Fix mathematical error in confidence interval calculation

[RektPunk]

Description

This PR addresses a mathematical inconsistency in the compute_confidence_intervals function.
Previously, the code applied a square root to the quantile value (q_alpha) when calculating the interval bounds.

However, based on the definition of Pearson residuals: $r_i = \frac{y_i - \hat{p}_i}{\sqrt{\hat{p}_i(1 - \hat{p}_i)}}$
To satisfy the condition , the bounds should be calculated as: $\hat{p}_i \pm q_a \sqrt{\hat{p}_i(1 - \hat{p}_i)}$

Key Changes

• Updated compute_confidence_intervals to use q_alpha * np.sqrt(...) instead of np.sqrt(q_alpha * ...).

Summary by Sourcery

Correct the computation of confidence intervals based on Pearson residuals and update linter configuration to the current Ruff schema.

Bug Fixes:

• Fix confidence interval bounds to use the quantile multiplier times the standard error instead of taking the square root of the product.

Enhancements:

• Refactor confidence interval computation to reuse a named standard error term for clarity.

Build:

• Update Ruff configuration keys in pyproject.toml to use the new lint.select and lint.ignore options.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Adjusts the confidence interval computation for Pearson residuals to use the quantile as a linear multiplier of the standard error, and updates Ruff configuration keys to the current schema.

File-Level Changes

Change	Details	Files
Correct confidence interval formula to match Pearson residual definition and improve numerical clarity.	Refactored confidence interval computation to factor out the standard error term sqrt(p*(1-p)). Changed lower and upper bounds to subtract/add q_alpha multiplied by the standard error instead of taking the square root of q_alpha * p * (1-p). Maintained clipping of bounds to [0, 1] using np.maximum and np.minimum.	pearsonify/utils.py
Update Ruff configuration keys to the newer lint.* schema.	Replaced top-level select option with lint.select in Ruff configuration. Replaced top-level ignore option with lint.ignore in Ruff configuration. Kept existing rule sets and ignored codes unchanged while aligning with updated Ruff config structure.	pyproject.toml

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[sourcery-ai]

Hey - I've found 1 issue, and left some high level feedback:

• Consider clarifying in the function (e.g., via parameter name or a short comment) whether q_alpha is expected to be a z-score, t-quantile, or something else, so its intended scaling in the interval formula is unambiguous to callers.
• The switch to lint.select / lint.ignore under [tool.ruff] assumes a newer Ruff config style; double-check that this matches the Ruff version used in this project, otherwise these options may be ignored.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider clarifying in the function (e.g., via parameter name or a short comment) whether `q_alpha` is expected to be a z-score, t-quantile, or something else, so its intended scaling in the interval formula is unambiguous to callers.
- The switch to `lint.select` / `lint.ignore` under `[tool.ruff]` assumes a newer Ruff config style; double-check that this matches the Ruff version used in this project, otherwise these options may be ignored.

## Individual Comments

### Comment 1
<location> `pearsonify/utils.py:4` </location>
<code_context>
 import numpy as np

+
 def compute_pearson_residuals(y_true, y_pred_proba):
     """Compute Pearson residuals for binary classification."""
     y_pred_proba = np.clip(y_pred_proba, 1e-10, 1 - 1e-10)
</code_context>

<issue_to_address>
**issue (bug_risk):** Align the statistical meaning of `q_alpha` with its new usage or rename it for clarity.

The old CI used `np.sqrt(q_alpha * p * (1 - p))`, consistent with `q_alpha` as a chi-square–style factor under the square root. The new form `p ± q_alpha * sqrt(p(1-p))` treats `q_alpha` as a z-score. If callers still pass values tuned for the old meaning, intervals will be incorrectly scaled. Please either update call sites/docs to reflect the z-score interpretation or rename the parameter (e.g., `z_alpha`) to make the change explicit.
</issue_to_address>

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[sourcery-ai]

issue (bug_risk): Align the statistical meaning of q_alpha with its new usage or rename it for clarity.

The old CI used np.sqrt(q_alpha * p * (1 - p)), consistent with q_alpha as a chi-square–style factor under the square root. The new form p ± q_alpha * sqrt(p(1-p)) treats q_alpha as a z-score. If callers still pass values tuned for the old meaning, intervals will be incorrectly scaled. Please either update call sites/docs to reflect the z-score interpretation or rename the parameter (e.g., z_alpha) to make the change explicit.

[RektPunk]

Hi @xRiskLab , I just wanted to follow up on this PR. I know you're likely busy, but I’d appreciate it if you could take a look when you have a moment. Let me know if there’s anything I should clarify or update. Thanks!