grakel.MultiscaleLaplacianFast¶
-
class
grakel.MultiscaleLaplacianFast(n_jobs=None, normalize=False, verbose=False, random_state=None, L=3, P=10, gamma=0.01, heta=0.01, n_samples=50)[source][source]¶ Laplacian Graph Kernel as proposed in [KP16].
- Parameters
- random_stateRandomState or int, default=None
A random number generator instance or an int to initialize a RandomState as a seed.
- Lint, default=3
The number of neighborhoods.
- gammaReal, default=0.01
A smoothing parameter of float value.
- hetafloat, default=0.01
A smoothing parameter of float value.
- Pint, default=10
Restrict the maximum number of eigenvalues, taken on eigenvalue decomposition.
- n_samplesint, default=50
The number of vertex samples.
- Attributes
- random_state_RandomState
A RandomState object handling all randomness of the class.
- _data_leveldict
A dictionary containing the feature basis information needed for each level calculation on transform.
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)FLG calculation for the fast multiscale laplacian.
parse_input(self, X)Fast ML Graph Kernel.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a multiscale_laplacian 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)FLG calculation for the fast multiscale laplacian.
parse_input(self, X)Fast ML Graph Kernel.
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, normalize=False, verbose=False, random_state=None, L=3, P=10, gamma=0.01, heta=0.01, n_samples=50)[source][source]¶ Initialise a multiscale_laplacian kernel.
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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.
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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)[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
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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][source]¶ FLG calculation for the fast multiscale laplacian.
- Parameters
- x, ytuple
An np.array of inverse and the log determinant of S (for the calculation of S matrices see the algorithm 1
of the supplement material in cite:kondor2016multiscale).
- Returns
- kernelnumber
The FLG core kernel value.
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parse_input(self, X)[source][source]¶ Fast ML Graph Kernel.
See supplementary material [KP16], algorithm 1.
- 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
A list of tuples with S matrices inverses and their 4th-root determinants.
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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