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Red Hat Bugzilla – Attachment 1979278 Details for
Bug 2226298
python-pyriemann: FTBFS in Fedora rawhide/f39
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build.log
build.log (text/plain), 32.00 KB, created by
Fedora Release Engineering
on 2023-07-25 19:42:07 UTC
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>hape (n_samples, n_features) > The input samples. > > x_squared_norms : ndarray of shape (n_samples,) > Squared euclidean norm of each data point. Pass it if you have it > at hands already to avoid it being recomputed here. > > init : {'k-means++', 'random'}, callable or ndarray of shape \ > (n_clusters, n_features) > Method for initialization. > > random_state : RandomState instance > Determines random number generation for centroid initialization. > See :term:`Glossary <random_state>`. > > init_size : int, default=None > Number of samples to randomly sample for speeding up the > initialization (sometimes at the expense of accuracy). > > n_centroids : int, default=None > Number of centroids to initialize. > If left to 'None' the number of centroids will be equal to > number of clusters to form (self.n_clusters) > > sample_weight : ndarray of shape (n_samples,), default=None > The weights for each observation in X. If None, all observations > are assigned equal weight. `sample_weight` is not used during > initialization if `init` is a callable or a user provided array. > > Returns > ------- > centers : ndarray of shape (n_clusters, n_features) > """ > n_samples = X.shape[0] > n_clusters = self.n_clusters if n_centroids is None else n_centroids > > if init_size is not None and init_size < n_samples: > init_indices = random_state.randint(0, n_samples, init_size) > X = X[init_indices] > x_squared_norms = x_squared_norms[init_indices] > n_samples = X.shape[0] > sample_weight = sample_weight[init_indices] > > if isinstance(init, str) and init == "k-means++": > centers, _ = _kmeans_plusplus( > X, > n_clusters, > random_state=random_state, > x_squared_norms=x_squared_norms, > sample_weight=sample_weight, > ) > elif isinstance(init, str) and init == "random": > seeds = random_state.choice( > n_samples, > size=n_clusters, > replace=False, >> p=sample_weight / sample_weight.sum(), > ) >E AttributeError: 'NoneType' object has no attribute 'sum' >/usr/lib64/python3.12/site-packages/sklearn/cluster/_kmeans.py:1030: AttributeError >_________________ test_km_init_metric[riemann-1-random-Kmeans] _________________ >clust = <class 'pyriemann.clustering.Kmeans'>, init = 'random', n_init = 1 >metric = 'riemann' >get_covmats = <function get_covmats.<locals>._gen_cov at 0xffff7487cae0> >get_labels = <function get_labels.<locals>._get_labels at 0xffff7487cea0> > @pytest.mark.parametrize("clust", [Kmeans, KmeansPerClassTransform]) > @pytest.mark.parametrize("init", ["random", "ndarray"]) > @pytest.mark.parametrize("n_init", [1, 5]) > @pytest.mark.parametrize("metric", get_metrics()) > def test_km_init_metric(clust, init, n_init, metric, get_covmats, get_labels): > n_clusters, n_matrices, n_channels = 2, 6, 3 > covmats = get_covmats(n_matrices, n_channels) > labels = get_labels(n_matrices, n_clusters) > if init == "ndarray": > clf = clust( > n_clusters=n_clusters, > metric=metric, > init=covmats[:n_clusters], > n_init=n_init, > ) > else: > clf = clust( > n_clusters=n_clusters, metric=metric, init=init, n_init=n_init > ) >> clf.fit(covmats, labels) >tests/test_clustering.py:182: >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:160: in fit > res.append(_fit_single(X, y, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:41: in _fit_single > mdm.covmeans_ = _init_centroids(X, n_clusters, init, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:17: in _init_centroids > return _KMeans(n_clusters=n_clusters, init=init)._init_centroids( >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >self = KMeans(init='random', n_clusters=2) >X = array([[[ 1.95533485, -0.04441076, 0.06991736], > [-0.04441076, 2.10294485, 0.07228122], > [ 0.06991736...6914, -0.01776901], > [ 0.00926914, 2.02707338, 0.00407719], > [-0.01776901, 0.00407719, 2.02005036]]]) >x_squared_norms = [12.322199809744472, 11.955609498474782, 12.106947520453508, 12.033080465981863, 11.490255833121225, 12.281145197961107] >init = 'random', random_state = RandomState(MT19937) at 0xFFFF7B414C40 >init_size = None, n_centroids = None, sample_weight = None > def _init_centroids( > self, > X, > x_squared_norms, > init, > random_state, > init_size=None, > n_centroids=None, > sample_weight=None, > ): > """Compute the initial centroids. > > Parameters > ---------- > X : {ndarray, sparse matrix} of shape (n_samples, n_features) > The input samples. > > x_squared_norms : ndarray of shape (n_samples,) > Squared euclidean norm of each data point. Pass it if you have it > at hands already to avoid it being recomputed here. > > init : {'k-means++', 'random'}, callable or ndarray of shape \ > (n_clusters, n_features) > Method for initialization. > > random_state : RandomState instance > Determines random number generation for centroid initialization. > See :term:`Glossary <random_state>`. > > init_size : int, default=None > Number of samples to randomly sample for speeding up the > initialization (sometimes at the expense of accuracy). > > n_centroids : int, default=None > Number of centroids to initialize. > If left to 'None' the number of centroids will be equal to > number of clusters to form (self.n_clusters) > > sample_weight : ndarray of shape (n_samples,), default=None > The weights for each observation in X. If None, all observations > are assigned equal weight. `sample_weight` is not used during > initialization if `init` is a callable or a user provided array. > > Returns > ------- > centers : ndarray of shape (n_clusters, n_features) > """ > n_samples = X.shape[0] > n_clusters = self.n_clusters if n_centroids is None else n_centroids > > if init_size is not None and init_size < n_samples: > init_indices = random_state.randint(0, n_samples, init_size) > X = X[init_indices] > x_squared_norms = x_squared_norms[init_indices] > n_samples = X.shape[0] > sample_weight = sample_weight[init_indices] > > if isinstance(init, str) and init == "k-means++": > centers, _ = _kmeans_plusplus( > X, > n_clusters, > random_state=random_state, > x_squared_norms=x_squared_norms, > sample_weight=sample_weight, > ) > elif isinstance(init, str) and init == "random": > seeds = random_state.choice( > n_samples, > size=n_clusters, > replace=False, >> p=sample_weight / sample_weight.sum(), > ) >E AttributeError: 'NoneType' object has no attribute 'sum' >/usr/lib64/python3.12/site-packages/sklearn/cluster/_kmeans.py:1030: AttributeError >________ test_km_init_metric[riemann-1-random-KmeansPerClassTransform] _________ >clust = <class 'pyriemann.clustering.KmeansPerClassTransform'>, init = 'random' >n_init = 1, metric = 'riemann' >get_covmats = <function get_covmats.<locals>._gen_cov at 0xffff7487d1c0> >get_labels = <function get_labels.<locals>._get_labels at 0xffff7487d800> > @pytest.mark.parametrize("clust", [Kmeans, KmeansPerClassTransform]) > @pytest.mark.parametrize("init", ["random", "ndarray"]) > @pytest.mark.parametrize("n_init", [1, 5]) > @pytest.mark.parametrize("metric", get_metrics()) > def test_km_init_metric(clust, init, n_init, metric, get_covmats, get_labels): > n_clusters, n_matrices, n_channels = 2, 6, 3 > covmats = get_covmats(n_matrices, n_channels) > labels = get_labels(n_matrices, n_clusters) > if init == "ndarray": > clf = clust( > n_clusters=n_clusters, > metric=metric, > init=covmats[:n_clusters], > n_init=n_init, > ) > else: > clf = clust( > n_clusters=n_clusters, metric=metric, init=init, n_init=n_init > ) >> clf.fit(covmats, labels) >tests/test_clustering.py:182: >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:264: in fit > self.km.fit(X[y == c]) >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:160: in fit > res.append(_fit_single(X, y, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:41: in _fit_single > mdm.covmeans_ = _init_centroids(X, n_clusters, init, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:17: in _init_centroids > return _KMeans(n_clusters=n_clusters, init=init)._init_centroids( >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >self = KMeans(init='random', n_clusters=2) >X = array([[[ 1.95533485, -0.04441076, 0.06991736], > [-0.04441076, 2.10294485, 0.07228122], > [ 0.06991736...2106, 0.06556988], > [-0.03502106, 2.00345189, -0.00595809], > [ 0.06556988, -0.00595809, 2.01727439]]]) >x_squared_norms = [12.322199809744472, 11.955609498474782, 12.106947520453508] >init = 'random', random_state = RandomState(MT19937) at 0xFFFF7B414F40 >init_size = None, n_centroids = None, sample_weight = None > def _init_centroids( > self, > X, > x_squared_norms, > init, > random_state, > init_size=None, > n_centroids=None, > sample_weight=None, > ): > """Compute the initial centroids. > > Parameters > ---------- > X : {ndarray, sparse matrix} of shape (n_samples, n_features) > The input samples. > > x_squared_norms : ndarray of shape (n_samples,) > Squared euclidean norm of each data point. Pass it if you have it > at hands already to avoid it being recomputed here. > > init : {'k-means++', 'random'}, callable or ndarray of shape \ > (n_clusters, n_features) > Method for initialization. > > random_state : RandomState instance > Determines random number generation for centroid initialization. > See :term:`Glossary <random_state>`. > > init_size : int, default=None > Number of samples to randomly sample for speeding up the > initialization (sometimes at the expense of accuracy). > > n_centroids : int, default=None > Number of centroids to initialize. > If left to 'None' the number of centroids will be equal to > number of clusters to form (self.n_clusters) > > sample_weight : ndarray of shape (n_samples,), default=None > The weights for each observation in X. If None, all observations > are assigned equal weight. `sample_weight` is not used during > initialization if `init` is a callable or a user provided array. > > Returns > ------- > centers : ndarray of shape (n_clusters, n_features) > """ > n_samples = X.shape[0] > n_clusters = self.n_clusters if n_centroids is None else n_centroids > > if init_size is not None and init_size < n_samples: > init_indices = random_state.randint(0, n_samples, init_size) > X = X[init_indices] > x_squared_norms = x_squared_norms[init_indices] > n_samples = X.shape[0] > sample_weight = sample_weight[init_indices] > > if isinstance(init, str) and init == "k-means++": > centers, _ = _kmeans_plusplus( > X, > n_clusters, > random_state=random_state, > x_squared_norms=x_squared_norms, > sample_weight=sample_weight, > ) > elif isinstance(init, str) and init == "random": > seeds = random_state.choice( > n_samples, > size=n_clusters, > replace=False, >> p=sample_weight / sample_weight.sum(), > ) >E AttributeError: 'NoneType' object has no attribute 'sum' >/usr/lib64/python3.12/site-packages/sklearn/cluster/_kmeans.py:1030: AttributeError >_________________ test_km_init_metric[riemann-5-random-Kmeans] _________________ >clust = <class 'pyriemann.clustering.Kmeans'>, init = 'random', n_init = 5 >metric = 'riemann' >get_covmats = <function get_covmats.<locals>._gen_cov at 0xffff7487c400> >get_labels = <function get_labels.<locals>._get_labels at 0xffff7487dc60> > @pytest.mark.parametrize("clust", [Kmeans, KmeansPerClassTransform]) > @pytest.mark.parametrize("init", ["random", "ndarray"]) > @pytest.mark.parametrize("n_init", [1, 5]) > @pytest.mark.parametrize("metric", get_metrics()) > def test_km_init_metric(clust, init, n_init, metric, get_covmats, get_labels): > n_clusters, n_matrices, n_channels = 2, 6, 3 > covmats = get_covmats(n_matrices, n_channels) > labels = get_labels(n_matrices, n_clusters) > if init == "ndarray": > clf = clust( > n_clusters=n_clusters, > metric=metric, > init=covmats[:n_clusters], > n_init=n_init, > ) > else: > clf = clust( > n_clusters=n_clusters, metric=metric, init=init, n_init=n_init > ) >> clf.fit(covmats, labels) >tests/test_clustering.py:182: >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:160: in fit > res.append(_fit_single(X, y, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:41: in _fit_single > mdm.covmeans_ = _init_centroids(X, n_clusters, init, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:17: in _init_centroids > return _KMeans(n_clusters=n_clusters, init=init)._init_centroids( >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >self = KMeans(init='random', n_clusters=2) >X = array([[[ 1.95533485, -0.04441076, 0.06991736], > [-0.04441076, 2.10294485, 0.07228122], > [ 0.06991736...6914, -0.01776901], > [ 0.00926914, 2.02707338, 0.00407719], > [-0.01776901, 0.00407719, 2.02005036]]]) >x_squared_norms = [12.322199809744472, 11.955609498474782, 12.106947520453508, 12.033080465981863, 11.490255833121225, 12.281145197961107] >init = 'random', random_state = RandomState(MT19937) at 0xFFFF7B414E40 >init_size = None, n_centroids = None, sample_weight = None > def _init_centroids( > self, > X, > x_squared_norms, > init, > random_state, > init_size=None, > n_centroids=None, > sample_weight=None, > ): > """Compute the initial centroids. > > Parameters > ---------- > X : {ndarray, sparse matrix} of shape (n_samples, n_features) > The input samples. > > x_squared_norms : ndarray of shape (n_samples,) > Squared euclidean norm of each data point. Pass it if you have it > at hands already to avoid it being recomputed here. > > init : {'k-means++', 'random'}, callable or ndarray of shape \ > (n_clusters, n_features) > Method for initialization. > > random_state : RandomState instance > Determines random number generation for centroid initialization. > See :term:`Glossary <random_state>`. > > init_size : int, default=None > Number of samples to randomly sample for speeding up the > initialization (sometimes at the expense of accuracy). > > n_centroids : int, default=None > Number of centroids to initialize. > If left to 'None' the number of centroids will be equal to > number of clusters to form (self.n_clusters) > > sample_weight : ndarray of shape (n_samples,), default=None > The weights for each observation in X. If None, all observations > are assigned equal weight. `sample_weight` is not used during > initialization if `init` is a callable or a user provided array. > > Returns > ------- > centers : ndarray of shape (n_clusters, n_features) > """ > n_samples = X.shape[0] > n_clusters = self.n_clusters if n_centroids is None else n_centroids > > if init_size is not None and init_size < n_samples: > init_indices = random_state.randint(0, n_samples, init_size) > X = X[init_indices] > x_squared_norms = x_squared_norms[init_indices] > n_samples = X.shape[0] > sample_weight = sample_weight[init_indices] > > if isinstance(init, str) and init == "k-means++": > centers, _ = _kmeans_plusplus( > X, > n_clusters, > random_state=random_state, > x_squared_norms=x_squared_norms, > sample_weight=sample_weight, > ) > elif isinstance(init, str) and init == "random": > seeds = random_state.choice( > n_samples, > size=n_clusters, > replace=False, >> p=sample_weight / sample_weight.sum(), > ) >E AttributeError: 'NoneType' object has no attribute 'sum' >/usr/lib64/python3.12/site-packages/sklearn/cluster/_kmeans.py:1030: AttributeError >________ test_km_init_metric[riemann-5-random-KmeansPerClassTransform] _________ >clust = <class 'pyriemann.clustering.KmeansPerClassTransform'>, init = 'random' >n_init = 5, metric = 'riemann' >get_covmats = <function get_covmats.<locals>._gen_cov at 0xffff7487de40> >get_labels = <function get_labels.<locals>._get_labels at 0xffff7487d300> > @pytest.mark.parametrize("clust", [Kmeans, KmeansPerClassTransform]) > @pytest.mark.parametrize("init", ["random", "ndarray"]) > @pytest.mark.parametrize("n_init", [1, 5]) > @pytest.mark.parametrize("metric", get_metrics()) > def test_km_init_metric(clust, init, n_init, metric, get_covmats, get_labels): > n_clusters, n_matrices, n_channels = 2, 6, 3 > covmats = get_covmats(n_matrices, n_channels) > labels = get_labels(n_matrices, n_clusters) > if init == "ndarray": > clf = clust( > n_clusters=n_clusters, > metric=metric, > init=covmats[:n_clusters], > n_init=n_init, > ) > else: > clf = clust( > n_clusters=n_clusters, metric=metric, init=init, n_init=n_init > ) >> clf.fit(covmats, labels) >tests/test_clustering.py:182: >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:264: in fit > self.km.fit(X[y == c]) >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:160: in fit > res.append(_fit_single(X, y, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:41: in _fit_single > mdm.covmeans_ = _init_centroids(X, n_clusters, init, >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/clustering.py:17: in _init_centroids > return _KMeans(n_clusters=n_clusters, init=init)._init_centroids( >_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ >self = KMeans(init='random', n_clusters=2) >X = array([[[ 1.95533485, -0.04441076, 0.06991736], > [-0.04441076, 2.10294485, 0.07228122], > [ 0.06991736...2106, 0.06556988], > [-0.03502106, 2.00345189, -0.00595809], > [ 0.06556988, -0.00595809, 2.01727439]]]) >x_squared_norms = [12.322199809744472, 11.955609498474782, 12.106947520453508] >init = 'random', random_state = RandomState(MT19937) at 0xFFFF7B416B40 >init_size = None, n_centroids = None, sample_weight = None > def _init_centroids( > self, > X, > x_squared_norms, > init, > random_state, > init_size=None, > n_centroids=None, > sample_weight=None, > ): > """Compute the initial centroids. > > Parameters > ---------- > X : {ndarray, sparse matrix} of shape (n_samples, n_features) > The input samples. > > x_squared_norms : ndarray of shape (n_samples,) > Squared euclidean norm of each data point. Pass it if you have it > at hands already to avoid it being recomputed here. > > init : {'k-means++', 'random'}, callable or ndarray of shape \ > (n_clusters, n_features) > Method for initialization. > > random_state : RandomState instance > Determines random number generation for centroid initialization. > See :term:`Glossary <random_state>`. > > init_size : int, default=None > Number of samples to randomly sample for speeding up the > initialization (sometimes at the expense of accuracy). > > n_centroids : int, default=None > Number of centroids to initialize. > If left to 'None' the number of centroids will be equal to > number of clusters to form (self.n_clusters) > > sample_weight : ndarray of shape (n_samples,), default=None > The weights for each observation in X. If None, all observations > are assigned equal weight. `sample_weight` is not used during > initialization if `init` is a callable or a user provided array. > > Returns > ------- > centers : ndarray of shape (n_clusters, n_features) > """ > n_samples = X.shape[0] > n_clusters = self.n_clusters if n_centroids is None else n_centroids > > if init_size is not None and init_size < n_samples: > init_indices = random_state.randint(0, n_samples, init_size) > X = X[init_indices] > x_squared_norms = x_squared_norms[init_indices] > n_samples = X.shape[0] > sample_weight = sample_weight[init_indices] > > if isinstance(init, str) and init == "k-means++": > centers, _ = _kmeans_plusplus( > X, > n_clusters, > random_state=random_state, > x_squared_norms=x_squared_norms, > sample_weight=sample_weight, > ) > elif isinstance(init, str) and init == "random": > seeds = random_state.choice( > n_samples, > size=n_clusters, > replace=False, >> p=sample_weight / sample_weight.sum(), > ) >E AttributeError: 'NoneType' object has no attribute 'sum' >/usr/lib64/python3.12/site-packages/sklearn/cluster/_kmeans.py:1030: AttributeError >=============================== warnings summary =============================== >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/utils/utils.py:29 >../../BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/utils/utils.py:29 > /builddir/build/BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/utils/utils.py:29: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. > this_version = LooseVersion(library.__version__) >../../../../usr/lib/python3.12/site-packages/setuptools/_distutils/version.py:345 >../../../../usr/lib/python3.12/site-packages/setuptools/_distutils/version.py:345 > /usr/lib/python3.12/site-packages/setuptools/_distutils/version.py:345: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. > other = LooseVersion(other) >../../../../usr/lib/python3.12/site-packages/pytz/tzinfo.py:27 > /usr/lib/python3.12/site-packages/pytz/tzinfo.py:27: DeprecationWarning: datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.fromtimestamp(timestamp, datetime.UTC). > _epoch = datetime.utcfromtimestamp(0) >tests/test_classification.py: 3 warnings >tests/test_sampling.py: 8 warnings >tests/test_transfer.py: 2 warnings > /usr/lib/python3.12/site-packages/joblib/externals/loky/backend/fork_exec.py:38: DeprecationWarning: This process (pid=567) is multi-threaded, use of fork() may lead to deadlocks in the child. > pid = os.fork() >tests/test_classification.py: 10 warnings >tests/test_transfer.py: 3 warnings > /builddir/build/BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/classification.py:527: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning. > out, _ = stats.mode(neighbors_classes[:, 0:self.n_neighbors], axis=1) >tests/test_embedding.py::TestEmbedding::test_embedding_build[SpectralEmbedding] >tests/test_embedding.py::TestEmbedding::test_embedding_build[LocallyLinearEmbedding] > /builddir/build/BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/utils/mean.py:470: UserWarning: Convergence not reached > warnings.warn('Convergence not reached') >tests/test_embedding.py: 20 warnings > /builddir/build/BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/embedding.py:395: DeprecationWarning: Keyword argument 'eigvals' is deprecated in favour of 'subset_by_index' keyword instead and will be removed in SciPy 1.12.0. > eigen_values, eigen_vectors = eigh( >tests/test_transfer.py::test_tlclassifiers[1.0-0.0-clf3] >tests/test_transfer.py::test_tlclassifiers[1.0-0.0-clf3] >tests/test_transfer.py::test_tlclassifiers[0.0-1.0-clf3] >tests/test_transfer.py::test_tlclassifiers[0.0-1.0-clf3] >tests/test_transfer.py::test_tlclassifiers[1.0-1.0-clf3] >tests/test_transfer.py::test_tlclassifiers[1.0-1.0-clf3] > /builddir/build/BUILDROOT/python-pyriemann-0.4-4.fc39.aarch64/usr/lib/python3.12/site-packages/pyriemann/classification.py:527: DeprecationWarning: Support for non-numeric arrays has been deprecated as of SciPy 1.9.0 and will be removed in 1.11.0. `pandas.DataFrame.mode` can be used instead, see https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html. > out, _ = stats.mode(neighbors_classes[:, 0:self.n_neighbors], axis=1) >-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html >=========================== short test summary info ============================ >FAILED tests/test_clustering.py::TestRiemannianClustering::test_two_clusters[Kmeans] >FAILED tests/test_clustering.py::TestRiemannianClustering::test_two_clusters[KmeansPerClassTransform] >FAILED tests/test_clustering.py::TestRiemannianClustering::test_three_clusters[Kmeans] >FAILED tests/test_clustering.py::TestRiemannianClustering::test_three_clusters[KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[euclid-1-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[euclid-1-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[euclid-5-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[euclid-5-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[logdet-1-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[logdet-1-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[logdet-5-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[logdet-5-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[logeuclid-1-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[logeuclid-1-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[logeuclid-5-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[logeuclid-5-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[kullback_sym-1-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[kullback_sym-1-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[kullback_sym-5-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[kullback_sym-5-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[riemann-1-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[riemann-1-random-KmeansPerClassTransform] >FAILED tests/test_clustering.py::test_km_init_metric[riemann-5-random-Kmeans] >FAILED tests/test_clustering.py::test_km_init_metric[riemann-5-random-KmeansPerClassTransform] >========= 24 failed, 2025 passed, 4 deselected, 59 warnings in 35.89s ========== >error: Bad exit status from /var/tmp/rpm-tmp.aP95FB (%check) >RPM build errors: > Bad exit status from /var/tmp/rpm-tmp.aP95FB (%check) >Child return code was: 1 >EXCEPTION: [Error('Command failed: \n # /usr/bin/systemd-nspawn -q -M 00a01f1288f1416f8af6f9d626e9d4ea -D /var/lib/mock/f39-build-44340647-5276606/root -a -u mockbuild --capability=cap_ipc_lock --bind=/tmp/mock-resolv.0spkuejb:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin --setenv=PROMPT_COMMAND=printf "\\033]0;<mock-chroot>\\007" --setenv=PS1=<mock-chroot> \\s-\\v\\$ --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c /usr/bin/rpmbuild -ba --noprep --noclean --target aarch64 --nodeps /builddir/build/SPECS/python-pyriemann.spec\n', 1)] >Traceback (most recent call last): > File "/usr/lib/python3.11/site-packages/mockbuild/trace_decorator.py", line 93, in trace > result = func(*args, **kw) > ^^^^^^^^^^^^^^^^^ > File "/usr/lib/python3.11/site-packages/mockbuild/util.py", line 597, in do_with_status > raise exception.Error("Command failed: \n # %s\n%s" % (command, output), child.returncode) >mockbuild.exception.Error: Command failed: > # /usr/bin/systemd-nspawn -q -M 00a01f1288f1416f8af6f9d626e9d4ea -D /var/lib/mock/f39-build-44340647-5276606/root -a -u mockbuild --capability=cap_ipc_lock --bind=/tmp/mock-resolv.0spkuejb:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin --setenv=PROMPT_COMMAND=printf "\033]0;<mock-chroot>\007" --setenv=PS1=<mock-chroot> \s-\v\$ --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c /usr/bin/rpmbuild -ba --noprep --noclean --target aarch64 --nodeps /builddir/build/SPECS/python-pyriemann.spec >
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