def test_kflk_perf_metric(self):
np.random.seed(0)
groundtruths = np.random.normal(0, 1.0, [4, 10]) + \
np.tile(np.array([1, 2, 3, 4]), [10, 1]).T
predictions = [1, 2, 3, 4]
metric = KflkPerfMetric(groundtruths, predictions)
result = metric.evaluate()
self.assertAlmostEqual(result['score'], 0.95, places=6)
self.assertAlmostEqual(result['AUC_BW'], 0.9166666666666666, places=6)
self.assertAlmostEqual(result['AUC_DS'], 0.95, places=6)
self.assertAlmostEqual(result['CC_0'], 1.0, places=6)
self.assertAlmostEqual(result['THR'], 3.0, places=6)
def test_kflk_perf_metric(self):
np.random.seed(0)
groundtruths = np.random.normal(0, 1.0, [4, 10]) + \
np.tile(np.array([1, 2, 3, 4]), [10, 1]).T
predictions = [1, 2, 3, 4]
metric = KflkPerfMetric(groundtruths, predictions)
result = metric.evaluate()
self.assertAlmostEqual(result['score'], 0.95, places=6)
self.assertAlmostEqual(result['AUC_BW'], 0.9166666666666666, places=6)
self.assertAlmostEqual(result['AUC_DS'], 0.95, places=6)
self.assertAlmostEqual(result['CC_0'], 1.0, places=6)
self.assertAlmostEqual(result['THR'], 3.0, places=6)
def test_kflk_metrics_performance(self):
mat_filepath = config.ROOT + '/python/test/resource/data_Toyama.mat'
mat_dict = scipy.io.loadmat(mat_filepath)
results = KflkPerfMetric._metrics_performance(mat_dict['objScoDif'], mat_dict['signif'])
self.assertAlmostEqual(np.mean(results['AUC_DS']), 0.69767003960902052, places=6)
self.assertAlmostEqual(np.mean(results['AUC_BW']), 0.94454700301894534, places=6)
self.assertAlmostEqual(np.mean(results['CC_0']), 0.88105386206276415, places=6)
self.assertAlmostEqual(np.mean(results['THR']), 6.2392849606450556, places=6)
def test_kflk_metrics_performance(self):
mat_filepath = config.ROOT + '/python/test/resource/data_Toyama.mat'
mat_dict = scipy.io.loadmat(mat_filepath)
results = KflkPerfMetric._metrics_performance(mat_dict['objScoDif'],
mat_dict['signif'])
self.assertAlmostEqual(np.mean(results['AUC_DS']),
0.69767003960902052,
places=6)
self.assertAlmostEqual(np.mean(results['AUC_BW']),
0.94454700301894534,
places=6)
self.assertAlmostEqual(np.mean(results['CC_0']),
0.88105386206276415,
places=6)
self.assertAlmostEqual(np.mean(results['THR']),
6.2392849606450556,
places=6)
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
