FIX: Warn on rank estimate with too few samples

Signed-off-by: Emmanuel Ferdman <[email protected]>

Author: emmanuel-ferdman

mne/decoding/tests/test_csp.py

@@ -431,7 +431,7 @@ def test_spoc():
     # check y
     pytest.raises(ValueError, spoc.fit, X, y * 0)
 
-    # Check that doesn't take CSP-spcific input
+    # Check that doesn't take CSP-specific input
     pytest.raises(TypeError, SPoC, cov_est="epoch")
 
     # Check mixing matrix on simulated data
@@ -492,6 +492,7 @@ def test_csp_component_ordering():
 
 
 @pytest.mark.filterwarnings("ignore:.*Only one sample available.*")
+@pytest.mark.filterwarnings("ignore:.*You've got fewer samples than channels.*")
 @parametrize_with_checks([CSP(), SPoC()])
 def test_sklearn_compliance(estimator, check):
     """Test compliance with sklearn."""

mne/rank.py

@@ -184,7 +184,7 @@ def _estimate_rank_meeg_signals(
     """
     picks_list = _picks_by_type(info)
     if data.shape[1] < data.shape[0]:
-       raise ValueError(
+       warn(
             "You've got fewer samples than channels, your "
             "rank estimate might be inaccurate."
         )
@@ -249,7 +249,7 @@ def _estimate_rank_meeg_cov(
     scalings = _handle_default("scalings_cov_rank", scalings)
     _apply_scaling_cov(data, picks_list, scalings)
     if data.shape[1] < data.shape[0]:
-       raise ValueError(
+       warn(
             "You've got fewer samples than channels, your "
             "rank estimate might be inaccurate."
         )

mne/simulation/tests/test_evoked.py

@@ -100,6 +100,7 @@ def test_simulate_evoked():
 @pytest.mark.filterwarnings("ignore:No average EEG reference present")
 @pytest.mark.filterwarnings("ignore:Too few samples")
 @pytest.mark.filterwarnings("ignore:Epochs are not baseline corrected")
+@pytest.mark.filterwarnings("ignore:.*You've got fewer samples than channels.*")
 def test_add_noise():
     """Test noise addition."""
     rng = np.random.default_rng(0)

mne/tests/test_cov.py

@@ -412,6 +412,7 @@ def _assert_cov(cov, cov_desired, tol=0.005, nfree=True):
 
 @pytest.mark.slowtest
 @pytest.mark.parametrize("rank", ("full", None))
+@pytest.mark.filterwarnings("ignore:.*You've got fewer samples than channels.*")
 def test_cov_estimation_with_triggers(rank, tmp_path):
     """Test estimation from raw with triggers."""
     raw = read_raw_fif(raw_fname)
@@ -781,6 +782,7 @@ def raw_epochs_events():
 
 
 @pytest.mark.parametrize("rank", (None, "full", "info"))
+@pytest.mark.filterwarnings("ignore:.*You've got fewer samples than channels.*")
 def test_low_rank_methods(rank, raw_epochs_events):
     """Test low-rank covariance matrix estimation."""
     pytest.importorskip("sklearn")
@@ -814,6 +816,7 @@ def test_low_rank_methods(rank, raw_epochs_events):
         assert these_bounds[0] < cov["loglik"] < these_bounds[1], (rank, method)
 
 
+@pytest.mark.filterwarnings("ignore:.*You've got fewer samples than channels.*")
 def test_low_rank_cov(raw_epochs_events):
     """Test additional properties of low rank computations."""
     pytest.importorskip("sklearn")
@@ -890,6 +893,7 @@ def test_low_rank_cov(raw_epochs_events):
 
 
 @testing.requires_testing_data
+@pytest.mark.filterwarnings("ignore:.*You've got fewer samples than channels.*")
 def test_cov_ctf():
     """Test basic cov computation on ctf data with/without compensation."""
     pytest.importorskip("sklearn")