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_rmse_perf_metric2(self):
groundtruths = [1, 2, 3, 4]
predictions = [1, 2, 3, 5]
metric = RmsePerfMetric(groundtruths, predictions)
result = metric.evaluate()
self.assertAlmostEqual(result['score'], 0.5, places=6)
def test_rmse_perf_metric_enable_mapping(self):
groundtruths = np.arange(0, 1, 0.0001)
predictions = np.arange(0, 1, 0.0001)
metric = RmsePerfMetric(groundtruths, predictions)
result = metric.evaluate(enable_mapping=True)
self.assertAlmostEqual(result['score'], 0.022753642178052261, places=6)
def test_rmse_perf_metric2(self):
groundtruths = [1, 2, 3, 4]
predictions = [1, 2, 3, 5]
metric = RmsePerfMetric(groundtruths, predictions)
result = metric.evaluate()
self.assertAlmostEqual(result['score'], 0.5, places=6)
def test_rmse_perf_metric_enable_mapping(self):
groundtruths = np.arange(0, 1, 0.0001)
predictions = np.arange(0, 1, 0.0001)
metric = RmsePerfMetric(groundtruths, predictions)
result = metric.evaluate(enable_mapping=True)
self.assertAlmostEqual(result['score'], 0.022753642178052261, places=6)