def fit_from_dict(self,
neighbors_dict,
attr,
spatially_extensive_attr,
threshold,
solver="cbc",
metric="euclidean"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
neighbors_dict : dict
Each key represents an area and each value is an iterable of
neighbors of this area.
attr : dict
A dict with the same keys as `neighbors_dict` and values
representing the attributes for calculating h**o-/heterogeneity. A
value can be scalar (e.g. `float` or `int`) or a
:class:`numpy.ndarray`.
spatially_extensive_attr : dict
A dict with the same keys as `neighbors_dict` and values
representing the spatially extensive attribute (scalar or iterable
of scalars). In the max-p-regions problem each region's sum of
spatially extensive attributes must be greater than a specified
threshold. In case of iterables of scalars as dict-values all
elements of the iterable have to fulfill the condition.
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`.
"""
if not isinstance(neighbors_dict, dict):
raise ValueError("The neighbors_dict argument must be dict.")
not_same_dict_keys_msg = "The {} argument has to be of type dict " \
"with the same keys as neighbors_dict."
if not isinstance(attr, dict) or attr.keys() != neighbors_dict.keys():
raise ValueError(not_same_dict_keys_msg.format("attr"))
if not isinstance(spatially_extensive_attr, dict) or \
spatially_extensive_attr.keys() != neighbors_dict.keys():
raise ValueError(
not_same_dict_keys_msg.format(spatially_extensive_attr))
adj = scipy_sparse_matrix_from_dict(neighbors_dict)
attr_arr = array_from_dict_values(attr)
spat_ext_attr_arr = array_from_dict_values(spatially_extensive_attr)
self.fit_from_scipy_sparse_matrix(adj,
attr_arr,
spat_ext_attr_arr,
threshold=threshold,
solver=solver,
metric=metric)
def fit_from_dict(self,
neighbor_dict,
attr,
n_regions,
initial_labels=None,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
neighbor_dict : `dict`
Each key is an area and each value is an iterable of the key area's
neighbors.
attr : `dict`
Each key is an area and each value is the corresponding
clustering-relevant attribute.
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
initial_labels : `dict` or None, default: None
Each key represents an area. Each value represents the region, the
corresponding 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`.
"""
sorted_areas = sorted(neighbor_dict)
adj = scipy_sparse_matrix_from_dict(neighbor_dict)
attr_arr = array_from_dict_values(attr, sorted_areas)
if initial_labels is not None:
initial_labels = array_from_dict_values(
initial_labels, sorted_areas, flat_output=True, dtype=np.int32)
self.fit_from_scipy_sparse_matrix(
adj,
attr_arr,
n_regions,
initial_labels,
objective_func=objective_func)
def fit_from_dict(self,
neighbor_dict,
attr,
n_regions,
initial_labels=None,
cooling_factor=0.85,
objective_func=ObjectiveFunctionPairwise()):
"""
Parameters
----------
neighbor_dict : `dict`
Refer to the corresponding argument in :meth:`AZP.fit_from_dict`.
attr : `dict`
Refer to the corresponding argument in :meth:`AZP.fit_from_dict`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
initial_labels : `dict` or None, default: None
Refer to the corresponding argument in :meth:`AZP.fit_from_dict`.
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`.
"""
sorted_areas = sorted(neighbor_dict)
adj = scipy_sparse_matrix_from_dict(neighbor_dict)
attr_arr = array_from_dict_values(attr, sorted_areas)
if initial_labels is not None:
initial_labels = array_from_dict_values(
initial_labels, sorted_areas, flat_output=True, dtype=np.int32)
self.fit_from_scipy_sparse_matrix(
adj,
attr_arr,
n_regions,
initial_labels=initial_labels,
cooling_factor=cooling_factor,
objective_func=objective_func)
def fit_from_dict(self,
neighbors_dict,
attr,
n_regions,
method="flow",
solver="cbc",
metric="euclidean"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
neighbors_dict : dict
Each key represents an area and each value is an iterable of
neighbors of this area.
attr : dict
A dict with the same keys as `neighbors_dict` and values
representing the clustering criteria. A value can be scalar (e.g.
float or int) or a :class:`numpy.ndarray`.
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`.
"""
if not isinstance(neighbors_dict, dict):
raise ValueError("The neighbors_dict argument must be dict.")
if not isinstance(attr, dict) or attr.keys() != neighbors_dict.keys():
raise ValueError("The attr argument has to be of type dict with "
"the same keys as neighbors_dict.")
adj = scipy_sparse_matrix_from_dict(neighbors_dict)
attr_arr = array_from_dict_values(attr)
self.fit_from_scipy_sparse_matrix(
adj,
attr_arr,
n_regions,
method=method,
solver=solver,
metric=metric)
def fit_from_dict(self,
neighbors_dict,
attr,
spatially_extensive_attr,
threshold,
max_it=10,
objective_func=ObjectiveFunctionPairwise()):
"""
Solve the max-p-regions problem in a heuristic way (see [DAR2012]_).
The resulting region labels are assigned to the instance's
:attr:`labels_` attribute.
Parameters
----------
neighbors_dict : dict
Each key represents an area and each value is an iterable of
neighbors of this area.
attr : dict
A dict with the same keys as `neighbors_dict` and values
representing the attributes for calculating h**o-/heterogeneity. A
value can be scalar (e.g. `float` or `int`) or a
:class:`numpy.ndarray`.
spatially_extensive_attr : dict
A dict with the same keys as `neighbors_dict` and values
representing the spatially extensive attribute (scalar or iterable
of scalars). In the max-p-regions problem each region's sum of
spatially extensive attributes must be greater than a specified
threshold. In case of iterables of scalars as dict-values all
elements of the iterable have to fulfill the condition.
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`.
"""
if not isinstance(neighbors_dict, dict):
raise ValueError("The neighbors_dict argument must be dict.")
not_same_dict_keys_msg = "The {} argument has to be of type dict " \
"with the same keys as neighbors_dict."
if not isinstance(attr, dict) or attr.keys() != neighbors_dict.keys():
raise ValueError(not_same_dict_keys_msg.format("attr"))
if not isinstance(spatially_extensive_attr, dict) or \
spatially_extensive_attr.keys() != neighbors_dict.keys():
raise ValueError(
not_same_dict_keys_msg.format(spatially_extensive_attr))
adj = scipy_sparse_matrix_from_dict(neighbors_dict)
attr_arr = array_from_dict_values(attr)
spat_ext_attr_arr = array_from_dict_values(spatially_extensive_attr)
self.fit_from_scipy_sparse_matrix(adj,
attr_arr,
spat_ext_attr_arr,
threshold=threshold,
max_it=max_it,
objective_func=objective_func)
