fix(visualize): use np.trapezoid for AUC/AP, restoring NumPy >=2.4 (#99)

[chaoming0625]

Summary

Hotfix: main CI went red after #98 because the new test_precision_recall_curve coverage test exercised braintools.visualize.precision_recall_curve, which (along with roc_curve) calls np.trapz.

np.trapz was renamed to np.trapezoid in NumPy 2.0 and removed in NumPy 2.4. CI installs numpy 2.4.6, so both functions raised:

AttributeError: module 'numpy' has no attribute 'trapz'. Did you mean: 'trace'?

It was invisible locally because local environments had NumPy < 2.4.

Fix

Resolve the integration routine once at import time, preferring np.trapezoid and falling back to np.trapz for the declared numpy>=1.15 floor:

_trapezoid = getattr(np, 'trapezoid', None) or np.trapz

Both call sites (roc_curve AUC, precision_recall_curve AP) now use _trapezoid. Same signature, same semantics.

Verification

pytest braintools/visualize/_statistical_extra_test.py braintools/visualize/_statistical_test.py → 43 passed locally, where _trapezoid resolves to np.trapezoid (the same path CI's NumPy 2.4.6 takes).

Closes #99

Summary by Sourcery

Ensure ROC and precision-recall visualization metrics work with newer NumPy versions by using a compatible trapezoidal integration helper.

Bug Fixes:

• Fix AttributeError in roc_curve and precision_recall_curve when running with NumPy >= 2.4 by resolving a compatible trapezoidal integration function at import time.

Documentation:

• Document the NumPy compatibility fix for roc_curve and precision_recall_curve in the changelog.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

This PR fixes compatibility with NumPy>=2.4 by resolving a shared trapezoidal integration helper at import time (preferring np.trapezoid, falling back to np.trapz) and updating ROC and precision–recall curve metrics to use it, plus documenting the fix in the changelog.

File-Level Changes

Change	Details	Files
Make trapezoidal integration robust to NumPy 2.x deprecations and removals and use it for ROC AUC and PR AP computations.	Introduce a module-level _trapezoid helper that resolves to np.trapezoid when available, otherwise np.trapz, with comments explaining the NumPy 2.0/2.4 behavior. Replace direct np.trapz calls in roc_curve with the new _trapezoid helper when computing AUC. Replace direct np.trapz calls in precision_recall_curve with the new _trapezoid helper when computing average precision.	braintools/visualize/_statistical.py
Document the NumPy compatibility fix for visualize metrics in the project changelog.	Add a changelog entry describing that roc_curve and precision_recall_curve now resolve np.trapezoid when available and fall back to np.trapz, fixing AttributeError on NumPy>=2.4.	changelog.md

Assessment against linked issues

Issue	Objective	Addressed	Explanation
#99	Update braintools.visualize.roc_curve and precision_recall_curve to avoid using np.trapz directly, instead resolving np.trapezoid when available and falling back to np.trapz, so that the functions work on NumPy >= 2.4 while retaining compatibility with the declared numpy>=1.15 floor.	✅
#99	Document the NumPy integration change (np.trapz -> np.trapezoid with fallback) in the project documentation/changelog.	✅

Possibly linked issues

• visualize.roc_curve/precision_recall_curve crash on NumPy >=2.4: np.trapz was removed #99: PR implements compatibility layer using np.trapezoid/np.trapz in visualize curves, exactly fixing the NumPy >=2.4 crash.

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