grakel.ShortestPath¶
- class grakel.ShortestPath(n_jobs=None, normalize=False, verbose=False, with_labels=True, algorithm_type='auto')[source][source]¶
The shortest path kernel class.
See [BK05].
- Parameters
- algorithm_typestr, default={“dijkstra”, “floyd_warshall”, “auto”}
Apply the dijkstra or floyd_warshall algorithm for calculating shortest path, or chose automatically (“auto”) based on the current graph format (“auto”).
- with_labelsbool, default=True, case_of_existence=(as_attributes==True)
Calculate shortest path using graph labels.
- Attributes
- Xdict
A dictionary of pairs between each input graph and a bins where the sampled graphlets have fallen.
- _with_labelsbool
Defines if the shortest path kernel considers also labels.
- _enumdict
A dictionary of graph bins holding pynauty objects
- _ltstr
A label type needed for build shortest path function.
- _lhashstr
A function for hashing labels, shortest paths.
- _nxint
Holds the number of sampled X graphs.
- _nyint
Holds the number of sampled Y graphs.
- _X_diagnp.array, shape=(_nx, 1)
Holds the diagonal of X kernel matrix in a numpy array, if calculated (fit_transform).
- _phi_Xnp.array, shape=(_nx, len(_graph_bins))
Holds the features of X in a numpy array, if calculated. (fit_transform).
Methods
diagonal()Calculate the kernel matrix diagonal for fitted data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X[, y])Fit and transform, on the same dataset.
get_params([deep])Get parameters for this estimator.
initialize()Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)Calculate a pairwise kernel between two elements.
parse_input(X)Parse and create features for “shortest path” kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
Initialize a shortest_path kernel.
- Attributes
- X
Methods
diagonal()Calculate the kernel matrix diagonal for fitted data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X[, y])Fit and transform, on the same dataset.
get_params([deep])Get parameters for this estimator.
initialize()Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)Calculate a pairwise kernel between two elements.
parse_input(X)Parse and create features for “shortest path” kernel.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
