Bump dependencies and support Python 3.13

.github/workflows/unittest.yml

@@ -14,7 +14,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        # There is a bug in Python 3.13.0 which breaks Vamb's tests, fixed in 3.13.1
+        python-version: ["3.10", "3.11", "3.12", "3.13.1"]
 
     steps:
     - uses: actions/checkout@v3

pyproject.toml

@@ -3,20 +3,20 @@
 dynamic = ["version"]
 name = "vamb"
 dependencies = [
-    "numpy == 1.26.4",
-    "torch == 2.3.1",
-    "pycoverm == 0.6.0",
-    "networkx == 3.2", # 3.3 drops Python 3.9 support
-    "scikit-learn == 1.5.0",
+    "numpy == 2.2.1",
+    "torch == 2.5.1",
+    "pycoverm == 0.6.2",
+    "networkx == 3.4.2",
+    "scikit-learn == 1.6.0",
     "dadaptation == 3.2",
-    "loguru == 0.7.2",
-    "pyhmmer == 0.10.12",
-    "pyrodigal == 3.4.1",
+    "loguru == 0.7.3",
+    "pyhmmer == 0.10.15",
+    "pyrodigal == 3.6.3",
 ]
-# Currently pycoverm does not have binaries for Python > 3.12.
-# The dependency resolver, will not error on Python 3.13, but attempt
+# Currently pycoverm does not have binaries for Python > 3.13.
+# The dependency resolver, will not error on Python 3.14, but attempt
 # to build pycoverm from source, but will not get the deps required for that.
-requires-python = "<3.13,>=3.9.0"
+requires-python = "<3.14,>=3.10.0"
 scripts = {vamb = "vamb.__main__:main"}
 
 [project.optional-dependencies]

test/test_vambtools.py

@@ -291,10 +291,10 @@ def test_axes(self):
         )
 
     def test_axis_bounds(self):
-        with self.assertRaises(np.AxisError):
+        with self.assertRaises(np.exceptions.AxisError):
             vamb.vambtools.zscore(self.arr, axis=-1)
 
-        with self.assertRaises(np.AxisError):
+        with self.assertRaises(np.exceptions.AxisError):
             vamb.vambtools.zscore(self.arr, axis=2)
 
     def test_integer(self):

vamb/encode.py

@@ -514,7 +514,9 @@ def load(
         """
 
         # Forcably load to CPU even if model was saves as GPU model
-        dictionary = _torch.load(path, map_location=lambda storage, loc: storage)
+        dictionary = _torch.load(
+            path, map_location=lambda storage, loc: storage, weights_only=True
+        )
 
         nsamples = dictionary["nsamples"]
         alpha = dictionary["alpha"]

vamb/semisupervised_encode.py

@@ -1125,7 +1125,9 @@ def load(cls, path, cuda=False, evaluate=True):
         """
 
         # Forcably load to CPU even if model was saves as GPU model
-        dictionary = _torch.load(path, map_location=lambda storage, loc: storage)
+        dictionary = _torch.load(
+            path, map_location=lambda storage, loc: storage, weights_only=False
+        )
 
         nsamples = dictionary["nsamples"]
         nlabels = dictionary["nlabels"]

vamb/taxvamb_encode.py

@@ -630,7 +630,9 @@ def load(cls, path, nodes, table_parent, cuda=False, evaluate=True):
         """
 
         # Forcably load to CPU even if model was saves as GPU model
-        dictionary = _torch.load(path, map_location=lambda storage, loc: storage)
+        dictionary = _torch.load(
+            path, map_location=lambda storage, loc: storage, weights_only=False
+        )
 
         nsamples = dictionary["nsamples"]
         nlabels = dictionary["nlabels"]

vamb/vambtools.py

@@ -239,7 +239,7 @@ def zscore(
     """
 
     if axis is not None and (axis >= array.ndim or axis < 0):
-        raise _np.AxisError(str(axis))
+        raise _np.exceptions.AxisError(str(axis))
 
     if inplace and not _np.issubdtype(array.dtype, _np.floating):
         raise TypeError("Cannot convert a non-float array to zscores")