def test_graph_dict_basic():
cluster_object = MaxPRegionsExact()
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_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_networkx(graph, double_attr_dict, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_geodataframe_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_geodataframe(gdf, double_attr_str, n_regions=2)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_dict_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_dict(neighbors_dict, double_attr_dict, n_regions=2)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_scipy_sparse_matrix(adj, double_attr, n_regions=2)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_w_basic():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_w(w, attr, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_graph_dict_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_networkx(graph, double_attr_dict, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
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_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_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_scipy_sparse_matrix_multi_attr():
cluster_object = MaxPRegionsExact()
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_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 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_w_basic():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_w(w, attr, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_geodataframe_multi_attr():
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_geodataframe(gdf, double_attr_str,
n_regions=n_reg)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_geodataframe_multi_attr():
cluster_object = MaxPRegionsExact()
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_dict():
value_dict = dataframe_to_dict(gdf, attr_str)
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_dict(neighbors_dict, value_dict, n_regions=n_reg)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_geodataframe_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_geodataframe(gdf, double_attr_str, n_regions=2)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_scipy_sparse_matrix(adj, double_attr, n_regions=2)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_dict_basic():
cluster_object = MaxPRegionsExact()
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_geodataframe_basic():
cluster_object = MaxPRegionsExact()
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(method):
value_dict = dataframe_to_dict(gdf, attr_str)
cluster_object = PRegionsExact()
cluster_object.fit_from_dict(neighbors_dict, value_dict, n_regions=2,
method=method)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_graph_str_basic(method):
nx.set_node_attributes(graph, attr_str, attr_dict)
cluster_object = PRegionsExact()
cluster_object.fit_from_networkx(graph, attr_str, n_regions=2,
method=method)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr():
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_scipy_sparse_matrix(adj, double_attr,
n_regions=n_reg)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_dict_multi_attr():
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_dict(neighbors_dict, double_attr_dict, n_regions=2)
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)
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_networkx(graph, attr_str, n_regions=n_reg)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_dict_multi_attr():
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_dict(neighbors_dict, double_attr_dict,
n_regions=n_reg)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_w_basic():
cluster_object = AZPReactiveTabu(max_iterations=max_it,
k1=k1,
k2=k2,
random_state=0)
cluster_object.fit_from_w(w, attr, n_regions=n_reg)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, 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_dict():
value_dict = dataframe_to_dict(gdf, attr_str)
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_dict(neighbors_dict, value_dict, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_geodataframe(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_geodataframe(gdf,
attr_str,
n_regions=2,
method=method)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_dict_multi_attr(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_dict(neighbors_dict,
double_attr_dict,
n_regions=2,
method=method)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_dict_multi_attr():
cluster_object = MaxPRegionsExact()
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_dict():
value_dict = dataframe_to_dict(gdf, attr_str)
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_dict(neighbors_dict, value_dict, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_scipy_sparse_matrix_multi_attr(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_scipy_sparse_matrix(adj,
double_attr,
n_regions=2,
method=method)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_graph_dict_multi_attr():
cluster_object = AZPReactiveTabu(max_iterations=max_it,
k1=k1,
k2=k2,
random_state=0)
cluster_object.fit_from_networkx(graph, double_attr_dict, n_regions=n_reg)
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_geodataframe_multi_attr(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_geodataframe(gdf,
double_attr_str,
n_regions=2,
method=method)
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)
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_networkx(graph, attr_str, n_regions=2)
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_graph_str_basic():
nx.set_node_attributes(graph, attr_dict, attr_str)
cluster_object = AZPSimulatedAnnealing(init_temperature=1,
max_iterations=2,
random_state=0)
cluster_object.fit_from_networkx(graph, attr_str, n_regions=2)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_graph_dict_basic(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_networkx(graph,
attr_dict,
n_regions=2,
method=method)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_geodataframe_multi_attr():
cluster_object = MaxPRegionsExact()
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_dict_multi_attr():
cluster_object = MaxPRegionsExact()
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_dict(method):
value_dict = dataframe_to_dict(gdf, attr_str)
cluster_object = PRegionsExact()
cluster_object.fit_from_dict(neighbors_dict,
value_dict,
n_regions=2,
method=method)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, 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 = MaxPRegionsExact()
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_w_basic():
cluster_object = MaxPRegionsExact()
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_geodataframe_multi_attr(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_geodataframe(gdf, double_attr_str, n_regions=2,
method=method)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_dict_multi_attr(method):
cluster_object = PRegionsExact()
cluster_object.fit_from_dict(neighbors_dict, double_attr_dict, n_regions=2,
method=method)
obtained = region_list_from_array(cluster_object.labels_)
compare_region_lists(obtained, optimal_clustering)
def test_graph_dict_multi_attr():
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_networkx(graph, double_attr_dict, n_regions=n_reg)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_w_basic():
cluster_object = AZPReactiveTabu(max_iterations=max_it, k1=k1, k2=k2,
random_state=0)
cluster_object.fit_from_w(w, attr, n_regions=n_reg)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
