def _compute_metrics()

in src/autofill_model.py [0:0]

• 14 lines of code
• 1 McCabe index (conditional complexity)

    def _compute_metrics(self, eval_preds: EvalPrediction):
        # load metrics
        f1_metric = evaluate.load('f1')
        precision_metric = evaluate.load('precision')
        recall_metric = evaluate.load('recall')

        # predictions
        logits, labels = eval_preds
        preds = np.argmax(logits, axis=-1)

        # compute metrics
        precision = precision_metric.compute(predictions=preds, references=labels, average='weighted')['precision']
        recall = recall_metric.compute(predictions=preds, references=labels, average='weighted')['recall']
        f1 = f1_metric.compute(predictions=preds, references=labels, average='weighted')['f1']
        return {
            'precision': precision,
            'recall': recall,
            'f1': f1,
        }