def get_stats(cls, ys_label, ys_label_pred, **kwargs): # cannot have None assert all(x is not None for x in ys_label) assert all(x is not None for x in ys_label_pred) # RMSE rmse = RmsePerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # spearman srcc = SrccPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # pearson pcc = PccPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # kendall kendall = KendallPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] stats = {'RMSE': rmse, 'SRCC': srcc, 'PCC': pcc, 'KENDALL': kendall, 'ys_label': list(ys_label), 'ys_label_pred': list(ys_label_pred)} ys_label_raw = kwargs['ys_label_raw'] if 'ys_label_raw' in kwargs else None if ys_label_raw is not None: try: # AUC auc = AucPerfMetric(ys_label_raw, ys_label_pred) \ .evaluate()['score'] stats['AUC'] = auc except TypeError: # AUC would not work with dictionary-style dataset stats['AUC'] = float('nan') try: # ResPow respow = ResolvingPowerPerfMetric(ys_label_raw, ys_label_pred) \ .evaluate()['score'] stats['ResPow'] = respow except TypeError: # ResPow would not work with dictionary-style dataset stats['ResPow'] = float('nan') if 'ys_label_stddev' in kwargs and 'ys_label_stddev' and kwargs['ys_label_stddev'] is not None: stats['ys_label_stddev'] = kwargs['ys_label_stddev'] return stats
def get_stats(cls, ys_label, ys_label_pred, **kwargs): # cannot have None assert all(x is not None for x in ys_label) assert all(x is not None for x in ys_label_pred) # RMSE rmse = RmsePerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # spearman srcc = SrccPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # pearson pcc = PccPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # kendall kendall = KendallPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] stats = { 'RMSE': rmse, 'SRCC': srcc, 'PCC': pcc, 'KENDALL': kendall, 'ys_label': list(ys_label), 'ys_label_pred': list(ys_label_pred) } ys_label_raw = kwargs[ 'ys_label_raw'] if 'ys_label_raw' in kwargs else None if ys_label_raw is not None: try: # KFLK kflk = KflkPerfMetric(ys_label_raw, ys_label_pred) \ .evaluate()['score'] stats['KFLK'] = kflk except TypeError: # KFLK would not work with dictionary-style dataset stats['KFLK'] = float('nan') return stats
def get_stats(cls, ys_label, ys_label_pred, ys_label_raw=None): # cannot have None assert all(x is not None for x in ys_label) assert all(x is not None for x in ys_label_pred) # RMSE rmse = RmsePerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # spearman srcc = SrccPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # pearson pcc = PccPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] # kendall kendall = KendallPerfMetric(ys_label, ys_label_pred) \ .evaluate(enable_mapping=True)['score'] stats = { 'RMSE': rmse, 'SRCC': srcc, 'PCC': pcc, 'KENDALL': kendall, 'ys_label': list(ys_label), 'ys_label_pred': list(ys_label_pred) } if ys_label_raw is not None: # KFLK kflk = KflkPerfMetric(ys_label_raw, ys_label_pred) \ .evaluate()['score'] stats['KFLK'] = kflk return stats
def test_pcc_perf_metric(self): groundtruths = [1, 2, 3, 4] predictions = [1, 2, 3, 5] metric = PccPerfMetric(groundtruths, predictions) result = metric.evaluate() self.assertAlmostEqual(result['score'], 0.98270762982399085, places=6)
def test_pcc_perf_metric(self): groundtruths = [1, 2, 3, 4] predictions = [1, 2, 3, 5] metric = PccPerfMetric(groundtruths, predictions) result = metric.evaluate() self.assertAlmostEqual(result['score'], 0.98270762982399085, places=6)