Beispiel #1
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 def test_make_clique_bipartite(self):
     G=self.G
     B=nx.make_clique_bipartite(G)
     assert_equal(sorted(B.nodes()),
                  [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
     H=nx.project_down(B)
     assert_equal(H.adj,G.adj)
     H1=nx.project_up(B)
     assert_equal(H1.nodes(),[1, 2, 3, 4, 5])
     H2=nx.make_max_clique_graph(G)
     assert_equal(H1.adj,H2.adj)
Beispiel #2
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 def test_make_clique_bipartite(self):
     G = self.G
     B = nx.make_clique_bipartite(G)
     assert sorted(B) == [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
     # Project onto the nodes of the original graph.
     H = nx.project(B, range(1, 12))
     assert H.adj == G.adj
     # Project onto the nodes representing the cliques.
     H1 = nx.project(B, range(-5, 0))
     # Relabel the negative numbers as positive ones.
     H1 = nx.relabel_nodes(H1, {-v: v for v in range(1, 6)})
     assert sorted(H1) == [1, 2, 3, 4, 5]
Beispiel #3
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 def test_make_clique_bipartite(self):
     G = self.G
     B = nx.make_clique_bipartite(G)
     assert_equal(sorted(B),
                  [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
     # Project onto the nodes of the original graph.
     H = nx.project(B, range(1, 12))
     assert_equal(H.adj, G.adj)
     # Project onto the nodes representing the cliques.
     H1 = nx.project(B, range(-5, 0))
     # Relabel the negative numbers as positive ones.
     H1 = nx.relabel_nodes(H1, {-v: v for v in range(1, 6)})
     assert_equal(sorted(H1), [1, 2, 3, 4, 5])
Beispiel #4
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 def test_make_clique_bipartite(self):
     G = self.G
     B = nx.make_clique_bipartite(G)
     assert_equal(sorted(B),
                  [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
     # Project onto the nodes of the original graph.
     H = nx.project(B, range(1, 12))
     assert_equal(H.adj, G.adj)
     # Project onto the nodes representing the cliques.
     H1 = nx.project(B, range(-5, 0))
     # Relabel the negative numbers as positive ones.
     H1 = nx.relabel_nodes(H1, dict((-v, v) for v in range(1, 6)))
     assert_equal(sorted(H1), [1, 2, 3, 4, 5])
Beispiel #5
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    def test_make_max_clique_graph(self):
        """Tests that the maximal clique graph is the same as the bipartite
        clique graph after being projected onto the nodes representing the
        cliques.

        """
        G = self.G
        B = nx.make_clique_bipartite(G)
        # Project onto the nodes representing the cliques.
        H1 = nx.project(B, range(-5, 0))
        # Relabel the negative numbers as nonnegative ones, starting at
        # 0.
        H1 = nx.relabel_nodes(H1, {-v: v - 1 for v in range(1, 6)})
        H2 = nx.make_max_clique_graph(G)
        assert H1.adj == H2.adj
Beispiel #6
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    def test_make_max_clique_graph(self):
        """Tests that the maximal clique graph is the same as the bipartite
        clique graph after being projected onto the nodes representing the
        cliques.

        """
        G = self.G
        B = nx.make_clique_bipartite(G)
        # Project onto the nodes representing the cliques.
        H1 = nx.project(B, range(-5, 0))
        # Relabel the negative numbers as nonnegative ones, starting at
        # 0.
        H1 = nx.relabel_nodes(H1, {-v: v - 1 for v in range(1, 6)})
        H2 = nx.make_max_clique_graph(G)
        assert_equal(H1.adj, H2.adj)
Beispiel #7
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def main():

    missing = file("missing.txt", "w")

    global di
    global u
    global v
    global gay_set
    global straight_set
    global gay_cliques
    global straight_cliques
    global B

    graph_file = file(sys.argv[1])
    u = pickle.load(graph_file)
    graph_file.close()

    for ego in u:
        if "orientation" in u.node[ego]:
            if u.node[ego]["orientation"] == 1:
                gay_set.add(ego)
            else:
                straight_set.add(ego)

    B = nx.make_clique_bipartite(u)

    for node in gay_set:
        gay_cliques = gay_cliques | set(B.neighbors(node))
    for node in straight_set:
        straight_cliques = straight_cliques | set(B.neighbors(node))

    print "Gay max cliques: %d" % len(gay_cliques)
    print "Straight max cliques: %d" % len(straight_cliques)
    print "Intersection: %d" % len(straight_cliques & gay_cliques)

    for ego in u:
        if "orientations" in u.node[ego]:
            B.node[ego]["orientation"] = u.node[ego]["orientation"]

    for (ego, alter) in B.edges():
        B[ego][alter]["embeddedness"] = 1.0

    bipartite_output_file = file(sys.argv[2], "w")
    pickle.dump(B, bipartite_output_file)
    bipartite_output_file.close()
Beispiel #8
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    lvl2.append(graphs_len[i])
    # lvl2.append(len(ClG_relabeled.nodes()[i])) #eval
    # lvl2.append(len(eval(ClG_relabeled.nodes()[i]))) #eval

# print 'lvl2'
# print lvl2
# print str(" ")

# plt.figure()
#
# nx.draw(ClG_relabeled,pos=posC,font_size=16,with_labels=False,node_size=[v * 100 for v in lvl2],node_color='g') #node_size=[v * 100 for v in lvl2],
# for p in posC:
#         posC[p][1] += 0.04
# nx.draw_networkx_labels(ClG_relabeled,posC)

BcG = nx.make_clique_bipartite(G, fpos=True)
bottom_nodes, top_nodes = bipartite.sets(BcG)
BcG_labels = dict()
Bcg_labels = dict()
for nd in top_nodes:
    tem = '['
    for cc in nx.all_neighbors(BcG, nd):
        tem += str(cc) + ', '
    tem = tem[:-2] + ']'
    BcG_labels[nd] = tem
    Bcg_labels[nd] = tem
for nd in bottom_nodes:
    BcG_labels[nd] = str(nd)

# print bottom_nodes
# print top_nodes
Beispiel #9
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#!/usr/bin/env python
# Funtion:
# Filename:

import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt

B = nx.Graph()
#添加一个项目101,它有3个参与者:201,202,203
B.add_edge(101, 201)
B.add_edge(101, None)
B.add_edge(101, 202)
B.add_edge(101, 203)
#添加一个项目102,它有2个参与者:203,202,2034
B.add_edge(102, 203)
B.add_edge(102, 204)

NSet = bipartite.sets(B)  #将二分图中的两类节点分别提取出来
print('NSet', NSet)
Act = nx.project(B, NSet[0])  #向项目节点投影
Actor = nx.project(B, NSet[1])  #向参与者节点投影
print(Act.edges())  #输出 [(101, 102)]
print(Actor.edges())  #输出 [(201, 202), (201, 203), (202, 203), (203, 204)]

G = nx.make_clique_bipartite(Actor)
print(G.edges())  #输出:[(201, -1), (202, -1), (203, -2), (203, -1), (204, -2)]

nx.draw(G)
plt.show()
Beispiel #10
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    # lvl2.append(len(eval(ClG_relabeled.nodes()[i]))) #eval

# print 'lvl2'
# print lvl2
# print str(" ")


# plt.figure()
#
# nx.draw(ClG_relabeled,pos=posC,font_size=16,with_labels=False,node_size=[v * 100 for v in lvl2],node_color='g') #node_size=[v * 100 for v in lvl2],
# for p in posC:
#         posC[p][1] += 0.04
# nx.draw_networkx_labels(ClG_relabeled,posC)


BcG = nx.make_clique_bipartite(G, fpos=True)
bottom_nodes, top_nodes = bipartite.sets(BcG)
BcG_labels = dict()
Bcg_labels = dict()
for nd in top_nodes:
    tem = "["
    for cc in nx.all_neighbors(BcG, nd):
        tem += str(cc) + ", "
    tem = tem[:-2] + "]"
    BcG_labels[nd] = tem
    Bcg_labels[nd] = tem
for nd in bottom_nodes:
    BcG_labels[nd] = str(nd)

# print bottom_nodes
# print top_nodes
Beispiel #11
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    def optimize(self):  # observation_set = ["weight", "nnet_outputs"]
        factors = self.factors

        all_variables = sorted(
            set(v for factor in factors for v in factor.names))
        factors = [
            new_factor for factor in factors
            for new_factor in factor.factorize()
        ]
        adjacency_matrix = np.eye(len(all_variables), dtype=bool)
        indices_to_factor = []
        for factor in factors:
            for v1 in (v for v in factor.names):
                for v2 in (v for v in factor.names):
                    adjacency_matrix[all_variables.index(v1),
                                     all_variables.index(v2)] = 1
            indices_to_factor.append({
                "indices": [all_variables.index(var) for var in factor.names],
                "factor":
                factor,
                "assigned":
                False
            })
        g = nx.from_numpy_matrix(adjacency_matrix)
        G = nx.make_clique_bipartite(g)
        cliques = [v for v in G.nodes() if G.nodes[v]['bipartite'] == 0]
        clique_graph = nx.project(G, cliques)

        # sort by decreasing number of neighbor cliques
        mapping = []
        new_cliques = []
        new_factors = []
        not_yet_mapped_names = set(self.names)
        changed_variables = set()
        ###################################
        # Merge ConstraintFactor together #
        # and compute new variables       #
        ###################################
        for clique_idx in sorted(
                clique_graph,
                key=lambda node_idx: len(clique_graph[node_idx]),
                reverse=True):
            # merge clique factors together
            clique_var_indices = list(G[clique_idx].keys())
            clique_factors = []
            for ind_fac in indices_to_factor:
                if set(ind_fac["indices"]) <= set(
                        clique_var_indices) and not ind_fac["assigned"]:
                    ind_fac["assigned"] = True
                    clique_factors.append(ind_fac["factor"])

            constraint_factors = [
                fac for fac in clique_factors
                if isinstance(fac, ConstraintFactor)
            ]
            non_constraint_factors = [
                fac for fac in clique_factors if fac not in constraint_factors
            ]
            clique_factors = ([
                ConstraintFactor(And(
                    *(fac.expr
                      for fac in constraint_factors))), *non_constraint_factors
            ] if len(constraint_factors) else non_constraint_factors)

            new_cliques.append(
                list(
                    range(len(new_factors),
                          len(new_factors) + len(clique_factors))))
            new_factors.extend(clique_factors)
            for factor in clique_factors:
                if isinstance(factor, ConstraintFactor):
                    variables_to_group = [
                        v for v in factor.names if v not in changed_variables
                    ]
                    valid_assignements = torch.unique(
                        factor.get_states(variables_to_group).long(),
                        dim=0).bool()
                    super_variable_name = "/".join(map(str,
                                                       variables_to_group))
                    indices_in_input = pd.factorize(
                        [*self.names,
                         *variables_to_group])[0][len(self.names):]
                    mapping.append((super_variable_name, variables_to_group,
                                    valid_assignements, indices_in_input))
                    not_yet_mapped_names -= set(variables_to_group)
                    changed_variables |= set(variables_to_group)
        for name in sorted(not_yet_mapped_names):
            indice_in_input = self.names.index(name)
            mapping.append((name, [name], None, [indice_in_input]))
        # new_variables.extend(set(all_variables) - changed_variables)
        factors = [factor.change_variables(mapping) for factor in new_factors]
        cliques = new_cliques

        new_cliques = []
        new_factors = []
        cluster_hints = []

        ##############################
        # Merge HintFactors together #
        ##############################
        for clique in cliques:
            clique_factors = [factors[i] for i in clique]
            clique_hint_factors = [
                fac for fac in clique_factors
                if isinstance(fac, (HintFactor, ObservationFactor))
                and isinstance(fac.fn, Indexer)
            ]
            cluster_hints = []

            for fac in clique_hint_factors:
                matching_cluster_hint = next(
                    (cluster_hint for cluster_hint in cluster_hints
                     if can_merge(cluster_hint, fac)), None)
                if matching_cluster_hint is None:
                    matching_cluster_hint = fac.clone()
                    matching_cluster_hint.mask = matching_cluster_hint.mask.long(
                    )
                    cluster_hints.append(matching_cluster_hint)
                else:
                    last_indexers_1 = matching_cluster_hint.fn.indexers[-1]
                    last_indexers_1 = list(last_indexers_1) if isinstance(
                        last_indexers_1, (list, tuple)) else [last_indexers_1]
                    last_indexers_2 = fac.fn.indexers[-1]
                    last_indexers_2 = list(last_indexers_2) if isinstance(
                        last_indexers_2, (list, tuple)) else [last_indexers_2]
                    new_last_indexer = last_indexers_1 + last_indexers_2
                    matching_cluster_hint.fn = Indexer[(*fac.fn.indexers[:-1],
                                                        new_last_indexer)]
                    offseted_mask = fac.mask.long() + len(last_indexers_1)
                    offseted_mask[~fac.mask] = 0
                    matching_cluster_hint.mask = matching_cluster_hint.mask + offseted_mask
            for cluster_hint in cluster_hints:
                cluster_hint.mask -= 1

            new_factors.extend((fac for fac in clique_factors
                                if fac not in clique_hint_factors))
            new_factors.extend(cluster_hints)

        factors_input_indices = factorize(
            values=[np.asarray(factor.names) for factor in new_factors],
            reference_values=[entry[0] for entry in mapping],
            freeze_reference=True)[0]

        return CRF(new_factors,
                   mapping,
                   names=self.names,
                   shape=self.shape,
                   factors_input_indices=factors_input_indices)
Beispiel #12
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def main():

    missing = file("missing.txt", "w")

    global di
    global u
    global v
    global gays
    global straights
    global gay_cliques
    global straight_cliques
    global B

    global labeled

    graph_file = file(sys.argv[3])
    u = pickle.load(graph_file)
    graph_file.close()

    # try w/ clique graph
    u = nx.make_clique_bipartite(u)
    for i, j in u.edges():
        u[i][j]["embeddedness"] = 1
    u = set_orientation_by_file(sys.argv[1], "orientation", u)
    u = set_orientation_by_file(sys.argv[2], "test_orientation", u)

    all = u.subgraph([x for x in u.node if "orientation" in u.node[x]])
    gay_graph = u.subgraph([
        y for y in [x for x in u.node if "orientation" in u.node[x]]
        if u.node[y]['orientation'] == 1
    ])
    straight_graph = u.subgraph([
        y for y in [x for x in u.node if "orientation" in u.node[x]]
        if u.node[y]['orientation'] == -1
    ])
    '''
    B = nx.make_clique_bipartite(u)
    '''
    for node in gay_graph:
        gay_cliques = gay_cliques | set(u.neighbors(node))
    for node in straight_graph:
        straight_cliques = straight_cliques | set(u.neighbors(node))

    #for node in straight_cliques & gay_cliques:
    #    print "Share clique: %s" % node
    #    print "Neighbors: %s " % u.neighbors(node)
    #    u.remove_node(node)

    ##################################
    #
    # Invent new node labels that snap will understand
    # i.e., from 0 to n-1
    # put the labeled training nodes first, followed by test nodes,
    # followed by the rest
    #
    # Not that u is not actually relabeled. The labels are applied
    # when the output file is written.
    #
    count = 0
    not_labeled = list()
    labeled = list()
    test_labeled = list()
    test_labels = list()
    labeled_data_for_snap = file("snap_labels.txt", "w")
    node_untrans = dict()

    for ego in u:
        if "orientation" in u.node[ego]:
            node_trans[ego] = count
            node_untrans[count] = ego
            labeled_data_for_snap.write("%d\n" % u.node[ego]["orientation"])
            labeled.append(u.node[ego]["orientation"])
            count += 1
        elif "test_orientation" in u.node[ego]:
            test_labeled.append(ego)
            test_labels.append(u.node[ego]["test_orientation"])
        else:
            not_labeled.append(ego)
    labeled_data_for_snap.close()

    for ego in test_labeled:
        node_trans[ego] = count
        node_untrans[count] = ego
        count += 1
    for ego in not_labeled:
        node_trans[ego] = count
        node_untrans[count] = ego
        count += 1

    ######################
    #
    # Output graph with labels and node translations
    # These are intermediate outputs that may later be
    # helpful in interpreting the data
    #
    pkl_file = open(sys.argv[6], "w")
    pickle.dump(u, pkl_file)
    pkl_file.close()

    #labeled = u.copy()

    pkl_file = open(sys.argv[7], "w")
    pickle.dump(node_trans, pkl_file)
    pkl_file.close()

    #u = label_by_voting (u)
    #u = label_by_weighted_voting (u, float(sys.argv[5]))
    #u = label_by_weighted_voting2 (u, float(sys.argv[5]), test_labeled)
    u = label_by_weighted_voting3(u, float(sys.argv[5]), test_labeled)
    #dump_tests (u, test_labeled)
    #u = label_by_revoting (u, float(sys.argv[5]), test_labeled)
    #dump_tests (u, test_labeled)

    write_to_snap(sys.argv[4], u, node_trans, node_untrans, labeled,
                  test_labels, float(sys.argv[5]))