def fit_from_w(self,
w,
attr,
n_regions,
initial_labels=None,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
w : :class:`libpysal.weights.weights.W`
W object representing the contiguity relation.
attr : :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
initial_labels : :class:`numpy.ndarray` or None, default: None
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 = scipy_sparse_matrix_from_w(w)
self.fit_from_scipy_sparse_matrix(
adj,
attr,
n_regions,
initial_labels,
objective_func=objective_func)
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_scipy_sparse_matrix(
self,
adj,
attr,
n_regions,
initial_labels=None,
objective_func=ObjectiveFunctionPairwise()):
"""
Perform the AZP algorithm as described in [OR1995]_.
The resulting region labels are assigned to the instance's
:attr:`labels_` attribute.
Parameters
----------
adj : :class:`scipy.sparse.csr_matrix`
Adjacency matrix representing the contiguity relation.
attr : :class:`numpy.ndarray`
Array (number of areas x number of attributes) of areas' attributes
relevant to clustering.
n_regions : `int`
Number of desired regions.
initial_labels : :class:`numpy.ndarray` or None, default: None
One-dimensional array of labels at the beginning of the algorithm.
If None, then a random initial clustering will be generated.
objective_func : :class:`region.objective_function.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
The objective function to use.
"""
if attr.ndim == 1:
attr = attr.reshape(adj.shape[0], -1)
self.allow_move_strategy.attr_all = attr
self.objective_func = objective_func
# step 1
if initial_labels is not None:
assert_feasible(initial_labels, adj, n_regions)
initial_labels_gen = separate_components(adj, initial_labels)
else:
initial_labels_gen = generate_initial_sol(adj, n_regions)
labels = -np.ones(adj.shape[0])
for labels_comp in initial_labels_gen:
comp_idx = np.where(labels_comp != -1)[0]
adj_comp = sub_adj_matrix(adj, comp_idx)
labels_comp = labels_comp[comp_idx]
attr_comp = attr[comp_idx]
self.allow_move_strategy.start_new_component(
labels_comp, attr_comp, self.objective_func, comp_idx)
labels_comp = self._azp_connected_component(
adj_comp, labels_comp, attr_comp)
labels[comp_idx] = labels_comp
self.n_regions = n_regions
self.labels_ = labels
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_geodataframe(self,
gdf,
attr,
spatially_extensive_attr,
threshold,
max_it=10,
objective_func=ObjectiveFunctionPairwise(),
contiguity="rook"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix:.
Parameters
----------
gdf : :class:`geopandas.GeoDataFrame`
attr : str or list
The clustering criteria (columns of the GeoDataFrame `gdf`) are
specified as string (for one column) or list of strings (for
multiple columns).
spatially_extensive_attr : str or list
The name (`str`) or names (`list` of strings) of column(s) in `gdf`
containing the spatially extensive attributes.
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`.
contiguity : {"rook", "queen"}, default: "rook"
Defines the contiguity relationship between areas. Possible
contiguity definitions are:
* "rook" - Rook contiguity.
* "queen" - Queen contiguity.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
spat_ext_attr = array_from_df_col(gdf, spatially_extensive_attr)
self.fit_from_w(w,
attr,
spat_ext_attr,
threshold=threshold,
max_it=max_it,
objective_func=objective_func)
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_geodataframe(self,
gdf,
attr,
n_regions,
contiguity="rook",
initial_labels=None,
cooling_factor=0.85,
objective_func=ObjectiveFunctionPairwise()):
"""
Parameters
----------
gdf : :class:`geopandas.GeoDataFrame`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
attr : `str` or `list`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
contiguity : `str`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
initial_labels : :class:`numpy.ndarray` or None, default: None
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
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`.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
self.fit_from_w(
w,
attr,
n_regions,
initial_labels,
cooling_factor=cooling_factor,
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_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_w(self,
w,
attr,
spatially_extensive_attr,
threshold,
max_it=10,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix:.
Parameters
----------
w : :class:`libpysal.weights.weights.W`
W object representing the contiguity relation.
attr : :class:`numpy.ndarray`
Each element specifies an area's attribute which is used for
calculating the objective function.
spatially_extensive_attr : :class:`numpy.ndarray`
Each element specifies an area's spatially extensive attribute
which is used to ensure that the sum of spatially extensive
attributes in each region adds up to a threshold defined by the
`threshold` argument.
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 = scipy_sparse_matrix_from_w(w)
self.fit_from_scipy_sparse_matrix(adj,
attr,
spatially_extensive_attr,
threshold,
max_it=max_it,
objective_func=objective_func)
def fit_from_geodataframe(self,
gdf,
attr,
n_regions,
contiguity="rook",
initial_labels=None,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
gdf : :class:`geopandas.GeoDataFrame`
attr : `str` or `list`
The clustering-relevant attributes (columns of the GeoDataFrame
`gdf`) are specified as string (for one column) or list of strings
(for multiple columns).
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
contiguity : {"rook", "queen"}, default: "rook"
Defines the contiguity relationship between areas. Possible
contiguity definitions are:
* "rook" - Rook contiguity.
* "queen" - Queen contiguity.
initial_labels : :class:`numpy.ndarray` or None, default: None
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`.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
self.fit_from_w(
w, attr, n_regions, initial_labels, objective_func=objective_func)
def fit_from_w(self,
w,
attr,
n_regions,
initial_labels=None,
cooling_factor=0.85,
objective_func=ObjectiveFunctionPairwise()):
"""
Parameters
----------
w : :class:`libpysal.weights.weights.W`
Refer to the corresponding argument in :meth:`AZP.fit_from_w`.
attr : :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
initial_labels : :class:`numpy.ndarray` or None, default: None
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
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 = scipy_sparse_matrix_from_w(w)
self.fit_from_scipy_sparse_matrix(
adj,
attr,
n_regions,
initial_labels,
cooling_factor=cooling_factor,
objective_func=objective_func)
def fit_from_scipy_sparse_matrix(
self,
adj,
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
----------
adj : :class:`scipy.sparse.csr_matrix`
Adjacency matrix representing the areas' contiguity relation.
attr : :class:`numpy.ndarray`
Array (number of areas x number of attributes) of areas' attributes
relevant to clustering.
spatially_extensive_attr : :class:`numpy.ndarray`
Array (number of areas x number of attributes) of areas' attributes
relevant to ensuring the threshold condition.
threshold : numbers.Real or :class:`numpy.ndarray`
The lower bound for a region's sum of spatially extensive
attributes. The argument's type is numbers.Real if there is only
one spatially extensive attribute per area, otherwise it is a
one-dimensional array with as many entries as there are spatially
extensive attributes per area.
max_it : int, default: 10
The maximum number of partitions produced in the algorithm's
construction phase.
objective_func : :class:`region.objective_function.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
The objective function to use.
"""
print("f_f_SCIPY got:\n",
attr,
"\n",
spatially_extensive_attr,
"\n",
threshold,
sep="")
weights = ps_api.WSP(adj).to_W()
areas_dict = weights.neighbors
self.metric = objective_func.metric
best_partition = None
best_obj_value = float("inf")
feasible_partitions = []
partitions_before_enclaves_assignment = []
max_p = 0 # maximum number of regions
# construction phase
# print("constructing")
for _ in range(max_it):
# print(" ", _)
partition, enclaves = self.grow_regions(adj, attr,
spatially_extensive_attr,
threshold)
n_regions = len(partition)
if n_regions > max_p:
partitions_before_enclaves_assignment = [(partition, enclaves)]
max_p = n_regions
elif n_regions == max_p:
partitions_before_enclaves_assignment.append(
(partition, enclaves))
# print("\n" + "assigning enclaves")
for partition, enclaves in partitions_before_enclaves_assignment:
# print(" cleaning up in partition", partition)
feasible_partitions.append(
self.assign_enclaves(partition, enclaves, areas_dict, attr))
for partition in feasible_partitions:
print(partition, "\n")
# local search phase
if self.local_search is None:
self.local_search = AZP()
self.local_search.allow_move_strategy = AllowMoveAZPMaxPRegions(
spatially_extensive_attr, threshold,
self.local_search.allow_move_strategy)
for partition in feasible_partitions:
self.local_search.fit_from_scipy_sparse_matrix(
adj,
attr,
max_p,
initial_labels=array_from_region_list(partition),
objective_func=objective_func)
partition = self.local_search.labels_
# print("optimized partition", partition)
obj_value = objective_func(partition, attr)
if obj_value < best_obj_value:
best_obj_value = obj_value
best_partition = partition
self.labels_ = best_partition
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)
def fit_from_scipy_sparse_matrix(
self,
adj,
attr,
n_regions,
initial_labels=None,
cooling_factor=0.85,
objective_func=ObjectiveFunctionPairwise()):
"""
Parameters
----------
adj : :class:`scipy.sparse.csr_matrix`
Refer to the corresponding argument in
:meth:`AZP.fit_from_scipy_sparse_matrix`.
attr : :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`AZP.fit_from_scipy_sparse_matrix`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`AZP.fit_from_scipy_sparse_matrix`.
initial_labels : :class:`numpy.ndarray` or None, default: None
Refer to the corresponding argument in
:meth:`AZP.fit_from_scipy_sparse_matrix`.
cooling_factor : float, default: 0.85
Float :math:`\\in (0, 1)` specifying the cooling factor for the
simulated annealing.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
if not (0 < cooling_factor < 1):
raise ValueError("The cooling_factor argument must be greater "
"than 0 and less than 1")
if attr.ndim == 1:
attr = attr.reshape(adj.shape[0], -1)
self.allow_move_strategy = AllowMoveAZPSimulatedAnnealing(
init_temperature=self.init_temperature,
sa_moves_term=self.sa_moves_term)
self.allow_move_strategy.register_sa_moves_term(self.sa_moves_alert)
self.allow_move_strategy.register_move_made(self.move_made_alert)
self.azp = AZP(
allow_move_strategy=self.allow_move_strategy,
random_state=self.random_state)
# step a
t = self.init_temperature
nonmoving_steps = 0
# step d: repeat step b and c
while nonmoving_steps < self.nonmoving_steps_before_stop:
it = 0
self.sa_moves_term_reached = False
self.allow_move_strategy.reset()
# step b
while it < self.maxit and not self.sa_moves_term_reached:
it += 1
old_sol = initial_labels
self.azp.fit_from_scipy_sparse_matrix(
adj, attr, n_regions, initial_labels, objective_func)
initial_labels = self.azp.labels_
if old_sol is not None:
if (old_sol == initial_labels).all():
break
# added termination condition (not in Openshaw & Rao (1995))
if self.visited.count(tuple(initial_labels)) \
>= self.reps_before_termination:
break
self.visited.append(tuple(initial_labels))
# step c
t *= cooling_factor
self.allow_move_strategy.update_temperature(t)
if self.move_made:
self.move_made = False
else:
nonmoving_steps += 1
self.labels_ = initial_labels