def pearson_spearman_scorer(
golds: ndarray,
probs: ndarray,
preds: Optional[ndarray],
uids: Optional[List[str]] = None,
) -> Dict[str, float]:
"""Average of Pearson and Spearman rank-order correlation coefficients.
Args:
golds: Ground truth values.
probs: Predicted probabilities.
preds: Predicted values.
uids: Unique ids, defaults to None.
Returns:
The average of Pearson correlation coefficient and Spearman rank-order
correlation coefficient.
"""
metrics = dict()
pearson_correlation = pearson_correlation_scorer(golds, probs, preds, uids)
spearman_correlation = spearman_correlation_scorer(golds, probs, preds,
uids)
metrics["pearson_spearman"] = mean([
pearson_correlation["pearson_correlation"],
spearman_correlation["spearman_correlation"],
])
return metrics
def pearson_spearman_scorer(golds, probs, preds, uids=None):
"""Average of Pearson correlation coefficient and Spearman rank-order
correlation coefficient.
:param golds: Ground truth (correct) target values.
:type golds: 1-d np.array
:param probs: Predicted target probabilities.
:type probs: 1-d np.array
:param preds: Predicted target values. (Not used!)
:type preds: 1-d np.array
:param uids: Unique ids.
:type uids: list
:return: Pearson correlation coefficient, the p-value and Spearman
rank-order correlation coefficient and the average.
:rtype: dict
"""
metrics = dict()
pearson_correlation = pearson_correlation_scorer(golds, probs, preds, uids)
spearman_correlation = spearman_correlation_scorer(golds, probs, preds,
uids)
metrics["pearson_spearman"] = np.mean([
pearson_correlation["pearson_correlation"],
spearman_correlation["spearman_correlation"],
])
return metrics
def test_pearson_correlation(caplog):
"""Unit test of pearson_correlation_scorer."""
caplog.set_level(logging.INFO)
metric_dict = pearson_correlation_scorer(GOLDS, UNARY_PROBS, None)
assert isequal(metric_dict, {"pearson_correlation": 0.5667402091575048})
metric_dict = pearson_correlation_scorer(GOLDS,
UNARY_PROBS,
None,
return_pvalue=True)
assert isequal(
metric_dict,
{
"pearson_correlation": 0.5667402091575048,
"pearson_pvalue": 0.24090659530906683,
},
)
def test_pearson_correlation(caplog):
"""Unit test of pearson_correlation_scorer"""
caplog.set_level(logging.INFO)
golds = np.array([1, 0, 1, 0, 1, 0])
probs = np.array([0.8, 0.6, 0.9, 0.7, 0.7, 0.2])
metric_dict = pearson_correlation_scorer(golds, probs, None)
assert isequal(metric_dict, {"pearson_correlation": 0.6764814252025461})
metric_dict = pearson_correlation_scorer(golds,
probs,
None,
return_pvalue=True)
assert isequal(
metric_dict,
{
"pearson_correlation": 0.6764814252025461,
"pearson_pvalue": 0.14006598491201777,
},
)
