pytext/metrics/__init__.py [1076:1100]:
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            predictions,
            label_names,
            recall_at_precision_thresholds,
            precision_at_recall_thresholds,
        )
        if average_precisions
        else None
    )

    if len(label_names) == 2:
        confusion_dict = per_label_confusions.label_confusions_map
        # Since MCC is symmetric, it doesn't matter which label is 0 and which is 1
        TP = confusion_dict[label_names[0]].TP
        FP = confusion_dict[label_names[0]].FP
        FN = confusion_dict[label_names[0]].FN
        TN = confusion_dict[label_names[1]].TP
        mcc: Optional[float] = compute_matthews_correlation_coefficients(TP, FP, FN, TN)
        roc_auc: Optional[float] = compute_roc_auc(predictions)
    else:
        mcc = None
        roc_auc = None

    return ClassificationMetrics(
        accuracy=accuracy,
        macro_prf1_metrics=macro_prf1_metrics,
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pytext/metrics/__init__.py [1242:1266]:
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            predictions,
            label_names,
            recall_at_precision_thresholds,
            precision_at_recall_thresholds,
        )
        if average_precisions
        else None
    )

    if len(label_names) == 2:
        confusion_dict = per_label_confusions.label_confusions_map
        # Since MCC is symmetric, it doesn't matter which label is 0 and which is 1
        TP = confusion_dict[label_names[0]].TP
        FP = confusion_dict[label_names[0]].FP
        FN = confusion_dict[label_names[0]].FN
        TN = confusion_dict[label_names[1]].TP
        mcc: Optional[float] = compute_matthews_correlation_coefficients(TP, FP, FN, TN)
        roc_auc: Optional[float] = compute_roc_auc(predictions)
    else:
        mcc = None
        roc_auc = None

    return ClassificationMetrics(
        accuracy=accuracy,
        macro_prf1_metrics=macro_prf1_metrics,
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