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import geopandas as gpd
import numpy as np
from scipy.sparse import linalg as spla
from geodatasets import get_path
from libpysal.graph import Graph
class TimeSuite:
def setup(self, *args, **kwargs):
self.gdf = gpd.read_file(get_path("geoda south"))
self.gdf_str = self.gdf.set_index(self.gdf.NAME + " " + self.gdf.STATE_NAME)
self.gdf_points = self.gdf.set_geometry(self.gdf.representative_point())
self.gdf_str_points = self.gdf_str.set_geometry(
self.gdf_str.representative_point()
)
self.graphs = {
"small_int": Graph.build_knn(self.gdf_points, k=10),
"large_int": Graph.build_knn(self.gdf_points, k=500),
"small_str": Graph.build_knn(self.gdf_str_points, k=10),
"large_str": Graph.build_knn(self.gdf_str_points, k=500),
}
self.ids = {
"int": self.gdf.index.to_series().sample(self.gdf.shape[0] // 5).values,
"str": self.gdf_str.index.to_series()
.sample(self.gdf_str.shape[0] // 5)
.values,
}
self.sparse_arrays = {
k+"_k":v.sparse for k,v in self.graphs.items()
}
self.sparse_arrays = {
"small_int": Graph.build_knn(self.gdf_points, k=10).sparse,
"large_int": Graph.build_knn(self.gdf_points, k=500).sparse,
'queen' : Graph.build_contiguity(self.gdf).sparse,
'rook' : Graph.build_contiguity(self.gdf).sparse,
'delaunay' : Graph.build_triangulation(self.gdf).sparse
'gabriel' : Graph.build_triangulation(self.gdf, method='gabriel').sparse
'relneigh' : Graph.build_triangulation(self.gdf, method='relative_neighborhoood').sparse
}
)
def time_queen(self, idx, strict):
Graph.build_contiguity(
self.gdf if idx == "int" else self.gdf_str,
strict=strict,
)
time_queen.params = (["int", "str"], [True, False])
time_queen.param_names = ["index", "strict"]
def time_knn(self, idx, k):
Graph.build_knn(self.gdf_points if idx == "int" else self.gdf_str_points, k=k)
time_knn.params = (["int", "str"], [10, 500])
time_knn.param_names = ["index", "k"]
def time_kernel(self, idx):
Graph.build_kernel(self.gdf_points if idx == "int" else self.gdf_str_points)
time_kernel.params = ["int", "str"]
time_kernel.param_names = ["index"]
def time_assign_self_weight(self, idx, size):
self.graphs[f"{size}_{idx}"].assign_self_weight()
time_assign_self_weight.params = (["int", "str"], ["small", "large"])
time_assign_self_weight.param_names = ["index", "graph_size"]
def time_sparse(self, idx, size):
s = self.graphs[f"{size}_{idx}"].sparse
time_sparse.params = (["int", "str"], ["small", "large"])
time_sparse.param_names = ["index", "graph_size"]
def time_subgraph(self, idx, size):
self.graphs[f"{size}_{idx}"].subgraph(self.ids[idx])
time_subgraph.params = (["int", "str"], ["small", "large"])
time_subgraph.param_names = ["index", "graph_size"]
def time_inverse(self, graph):
s = self.graphs[f"{graph}"].sparse
np.linalg.inv(np.eye(s.shape[0]) - .5*s)
def time_dense_solve(self, graph):
s = self.graphs[f"{graph}"].sparse
np.linalg.solve(
(np.eye(s.shape[0]) - .5*s).todense(),
np.arange(w.shape[0])
)
def time_sparse_solve(self, graph):
s = self.graphs[f"{graph}"].sparse
spla.spsolve(
sp.eye(s.shape[0]) - .5*s,
np.arange(w.shape[0])
)
def time_dense_slogdet(self, graph):
s = self.graphs[f"{graph}"].sparse
np.linalg.slogdet(np.eye(s.shape[0]) - .5*s)
def time_sparse_slogdet(self, graph):
s = self.graphs[f"{graph}"].sparse
LU = spla.splu(sp.eye(s.shape[0]) - .5*s)
np.sum(np.log(np.abs(LU.U.diagonal())))
# class MemSuite:
# def mem_list(self):
# return [0] * 256
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