#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jun 17 12:02:36 2020 @author: ljia """ import numpy as np import networkx as nx from gklearn.ged.env import Options, OptionsStringMap from gklearn.ged.env import GEDData class GEDEnv(object): def __init__(self): self._initialized = False self._new_graph_ids = [] self._ged_data = GEDData() # Variables needed for approximating ged_instance_. self._lower_bounds = {} self._upper_bounds = {} self._runtimes = {} self._node_maps = {} self._original_to_internal_node_ids = [] self._internal_to_original_node_ids = [] self._ged_method = None def set_edit_cost(self, edit_cost, edit_cost_constants=[]): """ /*! * @brief Sets the edit costs to one of the predefined edit costs. * @param[in] edit_costs Select one of the predefined edit costs. * @param[in] edit_cost_constants Constants passed to the constructor of the edit cost class selected by @p edit_costs. */ """ self._ged_data._set_edit_cost(edit_cost, edit_cost_constants) def add_graph(self, graph_name='', graph_class=''): """ /*! * @brief Adds a new uninitialized graph to the environment. Call init() after calling this method. * @param[in] graph_name The name of the added graph. Empty if not specified. * @param[in] graph_class The class of the added graph. Empty if not specified. * @return The ID of the newly added graph. */ """ # @todo: graphs are not uninitialized. self._initialized = False graph_id = self._ged_data._num_graphs_without_shuffled_copies self._ged_data._num_graphs_without_shuffled_copies += 1 self._new_graph_ids.append(graph_id) self._ged_data._graphs.append(nx.Graph()) self._ged_data._graph_names.append(graph_name) self._ged_data._graph_classes.append(graph_class) self._original_to_internal_node_ids.append({}) self._internal_to_original_node_ids.append({}) self._ged_data._strings_to_internal_node_ids.append({}) self._ged_data._internal_node_ids_to_strings.append({}) return graph_id def clear_graph(self, graph_id): """ /*! * @brief Clears and de-initializes a graph that has previously been added to the environment. Call init() after calling this method. * @param[in] graph_id ID of graph that has to be cleared. */ """ if graph_id > self._ged_data.num_graphs_without_shuffled_copies(): raise Exception('The graph', self.get_graph_name(graph_id), 'has not been added to the environment.') self._ged_data._graphs[graph_id].clear() self._original_to_internal_node_ids[graph_id].clear() self._internal_to_original_node_ids[graph_id].clear() self._ged_data._strings_to_internal_node_ids[graph_id].clear() self._ged_data._internal_node_ids_to_strings[graph_id].clear() self._initialized = False def add_node(self, graph_id, node_id, node_label): """ /*! * @brief Adds a labeled node. * @param[in] graph_id ID of graph that has been added to the environment. * @param[in] node_id The user-specific ID of the vertex that has to be added. * @param[in] node_label The label of the vertex that has to be added. Set to ged::NoLabel() if template parameter @p UserNodeLabel equals ged::NoLabel. */ """ # @todo: check ids. self._initialized = False internal_node_id = nx.number_of_nodes(self._ged_data._graphs[graph_id]) self._ged_data._graphs[graph_id].add_node(internal_node_id, label=node_label) self._original_to_internal_node_ids[graph_id][node_id] = internal_node_id self._internal_to_original_node_ids[graph_id][internal_node_id] = node_id self._ged_data._strings_to_internal_node_ids[graph_id][str(node_id)] = internal_node_id self._ged_data._internal_node_ids_to_strings[graph_id][internal_node_id] = str(node_id) self._ged_data._node_label_to_id(node_label) label_id = self._ged_data._node_label_to_id(node_label) # @todo: ged_data_.graphs_[graph_id].set_label def add_edge(self, graph_id, nd_from, nd_to, edge_label, ignore_duplicates=True): """ /*! * @brief Adds a labeled edge. * @param[in] graph_id ID of graph that has been added to the environment. * @param[in] tail The user-specific ID of the tail of the edge that has to be added. * @param[in] head The user-specific ID of the head of the edge that has to be added. * @param[in] edge_label The label of the vertex that has to be added. Set to ged::NoLabel() if template parameter @p UserEdgeLabel equals ged::NoLabel. * @param[in] ignore_duplicates If @p true, duplicate edges are ignores. Otherwise, an exception is thrown if an existing edge is added to the graph. */ """ # @todo: check everything. self._initialized = False # @todo: check ignore_duplicates. self._ged_data._graphs[graph_id].add_edge(self._original_to_internal_node_ids[graph_id][nd_from], self._original_to_internal_node_ids[graph_id][nd_to], label=edge_label) label_id = self._ged_data._edge_label_to_id(edge_label) # @todo: ged_data_.graphs_[graph_id].set_label def add_nx_graph(self, g, classe, ignore_duplicates=True) : """ Add a Graph (made by networkx) on the environment. Be careful to respect the same format as GXL graphs for labelling nodes and edges. :param g: The graph to add (networkx graph) :param ignore_duplicates: If True, duplicate edges are ignored, otherwise it's raise an error if an existing edge is added. True by default :type g: networkx.graph :type ignore_duplicates: bool :return: The ID of the newly added graphe :rtype: size_t .. note:: The NX graph must respect the GXL structure. Please see how a GXL graph is construct. """ graph_id = self.add_graph(g.name, classe) # check if the graph name already exists. for node in g.nodes: # @todo: if the keys of labels include int and str at the same time. self.add_node(graph_id, node, tuple(sorted(g.nodes[node].items(), key=lambda kv: kv[0]))) for edge in g.edges: self.add_edge(graph_id, edge[0], edge[1], tuple(sorted(g.edges[(edge[0], edge[1])].items(), key=lambda kv: kv[0])), ignore_duplicates) return graph_id def load_nx_graph(self, nx_graph, graph_id, graph_name='', graph_class=''): """ Loads NetworkX Graph into the GED environment. Parameters ---------- nx_graph : NetworkX Graph object The graph that should be loaded. graph_id : int or None The ID of a graph contained the environment (overwrite existing graph) or add new graph if `None`. graph_name : string, optional The name of newly added graph. The default is ''. Has no effect unless `graph_id` equals `None`. graph_class : string, optional The class of newly added graph. The default is ''. Has no effect unless `graph_id` equals `None`. Returns ------- int The ID of the newly loaded graph. """ if graph_id is None: # @todo: undefined. graph_id = self.add_graph(graph_name, graph_class) else: self.clear_graph(graph_id) for node in nx_graph.nodes: self.add_node(graph_id, node, tuple(sorted(nx_graph.nodes[node].items(), key=lambda kv: kv[0]))) for edge in nx_graph.edges: self.add_edge(graph_id, edge[0], edge[1], tuple(sorted(nx_graph.edges[(edge[0], edge[1])].items(), key=lambda kv: kv[0]))) return graph_id def init(self, init_type=Options.InitType.EAGER_WITHOUT_SHUFFLED_COPIES, print_to_stdout=False): if isinstance(init_type, str): init_type = OptionsStringMap.InitType[init_type] # Throw an exception if no edit costs have been selected. if self._ged_data._edit_cost is None: raise Exception('No edit costs have been selected. Call set_edit_cost() before calling init().') # Return if the environment is initialized. if self._initialized: return # Set initialization type. self._ged_data._init_type = init_type # @todo: Construct shuffled graph copies if necessary. # Re-initialize adjacency matrices (also previously initialized graphs must be re-initialized because of possible re-allocation). # @todo: setup_adjacency_matrix, don't know if neccessary. self._ged_data._max_num_nodes = np.max([nx.number_of_nodes(g) for g in self._ged_data._graphs]) self._ged_data._max_num_edges = np.max([nx.number_of_edges(g) for g in self._ged_data._graphs]) # Initialize cost matrices if necessary. if self._ged_data._eager_init(): pass # @todo: init_cost_matrices_: 1. Update node cost matrix if new node labels have been added to the environment; 2. Update edge cost matrix if new edge labels have been added to the environment. # Mark environment as initialized. self._initialized = True self._new_graph_ids.clear() def is_initialized(self): """ /*! * @brief Check if the environment is initialized. * @return True if the environment is initialized. */ """ return self._initialized def get_init_type(self): """ /*! * @brief Returns the initialization type of the last initialization. * @return Initialization type. */ """ return self._ged_data._init_type def set_label_costs(self, node_label_costs=None, edge_label_costs=None): """Set the costs between labels. """ if node_label_costs is not None: self._ged_data._node_label_costs = node_label_costs if edge_label_costs is not None: self._ged_data._edge_label_costs = edge_label_costs def set_method(self, method, options=''): """ /*! * @brief Sets the GEDMethod to be used by run_method(). * @param[in] method Select the method that is to be used. * @param[in] options An options string of the form @"[--@ @] [...]@" passed to the selected method. */ """ del self._ged_method if isinstance(method, str): method = OptionsStringMap.GEDMethod[method] if method == Options.GEDMethod.BRANCH: self._ged_method = Branch(self._ged_data) elif method == Options.GEDMethod.BRANCH_FAST: self._ged_method = BranchFast(self._ged_data) elif method == Options.GEDMethod.BRANCH_FAST: self._ged_method = BranchFast(self._ged_data) elif method == Options.GEDMethod.BRANCH_TIGHT: self._ged_method = BranchTight(self._ged_data) elif method == Options.GEDMethod.BRANCH_UNIFORM: self._ged_method = BranchUniform(self._ged_data) elif method == Options.GEDMethod.BRANCH_COMPACT: self._ged_method = BranchCompact(self._ged_data) elif method == Options.GEDMethod.PARTITION: self._ged_method = Partition(self._ged_data) elif method == Options.GEDMethod.HYBRID: self._ged_method = Hybrid(self._ged_data) elif method == Options.GEDMethod.RING: self._ged_method = Ring(self._ged_data) elif method == Options.GEDMethod.ANCHOR_AWARE_GED: self._ged_method = AnchorAwareGED(self._ged_data) elif method == Options.GEDMethod.WALKS: self._ged_method = Walks(self._ged_data) elif method == Options.GEDMethod.IPFP: self._ged_method = IPFP(self._ged_data) elif method == Options.GEDMethod.BIPARTITE: from gklearn.ged.methods import Bipartite self._ged_method = Bipartite(self._ged_data) elif method == Options.GEDMethod.SUBGRAPH: self._ged_method = Subgraph(self._ged_data) elif method == Options.GEDMethod.NODE: self._ged_method = Node(self._ged_data) elif method == Options.GEDMethod.RING_ML: self._ged_method = RingML(self._ged_data) elif method == Options.GEDMethod.BIPARTITE_ML: self._ged_method = BipartiteML(self._ged_data) elif method == Options.GEDMethod.REFINE: self._ged_method = Refine(self._ged_data) elif method == Options.GEDMethod.BP_BEAM: self._ged_method = BPBeam(self._ged_data) elif method == Options.GEDMethod.SIMULATED_ANNEALING: self._ged_method = SimulatedAnnealing(self._ged_data) elif method == Options.GEDMethod.HED: self._ged_method = HED(self._ged_data) elif method == Options.GEDMethod.STAR: self._ged_method = STAR(self._ged_data) # #ifdef GUROBI elif method == Options.GEDMethod.F1: self._ged_method = F1(self._ged_data) elif method == Options.GEDMethod.F2: self._ged_method = F2(self._ged_data) elif method == Options.GEDMethod.COMPACT_MIP: self._ged_method = CompactMIP(self._ged_data) elif method == Options.GEDMethod.BLP_NO_EDGE_LABELS: self._ged_method = BLPNoEdgeLabels(self._ged_data) self._ged_method.set_options(options) def run_method(self, g_id, h_id): """ /*! * @brief Runs the GED method specified by call to set_method() between the graphs with IDs @p g_id and @p h_id. * @param[in] g_id ID of an input graph that has been added to the environment. * @param[in] h_id ID of an input graph that has been added to the environment. */ """ if g_id >= self._ged_data.num_graphs(): raise Exception('The graph with ID', str(g_id), 'has not been added to the environment.') if h_id >= self._ged_data.num_graphs(): raise Exception('The graph with ID', str(h_id), 'has not been added to the environment.') if not self._initialized: raise Exception('The environment is uninitialized. Call init() after adding all graphs to the environment.') if self._ged_method is None: raise Exception('No method has been set. Call set_method() before calling run().') # Call selected GEDMethod and store results. if self._ged_data.shuffled_graph_copies_available() and (g_id == h_id): self._ged_method.run(g_id, self._ged_data.id_shuffled_graph_copy(h_id)) # @todo: why shuffle? else: self._ged_method.run(g_id, h_id) self._lower_bounds[(g_id, h_id)] = self._ged_method.get_lower_bound() self._upper_bounds[(g_id, h_id)] = self._ged_method.get_upper_bound() self._runtimes[(g_id, h_id)] = self._ged_method.get_runtime() self._node_maps[(g_id, h_id)] = self._ged_method.get_node_map() def init_method(self): """Initializes the method specified by call to set_method(). """ if not self._initialized: raise Exception('The environment is uninitialized. Call init() before calling init_method().') if self._ged_method is None: raise Exception('No method has been set. Call set_method() before calling init_method().') self._ged_method.init() def get_num_node_labels(self): """ /*! * @brief Returns the number of node labels. * @return Number of pairwise different node labels contained in the environment. * @note If @p 1 is returned, the nodes are unlabeled. */ """ return len(self._ged_data._node_labels) def get_all_node_labels(self): """ /*! * @brief Returns the list of all node labels. * @return List of pairwise different node labels contained in the environment. * @note If @p 1 is returned, the nodes are unlabeled. */ """ return self._ged_data._node_labels def get_node_label(self, label_id, to_dict=True): """ /*! * @brief Returns node label. * @param[in] label_id ID of node label that should be returned. Must be between 1 and num_node_labels(). * @return Node label for selected label ID. */ """ if label_id < 1 or label_id > self.get_num_node_labels(): raise Exception('The environment does not contain a node label with ID', str(label_id), '.') if to_dict: return dict(self._ged_data._node_labels[label_id - 1]) return self._ged_data._node_labels[label_id - 1] def get_num_edge_labels(self): """ /*! * @brief Returns the number of edge labels. * @return Number of pairwise different edge labels contained in the environment. * @note If @p 1 is returned, the edges are unlabeled. */ """ return len(self._ged_data._edge_labels) def get_all_edge_labels(self): """ /*! * @brief Returns the list of all edge labels. * @return List of pairwise different edge labels contained in the environment. * @note If @p 1 is returned, the edges are unlabeled. */ """ return self._ged_data._edge_labels def get_edge_label(self, label_id, to_dict=True): """ /*! * @brief Returns edge label. * @param[in] label_id ID of edge label that should be returned. Must be between 1 and num_node_labels(). * @return Edge label for selected label ID. */ """ if label_id < 1 or label_id > self.get_num_edge_labels(): raise Exception('The environment does not contain an edge label with ID', str(label_id), '.') if to_dict: return dict(self._ged_data._edge_labels[label_id - 1]) return self._ged_data._edge_labels[label_id - 1] def get_upper_bound(self, g_id, h_id): """ /*! * @brief Returns upper bound for edit distance between the input graphs. * @param[in] g_id ID of an input graph that has been added to the environment. * @param[in] h_id ID of an input graph that has been added to the environment. * @return Upper bound computed by the last call to run_method() with arguments @p g_id and @p h_id. */ """ if (g_id, h_id) not in self._upper_bounds: raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_upper_bound(' + str(g_id) + ',' + str(h_id) + ').') return self._upper_bounds[(g_id, h_id)] def get_lower_bound(self, g_id, h_id): """ /*! * @brief Returns lower bound for edit distance between the input graphs. * @param[in] g_id ID of an input graph that has been added to the environment. * @param[in] h_id ID of an input graph that has been added to the environment. * @return Lower bound computed by the last call to run_method() with arguments @p g_id and @p h_id. */ """ if (g_id, h_id) not in self._lower_bounds: raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_lower_bound(' + str(g_id) + ',' + str(h_id) + ').') return self._lower_bounds[(g_id, h_id)] def get_runtime(self, g_id, h_id): """ /*! * @brief Returns runtime. * @param[in] g_id ID of an input graph that has been added to the environment. * @param[in] h_id ID of an input graph that has been added to the environment. * @return Runtime of last call to run_method() with arguments @p g_id and @p h_id. */ """ if (g_id, h_id) not in self._runtimes: raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_runtime(' + str(g_id) + ',' + str(h_id) + ').') return self._runtimes[(g_id, h_id)] def get_init_time(self): """ /*! * @brief Returns initialization time. * @return Runtime of the last call to init_method(). */ """ return self._ged_method.get_init_time() def get_node_map(self, g_id, h_id): """ /*! * @brief Returns node map between the input graphs. * @param[in] g_id ID of an input graph that has been added to the environment. * @param[in] h_id ID of an input graph that has been added to the environment. * @return Node map computed by the last call to run_method() with arguments @p g_id and @p h_id. */ """ if (g_id, h_id) not in self._node_maps: raise Exception('Call run(' + str(g_id) + ',' + str(h_id) + ') before calling get_node_map(' + str(g_id) + ',' + str(h_id) + ').') return self._node_maps[(g_id, h_id)] def get_forward_map(self, g_id, h_id) : """ Returns the forward map (or the half of the adjacence matrix) between nodes of the two indicated graphs. :param g: The Id of the first compared graph :param h: The Id of the second compared graph :type g: size_t :type h: size_t :return: The forward map to the adjacence matrix between nodes of the two graphs :rtype: list[npy_uint32] .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_backward_map(), get_runtime(), quasimetric_cost(), get_node_map(), get_assignment_matrix() .. warning:: run_method() between the same two graph must be called before this function. .. note:: I don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! """ return self.get_node_map(g_id, h_id).forward_map def get_backward_map(self, g_id, h_id) : """ Returns the backward map (or the half of the adjacence matrix) between nodes of the two indicated graphs. :param g: The Id of the first compared graph :param h: The Id of the second compared graph :type g: size_t :type h: size_t :return: The backward map to the adjacence matrix between nodes of the two graphs :rtype: list[npy_uint32] .. seealso:: run_method(), get_upper_bound(), get_lower_bound(), get_forward_map(), get_runtime(), quasimetric_cost(), get_node_map(), get_assignment_matrix() .. warning:: run_method() between the same two graph must be called before this function. .. note:: I don't know how to connect the two map to reconstruct the adjacence matrix. Please come back when I know how it's work ! """ return self.get_node_map(g_id, h_id).backward_map def compute_induced_cost(self, g_id, h_id, node_map): """ /*! * @brief Computes the edit cost between two graphs induced by a node map. * @param[in] g_id ID of input graph. * @param[in] h_id ID of input graph. * @param[in,out] node_map Node map whose induced edit cost is to be computed. */ """ self._ged_data.compute_induced_cost(self._ged_data._graphs[g_id], self._ged_data._graphs[h_id], node_map) def get_nx_graph(self, graph_id): """ * @brief Returns NetworkX.Graph() representation. * @param[in] graph_id ID of the selected graph. """ graph = nx.Graph() # @todo: add graph attributes. graph.graph['id'] = graph_id nb_nodes = self.get_graph_num_nodes(graph_id) original_node_ids = self.get_original_node_ids(graph_id) node_labels = self.get_graph_node_labels(graph_id, to_dict=True) graph.graph['original_node_ids'] = original_node_ids for node_id in range(0, nb_nodes): graph.add_node(node_id, **node_labels[node_id]) edges = self.get_graph_edges(graph_id, to_dict=True) for (head, tail), labels in edges.items(): graph.add_edge(head, tail, **labels) return graph def get_graph_node_labels(self, graph_id, to_dict=True): """ Searchs and returns all the labels of nodes on a graph, selected by its ID. :param graph_id: The ID of the wanted graph :type graph_id: size_t :return: The list of nodes' labels on the selected graph :rtype: list[dict{string : string}] .. seealso:: get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_edges(), get_graph_adjacence_matrix() .. note:: These functions allow to collect all the graph's informations. """ graph = self._ged_data.graph(graph_id) node_labels = [] for n in graph.nodes(): node_labels.append(graph.nodes[n]['label']) if to_dict: return [dict(i) for i in node_labels] return node_labels def get_graph_edges(self, graph_id, to_dict=True): """ Searchs and returns all the edges on a graph, selected by its ID. :param graph_id: The ID of the wanted graph :type graph_id: size_t :return: The list of edges on the selected graph :rtype: dict{tuple(size_t, size_t) : dict{string : string}} .. seealso::get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_original_node_ids(), get_graph_node_labels(), get_graph_adjacence_matrix() .. note:: These functions allow to collect all the graph's informations. """ graph = self._ged_data.graph(graph_id) if to_dict: edges = {} for n1, n2, attr in graph.edges(data=True): edges[(n1, n2)] = dict(attr['label']) return edges return {(n1, n2): attr['label'] for n1, n2, attr in graph.edges(data=True)} def get_graph_name(self, graph_id): """ /*! * @brief Returns the graph name. * @param[in] graph_id ID of an input graph that has been added to the environment. * @return Name of the input graph. */ """ return self._ged_data._graph_names[graph_id] def get_graph_num_nodes(self, graph_id): """ /*! * @brief Returns the number of nodes. * @param[in] graph_id ID of an input graph that has been added to the environment. * @return Number of nodes in the graph. */ """ return nx.number_of_nodes(self._ged_data.graph(graph_id)) def get_original_node_ids(self, graph_id): """ Searchs and returns all th Ids of nodes on a graph, selected by its ID. :param graph_id: The ID of the wanted graph :type graph_id: size_t :return: The list of IDs's nodes on the selected graph :rtype: list[string] .. seealso::get_graph_internal_id(), get_graph_num_nodes(), get_graph_num_edges(), get_graph_node_labels(), get_graph_edges(), get_graph_adjacence_matrix() .. note:: These functions allow to collect all the graph's informations. """ return [i for i in self._internal_to_original_node_ids[graph_id].values()] def get_node_cost(self, node_label_1, node_label_2): return self._ged_data.node_cost(node_label_1, node_label_2) def get_node_rel_cost(self, node_label_1, node_label_2): """ /*! * @brief Returns node relabeling cost. * @param[in] node_label_1 First node label. * @param[in] node_label_2 Second node label. * @return Node relabeling cost for the given node labels. */ """ if isinstance(node_label_1, dict): node_label_1 = tuple(sorted(node_label_1.items(), key=lambda kv: kv[0])) if isinstance(node_label_2, dict): node_label_2 = tuple(sorted(node_label_2.items(), key=lambda kv: kv[0])) return self._ged_data._edit_cost.node_rel_cost_fun(node_label_1, node_label_2) # @todo: may need to use node_cost() instead (or change node_cost() and modify ged_method for pre-defined cost matrices.) def get_node_del_cost(self, node_label): """ /*! * @brief Returns node deletion cost. * @param[in] node_label Node label. * @return Cost of deleting node with given label. */ """ if isinstance(node_label, dict): node_label = tuple(sorted(node_label.items(), key=lambda kv: kv[0])) return self._ged_data._edit_cost.node_del_cost_fun(node_label) def get_node_ins_cost(self, node_label): """ /*! * @brief Returns node insertion cost. * @param[in] node_label Node label. * @return Cost of inserting node with given label. */ """ if isinstance(node_label, dict): node_label = tuple(sorted(node_label.items(), key=lambda kv: kv[0])) return self._ged_data._edit_cost.node_ins_cost_fun(node_label) def get_edge_cost(self, edge_label_1, edge_label_2): return self._ged_data.edge_cost(edge_label_1, edge_label_2) def get_edge_rel_cost(self, edge_label_1, edge_label_2): """ /*! * @brief Returns edge relabeling cost. * @param[in] edge_label_1 First edge label. * @param[in] edge_label_2 Second edge label. * @return Edge relabeling cost for the given edge labels. */ """ if isinstance(edge_label_1, dict): edge_label_1 = tuple(sorted(edge_label_1.items(), key=lambda kv: kv[0])) if isinstance(edge_label_2, dict): edge_label_2 = tuple(sorted(edge_label_2.items(), key=lambda kv: kv[0])) return self._ged_data._edit_cost.edge_rel_cost_fun(edge_label_1, edge_label_2) def get_edge_del_cost(self, edge_label): """ /*! * @brief Returns edge deletion cost. * @param[in] edge_label Edge label. * @return Cost of deleting edge with given label. */ """ if isinstance(edge_label, dict): edge_label = tuple(sorted(edge_label.items(), key=lambda kv: kv[0])) return self._ged_data._edit_cost.edge_del_cost_fun(edge_label) def get_edge_ins_cost(self, edge_label): """ /*! * @brief Returns edge insertion cost. * @param[in] edge_label Edge label. * @return Cost of inserting edge with given label. */ """ if isinstance(edge_label, dict): edge_label = tuple(sorted(edge_label.items(), key=lambda kv: kv[0])) return self._ged_data._edit_cost.edge_ins_cost_fun(edge_label) def get_all_graph_ids(self): return [i for i in range(0, self._ged_data._num_graphs_without_shuffled_copies)]