Python networkx.NetworkXUnfeasible方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def __init__(self, config: Dict, recipe_folder: str,
                 exclude: List[str] = None, nocatch: bool=False) ->None:
        self.config = config
        self.recipe_folder = recipe_folder
        self.skip = self.load_skips()
        self.exclude = exclude or []
        self.nocatch = nocatch
        self._messages = []

        dag = nx.DiGraph()
        dag.add_nodes_from(str(check) for check in get_checks())
        dag.add_edges_from(
            (str(check), str(check_dep))
            for check in get_checks()
            for check_dep in check.requires
        )
        self.checks_dag = dag

        try:
            self.checks_ordered = nx.topological_sort(dag, reverse=True)
        except nx.NetworkXUnfeasible:
            raise RunTimeError("Cycle in LintCheck requirements!")
        self.reload_checks() 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def bridge_augmentation(G, avail=None, weight=None):
    """Finds the a set of edges that bridge connects G.

    Adding these edges to G will make it 2-edge-connected.
    If no constraints are specified the returned set of edges is minimum an
    optimal, otherwise the solution is approximated.

    Notes
    -----
    If there are no constraints the solution can be computed in linear time
    using :func:`unconstrained_bridge_augmentation`. Otherwise, the problem
    becomes NP-hard and is the solution is approximated by
    :func:`weighted_bridge_augmentation`.
    """
    if G.number_of_nodes() < 3:
        raise nx.NetworkXUnfeasible(
            'impossible to bridge connect less than 3 nodes')
    if avail is None:
        return unconstrained_bridge_augmentation(G)
    else:
        return weighted_bridge_augmentation(G, avail, weight=weight)


# --- Algorithms and Helpers --- 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def sorted_nodes(self):
        """Returns a topological sort of the resource graph.

        Notes
        -----
        Topological sorts are not stable. Be wary of depending on order
        where you shouldn't.

        """
        if self._sorted_nodes is None:
            try:
                self._sorted_nodes = list(nx.algorithms.topological_sort(self.graph))
            except nx.NetworkXUnfeasible:
                raise ResourceError(f'The resource pool contains at least one cycle: '
                                    f'{nx.find_cycle(self.graph)}.')
        return self._sorted_nodes 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def resolve_hook_order(self, hook_tuples: List[HookTuple]) -> List[HookTuple]:
        dag = nx.DiGraph()

        for hook_tuple in hook_tuples:
            dag.add_node(hook_tuple.Hook.name, hook_tuple=hook_tuple)
            for dep_name in hook_tuple.Hook.run_after:
                dag.add_edge(hook_tuple.Hook.name, dep_name)
            for successor_name in hook_tuple.Hook.run_before:
                dag.add_edge(successor_name, hook_tuple.Hook.name)

        try:
            order = reversed(list(nx.topological_sort(dag)))
        except nx.NetworkXUnfeasible:
            msg = 'Circular dependency detected between hooks'
            problem_graph = ', '.join(f'{a} -> {b}'
                                      for a, b in nx.find_cycle(dag))
            raise Exception(f'{msg}: {problem_graph}')

        rv = []
        for hook_name in order:
            hook_tuple = dag.nodes[hook_name].get('hook_tuple')
            if hook_tuple:
                rv.append(hook_tuple)
        return rv 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def is_directed_acyclic_graph(G):
    """Return True if the graph G is a directed acyclic graph (DAG) or 
    False if not.

    Parameters
    ----------
    G : NetworkX graph
        A graph

    Returns
    -------
    is_dag : bool
        True if G is a DAG, false otherwise
    """
    if not G.is_directed():
        return False
    try:
        topological_sort(G, reverse=True)
        return True
    except nx.NetworkXUnfeasible:
        return False 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def test_reverse_topological_sort1(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, 2), (1, 3), (2, 3)])
        assert_equal(nx.topological_sort(DG, reverse=True), [3, 2, 1])
        assert_equal(
            nx.topological_sort_recursive(DG, reverse=True), [3, 2, 1])

        DG.add_edge(3, 2)
        assert_raises(nx.NetworkXUnfeasible,
                      nx.topological_sort, DG, reverse=True)
        assert_raises(nx.NetworkXUnfeasible,
                      nx.topological_sort_recursive, DG, reverse=True)

        DG.remove_edge(2, 3)
        assert_equal(nx.topological_sort(DG, reverse=True), [2, 3, 1])
        assert_equal(
            nx.topological_sort_recursive(DG, reverse=True), [2, 3, 1]) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def test_topological_sort3(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, i) for i in range(2, 5)])
        DG.add_edges_from([(2, i) for i in range(5, 9)])
        DG.add_edges_from([(6, i) for i in range(9, 12)])
        DG.add_edges_from([(4, i) for i in range(12, 15)])

        def validate(order):
            ok_(isinstance(order, list))
            assert_equal(set(order), set(DG))
            for u, v in combinations(order, 2):
                assert_false(nx.has_path(DG, v, u))
        validate(nx.topological_sort_recursive(DG))
        validate(nx.topological_sort(DG))

        DG.add_edge(14, 1)
        assert_raises(nx.NetworkXUnfeasible, nx.topological_sort, DG)
        assert_raises(nx.NetworkXUnfeasible, nx.topological_sort_recursive, DG) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def phase3(self):
        # build potential remaining edges and choose with rejection sampling
        potential_edges = combinations(self.remaining_degree, 2)
        # build auxilliary graph of potential edges not already in graph
        H = nx.Graph([(u,v) for (u,v) in potential_edges
                      if not self.graph.has_edge(u,v)])
        while self.remaining_degree:
            if not self.suitable_edge():
                raise nx.NetworkXUnfeasible('no suitable edges left')
            while True:
                u,v = sorted(random.choice(H.edges()))
                if random.random() < self.q(u,v):
                    break
            if random.random() < self.p(u,v): # accept edge
                self.graph.add_edge(u,v)
                self.update_remaining(u,v, aux_graph=H) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def test_topological_sort1(self):
        DG = nx.DiGraph([(1, 2), (1, 3), (2, 3)])

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_equal(tuple(algorithm(DG)), (1, 2, 3))

        DG.add_edge(3, 2)

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_raises(nx.NetworkXUnfeasible, consume, algorithm(DG))

        DG.remove_edge(2, 3)

        for algorithm in [nx.topological_sort,
                          nx.lexicographical_topological_sort]:
            assert_equal(tuple(algorithm(DG)), (1, 3, 2))

        DG.remove_edge(3, 2)

        assert_in(tuple(nx.topological_sort(DG)), {(1, 2, 3), (1, 3, 2)})
        assert_equal(tuple(nx.lexicographical_topological_sort(DG)), (1, 2, 3)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def test_topological_sort3(self):
        DG = nx.DiGraph()
        DG.add_edges_from([(1, i) for i in range(2, 5)])
        DG.add_edges_from([(2, i) for i in range(5, 9)])
        DG.add_edges_from([(6, i) for i in range(9, 12)])
        DG.add_edges_from([(4, i) for i in range(12, 15)])

        def validate(order):
            ok_(isinstance(order, list))
            assert_equal(set(order), set(DG))
            for u, v in combinations(order, 2):
                assert_false(nx.has_path(DG, v, u))
        validate(list(nx.topological_sort(DG)))

        DG.add_edge(14, 1)
        assert_raises(nx.NetworkXUnfeasible, consume, nx.topological_sort(DG)) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def test_all_topological_sorts_3(self):
        def unfeasible():
            DG = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 2), (4, 5)])
            # convert to list to execute generator
            list(nx.all_topological_sorts(DG))

        def not_implemented():
            G = nx.Graph([(1, 2), (2, 3)])
            # convert to list to execute generator
            list(nx.all_topological_sorts(G))

        def not_implemted_2():
            G = nx.MultiGraph([(1, 2), (1, 2), (2, 3)])
            list(nx.all_topological_sorts(G))
        assert_raises(nx.NetworkXUnfeasible, unfeasible)
        assert_raises(nx.NetworkXNotImplemented, not_implemented)
        assert_raises(nx.NetworkXNotImplemented, not_implemted_2) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def phase3(self):
        # build potential remaining edges and choose with rejection sampling
        potential_edges = combinations(self.remaining_degree, 2)
        # build auxiliary graph of potential edges not already in graph
        H = nx.Graph([(u, v) for (u, v) in potential_edges
                      if not self.graph.has_edge(u, v)])
        rng = self.rng
        while self.remaining_degree:
            if not self.suitable_edge():
                raise nx.NetworkXUnfeasible('no suitable edges left')
            while True:
                u, v = sorted(rng.choice(list(H.edges())))
                if rng.random() < self.q(u, v):
                    break
            if rng.random() < self.p(u, v):  # accept edge
                self.graph.add_edge(u, v)
                self.update_remaining(u, v, aux_graph=H) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def is_directed_acyclic_graph(G):
    """Return True if the graph `G` is a directed acyclic graph (DAG) or
    False if not.

    Parameters
    ----------
    G : NetworkX graph

    Returns
    -------
    bool
        True if `G` is a DAG, False otherwise
    """
    if not G.is_directed():
        return False
    try:
        consume(topological_sort(G))
        return True
    except nx.NetworkXUnfeasible:
        return False 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def weighted_one_edge_augmentation(G, avail, weight=None, partial=False):
    """Finds the minimum weight set of edges to connect G if one exists.

    This is a variant of the weighted MST problem.

    Example
    -------
    >>> G = nx.Graph([(1, 2), (2, 3), (4, 5)])
    >>> G.add_nodes_from([6, 7, 8])
    >>> # any edge not in avail has an implicit weight of infinity
    >>> avail = [(1, 3), (1, 5), (4, 7), (4, 8), (6, 1), (8, 1), (8, 2)]
    >>> sorted(weighted_one_edge_augmentation(G, avail))
    [(1, 5), (4, 7), (6, 1), (8, 1)]
    >>> # find another solution by giving large weights to edges in the
    >>> # previous solution (note some of the old edges must be used)
    >>> avail = [(1, 3), (1, 5, 99), (4, 7, 9), (6, 1, 99), (8, 1, 99), (8, 2)]
    >>> sorted(weighted_one_edge_augmentation(G, avail))
    [(1, 5), (4, 7), (6, 1), (8, 2)]
    """
    avail_uv, avail_w = _unpack_available_edges(avail, weight=weight, G=G)
    # Collapse CCs in the original graph into nodes in a metagraph
    # Then find an MST of the metagraph instead of the original graph
    C = collapse(G, nx.connected_components(G))
    mapping = C.graph['mapping']
    # Assign each available edge to an edge in the metagraph
    candidate_mapping = _lightest_meta_edges(mapping, avail_uv, avail_w)
    # nx.set_edge_attributes(C, name='weight', values=0)
    C.add_edges_from(
        (mu, mv, {'weight': w, 'generator': uv})
        for (mu, mv), uv, w in candidate_mapping
    )
    # Find MST of the meta graph
    meta_mst = nx.minimum_spanning_tree(C)
    if not partial and not nx.is_connected(meta_mst):
        raise nx.NetworkXUnfeasible(
            'Not possible to connect G with available edges')
    # Yield the edge that generated the meta-edge
    for mu, mv, d in meta_mst.edges(data=True):
        if 'generator' in d:
            edge = d['generator']
            yield edge 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import NetworkXUnfeasible [as 别名]
def find_pos_augment_edges(infr, pcc, k=None):
        """
        # [[1, 0], [0, 2], [1, 2], [3, 1]]
        pos_sub = nx.Graph([[0, 1], [1, 2], [0, 2], [1, 3]])
        """
        if k is None:
            pos_k = infr.params['redun.pos']
        else:
            pos_k = k
        pos_sub = infr.pos_graph.subgraph(pcc)

        # TODO:
        # weight by pairs most likely to be comparable

        # First try to augment only with unreviewed existing edges
        unrev_avail = list(nxu.edges_inside(infr.unreviewed_graph, pcc))
        try:
            check_edges = list(nxu.k_edge_augmentation(
                pos_sub, k=pos_k, avail=unrev_avail, partial=False))
        except nx.NetworkXUnfeasible:
            check_edges = None
        if not check_edges:
            # Allow new edges to be introduced
            full_sub = infr.graph.subgraph(pcc).copy()
            new_avail = ut.estarmap(infr.e_, nx.complement(full_sub).edges())
            full_avail = unrev_avail + new_avail
            n_max = (len(pos_sub) * (len(pos_sub) - 1)) // 2
            n_complement = n_max - pos_sub.number_of_edges()
            if len(full_avail) == n_complement:
                # can use the faster algorithm
                check_edges = list(nxu.k_edge_augmentation(
                    pos_sub, k=pos_k, partial=True))
            else:
                # have to use the slow approximate algo
                check_edges = list(nxu.k_edge_augmentation(
                    pos_sub, k=pos_k, avail=full_avail, partial=True))
        check_edges = set(it.starmap(e_, check_edges))
        return check_edges