Quentin patch buffers

pyproject.toml

@@ -79,7 +79,14 @@ doc = [
 ]
 
 [tool.setuptools]
-packages = ["radius_clustering"]
+packages.find = {where = ["src"], include = ["radius_clustering", "radius_clustering.*"]}
+
+[tool.pytest.ini_options]
+pythonpath = "src"
+testpaths = ["tests"]
+addopts = [
+    "--import-mode=importlib",
+]
 
 [tool.ruff]
 # Exclude a variety of commonly ignored directories.

setup.py

@@ -21,18 +21,18 @@
 extensions = [
     Extension(
         "radius_clustering.utils._emos",
-        ["radius_clustering/utils/emos.pyx", "radius_clustering/utils/main-emos.c"],
-        include_dirs=[np.get_include(), "radius_clustering/utils"],
+        ["src/radius_clustering/utils/emos.pyx", "src/radius_clustering/utils/main-emos.c"],
+        include_dirs=[np.get_include(), "src/radius_clustering/utils"],
         extra_compile_args=C_COMPILE_ARGS,
     ),
     Extension(
         "radius_clustering.utils._mds_approx",
         [
-            "radius_clustering/utils/mds.pyx",
-            "radius_clustering/utils/mds_core.cpp",
-            "radius_clustering/utils/random_manager.cpp",
+            "src/radius_clustering/utils/mds.pyx",
+            "src/radius_clustering/utils/mds_core.cpp",
+            "src/radius_clustering/utils/random_manager.cpp",
         ],
-        include_dirs=[np.get_include(), "radius_clustering/utils"],
+        include_dirs=[np.get_include(), "src/radius_clustering/utils"],
         language="c++",
         extra_compile_args=CXX_COMPILE_ARGS,
         extra_link_args=CXX_LINK_ARGS,

radius_clustering/radius_clustering.py → src/radius_clustering/radius_clustering.py

@@ -51,6 +51,10 @@ def __init__(self, manner="approx", threshold=0.5):
         self.threshold = threshold
 
     def _check_symmetric(self, a, tol=1e-8):
+        if a.ndim != 2:
+            raise ValueError("Input must be a 2D array.")
+        if a.shape[0] != a.shape[1]:
+            return False
         return np.allclose(a, a.T, atol=tol)
 
     def fit(self, X, y=None):
@@ -172,7 +176,7 @@ def _clustering_approx(self, n):
         This function uses the approximation method to solve the MDS problem.
         See [casado]_ for more details.
         """
-        result = solve_mds(n, self.edges.flatten(), self.nb_edges, "test")
+        result = solve_mds(n, self.edges.flatten().astype(np.int32), self.nb_edges, "test")
         self.centers_ = [x for x in result["solution_set"]]
         self._mds_exec_time = result["Time"]
 

radius_clustering/utils/mds.pyx → src/radius_clustering/utils/mds.pyx

@@ -39,7 +39,7 @@ cdef extern from "mds_core.cpp":
 
     cdef Result iterated_greedy_wrapper(int numNodes, const vector[int]& edges_list, int nb_edges, string name) nogil
 
-def solve_mds(int num_nodes, np.ndarray[int, ndim=1, mode="c"] edges not None, int nb_edges, str name):
+def solve_mds(int num_nodes, np.ndarray[np.uint32_t, ndim=1, mode="c"] edges not None, int nb_edges, str name):
     """
     Solve the Minimum Dominating Set problem for a given graph.
 

tests/conftest.py

@@ -0,0 +1,3 @@
+import sys
+from pathlib import Path
+