def test_w_multi_attr():
print(double_threshold)
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_w(w, double_attr, double_spatially_extensive_attr,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_geodataframe_basic():
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_geodataframe(gdf, attr_str,
spatially_extensive_attr_str,
threshold=threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_graph_dict_basic():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_networkx(graph, attr_dict,
spatially_extensive_attr_dict,
threshold=threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_graph_dict_basic():
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_networkx(graph, attr_dict,
spatially_extensive_attr_dict,
threshold=threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr():
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_scipy_sparse_matrix(
adj, double_attr, double_spatially_extensive_attr,
threshold=double_threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_geodataframe_basic():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_geodataframe(gdf, attr_str,
spatially_extensive_attr_str,
threshold=threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_dict_basic():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_dict(neighbors_dict, attr_dict,
spatially_extensive_attr_dict,
threshold=threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_scipy_sparse_matrix():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_scipy_sparse_matrix(adj, attr,
spatially_extensive_attr,
threshold=threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_w_multi_attr():
print(double_threshold)
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_w(w, double_attr, double_spatially_extensive_attr,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def max_p(X, w, threshold_variable="count", threshold=10, **kwargs):
"""Max-p clustering algorithm
:cite:`Duque2012`
Parameters
----------
X : array-like
n x k attribute data
w : PySAL W instance
spatial weights matrix
threshold_variable : str, default:"count"
attribute variable to use as floor when calculate
threshold : int, default:10
integer that defines the upper limit of a variable that can be grouped
into a single region
Returns
-------
model: region MaxPRegionsHeu instance
"""
model = MaxPRegionsHeu()
model.fit_from_w(w, X.values, threshold_variable, threshold)
return model
def test_dict_basic():
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_dict(neighbors_dict, attr_dict,
spatially_extensive_attr_dict,
threshold=threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_scipy_sparse_matrix(
adj, double_attr, double_spatially_extensive_attr,
threshold=double_threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_graph_dict_multi_attr():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_networkx(graph,
double_attr_dict,
double_spatially_extensive_attr_dict,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_w_basic():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_w(w,
attr,
spatially_extensive_attr,
threshold=threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_dict_multi_attr():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_dict(neighbors_dict,
double_attr_dict,
double_spatially_extensive_attr_dict,
threshold=double_threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_geodataframe_multi_attr():
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_geodataframe(gdf,
[attr_str] * 2,
[spatially_extensive_attr_str] * 2,
threshold=double_threshold)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_graph_str_basic():
nx.set_node_attributes(graph, attr_str, attr_dict)
nx.set_node_attributes(graph, spatially_extensive_attr_str,
spatially_extensive_attr_dict)
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_networkx(graph, attr_str,
spatially_extensive_attr_str,
threshold=threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_graph_str_multi_attr():
nx.set_node_attributes(graph, attr_str, attr_dict)
nx.set_node_attributes(graph, spatially_extensive_attr_str,
spatially_extensive_attr_dict)
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_networkx(graph,
[attr_str] * 2,
[spatially_extensive_attr_str] * 2,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_graph_str_multi_attr():
nx.set_node_attributes(graph, attr_str, attr_dict)
nx.set_node_attributes(graph, spatially_extensive_attr_str,
spatially_extensive_attr_dict)
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_networkx(graph,
[attr_str] * 2,
[spatially_extensive_attr_str] * 2,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)