def __init__(self,
algorithm: BaseRanking,
n_jobs: Optional[int] = None,
verbose: bool = False):
super(RankClassifier, self).__init__()
VerboseMixin.__init__(self, verbose)
self.algorithm = algorithm
self.n_jobs = check_n_jobs(n_jobs)
self.verbose = verbose
def __init__(self, embedding_method: BaseEmbedding = GSVD(10), n_neighbors: int = 5,
factor_distance: float = 2, leaf_size: int = 16, p: float = 2, tol_nn: float = 0.01,
n_jobs: Optional[int] = None):
super(KNN, self).__init__()
self.embedding_method = embedding_method
self.n_neighbors = n_neighbors
self.factor_distance = factor_distance
self.leaf_size = leaf_size
self.p = p
self.tol_nn = tol_nn
self.n_jobs = check_n_jobs(n_jobs)
if self.n_jobs is None:
self.n_jobs = -1
self.bipartite = None
def distance(adjacency: sparse.csr_matrix,
sources: Optional[Union[int, Iterable]] = None,
method: str = 'D',
return_predecessors: bool = False,
unweighted: bool = False,
n_jobs: Optional[int] = None):
"""Compute distances between nodes.
* Graphs
* Digraphs
Based on SciPy (scipy.sparse.csgraph.shortest_path)
Parameters
----------
adjacency :
The adjacency matrix of the graph
sources :
If specified, only compute the paths for the points at the given indices. Will not work with ``method =='FW'``.
method :
The method to be used.
* ``'D'`` (Dijkstra),
* ``'BF'`` (Bellman-Ford),
* ``'J'`` (Johnson).
return_predecessors :
If ``True``, the size predecessor matrix is returned
unweighted :
If ``True``, the weights of the edges are ignored
n_jobs :
If an integer value is given, denotes the number of workers to use (-1 means the maximum number will be used).
If ``None``, no parallel computations are made.
Returns
-------
dist_matrix : np.ndarray
The matrix of distances between graph nodes. ``dist_matrix[i,j]`` gives the shortest
distance from point ``i`` to point ``j`` along the graph.
If no path exists between nodes ``i`` and ``j``, then ``dist_matrix[i, j] = np.inf``.
predecessors : np.ndarray, optional
Returned only if ``return_predecessors == True``. The matrix of predecessors, which can be used to reconstruct
the shortest paths. Row i of the predecessor matrix contains information on the shortest paths from point ``i``:
each entry ``predecessors[i, j]`` gives the index of the previous node in the path from point ``i`` to point
``j``. If no path exists between nodes ``i`` and ``j``, then ``predecessors[i, j] = -9999``.
Examples
--------
>>> from sknetwork.data import cyclic_digraph
>>> adjacency = cyclic_digraph(3)
>>> distance(adjacency, sources=0)
array([0., 1., 2.])
>>> distance(adjacency, sources=0, return_predecessors=True)
(array([0., 1., 2.]), array([-9999, 0, 1]))
"""
n_jobs = check_n_jobs(n_jobs)
if method == 'FW' and n_jobs != 1:
raise ValueError(
'The Floyd-Warshall algorithm cannot be used with parallel computations.'
)
if sources is None:
sources = np.arange(adjacency.shape[0])
elif np.issubdtype(type(sources), np.integer):
sources = np.array([sources])
n = len(sources)
directed = not is_symmetric(adjacency)
local_function = partial(sparse.csgraph.shortest_path, adjacency, method,
directed, return_predecessors, unweighted, False)
if n_jobs == 1 or n == 1:
res = sparse.csgraph.shortest_path(adjacency, method, directed,
return_predecessors, unweighted,
False, sources)
else:
with Pool(n_jobs) as pool:
res = np.array(pool.map(local_function, sources))
if return_predecessors:
if n == 1:
return res[0].ravel(), res[1].astype(int).ravel()
else:
return res[0], res[1].astype(int)
else:
if n == 1:
return res.ravel()
else:
return res
def shortest_path(adjacency: sparse.csr_matrix,
method: str = 'auto',
directed: bool = True,
return_predecessors: bool = False,
unweighted: bool = False,
overwrite: bool = False,
indices: Optional[np.ndarray] = None,
n_jobs: Optional[int] = None):
"""Compute the shortest paths in the graph.
* Graphs
* Digraphs
Based on SciPy (scipy.sparse.csgraph.shortest_path)
Parameters
----------
adjacency:
The adjacency matrix of the graph
method:
The method to be used. Must be ``'auto'`` (default), ``'FW'`` (Floyd-Warshall), ``'D'`` (Dijkstra),
``'BF'`` (Bellman-Ford) or ``'J'`` (Johnson).
directed:
Denotes if the graph is directed
return_predecessors:
If ``True``, the size predecessor matrix is returned
unweighted:
If ``True``, the weights of the edges are ignored
overwrite:
If ``True`` and ``method == 'FW'``, overwrites the given adjacency
indices:
If specified, only compute the paths for the points at the given indices. Will not work with ``method =='FW'``.
n_jobs:
If an integer value is given, denotes the number of workers to use (-1 means the maximum number will be used).
If ``None``, no parallel computations are made.
Returns
-------
dist_matrix: np.ndarray
The matrix of distances between graph nodes. ``dist_matrix[i,j]`` gives the shortest
distance from point ``i`` to point ``j`` along the graph.
predecessors: np.ndarray, optional
Returned only if ``return_predecessors == True``. The matrix of predecessors, which can be used to reconstruct
the shortest paths. Row i of the predecessor matrix contains information on the shortest paths from point ``i``:
each entry ``predecessors[i, j]`` gives the index of the previous node in the path from point ``i`` to point
``j``. If no path exists between point ``i`` and ``j``, then ``predecessors[i, j] = -9999``.
"""
n_jobs = check_n_jobs(n_jobs)
if method == 'FW':
raise ValueError(
'The Floyd-Warshall algorithm cannot be used with parallel computations.'
)
if indices is None:
indices = np.arange(adjacency.shape[0])
local_function = partial(sparse.csgraph.shortest_path, adjacency, method,
directed, return_predecessors, unweighted,
overwrite)
with Pool(n_jobs) as pool:
res = pool.map(local_function, indices)
if return_predecessors:
paths = [el[0] for el in res]
predecessors = [el[1] for el in res]
return np.vstack(paths), np.vstack(predecessors)
else:
return np.vstack(res)
