maximumArray = await maximumTensor.data();
    numClasses = maximumArray[0] + 1;
    maximumTensor.dispose();
  }

  return Promise.all([labels.data(), predictions.data()])
      .then(([labelsArray, predsArray]) => {
        // Per class total counts
        const counts: number[] = Array(numClasses).fill(0);
        // Per class accuracy
        const accuracy: number[] = Array(numClasses).fill(0);

        for (let i = 0; i < labelsArray.length; i++) {
          const label = labelsArray[i];
          const pred = predsArray[i];

          counts[label] += 1;
          if (label === pred) {
            accuracy[label] += 1;
          }
        }

        const results: Array<{accuracy: number, count: number}> = [];
        for (let i = 0; i < counts.length; i++) {
          results.push({
            count: counts[i],
            accuracy: counts[i] === 0 ? 0 : accuracy[i] / counts[i],
          });
        }

        return results;
      });
}