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_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_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_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_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_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_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_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():
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)