Bump pypa/cibuildwheel from 2.17.0 to 2.18.0 (#50)

* Bump pypa/cibuildwheel from 2.17.0 to 2.18.0

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.17.0 to 2.18.0.
- [Release notes](https://github.com/pypa/cibuildwheel/releases)
- [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md)
- [Commits](pypa/cibuildwheel@v2.17.0...v2.18.0)

---
updated-dependencies:
- dependency-name: pypa/cibuildwheel
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* Relax OOB score test equality
* Relax docstring example outputs

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Reid Johnson <reidj@zillowgroup.com>

.github/workflows/publish.yml

@@ -19,7 +19,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.17.0
+        uses: pypa/cibuildwheel@v2.18.0
         env:
           CIBW_BUILD: cp3*-*
           CIBW_SKIP: pp* *i686* *win32 *musllinux*

quantile_forest/_quantile_forest.py

@@ -1125,7 +1125,7 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor):
     >>> qrf.fit(X[:1000], y[:1000])
     RandomForestQuantileRegressor(random_state=0)
     >>> qrf.score(X, y, quantiles=0.5)
-    0.3592...
+    0.359...
     """
 
     def __init__(
@@ -1456,7 +1456,7 @@ class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor):
     >>> qrf.fit(X[:1000], y[:1000])
     ExtraTreesQuantileRegressor(random_state=0)
     >>> qrf.score(X, y, quantiles=0.5)
-    0.3352...
+    0.3...
     """
 
     def __init__(

quantile_forest/tests/test_quantile_forest.py

@@ -932,7 +932,7 @@ def check_predict_oob(
     else:
         assert y_pred.shape == (len(X),)
     if quantiles == "mean" and aggregate_leaves_first is False:
-        assert est.oob_score_ == y_pred_score
+        assert math.isclose(est.oob_score_, y_pred_score)
     else:
         assert abs(est.oob_score_ - y_pred_score) < 0.1