Fix/umap pkg resources

docs/requirements.txt

@@ -6,10 +6,10 @@ torch>=2.0
 tqdm>=4.60
 seaborn>=0.12
 matplotlib>=3.6
-scikit-learn>=1.0
+scikit-learn>=1.6
 scikit-misc>=0.1
 leidenalg>=0.9
-umap-learn>=0.5
+umap-learn>=0.5.7
 anndata>=0.8
 sphinx>=7.0
 sphinx-rtd-theme>=2.0

requirements.txt

@@ -1,11 +1,11 @@
 scanpy==1.9.5
 numpy==1.26.1
-umap-learn==0.5.3
+umap-learn>=0.5.7
 tqdm==4.66.1
 seaborn==0.13.0
 matplotlib==3.8.0
 scipy==1.11.3
-scikit-learn==1.3.1
+scikit-learn>=1.6
 pandas==2.1.1
 torch==2.1.0
 scikit-misc==0.3.0

tests/conftest.py

@@ -0,0 +1,99 @@
+"""Shared pytest fixtures for aggregation and graph-target tests."""
+
+import numpy as np
+import pytest
+from scipy.sparse import csr_matrix
+
+
+@pytest.fixture
+def tiny_graph():
+    return csr_matrix(np.array([
+        [0, 1, 0, 0],
+        [1, 0, 1, 0],
+        [0, 1, 0, 1],
+        [0, 0, 1, 0],
+    ], dtype=np.float64))
+
+
+@pytest.fixture
+def star_graph():
+    adj = np.zeros((5, 5), dtype=np.float64)
+    for i in range(1, 5):
+        adj[0, i] = 1.0
+        adj[i, 0] = 1.0
+    return csr_matrix(adj)
+
+
+@pytest.fixture
+def ring_with_isolate():
+    adj = np.zeros((6, 6), dtype=np.float64)
+    for i in range(5):
+        j = (i + 1) % 5
+        adj[i, j] = 1.0
+        adj[j, i] = 1.0
+    return csr_matrix(adj)
+
+
+@pytest.fixture
+def tiny_X(tiny_graph):
+    X = np.array([
+        [1., 0.],
+        [0., 2.],
+        [3., 1.],
+        [2., 4.],
+    ], dtype=np.float64)
+    return X, tiny_graph
+
+
+@pytest.fixture
+def uniform_X_star(star_graph):
+    X = np.ones((5, 3), dtype=np.float64)
+    return X, star_graph
+
+
+@pytest.fixture
+def random_X_ring(ring_with_isolate):
+    X = np.random.RandomState(42).rand(6, 10)
+    return X, ring_with_isolate
+
+
+@pytest.fixture
+def star_distances(star_graph):
+    row, col = star_graph.nonzero()
+    data = np.zeros(len(row), dtype=np.float64)
+    for k in range(len(row)):
+        leaf = max(row[k], col[k])
+        data[k] = float(leaf)
+    distances = csr_matrix((data, (row, col)), shape=(5, 5))
+    return star_graph, distances
+
+
+@pytest.fixture
+def tiny_adata():
+    anndata = pytest.importorskip("anndata")
+    import pandas as pd
+
+    rng = np.random.RandomState(42)
+    X = csr_matrix(rng.poisson(2, size=(20, 6)).astype(np.float64))
+    gene_names = [f"GENE{i}" for i in range(6)]
+    cell_names = [f"CELL{i}" for i in range(20)]
+
+    adata = anndata.AnnData(
+        X=X,
+        var=pd.DataFrame(index=gene_names),
+        obs=pd.DataFrame(index=cell_names),
+    )
+
+    conn = csr_matrix(rng.random((20, 20)) < 0.15, dtype=np.float64)
+    conn = conn + conn.T
+    conn.data[:] = 1.0
+    conn.setdiag(0)
+    conn.eliminate_zeros()
+    adata.obsp["spatial_connectivities"] = conn
+
+    dist_vals = rng.rand(conn.nnz) * 4.5 + 0.5
+    distances = conn.copy()
+    distances.data[:] = dist_vals
+    adata.obsp["spatial_distances"] = distances
+
+    return adata