gklearn.utils.utils¶

class SpecialLabel[source]¶

Bases: enum.Enum

can be used to define special labels.

DUMMY = 1¶

compute_distance_matrix(gram_matrix)[source]¶

compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save='', irrelevant_labels=None, edge_required=False)[source]¶

direct_product(G1, G2, node_label, edge_label)[source]¶

Return the direct/tensor product of directed graphs G1 and G2.

G1, G2 : NetworkX graph: The original graphs.
node_label : string: node attribute used as label. The default node label is ‘atom’.
edge_label : string: edge attribute used as label. The default edge label is ‘bond_type’.

gt : NetworkX graph: The direct product graph of G1 and G2.

This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.

[1]	Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003.

direct_product_graph(G1, G2, node_labels, edge_labels)[source]¶

Return the direct/tensor product of directed graphs G1 and G2.

G1, G2 : NetworkX graph: The original graphs.
node_labels : list: A list of node attributes used as labels.
edge_labels : list: A list of edge attributes used as labels.

gt : NetworkX graph: The direct product graph of G1 and G2.

This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.

[1]	Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003.

dummy_edge()[source]¶

/*!

@brief Returns a dummy edge.
@return ID of dummy edge.

*/

dummy_node()[source]¶

/*!

@brief Returns a dummy node.
@return ID of dummy node.

*/

find_all_paths(G, length, is_directed)[source]¶

Find all paths with a certain length in a graph. A recursive depth first search is applied.

G : NetworkX graphs: The graph in which paths are searched.
length : integer: The length of paths.

path : list of list: List of paths retrieved, where each path is represented by a list of nodes.

find_paths(G, source_node, length)[source]¶

Find all paths with a certain length those start from a source node. A recursive depth first search is applied.

G : NetworkX graphs: The graph in which paths are searched.
source_node : integer: The number of the node from where all paths start.
length : integer: The length of paths.

path : list of list: List of paths retrieved, where each path is represented by a list of nodes.

floydTransformation(G, edge_weight=None)[source]¶

Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.

G : NetworkX graph: The graph to be tramsformed.
edge_weight : string: edge attribute corresponding to the edge weight. The default edge weight is bond_type.

S : NetworkX graph: The shortest-paths graph corresponding to G.

[1]	Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.

getSPGraph(G, edge_weight=None)[source]¶

Transform graph G to its corresponding shortest-paths graph.

G : NetworkX graph: The graph to be tramsformed.
edge_weight : string: edge attribute corresponding to the edge weight.

S : NetworkX graph: The shortest-paths graph corresponding to G.

For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.

[1]	Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.

getSPLengths(G1)[source]¶

get_edge_labels(Gn, edge_label)[source]¶: Get edge labels of dataset Gn.

get_graph_kernel_by_name(name, node_labels=None, edge_labels=None, node_attrs=None, edge_attrs=None, ds_infos=None, kernel_options={})[source]¶

get_mlti_dim_edge_attrs(G, attr_names)[source]¶

get_mlti_dim_node_attrs(G, attr_names)[source]¶

get_node_labels(Gn, node_label)[source]¶: Get node labels of dataset Gn.

get_shortest_paths(G, weight, directed)[source]¶

Get all shortest paths of a graph.

G : NetworkX graphs: The graphs whose paths are calculated.
weight : string/None: edge attribute used as weight to calculate the shortest path.
directed: boolean: Whether graph is directed.

sp : list of list: List of shortest paths of the graph, where each path is represented by a list of nodes.

graph_deepcopy(G)[source]¶

Deep copy a graph, including deep copy of all nodes, edges and attributes of the graph, nodes and edges.

It is the same as the NetworkX function graph.copy(), as far as I know.

graph_isIdentical(G1, G2)[source]¶

Check if two graphs are identical, including: same nodes, edges, node labels/attributes, edge labels/attributes.

The type of graphs has to be the same.
Global/Graph attributes are neglected as they may contain names for graphs.

normalize_gram_matrix(gram_matrix)[source]¶

undefined_node()[source]¶

/*!

@brief Returns an undefined node.
@return ID of undefined node.

*/

untotterTransformation(G, node_label, edge_label)[source]¶

Transform graph G according to Mahé et al.’s work to filter out tottering patterns of marginalized kernel and tree pattern kernel.

G : NetworkX graph: The graph to be tramsformed.
node_label : string: node attribute used as label. The default node label is ‘atom’.
edge_label : string: edge attribute used as label. The default edge label is ‘bond_type’.

gt : NetworkX graph: The transformed graph corresponding to G.

[1]	Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.