running pytest
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writing dependency_links to networkx.egg-info/dependency_links.txt
writing requirements to networkx.egg-info/requires.txt
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running build_ext
============================= test session starts ==============================
platform linux -- Python 3.10.1, pytest-6.2.5, py-1.11.0, pluggy-0.13.1
rootdir: /build/python-networkx/src/networkx-networkx-2.6.3
collected 4328 items / 27 skipped / 4301 selected

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=================================== FAILURES ===================================
____________________________ test_tournament_matrix ____________________________

    def test_tournament_matrix():
        np = pytest.importorskip("numpy")
        npt = pytest.importorskip("numpy.testing")
        G = DiGraph([(0, 1)])
>       m = tournament_matrix(G)

networkx/algorithms/tests/test_tournament.py:125: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
networkx/utils/decorators.py:795: in func
    return argmap._lazy_compile(__wrapper)(*args, **kwargs)
<class 'networkx.utils.decorators.argmap'> compilation 1078:5: in argmap_tournament_matrix_1074
    ???
networkx/algorithms/tournament.py:230: in tournament_matrix
    A = nx.adjacency_matrix(G)
networkx/linalg/graphmatrix.py:157: in adjacency_matrix
    return nx.to_scipy_sparse_matrix(G, nodelist=nodelist, dtype=dtype, weight=weight)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

G = <networkx.classes.digraph.DiGraph object at 0x400c51d7e0>, nodelist = None
dtype = None, weight = 'weight', format = 'csr'

    def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, weight="weight", format="csr"):
        """Returns the graph adjacency matrix as a SciPy sparse matrix.
    
        Parameters
        ----------
        G : graph
            The NetworkX graph used to construct the sparse matrix.
    
        nodelist : list, optional
           The rows and columns are ordered according to the nodes in `nodelist`.
           If `nodelist` is None, then the ordering is produced by G.nodes().
    
        dtype : NumPy data-type, optional
            A valid NumPy dtype used to initialize the array. If None, then the
            NumPy default is used.
    
        weight : string or None   optional (default='weight')
            The edge attribute that holds the numerical value used for
            the edge weight.  If None then all edge weights are 1.
    
        format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
            The type of the matrix to be returned (default 'csr').  For
            some algorithms different implementations of sparse matrices
            can perform better.  See [1]_ for details.
    
        Returns
        -------
        M : SciPy sparse matrix
           Graph adjacency matrix.
    
        Notes
        -----
        For directed graphs, matrix entry i,j corresponds to an edge from i to j.
    
        The matrix entries are populated using the edge attribute held in
        parameter weight. When an edge does not have that attribute, the
        value of the entry is 1.
    
        For multiple edges the matrix values are the sums of the edge weights.
    
        When `nodelist` does not contain every node in `G`, the adjacency matrix
        is built from the subgraph of `G` that is induced by the nodes in
        `nodelist`.
    
        The convention used for self-loop edges in graphs is to assign the
        diagonal matrix entry value to the weight attribute of the edge
        (or the number 1 if the edge has no weight attribute).  If the
        alternate convention of doubling the edge weight is desired the
        resulting Scipy sparse matrix can be modified as follows:
    
        >>> G = nx.Graph([(1, 1)])
        >>> A = nx.to_scipy_sparse_matrix(G)
        >>> print(A.todense())
        [[1]]
        >>> A.setdiag(A.diagonal() * 2)
        >>> print(A.todense())
        [[2]]
    
        Examples
        --------
        >>> G = nx.MultiDiGraph()
        >>> G.add_edge(0, 1, weight=2)
        0
        >>> G.add_edge(1, 0)
        0
        >>> G.add_edge(2, 2, weight=3)
        0
        >>> G.add_edge(2, 2)
        1
        >>> S = nx.to_scipy_sparse_matrix(G, nodelist=[0, 1, 2])
        >>> print(S.todense())
        [[0 2 0]
         [1 0 0]
         [0 0 4]]
    
        References
        ----------
        .. [1] Scipy Dev. References, "Sparse Matrices",
           https://docs.scipy.org/doc/scipy/reference/sparse.html
        """
>       import scipy as sp
E       ModuleNotFoundError: No module named 'scipy'

networkx/convert_matrix.py:851: ModuleNotFoundError
=============================== warnings summary ===============================
networkx/utils/decorators.py:289
  /build/python-networkx/src/networkx-networkx-2.6.3/networkx/utils/decorators.py:289: DeprecationWarning: preserve_random_state is deprecated and will be removed in 3.0.
    warnings.warn(msg, DeprecationWarning)

networkx/classes/tests/test_ordered.py::TestOrderedFeatures::test_subgraph_order
  /build/python-networkx/src/networkx-networkx-2.6.3/networkx/classes/tests/test_ordered.py:22: DeprecationWarning: OrderedDiGraph is deprecated and will be removed in version 3.0.
  Use `DiGraph` instead, which guarantees order is preserved for
  Python >= 3.7
  
    cls.G = nx.OrderedDiGraph()

networkx/generators/tests/test_random_graphs.py: 4830 warnings
  /build/python-networkx/src/networkx-networkx-2.6.3/networkx/generators/random_graphs.py:227: DeprecationWarning: non-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent version
    if seed.randrange(mmax - t) < m - k:

-- Docs: https://docs.pytest.org/en/stable/warnings.html
=========================== short test summary info ============================
FAILED networkx/algorithms/tests/test_tournament.py::test_tournament_matrix
==== 1 failed, 4162 passed, 192 skipped, 4832 warnings in 610.19s (0:10:10) ====
[1m[31m==> ERROR:[m[1m A failure occurred in check().[m
[1m    Aborting...[m
