[BUG] Fix missing negative sign in SPLL metric calculates negative loss for censored data

[KaranSinghDev]

What does this implement/fix? Explain your changes.

Fixes a mathematical sign error in the SPLL (Survival Process Logarithmic Loss) metric calculation for censored data.

In _spll.py, the docstring correctly defines the loss for a censored instance ($\Delta = 1$) as:
$L = - \Delta \log d.S(y)$

However, the implementation was missing negative sign for the discrete term:

# Before
disc_term = np.log(1 - y_pred.cdf(y_true)) * C_true.to_numpy()

Because $S(y) = 1 - CDF \le 1$, its logarithm is inherently negative. Without the leading minus sign, the metric was returning a negative loss for censored instances. Since lower_is_better=True, this should inadvertently reward models that assigned lower survival probabilities to censored data (e.g., an Exponential model predicting $S(y)=e^{-1}$ was returning a score of -1.0 instead of 1.0).

Does your contribution introduce a new dependency? If yes, which one?

No

What should a reviewer concentrate their feedback on?

Verification of the mathematical correction in _spll.py.

Did you add any tests for the change?

No, I have made a regression test but would like to discuss whether to add seperately or append in any of the tests .

Any other comments?

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[fkiraly]

Yes, this is indeed a typo. Thanks for fixing!