grakel.WeisfeilerLehman¶
-
class
grakel.WeisfeilerLehman(n_jobs=None, verbose=False, normalize=False, n_iter=5, base_graph_kernel=<class 'grakel.kernels.vertex_histogram.VertexHistogram'>)[source][source]¶ Compute the Weisfeiler Lehman Kernel.
See [SSVL+11].
- Parameters
- n_iterint, default=5
The number of iterations.
- base_graph_kernelgrakel.kernels.Kernel or tuple, default=None
If tuple it must consist of a valid kernel object and a dictionary of parameters. General parameters concerning normalization, concurrency, .. will be ignored, and the ones of given on __init__ will be passed in case it is needed. Default base_graph_kernel is VertexHistogram.
- Attributes
- Xdict
Holds a dictionary of fitted subkernel modules for all levels.
- _nxnumber
Holds the number of inputs.
- _n_iterint
Holds the number, of iterations.
- _base_graph_kernelfunction
A void function that initializes a base kernel object.
- _inv_labelsdict
An inverse dictionary, used for relabeling on each iteration.
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 input for weisfeiler lehman.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a weisfeiler_lehman 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 input for weisfeiler lehman.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
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__init__(self, n_jobs=None, verbose=False, normalize=False, n_iter=5, base_graph_kernel=<class 'grakel.kernels.vertex_histogram.VertexHistogram'>)[source][source]¶ Initialise a weisfeiler_lehman kernel.
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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.
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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.
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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). If None the kernel matrix is calculated upon fit data. The test samples.
- 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
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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.
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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.
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parse_input(self, X)[source][source]¶ Parse input for weisfeiler lehman.
- 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
- base_graph_kernelobject
- Returns base_graph_kernel.
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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
