def __init__(self,
resolution: float = 1,
modularity: str = 'dugue',
tol_optimization: float = 1e-3,
tol_aggregation: float = 1e-3,
n_aggregations: int = -1,
shuffle_nodes: bool = False,
sort_clusters: bool = True,
return_membership: bool = True,
return_aggregate: bool = True,
random_state: Optional[Union[np.random.RandomState,
int]] = None,
verbose: bool = False):
super(Louvain, self).__init__(sort_clusters=sort_clusters,
return_membership=return_membership,
return_aggregate=return_aggregate)
VerboseMixin.__init__(self, verbose)
self.resolution = np.float32(resolution)
self.modularity = modularity
self.tol = np.float32(tol_optimization)
self.tol_aggregation = tol_aggregation
self.n_aggregations = n_aggregations
self.shuffle_nodes = shuffle_nodes
self.random_state = check_random_state(random_state)
def __init__(self,
adjacency: sparse.csr_matrix,
damping_factor: float = 0.85,
personalization=None,
fb_mode: bool = False,
verbose: bool = False):
VerboseMixin.__init__(self, verbose)
n1, n2 = adjacency.shape
restart_prob: np.ndarray = restart_probability(n1, personalization)
if fb_mode:
restart_prob = np.hstack((restart_prob, np.zeros(n2)))
adjacency = bipartite2undirected(adjacency)
LinearOperator.__init__(self, shape=adjacency.shape, dtype=float)
n = adjacency.shape[0]
out_degrees = adjacency.dot(np.ones(n))
damping_matrix = damping_factor * sparse.eye(n, format='csr')
if fb_mode:
damping_matrix.data[n1:] = 1
self.a = (damping_matrix.dot(transition_matrix(adjacency))).T.tocsr()
self.b = (np.ones(n) -
damping_factor * out_degrees.astype(bool)) * restart_prob
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,
verbose: bool = False,
n_iter: int = 0,
n_jobs: Optional[int] = None):
super(MultiDiff, self).__init__()
VerboseMixin.__init__(self, verbose)
self.verbose = verbose
self.n_iter = n_iter
self.n_jobs = check_n_jobs(n_jobs)
def __init__(self,
n_iter: int = 10,
damping_factor: Optional[float] = None,
verbose: bool = False):
super(Dirichlet, self).__init__()
VerboseMixin.__init__(self, verbose)
self.n_iter = n_iter
if damping_factor is None:
damping_factor = 1.
check_is_proba(damping_factor, 'Damping factor')
self.damping_factor = damping_factor
def __init__(self, engine: str = 'default', algorithm: Union[str, Optimizer] = 'default', resolution: float = 1,
tol: float = 1e-3, agg_tol: float = 1e-3, max_agg_iter: int = -1, shuffle_nodes: bool = False,
sorted_cluster: bool = True, random_state: Optional[Union[np.random.RandomState, int]] = None,
verbose: bool = False):
super(Louvain, self).__init__()
VerboseMixin.__init__(self, verbose)
self.random_state = check_random_state(random_state)
if algorithm == 'default':
self.algorithm = GreedyModularity(resolution, tol, engine=check_engine(engine))
elif isinstance(algorithm, Optimizer):
self.algorithm = algorithm
else:
raise TypeError('Algorithm must be \'auto\' or a valid algorithm.')
if type(max_agg_iter) != int:
raise TypeError('The maximum number of iterations must be an integer.')
self.agg_tol = agg_tol
self.max_agg_iter = max_agg_iter
self.shuffle_nodes = shuffle_nodes
self.sorted_cluster = sorted_cluster
self.iteration_count_ = None
self.aggregate_graph_ = None
def test_prints(self):
verbose = VerboseMixin(verbose=True)
verbose.log.print('There are', 4, 'seasons in a year')
self.assertEqual(str(verbose.log), 'There are 4 seasons in a year\n')
def __init__(self, n_iter: int = 10, verbose: bool = False):
super(Diffusion, self).__init__()
VerboseMixin.__init__(self, verbose)
self.n_iter = n_iter
