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(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)Lovasz theta kernel as proposed in [JJDB14].
parse_input(self, X)Parse and create features for lovasz_theta kernel.
set_params(self, \*\*params)Call the parent method.
transform(self, X)Calculate the kernel matrix, between given and fitted dataset.
Initialise a lovasz_theta 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)Lovasz theta kernel as proposed in [JJDB14].
parse_input(self, X)Parse and create features for lovasz_theta 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, random_state=None, n_samples=50, subsets_size_range=(2, 8), max_dim=None, base_kernel=None)[source][source]¶ Initialise a lovasz_theta 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]¶ Lovasz theta kernel as proposed in [JJDB14].
- Parameters
- x, ydict
Subgraph samples metric dictionaries for all levels.
- Returns
- kernelnumber
The kernel value.
-
parse_input(self, X)[source][source]¶ Parse and create features for lovasz_theta 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 lovasz metrics for the 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