[python-package] Do not reset the global NumPy random state in xgboost.cv

[SulimanAbdulrazzaq]

mknfold called np.random.seed(seed) and then drew the folds from the global generator, so every call of xgboost.cv reseeded the caller's global NumPy random state (with the default seed=0). Anything drawn from np.random after a cv call is then the same on every call, for example the hyper-parameters of a random search that runs xgb.cv in its loop.

The folds are now drawn from a private np.random.RandomState(seed). np.random.seed(seed) followed by np.random.permutation(n) and RandomState(seed).permutation(n) give the same permutation, so the folds, and therefore all cv results, stay the same for a given seed; only the side effect on the global state is gone. mkgroupfold (ranking data) receives the generator as a new required keyword rng; it is only called from mknfold. The seed docstring no longer says it is passed to numpy.random.seed.

Demonstration, same script on master (red) and on this change (green), fork CI:

master: draws after seed(123), without cv:                 [0.69646919 0.28613933 0.22685145]
master: draws after seed(123), with a cv call in between:  [0.54723513 0.31020924 0.28271973]
master: random-search sample 1 0.3241410077932141
master: random-search sample 2 0.3241410077932141
change: draws after seed(123), with a cv call in between:  [0.69646919 0.28613933 0.22685145]
change: random-search sample 0 0.22199317108973948 / 1 0.8707323061773764 / 2 0.20671915533942642

Tests (tests/python/test_basic.py): test_cv_keeps_global_random_state (plain and ranking/group data; fails on master, passes here) and test_cv_folds_follow_seed (the folds equal np.array_split(RandomState(seed).permutation(n), nfold); passes on both, so the folds did not change). Full pytest tests/python on the change: 575 passed, 11 skipped; ruff (pre-commit version) and mypy on training.py are clean.

Not changed on purpose: the stratified path (already uses its own random_state=seed), custom folds, and the R / JVM packages.

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Walkthrough

Cross-validation fold generation now uses a local seeded RandomState for grouped and ordinary folds. It no longer resets or consumes NumPy’s global random state. The cv seed documentation describes this behavior. New tests check global-state preservation for ordinary and ranking cross-validation, and verify that test folds match the seeded permutation.

Priority: ⬇️ Low

Merge Risk: ⚪ Minimal · up to 860c3

The fold test checks all expected folds, and the change is compatible with supported Python versions. No issue identified here needs resolution before merge.

Security Architecture Review

Security architecture risk: ⚪ Minimal · up to 8022b

The change isolates cross-validation randomness without adding privileges or expanding access to data or callbacks. The reviewed implementation introduces no material security risk.

Retained concerns
No architecture-level concerns identified.

Security review details

Security Blast Radius

• inferred — The demonstrated exposure change is removal of interference with the caller’s process-wide NumPy RNG. The reviewed path does not introduce a new cross-service, tenant, credential, or privileged-resource access path.

Trust Boundaries and Controls

• inferred — Caller-supplied splitters, preprocessing, and callbacks remain existing execution extension points; this change adds no authority to them. Such extensions can independently consume or mutate global randomness, so the isolation guarantee applies to library-owned fold generation rather than arbitrary user code.

Resilience and Maintainability Implications

• inferred — Because fold generation neither installs its RNG globally nor saves and restores global state, exceptions or interruption cannot strand a library-induced global RNG reset. Repeated and concurrent invocations have independent RNG ownership; this does not establish thread safety for training or user extensions generally.

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Inline comments:
Review comments at @tests/python/test_basic.py:
- Line 275: Update the zip call in the fold-comparison loop to use strict=True,
so mismatched lengths between cb.test_labels and np.array_split(idx, nfold)
raise an error instead of silently truncating.

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