python-mne fails to build with Python 3.9.0b1. ________________________________ test_get_coef _________________________________ @requires_sklearn def test_get_coef(): """Test getting linear coefficients (filters/patterns) from estimators.""" from sklearn.base import TransformerMixin, BaseEstimator from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn import svm from sklearn.linear_model import Ridge, LinearRegression from sklearn.model_selection import GridSearchCV lm_classification = LinearModel() assert (is_classifier(lm_classification)) lm_regression = LinearModel(Ridge()) assert (is_regressor(lm_regression)) parameters = {'kernel': ['linear'], 'C': [1, 10]} lm_gs_classification = LinearModel( GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1)) assert (is_classifier(lm_gs_classification)) lm_gs_regression = LinearModel( GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1)) assert (is_regressor(lm_gs_regression)) # Define a classifier, an invertible transformer and an non-invertible one. class Clf(BaseEstimator): def fit(self, X, y): return self class NoInv(TransformerMixin): def fit(self, X, y): return self def transform(self, X): return X class Inv(NoInv): def inverse_transform(self, X): return X X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1) # I. Test inverse function # Check that we retrieve the right number of inverse functions even if # there are nested pipelines good_estimators = [ (1, make_pipeline(Inv(), Clf())), (2, make_pipeline(Inv(), Inv(), Clf())), (3, make_pipeline(Inv(), make_pipeline(Inv(), Inv()), Clf())), ] for expected_n, est in good_estimators: est.fit(X, y) assert (expected_n == len(_get_inverse_funcs(est))) bad_estimators = [ Clf(), # no preprocessing Inv(), # final estimator isn't classifier make_pipeline(NoInv(), Clf()), # first step isn't invertible make_pipeline(Inv(), make_pipeline( Inv(), NoInv()), Clf()), # nested step isn't invertible ] for est in bad_estimators: est.fit(X, y) invs = _get_inverse_funcs(est) assert_equal(invs, list()) # II. Test get coef for classification/regression estimators and pipelines rng = np.random.RandomState(0) for clf in (lm_regression, lm_gs_classification, make_pipeline(StandardScaler(), lm_classification), make_pipeline(StandardScaler(), lm_gs_regression)): # generate some categorical/continuous data # according to the type of estimator. if is_classifier(clf): n, n_features = 1000, 3 X = rng.rand(n, n_features) y = np.arange(n) % 2 else: X, y, A = _make_data(n_samples=1000, n_features=3, n_targets=1) y = np.ravel(y) clf.fit(X, y) # Retrieve final linear model filters = get_coef(clf, 'filters_', False) if hasattr(clf, 'steps'): if hasattr(clf.steps[-1][-1].model, 'best_estimator_'): # Linear Model with GridSearchCV coefs = clf.steps[-1][-1].model.best_estimator_.coef_ else: # Standard Linear Model coefs = clf.steps[-1][-1].model.coef_ else: if hasattr(clf.model, 'best_estimator_'): # Linear Model with GridSearchCV coefs = clf.model.best_estimator_.coef_ else: # Standard Linear Model coefs = clf.model.coef_ if coefs.ndim == 2 and coefs.shape[0] == 1: coefs = coefs[0] assert_array_equal(filters, coefs) patterns = get_coef(clf, 'patterns_', False) assert (filters[0] != patterns[0]) n_chans = X.shape[1] assert_array_equal(filters.shape, patterns.shape, [n_chans, n_chans]) # Inverse transform linear model filters_inv = get_coef(clf, 'filters_', True) assert (filters[0] != filters_inv[0]) patterns_inv = get_coef(clf, 'patterns_', True) assert (patterns[0] != patterns_inv[0]) # Check with search_light and combination of preprocessing ending with sl: slider = SlidingEstimator(make_pipeline(StandardScaler(), lm_regression)) X = np.transpose([X, -X], [1, 2, 0]) # invert X across 2 time samples > clfs = (make_pipeline(Scaler(None, scalings='mean'), slider), slider) A = array([[-0.42231482], [ 0.10471403], [ 0.22805333]]) BaseEstimator = <class 'sklearn.base.BaseEstimator'> Clf = <class 'mne.decoding.tests.test_base.test_get_coef.<locals>.Clf'> GridSearchCV = <class 'sklearn.model_selection._search.GridSearchCV'> Inv = <class 'mne.decoding.tests.test_base.test_get_coef.<locals>.Inv'> LinearRegression = <class 'sklearn.linear_model._base.LinearRegression'> NoInv = <class 'mne.decoding.tests.test_base.test_get_coef.<locals>.NoInv'> Ridge = <class 'sklearn.linear_model._ridge.Ridge'> StandardScaler = <class 'sklearn.preprocessing._data.StandardScaler'> TransformerMixin = <class 'sklearn.base.TransformerMixin'> X = array([[[-0.51151882, 0.51151882], [ 1.46567234, -1.46567234], [-0.53831733, 0.53831733]], [...31359]], [[-0.32358505, 0.32358505], [ 2.03200796, -2.03200796], [-0.52246677, 0.52246677]]]) bad_estimators = [Clf(), <mne.decoding.tests.test_base.test_get_coef.<locals>.Inv object at 0x7f772eed2190>, Pipeline(steps=[('noinv', ....decoding.tests.test_base.test_get_coef.<locals>.NoInv object at 0x7f7742cadca0>)])), ('clf', Clf())])] clf = Pipeline(steps=[('standardscaler', StandardScaler()), ('linearmodel', LinearModel(model=GridSearchCV(cv=2, estimator=SVR(), n_jobs=1, param_grid={'C': [1, 10], 'kernel': ['linear']})))]) coefs = array([-1.96881649, 0.3818861 , 0.83271859]) est = Pipeline(steps=[('inv', <mne.decoding.tests.test_base.test_get_coef.<locals>.Inv object at 0x7f772eed...e.decoding.tests.test_base.test_get_coef.<locals>.NoInv object at 0x7f7742cadca0>)])), ('clf', Clf())]) expected_n = 3 filters = array([-1.96881649, 0.3818861 , 0.83271859]) filters_inv = array([-2.56903749, 0.9679832 , 1.49243367]) good_estimators = [(1, Pipeline(steps=[('inv', <mne.decoding.tests.test_base.test_get_coef.<locals>.Inv object at 0x7f7...e.decoding.tests.test_base.test_get_coef.<locals>.Inv object at 0x7f772eed22b0>)])), ('clf', Clf())]))] invs = [] lm_classification = LinearModel(model=LogisticRegression(solver='liblinear')) lm_gs_classification = LinearModel(model=GridSearchCV(cv=2, estimator=SVC(), n_jobs=1, param_grid={'C': [1, 10], 'kernel': ['linear']})) lm_gs_regression = LinearModel(model=GridSearchCV(cv=2, estimator=SVR(), n_jobs=1, param_grid={'C': [1, 10], 'kernel': ['linear']})) lm_regression = LinearModel(model=Ridge()) make_pipeline = <function make_pipeline at 0x7f772eeee820> n = 1000 n_chans = 3 n_features = 3 parameters = {'C': [1, 10], 'kernel': ['linear']} patterns = array([-2.48265066, 1.06274699, 1.88762667]) patterns_inv = array([-3.4191915 , 1.68874717, 2.77278389]) rng = RandomState(MT19937) at 0x7F77318A7840 slider = <SlidingEstimator(base_estimator=Pipeline(steps=[('standardscaler', StandardScaler()), ('linearmodel', LinearModel(model=Ridge()))]), n_jobs=1, scoring=None, verbose=None)> svm = <module 'sklearn.svm' from '/usr/lib64/python3.9/site-packages/sklearn/svm/__init__.py'> y = array([ 3.89402630e+00, 7.99337228e+00, 6.78081920e+00, -2.45890531e+00, 3.80097555e+00, 2.23597243e-01, 3...4990e+00, 7.08108388e-02, 1.02111595e+00, -3.01212899e+00, -4.47927084e-01, 2.52089705e+00, 3.61384968e+00]) mne/decoding/tests/test_base.py:180: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ mne/decoding/transformer.py:126: in __init__ self._scaler = StandardScaler(self.with_mean, self.with_std) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<[FutureWarning("From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an ...e attribute. Previously it would return None.") raised in repr()] StandardScaler object at 0x7f772eeac6a0>, True, True) kwargs = {}, extra_args = 2, args_msg = ['copy=True', 'with_mean=True'] @wraps(f) def inner_f(*args, **kwargs): extra_args = len(args) - len(all_args) if extra_args > 0: # ignore first 'self' argument for instance methods args_msg = ['{}={}'.format(name, arg) for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])] > warnings.warn("Pass {} as keyword args. From version 0.25 " "passing these as positional arguments will " "result in an error".format(", ".join(args_msg)), FutureWarning) E FutureWarning: Pass copy=True, with_mean=True as keyword args. From version 0.25 passing these as positional arguments will result in an error all_args = ['self'] args = (<[FutureWarning("From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an ...e attribute. Previously it would return None.") raised in repr()] StandardScaler object at 0x7f772eeac6a0>, True, True) args_msg = ['copy=True', 'with_mean=True'] extra_args = 2 f = <function StandardScaler.__init__ at 0x7f7732a989d0> kwargs = {} kwonly_args = ['copy', 'with_mean', 'with_std'] sig = <Signature (self, *, copy=True, with_mean=True, with_std=True)> /usr/lib64/python3.9/site-packages/sklearn/utils/validation.py:68: FutureWarning _________________________________ test_scaler __________________________________ def test_scaler(): """Test methods of Scaler.""" raw = io.read_raw_fif(raw_fname) events = read_events(event_name) picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads') picks = picks[1:13:3] epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True) epochs_data = epochs.get_data() y = epochs.events[:, -1] methods = (None, dict(mag=5, grad=10, eeg=20), 'mean', 'median') infos = (epochs.info, epochs.info, None, None) epochs_data_t = epochs_data.transpose([1, 0, 2]) for method, info in zip(methods, infos): if method in ('mean', 'median') and not check_version('sklearn'): with pytest.raises(ImportError, match='No module'): Scaler(info, method) continue > scaler = Scaler(info, method) X = array([[[ 2.45472304e-11, 1.49036756e-11, -4.38343400e-12, ..., -1.40269888e-11, -2.36705436e-11, -4.3834340...[ 2.00840976e-11, -8.84656681e-12, 7.96988001e-13, ..., 5.86583168e-11, 2.97276524e-11, 2.97276524e-11]]]) X2 = array([[[ 2.45472304e-11, 1.49036756e-11, -4.38343400e-12, ..., -1.40269888e-11, -2.36705436e-11, -4.3834340...[ 2.00840976e-11, -8.84656681e-12, 7.96988001e-13, ..., 5.86583168e-11, 2.97276524e-11, 2.97276524e-11]]]) Xi = array([[[ 2.45472304e-12, 1.49036756e-12, -4.38343400e-13, ..., -1.40269888e-12, -2.36705436e-12, -4.3834340...[ 2.00840976e-12, -8.84656681e-13, 7.96988001e-14, ..., 5.86583168e-12, 2.97276524e-12, 2.97276524e-12]]]) epochs = <Epochs | 14 events (all good), -0.199795 - 0.499488 sec, baseline [None, 0], ~3.2 MB, data loaded, 'aud_l': 7 'vis_l': 7> epochs_data = array([[[ 2.45472304e-12, 1.49036756e-12, -4.38343400e-13, ..., -1.40269888e-12, -2.36705436e-12, -4.3834340...[ 2.00840976e-12, -8.84656681e-13, 7.96988001e-14, ..., 5.86583168e-12, 2.97276524e-12, 2.97276524e-12]]]) epochs_data_t = array([[[ 2.45472304e-12, 1.49036756e-12, -4.38343400e-13, ..., -1.40269888e-12, -2.36705436e-12, -4.3834340...[ 2.00840976e-12, -8.84656681e-13, 7.96988001e-14, ..., 5.86583168e-12, 2.97276524e-12, 2.97276524e-12]]]) events = array([[27977, 0, 2], [28345, 0, 3], [28771, 0, 1], [29219, 0, 4]... [38711, 0, 1], [39130, 0, 4], [39563, 0, 2], [39926, 0, 3]]) info = None infos = (<Info | 20 non-empty values acq_pars: ACQch001 110113 ACQch002 110112 ACQch003 110111 ACQch004 110122 ... bads: [] ... proj_id: 1 item (ndarray) proj_name: test projs: PCA-v1: on, PCA-v2: on, PCA-v3: on sfreq: 600.6 Hz >, None, None) key = 'grad' means = array([0., 0., 0., 0.]) method = 'mean' methods = (None, {'eeg': 20, 'grad': 10, 'mag': 5}, 'mean', 'median') picks = array([ 1, 4, 7, 10]) raw = <Raw | test_raw.fif, 376 x 14400 (24.0 s), ~3.6 MB, data not loaded> scaler = Scaler(info=<Info | 20 non-empty values acq_pars: ACQch001 110113 ACQch002 110112 ACQch003 110111 ACQch004 110122 ......: on, PCA-v3: on sfreq: 600.6 Hz >, scalings={'mag': 5, 'grad': 10, 'eeg': 20}, with_mean=True, with_std=True) sd = {'eeg': 20, 'grad': 10, 'mag': 5} stds = array([0.1, 0.1, 0.1, 0.1]) y = array([3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1]) mne/decoding/tests/test_transformer.py:49: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ mne/decoding/transformer.py:126: in __init__ self._scaler = StandardScaler(self.with_mean, self.with_std) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ args = (<[FutureWarning("From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an ...e attribute. Previously it would return None.") raised in repr()] StandardScaler object at 0x7f772eead4c0>, True, True) kwargs = {}, extra_args = 2, args_msg = ['copy=True', 'with_mean=True'] @wraps(f) def inner_f(*args, **kwargs): extra_args = len(args) - len(all_args) if extra_args > 0: # ignore first 'self' argument for instance methods args_msg = ['{}={}'.format(name, arg) for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])] > warnings.warn("Pass {} as keyword args. From version 0.25 " "passing these as positional arguments will " "result in an error".format(", ".join(args_msg)), FutureWarning) E FutureWarning: Pass copy=True, with_mean=True as keyword args. From version 0.25 passing these as positional arguments will result in an error all_args = ['self'] args = (<[FutureWarning("From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an ...e attribute. Previously it would return None.") raised in repr()] StandardScaler object at 0x7f772eead4c0>, True, True) args_msg = ['copy=True', 'with_mean=True'] extra_args = 2 f = <function StandardScaler.__init__ at 0x7f7732a989d0> kwargs = {} kwonly_args = ['copy', 'with_mean', 'with_std'] sig = <Signature (self, *, copy=True, with_mean=True, with_std=True)> /usr/lib64/python3.9/site-packages/sklearn/utils/validation.py:68: FutureWarning ----------------------------- Captured stdout call ----------------------------- Opening raw data file /builddir/build/BUILD/mne-python-0.20.4/mne/decoding/tests/../../io/tests/data/test_raw.fif... Read a total of 3 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Range : 25800 ... 40199 = 42.956 ... 66.930 secs Ready. Current compensation grade : 0 15 matching events found Applying baseline correction (mode: mean) Not setting metadata 3 projection items activated Loading data for 15 events and 421 original time points ... 1 bad epochs dropped For the build logs, see: https://copr-be.cloud.fedoraproject.org/results/@python/python3.9/fedora-rawhide-x86_64/01400181-python-mne/ For all our attempts to build python-mne with Python 3.9, see: https://copr.fedorainfracloud.org/coprs/g/python/python3.9/package/python-mne/ Testing and mass rebuild of packages is happening in copr. You can follow these instructions to test locally in mock if your package builds with Python 3.9: https://copr.fedorainfracloud.org/coprs/g/python/python3.9/ Let us know here if you have any questions. Python 3.9 will be included in Fedora 33. To make that update smoother, we're building Fedora packages with early pre-releases of Python 3.9. A build failure prevents us from testing all dependent packages (transitive [Build]Requires), so if this package is required a lot, it's important for us to get it fixed soon. We'd appreciate help from the people who know this package best, but if you don't want to work on this now, let us know so we can try to work around it on our side.
See also https://koschei.fedoraproject.org/package/python-mne?collection=f33
This comment is mass posted to all bugs blocking the Python 3.9 tracker, sorry if it is not 100 % relevant. When in doubt, please ask. The Python 3.9 rebuild is in progress in a Koji side tag. If you fix this bug, please don't rebuild the package in regular rawhide, but do it in the side tag with: $ fedpkg build --target=f33-python The rebuild is progressing slowly and it is possible this package won't have all the required build dependencies yet. If that's the case, please just leave the fix committed and pushed and we will eventually rebuild it for you. You are not asked to go and try rebuild all the missing dependencies yourself. If you know there is a bootstrap loop in the dependencies, let me know and we can untangle it together. If you want to test your fix or reproduce the failure, you can still use the Copr repo mentioned in the initial comment of this bug: https://copr.fedorainfracloud.org/coprs/g/python/python3.9/
Python 3.9 update: The f33-python side tag is currently being merged. New builds in f33-python are no longer possible, but python3 is not yet updated to Python 3.9 in rawhide. You can check when Python is Python 3.9 with: $ koji wait-repo f33-build --build python3.9-3.9.0~b1-3.fc3 And build the packages normally after that.