ENH, FIX i) `build_oob_forest` backwards compatiblility with sklearn and ii) HonestForest stratification during bootstrap

[adam2392]

Changes proposed in this pull request:

• build_oob_forest will work with any sklearn Forest that has estimators_samples_ (in-bag sample indices)
• HonestForest will now stratify during bootstrap step. This fixes a major bug where the posterior estimates are biased, resulting in a biased AUC score on independent data.

Stratification should occur every time we sample the dataset whether its subsampling, or bootstrapping.

1. when we bootstrap sample the dataset to get the in-bag and oob samples, we stratify using sklearn.utils.resample (this PR)
2. when we split the in-bag unique samples in halves to get structure and honest dataset, we stratify using StratifiedShuffleSplit.

Summary

On main branch, using the following test:

def test_honest_forest_posteriors_on_independent():
    from sktree.datasets import make_trunk_classification

    seed = 12345
    scores = []
    for idx in range(5):
        X, y = make_trunk_classification(
            n_samples=128, n_dim=4096, n_informative=1, mu_0=0.0, mu_1=0.0, seed=idx
        )
        clf = HonestForestClassifier(
            n_estimators=100,
            random_state=idx,
            bootstrap=True,
            max_samples=1.6,
            n_jobs=-1,
            honest_prior="ignore",
            stratify=True,
        )
        clf.fit(X, y)

        oob_posteriors = clf.predict_proba_per_tree(X, clf.oob_samples_)
        auc_score = roc_auc_score(y, np.nanmean(oob_posteriors, axis=0)[:, 1])
        scores.append(auc_score)

    print(np.mean(scores), scores)
    assert np.mean(scores) > 0.49, f"{np.mean(scores)} {scores}"
    assert False

we get the error:

(sktree) (base) adam2392@arm64-apple-darwin20 scikit-tree % pytest ./sktree/tests/test_honest_forest.py::test_honest_forest_posteriors_on_independent
==================================================================== test session starts ====================================================================
platform darwin -- Python 3.9.18, pytest-8.2.2, pluggy-1.5.0 -- /Users/adam2392/miniforge3/envs/sktree/bin/python3.9
cachedir: .pytest_cache
rootdir: /Users/adam2392/Documents/scikit-tree
configfile: pyproject.toml
plugins: cov-5.0.0, flaky-3.8.1
collected 1 item                                                                                                                                            

sktree/tests/test_honest_forest.py::test_honest_forest_posteriors_on_independent FAILED                                                               [100%]

========================================================================= FAILURES ==========================================================================
_______________________________________________________ test_honest_forest_posteriors_on_independent ________________________________________________________

    def test_honest_forest_posteriors_on_independent():
        from sktree.datasets import make_trunk_classification

        seed = 12345
        scores = []
        for idx in range(5):
            X, y = make_trunk_classification(
                n_samples=128, n_dim=4096, n_informative=1, mu_0=0.0, mu_1=0.0, seed=idx
            )
            clf = HonestForestClassifier(
                n_estimators=100,
                random_state=idx,
                bootstrap=True,
                max_samples=1.6,
                n_jobs=-1,
                honest_prior="ignore",
                stratify=True,
            )
            clf.fit(X, y)

            oob_posteriors = clf.predict_proba_per_tree(X, clf.oob_samples_)
            auc_score = roc_auc_score(y, np.nanmean(oob_posteriors, axis=0)[:, 1])
            scores.append(auc_score)

        print(np.mean(scores), scores)
>       assert np.mean(scores) > 0.49, f"{np.mean(scores)} {scores}"
E       AssertionError: 0.47548828125 [0.49951171875, 0.479736328125, 0.408203125, 0.464111328125, 0.52587890625]

sktree/tests/test_honest_forest.py:519: AssertionError

However, if we run it on this branch, we get 0.50498046875 [0.484375, 0.53076171875, 0.513671875, 0.46533203125, 0.53076171875], which shows the stratification fixes the bias.

[adam2392]

Interestingly, this is not an issue on RandomForestClassifier, so I suspect there is a relationship to the empty leaves, or the fact that we use a separate dataset to estimate the posteriors

[codecov[bot]]

Codecov Report

Attention: Patch coverage is 82.14286% with 10 lines in your changes missing coverage. Please review.

Project coverage is 78.55%. Comparing base (b8da7b0) to head (290c5f6). Report is 1 commits behind head on main.

Files	Patch %	Lines
sktree/ensemble/_honest_forest.py	77.77%	4 Missing and 2 partials :warning:
sktree/tree/_honest_tree.py	80.00%	2 Missing and 1 partial :warning:
sktree/stats/forestht.py	88.88%	0 Missing and 1 partial :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #283 +/- ## ========================================== + Coverage 76.79% 78.55% +1.75% ========================================== Files 25 24 -1 Lines 2267 2252 -15 Branches 409 414 +5 ========================================== + Hits 1741 1769 +28 + Misses 402 352 -50 - Partials 124 131 +7 ```

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