def _multi_target_sk_metric(preds, target, adjusted, multioutput):
sk_preds = preds.view(-1, num_targets).numpy()
sk_target = target.view(-1, num_targets).numpy()
r2_score = sk_r2score(sk_target, sk_preds, multioutput=multioutput)
if adjusted != 0:
r2_score = 1 - (1 - r2_score) * (sk_preds.shape[0] - 1) / (sk_preds.shape[0] - adjusted - 1)
return r2_score
def _multi_target_sk_r2score(preds,
target,
adjusted=0,
multioutput="raw_values"):
"""Compute R2 score over multiple outputs."""
sk_preds = preds.view(-1, num_targets).numpy()
sk_target = target.view(-1, num_targets).numpy()
r2_score = sk_r2score(sk_target, sk_preds, multioutput=multioutput)
if adjusted != 0:
r2_score = 1 - (1 - r2_score) * (sk_preds.shape[0] - 1) / (
sk_preds.shape[0] - adjusted - 1)
return r2_score
