v1.0.2: fixes python 3.9 dependencies

conversion/guacamol.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import os
+from typing import Union
 
 import torch
 from loguru import logger
@@ -108,7 +109,11 @@ def check_smiles_graph_mapping_worker(smile_idx, smile):
 
 
 def process(
-    split: str, raw_dir: str, n_jobs: int, limit: int | None, chunk_size: int
+    split: str,
+    raw_dir: str,
+    n_jobs: int,
+    limit: Union[int, None],
+    chunk_size: int,
 ) -> None:
     path = os.path.join(raw_dir, f"guacamol_v1_{split}.smiles")
     smile_list = [

polygraph/metrics/base/polygraphdiscrepancy.py

@@ -251,7 +251,7 @@ def _descriptions_to_classifier_metric(
     variant: Literal["informedness", "jsd"] = "jsd",
     classifier: Optional[ClassifierProtocol] = None,
     rng: Optional[np.random.Generator] = None,
-) -> Tuple[float, int | float]:
+) -> Tuple[float, Union[int, float]]:
     rng = np.random.default_rng(0) if rng is None else rng
 
     if isinstance(ref_descriptions, csr_array):

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "polygraph-benchmark"
-version = "1.0.1"
+version = "1.0.2"
 description = "Evaluation benchmarks for graph generative models"
 readme = "README.md"
 authors = [
@@ -13,22 +13,22 @@ authors = [
     { name = "Dexiong Chen", email = "[email protected]" },
     { name = "Karsten Borgwardt", email = "[email protected]" },
 ]
-requires-python = ">=3.7"
+requires-python = ">=3.9"
 dependencies = [
     "numpy>=1.26.4,<3.0",
     "torch>=2.4.0,<3.0",
     "torch_geometric>=2.6.1,<3.0",
     "rich",
-    "scipy>=1.14.0,<2.0",
+    "scipy>=1.12.0,<2.0",
     "pydantic~=2.11.7",
-    "networkx>=3.4,<4.0",
+    "networkx>=3.2,<4.0",
     "joblib",
     "appdirs",
     "loguru",
     "rdkit",
     "pandas",
     "orbit-count",
-    "numba~=0.61.2",
+    "numba>=0.60.0,<0.62.0",
     "scikit-learn>=1.6.1,<2.0",
     "tabpfn==2.0.9",
     "fcd~=1.2.2"

tests/test_mmd.py

@@ -48,8 +48,6 @@
 from polygraph.utils.mmd_utils import mmd_from_gram
 from polygraph.metrics.base.metric_interval import MetricInterval
 
-import grakel
-
 
 class WeisfeilerLehmanMMD2(DescriptorMMD2):
     def __init__(self, reference_graphs, iterations=3):
@@ -67,6 +65,8 @@ def __init__(self, reference_graphs, iterations=3):
 def grakel_wl_mmd(
     reference_graphs, test_graphs, is_parallel=False, iterations=3
 ):
+    import grakel
+
     grakel_kernel = grakel.WeisfeilerLehman(n_iter=iterations)
     all_graphs = reference_graphs + test_graphs
     for g in all_graphs: