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(self)Calculate the kernel matrix diagonal for fitted data.
fit(self, X[, y])Fit a dataset, for a transformer.
fit_transform(self, X[, y])Fit and transform, on the same dataset.
get_params(self[, deep])Get parameters for this estimator.
initialize(self)Initialize all transformer arguments, needing initialization.
pairwise_operation(self, x, y)Calculate a pairwise kernel between two elements.
parse_input(self, X)Parse and create features for “shortest path” kernel.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
Initialize a shortest_path kernel.
- Attributes
- X
Methods
diagonal(self)Calculate the kernel matrix diagonal for fitted data.
fit(self, X[, y])Fit a dataset, for a transformer.
fit_transform(self, X[, y])Fit and transform, on the same dataset.
get_params(self[, deep])Get parameters for this estimator.
initialize(self)Initialize all transformer arguments, needing initialization.
pairwise_operation(self, x, y)Calculate a pairwise kernel between two elements.
parse_input(self, X)Parse and create features for “shortest path” kernel.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
-
__init__(self, n_jobs=None, normalize=False, verbose=False, with_labels=True, algorithm_type='auto')[source][source]¶ Initialize a shortest_path kernel.
-
diagonal(self)[source][source]¶ Calculate the kernel matrix diagonal for fitted data.
A funtion called on transform on a seperate dataset to apply normalization on the exterior.
- Parameters
- None.
- Returns
- X_diagnp.array
The diagonal of the kernel matrix, of the fitted data. This consists of kernel calculation for each element with itself.
- Y_diagnp.array
The diagonal of the kernel matrix, of the transformed data. This consists of kernel calculation for each element with itself.
-
fit(self, X, y=None)[source]¶ Fit a dataset, for a transformer.
- Parameters
- Xiterable
Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that fitting the given graph format). The train samples.
- yNone
There is no need of a target in a transformer, yet the pipeline API requires this parameter.
- Returns
- selfobject
- Returns self.
-
fit_transform(self, X, y=None)[source][source]¶ Fit and transform, on the same dataset.
- Parameters
- Xiterable
Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that fitting the given graph format).
- yObject, default=None
Ignored argument, added for the pipeline.
- Returns
- Knumpy array, shape = [n_targets, n_input_graphs]
corresponding to the kernel matrix, a calculation between all pairs of graphs between target an features
-
get_params(self, deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
pairwise_operation(self, x, y)[source]¶ Calculate a pairwise kernel between two elements.
- Parameters
- x, yObject
Objects as occur from parse_input.
- Returns
- kernelnumber
The kernel value.
-
parse_input(self, X)[source][source]¶ Parse and create features for “shortest path” kernel.
- Parameters
- Xiterable
For the input to pass the test, we must have: Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that correspond to the given graph format). A valid input also consists of graph type objects.
- Returns
- sp_countsdict
A dictionary that for each vertex holds the counts of shortest path tuples.
-
transform(self, X)[source][source]¶ Calculate the kernel matrix, between given and fitted dataset.
- Parameters
- Xiterable
Each element must be an iterable with at most three features and at least one. The first that is obligatory is a valid graph structure (adjacency matrix or edge_dictionary) while the second is node_labels and the third edge_labels (that fitting the given graph format). If None the kernel matrix is calculated upon fit data. The test samples.
- Returns
- Knumpy array, shape = [n_targets, n_input_graphs]
corresponding to the kernel matrix, a calculation between all pairs of graphs between target an features
