Change required versions of numpy >= 2.0, required versions of python?=3.9, and required versions of torch >= 2.5. As this will ensure they can work together.

Author: jdjakem

environment.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
   - python>=3.9
   #- numpy>=1.20,<=1.26.4
-  - numpy>=1.20
+  - numpy>=2.0
   - matplotlib
   - scipy
   - jupyter
@@ -17,7 +17,7 @@ dependencies:
   - setuptools
   - sympy
   - networkx
-  - pytorch
+  - pytorch>=2.5
   - numba
   - cvxpy
   # make html in docs folder

environment_w_extras.yml

@@ -6,7 +6,7 @@ channels:
 dependencies:
   - python>=3.9
   #- numpy>=1.20,<=1.26.4
-  - numpy>=1.20
+  - numpy>=2.0
   - matplotlib
   - scipy
   - jupyter
@@ -17,7 +17,7 @@ dependencies:
   - setuptools
   - sympy
   - networkx
-  - pytorch
+  - pytorch>=2.5
   - numba
   - cvxpy
   # gpu support

pyapprox/surrogates/affine/linearsystemsolvers.py

@@ -1354,7 +1354,7 @@ def _constraint_bounds(self):
         )
 
 
-class BasisPursuitDensoisingCVXRegressionSolver(
+class BasisPursuitDenoisingCVXRegressionSolver(
     CVXOPTOptionsMixin, LinearSystemSolver
 ):
     def __init__(self, penalty: float, backend: BackendMixin = NumpyMixin):

pyapprox/surrogates/affine/tests/test_linsolvers.py

@@ -8,7 +8,7 @@
     LinearlyConstrainedLstSqSolver,
     OMPSolver,
     BasisPursuitRegressionSolver,
-    BasisPursuitDensoisingCVXRegressionSolver,
+    BasisPursuitDenoisingCVXRegressionSolver,
     QuantileRegressionSolver,
     EntropicLoss,
     EntropicRegressionSolver,
@@ -103,7 +103,7 @@ def test_basis_pursuit_denoising_cvxopt(self):
         true_coef[np.random.permutation(true_coef.shape[0])[:sparsity]] = 1.0
         vals = basis_matrix @ true_coef
 
-        solver = BasisPursuitDensoisingCVXRegressionSolver(0.001, backend=bkd)
+        solver = BasisPursuitDenoisingCVXRegressionSolver(0.001, backend=bkd)
         solver.set_options(
             {"abstol": 1e-14, "reltol": 1e-14, "feastol": 1e-14}
         )

pyproject.toml

@@ -11,7 +11,7 @@ requires = [
 name = "pyapprox"
 version = "1.0.3"
 description = "High-dimensional function approximation and estimation"
-requires-python = '>=3.6'
+requires-python = '>=3.9'
 license = {file = 'LICENSE'}
 readme = "README.md"
 authors = [
@@ -23,11 +23,11 @@ classifiers=[
     "Operating System :: OS Independent",
 ]    
 dependencies = [
-    'numpy >= 1.20',
+    'numpy >= 2.0',
     'matplotlib',
     'scipy >= 1.0.0',
     'sympy',
-    'torch',
+    'torch>=2.5',
     'coverage>=6.0',
     'pytest-cov',
     'pytest>=4.6',

setup.py

@@ -26,7 +26,7 @@
     long_description_content_type="text/markdown",
     url="https://github.com/sandialabs/pyapprox",
     packages=setuptools.find_packages(),
-    python_requires=">=3.8",
+    python_requires=">=3.9",
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",
@@ -36,16 +36,16 @@
     setup_requires=[
         "setuptools",
         # "numpy >= 1.20, <=1.26.4",
-        "numpy >= 1.20",
+        "numpy >= 2.0",
         "scipy >= 1.0.0",
     ],
     install_requires=[
         # "numpy >= 1.20, <=1.26.4",
-        "numpy >= 1.20",
+        "numpy >= 2.0",
         "matplotlib",
         "scipy >= 1.0.0",
         "sympy",
-        "torch",
+        "torch>=2.5",
         # coverage > 6.0 causes a wierd bug with numpy
         # e.g
         # test_scipy_gauss_legendre_pts_wts_1D (__main__.TestNumpyUtilities)

tutorials/surrogates/plot_basis_pursuit_denoising.py

@@ -102,7 +102,7 @@
 from pyapprox.util.backends.numpy import NumpyMixin as bkd
 from pyapprox.variables.joint import IndependentMarginalsVariable
 from pyapprox.surrogates.affine.linearsystemsolvers import (
-    BasisPursuitDensoisingCVXRegressionSolver,
+    BasisPursuitDenoisingCVXRegressionSolver,
 )
 from pyapprox.surrogates.univariate.orthopoly import (
     setup_univariate_orthogonal_polynomial_from_marginal,
@@ -144,7 +144,7 @@
 nsamples = 50
 train_samples = sampler(nsamples)
 train_values = model(train_samples)
-solver = BasisPursuitDensoisingCVXRegressionSolver(0.001, backend=bkd)
+solver = BasisPursuitDenoisingCVXRegressionSolver(0.001, backend=bkd)
 solver.set_options({"abstol": 1e-14, "reltol": 1e-14, "feastol": 1e-14})
 solver.set_weights(sampler.christoffel_function(train_samples))
 bexp.set_solver(solver)