fix: ruff import order + drop unused import; correct the gamma test invariant

- ruff: SchurLedoitWolf import sorts before schurcov in __init__/registry; remove unused
  pytest import in the new test.
- the previous 'gamma rises with n' test asserted a false property for an EWA estimator
  (effective sample ~1/r, not n, so gamma converges/forgets rather than climbing with n).
  Replace with the correct invariants: gamma rises with cross-block coupling STRENGTH and
  as the decay r shrinks (larger effective sample). Verified: ruff, mypy, pytest (204) all pass.

Author: microprediction

precise/__init__.py

@@ -36,8 +36,8 @@
 from precise.partialmoments import PartialMomentsCovariance
 from precise.recommend import covariance_features, suggest
 from precise.registry import all_estimators, estimator_from_name, estimator_names
-from precise.schurcov import SchurCovariance
 from precise.schur_ledoit_wolf import SchurLedoitWolfCovariance
+from precise.schurcov import SchurCovariance
 from precise.shrunk import ShrunkCovariance
 from precise.tyler import TylerCovariance
 

precise/registry.py

@@ -19,8 +19,8 @@
 from precise.ledoitwolf import LedoitWolfCovariance
 from precise.oas import OASCovariance
 from precise.partialmoments import PartialMomentsCovariance
-from precise.schurcov import SchurCovariance
 from precise.schur_ledoit_wolf import SchurLedoitWolfCovariance
+from precise.schurcov import SchurCovariance
 from precise.shrunk import ShrunkCovariance
 from precise.tyler import TylerCovariance
 

tests/test_schur_ledoit_wolf.py

@@ -8,7 +8,6 @@
 from __future__ import annotations
 
 import numpy as np
-import pytest
 
 from precise import SchurLedoitWolfCovariance, all_estimators
 
@@ -33,15 +32,23 @@ def test_gamma_in_unit_interval_and_pd():
     assert np.all(np.linalg.eigvalsh(C) > 0)
 
 
-def test_gamma_rises_with_sample_size_when_coupling_is_real():
-    # more data => the cross-block coupling becomes more reliable => larger gamma_
-    gammas = []
-    for n in (300, 3000):
-        e = SchurLedoitWolfCovariance(n_blocks=3, r=0.005)
-        e.partial_fit(_block_data(n, seed=1))
-        _ = e.covariance_
-        gammas.append(e.gamma_)
-    assert gammas[1] > gammas[0]
+def _gamma(cross, r=0.02, n=2000):
+    e = SchurLedoitWolfCovariance(n_blocks=3, r=r)
+    e.partial_fit(_block_data(n, within=0.7, cross=cross, seed=1))
+    _ = e.covariance_
+    return e.gamma_
+
+
+def test_gamma_rises_with_coupling_strength():
+    # stronger true cross-block coupling => higher reliability => larger gamma_
+    # (the right invariant for an EWA estimator, whose effective sample is ~1/r, not n)
+    g = [_gamma(c) for c in (0.0, 0.1, 0.3, 0.6)]
+    assert g[0] < g[1] < g[2] < g[3]
+
+
+def test_gamma_rises_as_decay_shrinks():
+    # smaller r => larger effective sample => the coupling is more reliably estimated
+    assert _gamma(0.5, r=0.05) < _gamma(0.5, r=0.005)
 
 
 def test_gamma_low_when_coupling_is_noise():