grakel.SubgraphMatching¶
-
class
grakel.SubgraphMatching(n_jobs=None, verbose=False, normalize=False, k=5, kv=<function _dirac>, ke=<function _dirac>, lw='uniform')[source][source]¶ Calculate the subgraph matching kernel.
See [KM12].
- Parameters
- kint, default=5
The upper bound for the maximum size of subgraphs.
- lwstr, valid_values={“uniform”, “increasing”, “decreasing”, “strong_decreasing”},
- default=”uniform” | iterable, size=k+1,
- | callable, num_of_arguments=1, argument_type=int
The lambda weights applied to the clique sizes.
- kvfunction (vertex_label, `vertex_label, -> number), or None
- default=:math:`k_{v}^{default}(l(a), l(b))= delta(l(a), l(b))`
The kernel function between two vertex_labels. If no function is provided, this is equivalent with not taking into account node labels.
- kefunction (edge_label, edge_label -> number),
- default=:math:`k_{e}^{default}(l(e), l(e’))= delta(l(e), l(e’))`
The kernel function between two edge_labels. If no function is provided, this is equivalent with not taking into account edge labels.
- Attributes
- lambdas_np.array, shape=(1, k+1)
All the lambdas corresponding to all the valid sizes of subgraphs.
Methods
diagonal(self)Calculate the kernel matrix diagonal of the fit/transformed data.
fit(self, X[, y])Fit a dataset, for a transformer.
fit_transform(self, X)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 the subgraph_matching kernel.
parse_input(self, X)Parse and create features for the subgraph_matching kernel.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a subgraph_matching kernel.
- Attributes
- X
Methods
diagonal(self)Calculate the kernel matrix diagonal of the fit/transformed data.
fit(self, X[, y])Fit a dataset, for a transformer.
fit_transform(self, X)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 the subgraph_matching kernel.
parse_input(self, X)Parse and create features for the subgraph_matching 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, verbose=False, normalize=False, k=5, kv=<function _dirac at 0x7f183e49f7b8>, ke=<function _dirac at 0x7f183e49f7b8>, lw='uniform')[source][source]¶ Initialise a subgraph_matching kernel.
-
diagonal(self)[source]¶ Calculate the kernel matrix diagonal of the fit/transformed data.
- Parameters
- None.
- Returns
- X_diagnp.array
The diagonal of the kernel matrix between the fitted data. This consists of each element calculated with itself.
- Y_diagnp.array
The diagonal of the kernel matrix, of the transform. This consists of each element calculated 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)[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). If None the kernel matrix is calculated upon fit data. The test samples.
- yNone
There is no need of a target in a transformer, yet the pipeline API requires this parameter.
- 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][source]¶ Calculate the subgraph_matching kernel.
See [KM12].
- Parameters
- x, ytuples
Vertex-set, edge-dictionary, node-label-dictionary, edge-labels-dictionary tuple.
- Returns
- kernelnumber
The kernel value.
-
parse_input(self, X)[source][source]¶ Parse and create features for the subgraph_matching 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
- outlist
The extracted adjacency matrices for any given input.
-
transform(self, X)[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