Upgrade Python versions to 3.9–3.13 (tensorly#576)

* Upgrade python versions

* pyupgrade recommendations

* Stick with py 3.12 for now

* py 3.13 is now out

.github/workflows/deploy_pypi.yml

@@ -16,7 +16,7 @@ jobs:
       - name: Install Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.12'
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

.github/workflows/lint.yml

@@ -16,7 +16,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.10'
+        python-version: '3.12'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/test.yml

@@ -12,13 +12,13 @@ jobs:
         BACKEND: ['numpy', 'pytorch', 'paddle']
         python-version: ['3.12']
         include:
-          - BACKEND: 'numpy'
-            python-version: '3.8'
           - BACKEND: 'numpy'
             python-version: '3.9'
+          - BACKEND: 'numpy'
+            python-version: '3.11'
             TENSORLY_TENALG_BACKEND: ['einsum']
           - BACKEND: 'jax'
-            python-version: '3.12'
+            python-version: '3.13'
             TENSORLY_TENALG_BACKEND: ['einsum']
           - BACKEND: 'tensorflow'
             python-version: '3.12'
@@ -67,7 +67,7 @@ jobs:
         TENSORLY_BACKEND=${{matrix.BACKEND}} pytest -vv --cov tensorly --cov-report xml --durations=10 tensorly
 
     - name: Check coverage with CodeCov
-      uses: codecov/codecov-action@v3
+      uses: codecov/codecov-action@v4
       with:
         file: ./coverage.xml
         verbose: true

doc/conf.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 #
 # tensorly documentation build configuration file
 #
@@ -75,9 +74,9 @@
 master_doc = "index"
 
 # General information about the project.
-project = u'tensorly'
+project = 'tensorly'
 year = datetime.now().year
-copyright = "2016 - {}, TensorLy Developers".format(year)
+copyright = f"2016 - {year}, TensorLy Developers"
 author = "Jean Kossaifi"
 
 # The version info for the project you're documenting, acts as replacement for

doc/minify.py

@@ -12,7 +12,7 @@
     if ".min." in str(path):
         continue
     target_path = path.with_suffix(".min.js")
-    with open(path.as_posix(), "r") as f:
+    with open(path.as_posix()) as f:
         text = f.read()
     minified = jsmin(text, mangle=True, mangle_toplevel=True)
     with open(target_path.as_posix(), "w") as f:
@@ -23,7 +23,7 @@
     if ".min." in str(path):
         continue
     target_path = path.with_suffix(".min.css")
-    with open(path.as_posix(), "r") as f:
+    with open(path.as_posix()) as f:
         text = f.read()
     minified = cssmin(text)
     with open(target_path.as_posix(), "w") as f:

examples/decomposition/plot_nn_cp_hals.py

@@ -119,9 +119,9 @@
 # -----------------------
 # First comparison option is processing time for each algorithm:
 
-print(str("{:.2f}".format(time_mu)) + " " + "seconds")
-print(str("{:.2f}".format(time_hals)) + " " + "seconds")
-print(str("{:.2f}".format(time_exact_hals)) + " " + "seconds")
+print(str(f"{time_mu:.2f}") + " " + "seconds")
+print(str(f"{time_hals:.2f}") + " " + "seconds")
+print(str(f"{time_exact_hals:.2f}") + " " + "seconds")
 
 ##############################################################################
 # As it is expected, the exact solution takes much longer than the approximate

examples/decomposition/plot_nn_tucker.py

@@ -109,9 +109,9 @@
 # To compare the various methods, first we may look at each algorithm
 # processing time:
 
-print("time for tensorly nntucker:" + " " + str("{:.2f}".format(time_mu)))
-print("time for HALS with fista:" + " " + str("{:.2f}".format(time_fista)))
-print("time for HALS with as:" + " " + str("{:.2f}".format(time_as)))
+print("time for tensorly nntucker:" + " " + str(f"{time_mu:.2f}"))
+print("time for HALS with fista:" + " " + str(f"{time_fista:.2f}"))
+print("time for HALS with as:" + " " + str(f"{time_as:.2f}"))
 
 ##############################################################################
 # All algorithms should run with about the same number of iterations on our

examples/decomposition/plot_parafac2.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 
 """
 Demonstration of PARAFAC2

examples/plot_tensor.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 Basic tensor operations
 =======================
@@ -13,12 +12,12 @@
 ###########################################################################
 # A tensor is simply a numpy array
 tensor = tl.tensor(np.arange(24).reshape((3, 4, 2)))
-print("* original tensor:\n{}".format(tensor))
+print(f"* original tensor:\n{tensor}")
 
 ###########################################################################
 # Unfolding a tensor is easy
 for mode in range(tensor.ndim):
-    print("* mode-{} unfolding:\n{}".format(mode, tl.unfold(tensor, mode)))
+    print(f"* mode-{mode} unfolding:\n{tl.unfold(tensor, mode)}")
 
 ###########################################################################
 # Re-folding the tensor is as easy:

examples/regression/plot_cp_regression.py

@@ -67,7 +67,7 @@
         ax.set_axis_off()
 
         if i == 0:
-            ax.set_title("Learned\nrank = {}".format(rank))
+            ax.set_title(f"Learned\nrank = {rank}")
 
 plt.suptitle("CP tensor regression")
 plt.show()

examples/regression/plot_tucker_regression.py

@@ -31,7 +31,7 @@
 
 for i, pattern in enumerate(patterns):
 
-    print("fitting pattern n.{}".format(i))
+    print(f"fitting pattern n.{i}")
 
     # Generate the original image
     weight_img = gen_image(
@@ -50,7 +50,7 @@
         ax.set_title("Original\nweights")
 
     for j, rank in enumerate(ranks):
-        print("fitting for rank = {}".format(rank))
+        print(f"fitting for rank = {rank}")
 
         # Create a tensor Regressor estimator
         estimator = TuckerRegressor(
@@ -69,7 +69,7 @@
         ax.set_axis_off()
 
         if i == 0:
-            ax.set_title("Learned\nrank = {}".format(rank))
+            ax.set_title(f"Learned\nrank = {rank}")
 
 plt.suptitle("Tucker tensor regression")
 plt.show()

examples/sparse_parafac.ipynb

@@ -54,8 +54,7 @@
     "approx_error = tl.norm(t - approx)\n",
     "nonzero_elems_count = sum(t_shape)*r\n",
     "\n",
-    "print('approx error: {}, non-zero elements in approx: {}'.format(approx_error,\\\n",
-    "                                                                 nonzero_elems_count))"
+    "print(f'approx error: {approx_error}, non-zero elements in approx: {nonzero_elems_count}')"
    ]
   },
   {
@@ -82,8 +81,7 @@
     "approx_error = tl.norm(t - approx_sp)\n",
     "nonzero_elems_count = sum(t_shape)*r + sparsity*np.prod(t_shape)\n",
     "\n",
-    "print('approx error: {}, non-zero elements in approx: {}'.format(approx_error,\\\n",
-    "                                                                 nonzero_elems_count))"
+    "print(f'approx error: {approx_error}, non-zero elements in approx: {nonzero_elems_count}')"
    ]
   },
   {

tensorly/backend/__init__.py

@@ -10,7 +10,7 @@
 import sys
 
 
-class dynamically_dispatched_class_attribute(object):
+class dynamically_dispatched_class_attribute:
     """Enable dynamically dispatched attributes such as dtype
 
     Parameters

tensorly/backend/core.py

@@ -88,7 +88,7 @@ def __name__(self):
         return "Index"
 
 
-class Backend(object):
+class Backend:
     _available_backends = dict()
 
     def __init_subclass__(cls, backend_name, **kwargs):

tensorly/contrib/decomposition/_tt_cross.py

@@ -106,14 +106,10 @@ def tensor_train_cross(input_tensor, rank, tol=1e-4, n_iter_max=100, random_stat
 
     # Initialize rank
     if rank[0] != 1:
-        message = "Provided rank[0] == {} but boundary conditions dictate rank[0] == rank[-1] == 1.".format(
-            rank[0]
-        )
+        message = f"Provided rank[0] == {rank[0]} but boundary conditions dictate rank[0] == rank[-1] == 1."
         raise ValueError(message)
     if rank[-1] != 1:
-        message = "Provided rank[-1] == {} but boundary conditions dictate rank[0] == rank[-1] == 1.".format(
-            rank[-1]
-        )
+        message = f"Provided rank[-1] == {rank[-1]} but boundary conditions dictate rank[0] == rank[-1] == 1."
         raise ValueError(message)
 
     # list col_idx: column indices (right indices) for skeleton-decomposition: indicate which columns used in each core.

tensorly/contrib/decomposition/tests/test_tt_TTOI.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 Testing of applying TTOI
 

tensorly/contrib/sparse/backend/numpy_backend.py

@@ -14,7 +14,7 @@
 _MIN_SPARSE_VERSION = Version("0.4.1+10.g81eccee")
 if Version(sparse.__version__) < _MIN_SPARSE_VERSION:
     raise ImportError(
-        "numpy sparse backend requires `sparse` version >= %r" % _MIN_SPARSE_VERSION
+        f"NumPy sparse backend requires `sparse` version >= {_MIN_SPARSE_VERSION!r}"
     )
 
 

tensorly/random/base.py

@@ -212,14 +212,10 @@ def random_tt(shape, rank, full=False, random_state=None, **context):
 
     # Initialization
     if rank[0] != 1:
-        message = "Provided rank[0] == {} but boundaring conditions dictatate rank[0] == rank[-1] == 1.".format(
-            rank[0]
-        )
+        message = f"Provided rank[0] == {rank[0]} but boundaring conditions dictatate rank[0] == rank[-1] == 1."
         raise ValueError(message)
     if rank[-1] != 1:
-        message = "Provided rank[-1] == {} but boundaring conditions dictatate rank[0] == rank[-1] == 1.".format(
-            rank[-1]
-        )
+        message = f"Provided rank[-1] == {rank[-1]} but boundaring conditions dictatate rank[0] == rank[-1] == 1."
         raise ValueError(message)
 
     rns = T.check_random_state(random_state)

tensorly/tenalg/proximal.py

@@ -550,8 +550,8 @@ def unimodality_prox(tensor):
     )
     # Next line finds mutual peak points
     values = tl.tensor(
-        tl.to_numpy((tensor - monotone_decreasing >= 0))
-        * tl.to_numpy((tensor - monotone_increasing >= 0)),
+        tl.to_numpy(tensor - monotone_decreasing >= 0)
+        * tl.to_numpy(tensor - monotone_increasing >= 0),
         **tl.context(tensor),
     )
 

tensorly/tenalg/tests/test_batched_tensordot.py

@@ -8,8 +8,8 @@
 
 def test_batched_tensordot():
     shape = [3, 4, 2]
-    vecs = [random.random_tensor((s)) for s in shape]
-    tensor = random.random_tensor((shape))
+    vecs = [random.random_tensor(s) for s in shape]
+    tensor = random.random_tensor(shape)
 
     # Equivalence with inner product when contracting with self along all modes
     res = tensordot(tensor, tensor, modes=3)  # [[0, 1, 2], [0, 1, 2]])

tensorly/tenalg/tests/test_outer_product.py

@@ -12,7 +12,7 @@ def test_outer_product():
 
     X = tl.tensor(rng.random_sample((4, 5, 6)))
     Y = tl.tensor(rng.random_sample((3, 4)))
-    Z = tl.tensor(rng.random_sample((2)))
+    Z = tl.tensor(rng.random_sample(2))
     tdot = outer([X, Y, Z])
     true_dot = tenalg.tensordot(X, Y, ())
     true_dot = tenalg.tensordot(true_dot, Z, ())

tensorly/tt_tensor.py

@@ -260,14 +260,10 @@ def validate_tt_rank(
 
         # Initialization
         if rank[0] != 1:
-            message = "Provided rank[0] == {} but boundary conditions dictate rank[0] == rank[-1] == 1.".format(
-                rank[0]
-            )
+            message = f"Provided rank[0] == {rank[0]} but boundary conditions dictate rank[0] == rank[-1] == 1."
             raise ValueError(message)
         if rank[-1] != 1:
-            message = "Provided rank[-1] == {} but boundary conditions dictate rank[0] == rank[-1] == 1.".format(
-                rank[-1]
-            )
+            message = f"Provided rank[-1] == {rank[-1]} but boundary conditions dictate rank[0] == rank[-1] == 1."
             raise ValueError(message)
 
     if allow_overparametrization:

tensorly/utils/deprecation.py

@@ -19,7 +19,7 @@ def deprecated(deprecated_by, msg="", use_deprecated=True):
         if False, the new class/function will be used along with the deprecation warning for the old name
     """
 
-    class DeprecatedBy(object):
+    class DeprecatedBy:
         def __init__(self):
             self.deprecated_by = deprecated_by
             self.use_deprecated = use_deprecated
@@ -67,7 +67,7 @@ def __init__(wrapped_self, *args, **kwargs):
     return DeprecatedBy()
 
 
-class DefineDeprecated(object):
+class DefineDeprecated:
     """Creates a dummy class or function that returns the class/fun it is deprecated by,
             along with a warning
 

tensorly/utils/tests/test_deprecation.py

@@ -2,7 +2,7 @@
 
 
 def test_deprecated():
-    class Dummy(object):
+    class Dummy:
         def __init__(self, arg=1):
             self.arg = arg + 1