Attachment 1979278 Details for Bug 2226298 – build.log

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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Created: 2023-07-25 19:42:07 UTC

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obsolete

>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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Attachments on bug 2226298: 1979278 | 1979279 | 1979280