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_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_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_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_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_dict():
value_dict = dataframe_to_dict(gdf, attr_str)
cluster_object = AZP(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)
np.array([0, 0, 0, 1, 0, 0, 1, 1, 1]))
attr_str = "attr"
spatially_extensive_attr_str = "spatially_extensive_attr"
gdf = GeoDataFrame(
{
attr_str: attr,
spatially_extensive_attr_str: spatially_extensive_attr
},
geometry=[
Polygon([(x, y), (x, y + 1), (x + 1, y + 1), (x + 1, y)])
for y in range(3) for x in range(3)
])
# for tests with scalar attr & spatially_extensive_attr per area
attr = attr.reshape(-1, 1)
spatially_extensive_attr = spatially_extensive_attr.reshape(-1, 1)
adj, graph, neighbors_dict, w = convert_from_geodataframe(gdf)
attr_dict = dataframe_to_dict(gdf, attr_str)
spatially_extensive_attr_dict = dataframe_to_dict(
gdf, spatially_extensive_attr_str)
# for tests where attr & spatially_extensive_attr are vectors in each area
double_attr = np.column_stack((attr, attr))
double_spatially_extensive_attr = np.column_stack(
(spatially_extensive_attr, spatially_extensive_attr))
double_threshold = np.hstack((threshold, threshold))
double_attr_dict = dataframe_to_dict(gdf, [attr_str] * 2)
double_spatially_extensive_attr_dict = dataframe_to_dict(
gdf, [spatially_extensive_attr_str] * 2)
from region.tests.util import region_list_from_array, convert_from_geodataframe
from region.util import dataframe_to_dict
attr = np.array([726.7, 623.6, 487.3,
200.4, 245.0, 481.0,
170.9, 225.9, 226.9])
attr_str = "attr"
gdf = GeoDataFrame(
{attr_str: attr},
geometry=[Polygon([(x, y), # 3x3-grid
(x, y+1),
(x+1, y+1),
(x+1, y)]) for y in range(3) for x in range(3)]
)
optimal_clustering = region_list_from_array(np.array([0, 0, 0,
1, 1, 0,
1, 1, 1]))
# for tests with scalar attr & spatially_extensive_attr per area
attr = attr.reshape(-1, 1)
adj, graph, neighbors_dict, w = convert_from_geodataframe(gdf)
attr_dict = dataframe_to_dict(gdf, attr_str)
# for tests where attr & spatially_extensive_attr are vectors in each area
double_attr = np.column_stack((attr, attr))
double_attr_dict = dataframe_to_dict(gdf, [attr_str] * 2)
double_attr_str = [attr_str] * 2
threshold = 2
optimal_clustering = region_list_from_array(np.array([0, 0,
1, 1]))
attr_str = "attr"
spatially_extensive_attr_str = "spatially_extensive_attr"
gdf = GeoDataFrame(
{attr_str: attr,
spatially_extensive_attr_str: spatially_extensive_attr},
geometry=[Polygon([(x, y),
(x, y+1),
(x+1, y+1),
(x+1, y)]) for y in range(2) for x in range(2)]
)
# for tests with scalar attr & spatially_extensive_attr per area
attr = attr.reshape(-1, 1)
spatially_extensive_attr = spatially_extensive_attr.reshape(-1, 1)
adj, graph, neighbors_dict, w = convert_from_geodataframe(gdf)
attr_dict = dataframe_to_dict(gdf, attr_str)
spatially_extensive_attr_dict = dataframe_to_dict(gdf,
spatially_extensive_attr_str)
# for tests where attr & spatially_extensive_attr are vectors in each area
double_attr = np.column_stack((attr, attr))
double_spatially_extensive_attr = np.column_stack((spatially_extensive_attr,
spatially_extensive_attr))
double_threshold = np.hstack((threshold, threshold))
double_attr_dict = dataframe_to_dict(gdf, [attr_str] * 2)
double_spatially_extensive_attr_dict = dataframe_to_dict(
gdf, [spatially_extensive_attr_str] * 2)