grakel.LovaszTheta#
- class grakel.LovaszTheta(n_jobs=None, normalize=False, verbose=False, random_state=None, n_samples=50, subsets_size_range=(2, 8), max_dim=None, base_kernel=None)[source][source]#
Lovasz theta kernel as proposed in [JJDB14].
- Parameters:
- X,Yvalid-graph-format
The pair of graphs on which the kernel is applied.
- n_samplesint, default=50
The number of samples.
- subsets_size_rangetuple, len=2, default=(2,8)
(min, max) size of the vertex set of sampled subgraphs.
- random_stateRandomState or int, default=None
A random number generator instance or an int to initialize a RandomState as a seed.
- base_kernelfunction (np.1darray, np.1darray -> number), default=None
The applied metric between the lovasz_theta numbers of subgraphs. If None \(f(x,y) = x*y\)
- max_dimint, default=None
The maximum graph size that can appear both in fit or transform. When None, max_dim is calculated based on the size of the biggest graph on fit. This can lead to a crash in case a graph appears in transform with size bigger than in fit.
- Attributes:
- d_int,
The maximum matrix dimension of fit plus 1. Signifies the number of features assigned for lovasz labelling.
- random_state_RandomState
A RandomState object handling all randomness of the class.
Methods
diagonal()Calculate the kernel matrix diagonal of the fit/transformed data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X[, y])Fit and transform, on the same dataset.
get_metadata_routing()Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
initialize()Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)Lovasz theta kernel as proposed in [JJDB14].
parse_input(X)Parse and create features for lovasz_theta kernel.
set_output(*[, transform])Set output container.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a lovasz_theta kernel.
- Attributes:
- X
Methods
diagonal()Calculate the kernel matrix diagonal of the fit/transformed data.
fit(X[, y])Fit a dataset, for a transformer.
fit_transform(X[, y])Fit and transform, on the same dataset.
get_metadata_routing()Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
initialize()Initialize all transformer arguments, needing initialization.
pairwise_operation(x, y)Lovasz theta kernel as proposed in [JJDB14].
parse_input(X)Parse and create features for lovasz_theta kernel.
set_output(*[, transform])Set output container.
set_params(**params)Call the parent method.
transform(X)Calculate the kernel matrix, between given and fitted dataset.