def fit_from_networkx(self,
graph,
attr,
spatially_extensive_attr,
threshold,
max_it=10,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix:.
Parameters
----------
graph : `networkx.Graph`
attr : str, list or dict
If the clustering criteria are present in the networkx.Graph
`graph` as node attributes, then they can be specified as a string
(for one criterion) or as a list of strings (for multiple
criteria).
Alternatively, a dict can be used with each key being a node of the
networkx.Graph `graph` and each value being the corresponding
clustering criterion (a scalar (e.g. `float` or `int`) or a
:class:`numpy.ndarray`).
If there are no clustering criteria present in the networkx.Graph
`graph` as node attributes, then a dictionary must be used for this
argument. Refer to the corresponding argument in
:meth:`fit_from_dict` for more details about the expected dict.
spatially_extensive_attr : str, list or dict
If the spatially extensive attribute is present in the
networkx.Graph `graph` as node attributes, then they can be
specified as a string (for one attribute) or as a list of
strings (for multiple attributes).
Alternatively, a dict can be used with each key being a node of the
networkx.Graph `graph` and each value being the corresponding
spatially extensive attribute (a scalar (e.g. `float` or `int`) or
a :class:`numpy.ndarray`).
If there are no spatially extensive attributes present in the
networkx.Graph `graph` as node attributes, then a dictionary must
be used for this argument. Refer to the corresponding argument in
:meth:`fit_from_dict` for more details about the expected dict.
threshold : numbers.Real or :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
max_it : int, default: 10
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
adj = nx.to_scipy_sparse_matrix(graph)
attr = array_from_graph_or_dict(graph, attr)
sp_ext_attr = array_from_graph_or_dict(graph, spatially_extensive_attr)
self.fit_from_scipy_sparse_matrix(adj,
attr,
sp_ext_attr,
threshold=threshold,
max_it=max_it,
objective_func=objective_func)
def fit_from_networkx(self,
graph,
attr,
spatially_extensive_attr,
threshold,
solver="cbc",
metric="euclidean"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
graph : `networkx.Graph`
attr : str, list or dict
If the clustering criteria are present in the networkx.Graph
`graph` as node attributes, then they can be specified as a string
(for one criterion) or as a list of strings (for multiple
criteria).
Alternatively, a dict can be used with each key being a node of the
networkx.Graph `graph` and each value being the corresponding
clustering criterion (a scalar (e.g. `float` or `int`) or a
:class:`numpy.ndarray`).
If there are no clustering criteria present in the networkx.Graph
`graph` as node attributes, then a dictionary must be used for this
argument. Refer to the corresponding argument in
:meth:`fit_from_dict` for more details about the expected dict.
spatially_extensive_attr : str, list or dict
If the spatially extensive attribute is present in the
networkx.Graph `graph` as node attributes, then they can be
specified as a string (for one attribute) or as a list of
strings (for multiple attributes).
Alternatively, a dict can be used with each key being a node of the
networkx.Graph `graph` and each value being the corresponding
spatially extensive attribute (a scalar (e.g. `float` or `int`) or
a :class:`numpy.ndarray`).
If there are no spatially extensive attributes present in the
networkx.Graph `graph` as node attributes, then a dictionary must
be used for this argument. Refer to the corresponding argument in
:meth:`fit_from_dict` for more details about the expected dict.
threshold : numbers.Real or :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
solver : {"cbc", "cplex", "glpk", "gurobi"}, default: "cbc"
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
metric : str or function, default: "euclidean"
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
adj = nx.to_scipy_sparse_matrix(graph)
attr = array_from_graph_or_dict(graph, attr)
sp_ext_attr = array_from_graph_or_dict(graph, spatially_extensive_attr)
self.fit_from_scipy_sparse_matrix(adj,
attr,
sp_ext_attr,
threshold=threshold,
solver=solver,
metric=metric)
def fit_from_networkx(self,
graph,
attr,
n_regions,
initial_labels=None,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
graph : `networkx.Graph`
Graph representing the contiguity relation.
attr : str, list or dict
If the clustering criteria are present in the networkx.Graph
`graph` as node attributes, then they can be specified as a string
(for one criterion) or as a list of strings (for multiple
criteria).
Alternatively, a dict can be used with each key being a node of the
networkx.Graph `graph` and each value being the corresponding
clustering criterion (a scalar (e.g. `float` or `int`) or a
:class:`numpy.ndarray`).
If there are no clustering criteria present in the networkx.Graph
`graph` as node attributes, then a dictionary must be used for this
argument. Refer to the corresponding argument in
:meth:`fit_from_dict` for more details about the expected dict.
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
initial_labels : str or dict or None, default: None
If str, then the string names the graph's attribute holding the
information about the initial clustering.
If dict, then each key is a node and each value is the region the
key area is assigned to at the beginning of the algorithm.
If None, then a random initial clustering will be generated.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
adj = nx.to_scipy_sparse_matrix(graph)
attr = array_from_graph_or_dict(graph, attr)
if initial_labels is not None:
initial_labels = array_from_graph_or_dict(graph, initial_labels)
self.fit_from_scipy_sparse_matrix(
adj,
attr,
n_regions,
initial_labels,
objective_func=objective_func)
def fit_from_networkx(self,
graph,
attr,
n_regions,
initial_labels=None,
cooling_factor=0.85,
objective_func=ObjectiveFunctionPairwise()):
"""
Parameters
----------
graph : `networkx.Graph`
Refer to the corresponding argument in
:meth:`AZP.fit_from_networkx`.
attr : str, list or dict
Refer to the corresponding argument in
:meth:`AZP.fit_from_networkx`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
initial_labels : str or dict or None, default: None
Refer to the corresponding argument in
:meth:`AZP.fit_from_networkx`.
cooling_factor : float, default: 0.85
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
adj = nx.to_scipy_sparse_matrix(graph)
attr = array_from_graph_or_dict(graph, attr)
if initial_labels is not None:
initial_labels = array_from_graph_or_dict(graph, initial_labels)
self.fit_from_scipy_sparse_matrix(
adj,
attr,
n_regions,
initial_labels,
cooling_factor=cooling_factor,
objective_func=objective_func)
def fit_from_networkx(self,
graph,
attr,
n_regions,
method="flow",
solver="cbc",
metric="euclidean"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix:.
Parameters
----------
graph : `networkx.Graph`
Graph representing the areas' contiguity relation.
attr : str, list or dict
If the clustering criteria are present in the networkx.Graph
`graph` as node attributes, then they can be specified as a string
(for one criterion) or as a list of strings (for multiple
criteria).
Alternatively, a dict can be used with each key being a node of the
networkx.Graph `graph` and each value being the corresponding
clustering criterion (a scalar (e.g. `float` or `int`) or a
:class:`numpy.ndarray`).
If there are no clustering criteria present in the networkx.Graph
`graph` as node attributes, then a dictionary must be used for this
argument. Refer to the corresponding argument in
:meth:`fit_from_dict` for more details about the expected dict.
n_regions : int
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
method : str
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
solver : str
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
metric : str or function, default: "euclidean"
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
adj = nx.to_scipy_sparse_matrix(graph)
attr = array_from_graph_or_dict(graph, attr)
self.fit_from_scipy_sparse_matrix(adj,
attr,
n_regions,
method=method,
solver=solver,
metric=metric)
