FIX: Better

mne/channels/tests/test_channels.py

@@ -50,7 +50,6 @@
     Epochs,
 )
 from mne.datasets import testing
-from mne.utils import requires_pandas, requires_version
 from mne.parallel import parallel_func
 
 io_dir = Path(__file__).parent.parent.parent / "io"
@@ -410,10 +409,10 @@ def test_get_set_sensor_positions():
     assert_array_equal(raw1.info["chs"][13]["loc"], raw2.info["chs"][13]["loc"])
 
 
-@requires_version("pymatreader")
 @testing.requires_testing_data
 def test_1020_selection():
     """Test making a 10/20 selection dict."""
+    pytest.importorskip("pymatreader")
     raw_fname = testing_path / "EEGLAB" / "test_raw.set"
     loc_fname = testing_path / "EEGLAB" / "test_chans.locs"
     raw = read_raw_eeglab(raw_fname, preload=True)
@@ -676,11 +675,9 @@ def test_combine_channels():
     assert len(record) == 3
 
 
-@requires_pandas
 def test_combine_channels_metadata():
     """Test if metadata is correctly retained in combined object."""
-    import pandas as pd
-
+    pd = pytest.importorskip("pandas")
     raw = read_raw_fif(raw_fname, preload=True)
     epochs = Epochs(raw, read_events(eve_fname), preload=True)
 

mne/decoding/tests/test_base.py

@@ -14,7 +14,6 @@
 import pytest
 
 from mne import create_info, EpochsArray
-from mne.utils import requires_sklearn
 from mne.decoding.base import (
     _get_inverse_funcs,
     LinearModel,
@@ -26,6 +25,9 @@
 from mne.decoding import Scaler, TransformerMixin, Vectorizer, GeneralizingEstimator
 
 
+pytest.importorskip("sklearn")
+
+
 def _make_data(n_samples=1000, n_features=5, n_targets=3):
     """Generate some testing data.
 
@@ -65,7 +67,6 @@ def _make_data(n_samples=1000, n_features=5, n_targets=3):
     return X, Y, A
 
 
-@requires_sklearn
 def test_get_coef():
     """Test getting linear coefficients (filters/patterns) from estimators."""
     from sklearn.base import (
@@ -204,7 +205,6 @@ def transform(self, X):
     inverse_transform = transform
 
 
-@requires_sklearn
 @pytest.mark.parametrize("inverse", (True, False))
 @pytest.mark.parametrize(
     "Scale, kwargs",
@@ -241,7 +241,6 @@ def test_get_coef_inverse_transform(inverse, Scale, kwargs):
             assert_array_equal(filters_t, filters[:, t])
 
 
-@requires_sklearn
 @pytest.mark.parametrize("n_features", [1, 5])
 @pytest.mark.parametrize("n_targets", [1, 3])
 def test_get_coef_multiclass(n_features, n_targets):
@@ -288,7 +287,6 @@ def test_get_coef_multiclass(n_features, n_targets):
     lm.fit(X, Y, sample_weight=np.ones(len(Y)))
 
 
-@requires_sklearn
 @pytest.mark.parametrize(
     "n_classes, n_channels, n_times",
     [
@@ -336,7 +334,6 @@ def test_get_coef_multiclass_full(n_classes, n_channels, n_times):
     assert_allclose(patterns[:, 1:], 0.0, atol=1e-7)  # no other channels useful
 
 
-@requires_sklearn
 def test_linearmodel():
     """Test LinearModel class for computing filters and patterns."""
     # check categorical target fit in standard linear model
@@ -394,7 +391,6 @@ def test_linearmodel():
         clf.fit(X, wrong_y)
 
 
-@requires_sklearn
 def test_cross_val_multiscore():
     """Test cross_val_multiscore for computing scores on decoding over time."""
     from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score

mne/decoding/tests/test_csp.py

@@ -13,7 +13,6 @@
 
 from mne import io, Epochs, read_events, pick_types
 from mne.decoding.csp import CSP, _ajd_pham, SPoC
-from mne.utils import requires_sklearn
 
 data_dir = Path(__file__).parent.parent.parent / "io" / "tests" / "data"
 raw_fname = data_dir / "test_raw.fif"
@@ -245,9 +244,9 @@ def test_csp():
         assert np.abs(corr) > 0.95
 
 
-@requires_sklearn
 def test_regularized_csp():
     """Test Common Spatial Patterns algorithm using regularized covariance."""
+    pytest.importorskip("sklearn")
     raw = io.read_raw_fif(raw_fname)
     events = read_events(event_name)
     picks = pick_types(
@@ -281,9 +280,9 @@ def test_regularized_csp():
         assert sources.shape[1] == n_components
 
 
-@requires_sklearn
 def test_csp_pipeline():
     """Test if CSP works in a pipeline."""
+    pytest.importorskip("sklearn")
     from sklearn.svm import SVC
     from sklearn.pipeline import Pipeline
 

mne/decoding/tests/test_ems.py

@@ -9,7 +9,6 @@
 import pytest
 
 from mne import io, Epochs, read_events, pick_types
-from mne.utils import requires_sklearn
 from mne.decoding import compute_ems, EMS
 
 data_dir = Path(__file__).parent.parent.parent / "io" / "tests" / "data"
@@ -18,8 +17,9 @@
 tmin, tmax = -0.2, 0.5
 event_id = dict(aud_l=1, vis_l=3)
 
+pytest.importorskip("sklearn")
+
 
-@requires_sklearn
 def test_ems():
     """Test event-matched spatial filters."""
     from sklearn.model_selection import StratifiedKFold

mne/decoding/tests/test_receptive_field.py

@@ -10,7 +10,6 @@
 from numpy.fft import rfft, irfft
 from numpy.testing import assert_array_equal, assert_allclose, assert_equal
 
-from mne.utils import requires_sklearn
 from mne.decoding import ReceptiveField, TimeDelayingRidge
 from mne.decoding.receptive_field import (
     _delay_time_series,
@@ -78,10 +77,10 @@ def test_compute_reg_neighbors():
                 )
 
 
-@requires_sklearn
 def test_rank_deficiency():
     """Test signals that are rank deficient."""
     # See GH#4253
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
 
     N = 256
@@ -174,9 +173,9 @@ def test_time_delay():
 
 @pytest.mark.slowtest  # slow on Azure
 @pytest.mark.parametrize("n_jobs", n_jobs_test)
-@requires_sklearn
 def test_receptive_field_basic(n_jobs):
     """Test model prep and fitting."""
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
 
     # Make sure estimator pulling works
@@ -372,9 +371,9 @@ def test_time_delaying_fast_calc(n_jobs):
 
 
 @pytest.mark.parametrize("n_jobs", n_jobs_test)
-@requires_sklearn
 def test_receptive_field_1d(n_jobs):
     """Test that the fast solving works like Ridge."""
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
 
     rng = np.random.RandomState(0)
@@ -433,9 +432,9 @@ def test_receptive_field_1d(n_jobs):
 
 
 @pytest.mark.parametrize("n_jobs", n_jobs_test)
-@requires_sklearn
 def test_receptive_field_nd(n_jobs):
     """Test multidimensional support."""
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
 
     # multidimensional
@@ -552,9 +551,9 @@ def _make_data(n_feats, n_targets, n_samples, tmin, tmax):
     return X, y
 
 
-@requires_sklearn
 def test_inverse_coef():
     """Test inverse coefficients computation."""
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
 
     tmin, tmax = 0.0, 10.0
@@ -583,9 +582,9 @@ def test_inverse_coef():
         assert_allclose(np.dot(c0, c1.T), np.eye(c0.shape[0]), atol=0.2)
 
 
-@requires_sklearn
 def test_linalg_warning():
     """Test that warnings are issued when no regularization is applied."""
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
 
     n_feats, n_targets, n_samples = 5, 60, 50
@@ -598,9 +597,9 @@ def test_linalg_warning():
             rf.fit(y, X)
 
 
-@requires_sklearn
 def test_tdr_sklearn_compliance():
     """Test sklearn estimator compliance."""
+    pytest.importorskip("sklearn")
     from sklearn.utils.estimator_checks import check_estimator
 
     tdr = TimeDelayingRidge(0, 10, 1.0, 0.1, "laplacian", n_jobs=1)
@@ -619,9 +618,9 @@ def test_tdr_sklearn_compliance():
         check(est)
 
 
-@requires_sklearn
 def test_rf_sklearn_compliance():
     """Test sklearn RF compliance."""
+    pytest.importorskip("sklearn")
     from sklearn.linear_model import Ridge
     from sklearn.utils.estimator_checks import check_estimator
 

mne/decoding/tests/test_search_light.py

@@ -8,10 +8,12 @@
 from numpy.testing import assert_array_equal, assert_equal
 import pytest
 
-from mne.utils import requires_sklearn, _record_warnings, use_log_level
+from mne.utils import _record_warnings, use_log_level
 from mne.decoding.search_light import SlidingEstimator, GeneralizingEstimator
 from mne.decoding.transformer import Vectorizer
 
+pytest.importorskip("sklearn")
+
 
 def make_data():
     """Make data."""
@@ -25,7 +27,6 @@ def make_data():
     return X, y
 
 
-@requires_sklearn
 def test_search_light():
     """Test SlidingEstimator."""
     from sklearn.linear_model import Ridge, LogisticRegression
@@ -167,7 +168,6 @@ def transform(self, X):
         assert isinstance(pipe.estimators_[0], BaggingClassifier)
 
 
-@requires_sklearn
 def test_generalization_light():
     """Test GeneralizingEstimator."""
     from sklearn.pipeline import make_pipeline
@@ -254,7 +254,6 @@ def test_generalization_light():
     assert_array_equal(y_preds[0], y_preds[1])
 
 
-@requires_sklearn
 @pytest.mark.parametrize(
     "n_jobs, verbose", [(1, False), (2, False), (1, True), (2, "info")]
 )
@@ -280,7 +279,6 @@ def test_verbose_arg(capsys, n_jobs, verbose):
                 assert any(len(channel) > 0 for channel in (stdout, stderr))
 
 
-@requires_sklearn
 def test_cross_val_predict():
     """Test cross_val_predict with predict_proba."""
     from sklearn.linear_model import LinearRegression
@@ -317,7 +315,6 @@ def predict_proba(self, X):
 
 
 @pytest.mark.slowtest
-@requires_sklearn
 def test_sklearn_compliance():
     """Test LinearModel compliance with sklearn."""
     from sklearn.utils.estimator_checks import check_estimator

mne/decoding/tests/test_ssd.py

@@ -9,7 +9,6 @@
 from mne import io
 from mne.time_frequency import psd_array_welch
 from mne.decoding.ssd import SSD
-from mne.utils import requires_sklearn
 from mne.filter import filter_data
 from mne import create_info
 from mne.decoding import CSP
@@ -296,9 +295,9 @@ def test_ssd_epoched_data():
     )
 
 
-@requires_sklearn
 def test_ssd_pipeline():
     """Test if SSD works in a pipeline."""
+    pytest.importorskip("sklearn")
     from sklearn.pipeline import Pipeline
 
     sf = 250

mne/decoding/tests/test_time_frequency.py

@@ -7,13 +7,12 @@
 from numpy.testing import assert_array_equal
 import pytest
 
-from mne.utils import requires_sklearn
 from mne.decoding.time_frequency import TimeFrequency
 
 
-@requires_sklearn
 def test_timefrequency():
     """Test TimeFrequency."""
+    pytest.importorskip("sklearn")
     from sklearn.base import clone
 
     # Init

mne/decoding/tests/test_transformer.py

@@ -25,7 +25,7 @@
     TemporalFilter,
 )
 from mne.defaults import DEFAULTS
-from mne.utils import requires_sklearn, check_version, use_log_level
+from mne.utils import check_version, use_log_level
 
 tmin, tmax = -0.2, 0.5
 event_id = dict(aud_l=1, vis_l=3)
@@ -217,9 +217,9 @@ def test_vectorizer():
     pytest.raises(ValueError, vect.inverse_transform, np.random.rand(102, 12, 12))
 
 
-@requires_sklearn
 def test_unsupervised_spatial_filter():
     """Test unsupervised spatial filter."""
+    pytest.importorskip("sklearn")
     from sklearn.decomposition import PCA
     from sklearn.kernel_ridge import KernelRidge
 

mne/io/tests/test_what.py

@@ -11,16 +11,16 @@
 from mne.datasets import testing
 from mne.io import RawArray
 from mne.preprocessing import ICA
-from mne.utils import requires_sklearn, _record_warnings
+from mne.utils import _record_warnings
 
 data_path = testing.data_path(download=False)
 
 
 @pytest.mark.slowtest
-@requires_sklearn
 @testing.requires_testing_data
 def test_what(tmp_path, verbose_debug):
     """Test mne.what."""
+    pytest.importorskip("sklearn")
     # ICA
     ica = ICA(max_iter=1)
     raw = RawArray(np.random.RandomState(0).randn(3, 10), create_info(3, 1000.0, "eeg"))