def fit(
self, biadjacency: Union[sparse.csr_matrix,
np.ndarray]) -> 'BiLouvainHierarchy':
"""Applies Louvain hierarchical clustering to
:math:`A = \\begin{bmatrix} 0 & B \\\\ B^T & 0 \\end{bmatrix}`
where :math:`B` is the biadjacency matrix of the graphs.
Parameters
----------
biadjacency:
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiLouvainHierarchy`
"""
biadjacency = check_format(biadjacency)
adjacency = bipartite2undirected(biadjacency)
self.dendrogram_ = self.louvain_hierarchy.fit_transform(adjacency)
self._split_vars(biadjacency.shape)
return self
def fit(self,
biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Union[np.ndarray, dict],
seeds_col: Union[np.ndarray, dict,
None] = None) -> 'RankClassifier':
"""Compute labels.
Parameters
----------
biadjacency :
Biadjacency matrix of the graph.
seeds_row :
Seed rows (labels as dictionary or array; negative values ignored).
seeds_col :
Seed columns (optional). Same format.
Returns
-------
self: :class:`BiPageRankClassifier`
"""
n_row, n_col = biadjacency.shape
labels = stack_seeds(n_row, n_col, seeds_row, seeds_col)
adjacency = bipartite2undirected(biadjacency)
RankClassifier.fit(self, adjacency, labels)
self.labels_row_ = self.labels_[:n_row]
self.labels_col_ = self.labels_[n_row:]
self.labels_ = self.labels_row_
self.membership_row_ = self.membership_[:n_row]
self.membership_col_ = self.membership_[n_row:]
self.membership_ = self.membership_row_
return self
def test_is_bipartite(self):
biadjacency = star_wars(metadata=False)
adjacency = bipartite2undirected(biadjacency)
self.assertTrue(is_bipartite(adjacency))
bipartite, pred = is_bipartite(adjacency, return_biadjacency=True)
self.assertEqual(bipartite, True)
self.assertEqual(np.all(biadjacency.data == pred.data), True)
adjacency = sparse.identity(2, format='csr')
bipartite, biadjacency = is_bipartite(adjacency,
return_biadjacency=True)
self.assertEqual(bipartite, False)
self.assertIsNone(biadjacency)
adjacency = directed2undirected(cyclic_digraph(3))
bipartite, biadjacency = is_bipartite(adjacency,
return_biadjacency=True)
self.assertEqual(bipartite, False)
self.assertIsNone(biadjacency)
with self.assertRaises(ValueError):
is_bipartite(cyclic_digraph(3))
self.assertTrue(~is_bipartite(sparse.eye(3)))
def fit(
self, biadjacency: Union[sparse.csr_matrix, np.ndarray]
) -> 'BiPropagationClustering':
"""Clustering.
Parameters
----------
biadjacency :
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiPropagationClustering`
"""
n_row, n_col = biadjacency.shape
biadjacency = check_format(biadjacency)
adjacency = bipartite2undirected(biadjacency)
propagation = PropagationClustering(self.n_iter, self.node_order,
self.weighted)
self.labels_ = propagation.fit_transform(adjacency)
self._split_vars(n_row)
self._secondary_outputs(biadjacency)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Optional[Union[dict, np.ndarray]] = None, seeds_col: Optional[Union[dict, np.ndarray]] = None) \
-> 'BiPageRank':
"""Fit algorithm to data.
Parameters
----------
biadjacency :
Biadjacency matrix.
seeds_row, seeds_col :
Parameter to be used for Personalized BiPageRank.
Restart distribution as vectors or dicts on rows, columns (node: weight).
If both seeds_row and seeds_col are ``None`` (default), the uniform distribution on rows is used.
Returns
-------
self: :class:`BiPageRank`
"""
biadjacency = check_format(biadjacency)
n_row, n_col = biadjacency.shape
adjacency = bipartite2undirected(biadjacency)
seeds = stack_seeds(n_row, n_col, seeds_row, seeds_col)
PageRank.fit(self, adjacency, seeds)
self._split_vars(n_row)
self.scores_row_ /= self.scores_row_.sum()
self.scores_col_ /= self.scores_col_.sum()
self.scores_ = self.scores_row_
return self
def fit(
self,
biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Union[np.ndarray, dict],
seeds_col: Optional[Union[np.ndarray,
dict]] = None) -> 'BiPropagation':
"""Node classification by k-nearest neighbors in the embedding space.
Parameters
----------
biadjacency :
Biadjacency matrix of the graph.
seeds_row :
Seed rows. Can be a dict {node: label} or an array where "-1" means no label.
seeds_col :
Seed columns (optional). Same format.
Returns
-------
self: :class:`BiPropagation`
"""
n_row, n_col = biadjacency.shape
biadjacency = check_format(biadjacency)
adjacency = bipartite2undirected(biadjacency)
seeds = stack_seeds(n_row, n_col, seeds_row, seeds_col).astype(int)
Propagation.fit(self, adjacency, seeds)
self._split_vars(n_row)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Optional[Union[dict, np.ndarray]] = None, seeds_col: Optional[Union[dict, np.ndarray]] = None) \
-> 'BiPageRank':
"""Fit algorithm to data.
Parameters
----------
biadjacency :
Biadjacency matrix.
seeds_row :
Seed rows, as a dict or a vector.
seeds_col :
Seed columns, as a dict or a vector.
If both seeds_row and seeds_col are ``None``, the uniform distribution is used.
Returns
-------
self: :class:`BiPageRank`
"""
biadjacency = check_format(biadjacency)
n_row, n_col = biadjacency.shape
adjacency = bipartite2undirected(biadjacency)
seeds = stack_seeds(n_row, n_col, seeds_row, seeds_col)
PageRank.fit(self, adjacency, seeds)
self._split_vars(n_row)
self.scores_row_ /= self.scores_row_.sum()
self.scores_col_ /= self.scores_col_.sum()
self.scores_ = self.scores_row_
return self
def fit(self,
biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Optional[Union[dict, np.ndarray]] = None,
seeds_col: Optional[Union[dict, np.ndarray]] = None,
init: Optional[float] = None) -> 'BiDirichlet':
"""Compute the solution to the Dirichlet problem (temperatures at equilibrium).
Parameters
----------
biadjacency :
Biadjacency matrix, shape (n_row, n_col).
seeds_row :
Temperatures of seed rows (dictionary or vector of size n_row). Negative temperatures ignored.
seeds_col :
Temperatures of seed columns (dictionary or vector of size n_col). Negative temperatures ignored.
init :
Temperature of non-seed nodes in initial state.
If ``None``, use the average temperature of seed nodes (default).
Returns
-------
self: :class:`BiDirichlet`
"""
biadjacency = check_format(biadjacency)
n_row, n_col = biadjacency.shape
seeds = stack_seeds(n_row, n_col, seeds_row, seeds_col)
adjacency = bipartite2undirected(biadjacency)
Dirichlet.fit(self, adjacency, seeds, init)
self._split_vars(n_row)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Optional[Union[dict, np.ndarray]] = None, seeds_col: Optional[Union[dict, np.ndarray]] = None,
initial_state: Optional = None) -> 'BiDiffusion':
"""Compute the diffusion (temperature at equilibrium).
Parameters
----------
biadjacency :
Biadjacency matrix, shape (n_row, n_col).
seeds_row :
Temperatures of row border nodes (dictionary or vector of size n_row). Negative temperatures ignored.
seeds_col :
Temperatures of column border nodes (dictionary or vector of size n_row). Negative temperatures ignored.
initial_state :
Initial state of temperatures.
Returns
-------
self: :class:`BiDiffusion`
"""
biadjacency = check_format(biadjacency)
n_row, n_col = biadjacency.shape
seeds = stack_seeds(n_row, n_col, seeds_row, seeds_col)
adjacency = bipartite2undirected(biadjacency)
Diffusion.fit(self, adjacency, seeds)
self._split_vars(n_row)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix, np.ndarray]) -> 'BiRandomProjection':
"""Compute the embedding.
Parameters
----------
biadjacency:
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiRandomProjection`
"""
biadjacency = check_format(biadjacency)
n_row, _ = biadjacency.shape
RandomProjection.fit(self, bipartite2undirected(biadjacency))
self._split_vars(n_row)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix, np.ndarray]) -> 'BiKatz':
"""Katz centrality.
Parameters
----------
biadjacency :
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiKatz`
"""
biadjacency = check_format(biadjacency)
n_row, n_col = biadjacency.shape
adjacency = bipartite2undirected(biadjacency)
Katz.fit(self, adjacency)
self._split_vars(n_row)
return self
def fit(
self, biadjacency: Union[sparse.csr_matrix, np.ndarray]
) -> 'BiHLouvainEmbedding':
"""Embedding of graphs from a clustering obtained with Louvain.
Parameters
----------
biadjacency:
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiLouvainNE`
"""
biadjacency = check_format(biadjacency)
n_row, _ = biadjacency.shape
HLouvainEmbedding.fit(self, bipartite2undirected(biadjacency))
self._split_vars(n_row)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix,
np.ndarray]) -> 'BiLouvain':
"""Apply the Louvain algorithm to the corresponding directed graph, with adjacency matrix:
:math:`A = \\begin{bmatrix} 0 & B \\\\ 0 & 0 \\end{bmatrix}`
where :math:`B` is the input (biadjacency matrix).
Parameters
----------
biadjacency:
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiLouvain`
"""
louvain = Louvain(resolution=self.resolution,
modularity=self.modularity,
tol_aggregation=self.tol_aggregation,
n_aggregations=self.n_aggregations,
shuffle_nodes=self.shuffle_nodes,
sort_clusters=self.sort_clusters,
return_membership=self.return_membership,
return_aggregate=False,
random_state=self.random_state,
verbose=self.log.verbose)
biadjacency = check_format(biadjacency)
n_row, _ = biadjacency.shape
if self.modularity == 'dugue':
adjacency = bipartite2directed(biadjacency)
else:
adjacency = bipartite2undirected(biadjacency)
louvain.fit(adjacency)
self.labels_ = louvain.labels_
self._split_vars(n_row)
self._secondary_outputs(biadjacency)
return self
def fit(self, biadjacency: Union[sparse.csr_matrix,
np.ndarray]) -> 'BiSpectral':
"""Spectral embedding of the bipartite graph considered as undirected, with adjacency matrix:
:math:`A = \\begin{bmatrix} 0 & B \\\\ B^T & 0 \\end{bmatrix}`
where :math:`B` is the biadjacency matrix (or the adjacency matrix of a directed graph).
Parameters
----------
biadjacency:
Biadjacency matrix of the graph.
Returns
-------
self: :class:`BiSpectral`
"""
biadjacency = check_format(biadjacency)
n_row, _ = biadjacency.shape
Spectral.fit(self, bipartite2undirected(biadjacency))
self._split_vars(n_row)
return self
def fit(self,
biadjacency: Union[sparse.csr_matrix, np.ndarray],
seeds_row: Optional[Union[dict, np.ndarray]] = None,
seeds_col: Optional[Union[dict, np.ndarray]] = None,
init: Optional[float] = None) -> 'BiDiffusion':
"""Compute the diffusion (temperatures at equilibrium).
Parameters
----------
biadjacency :
Biadjacency matrix, shape (n_row, n_col).
seeds_row :
Temperatures of seed rows in initial state (dictionary or vector of size n_row).
Negative temperatures ignored.
seeds_col :
Temperatures of seed columns in initial state (dictionary or vector of size n_col).
Negative temperatures ignored.
init :
Temperature of non-seed nodes in initial state.
If ``None``, use the average temperature of seed nodes (default).
Returns
-------
self: :class:`BiDiffusion`
"""
biadjacency = check_format(biadjacency)
n_row, n_col = biadjacency.shape
seeds = stack_seeds(n_row, n_col, seeds_row, seeds_col)
adjacency = bipartite2undirected(biadjacency)
Diffusion.fit(self, adjacency, seeds, init)
# average over 2 successive iterations because the graph is bipartite
diffusion = DirichletOperator(adjacency, self.damping_factor)
self.scores_ += diffusion.dot(self.scores_)
self.scores_ /= 2
self._split_vars(n_row)
return self
