updated Python version requirements

.github/workflows/fvGP-CI.yml

@@ -17,7 +17,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.8', '3.9' ]
+        python-version: [ '3.9', '3.10' ]
 
     steps:
       - uses: actions/checkout@v2
@@ -52,10 +52,10 @@ jobs:
     steps:
       - uses: actions/checkout@v2
 
-      - name: Setup Python 3.8
+      - name: Setup Python 3.9
         uses: actions/setup-python@v2
         with:
-          python-version: '3.8'
+          python-version: '3.9'
 
       - name: Setup deploy
         run: |

CONTRIBUTING.rst

@@ -102,7 +102,7 @@ Before you submit a pull request, check that it meets these guidelines:
 2. If the pull request adds functionality, the docs should be updated. Put
    your new functionality into a function with a docstring, and add the
    feature to the list in README.rst.
-3. The pull request should work for Python 3.5, 3.6, 3.7 and 3.8, and for PyPy. Check
+3. The pull request should work for Python 3.9, 3.10, and for PyPy. Check
    https://travis-ci.com/MarcusMichaelNoack/fvgp/pull_requests
    and make sure that the tests pass for all supported Python versions.
 

fvgp/.travis.yml

@@ -2,10 +2,8 @@
 
 language: python
 python:
-  - 3.8
-  - 3.7
-  - 3.6
-  - 3.5
+  - 3.10
+  - 3.9
 
 # Command to install dependencies, e.g. pip install -r requirements.txt --use-mirrors
 install: pip install -U tox-travis

requirements.txt

@@ -2,8 +2,8 @@ wheel
 versioneer
 matplotlib
 plotly
-scipy
-numpy
+scipy == 1.11.2
+numpy == 1.25.2
 dask >= 2021.6.2
 distributed >= 2021.6.2
 hgdl == 2.1.2

setup.py

@@ -28,17 +28,15 @@
 setup(
     author="Marcus Michael Noack",
     author_email='MarcusNoack@lbl.gov',
-    python_requires='>=3.7',
+    python_requires='>=3.9',
     classifiers=[
         'Development Status :: 2 - Pre-Alpha',
         'Intended Audience :: Developers',
         'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
         'Natural Language :: English',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.5',
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
-        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
     description="Python package for highly flexible function-valued Gaussian processes (fvGP)",
     entry_points={

tests/test_fvgp.py

@@ -70,13 +70,13 @@ def test_single_task_init_advanced(self):
     def test_train_basic(self):
         my_gp1 = GP(input_dim, x_data, y_data, np.array([1, 1, 1, 1, 1, 1]))
         my_gp1.train(np.array([[0.01,1],[0.01,10],[0.01,10],[0.01,10],[0.01,10],[0.01,10]]),
-                method = "local", pop_size = 10, tolerance = 0.001,max_iter = 5)
+                method = "local", pop_size = 10, tolerance = 0.001,max_iter = 2)
         my_gp1.train(np.array([[0.01,1],[0.01,10],[0.01,10],[0.01,10],[0.01,10],[0.01,10]]),
-                method = "global", pop_size = 10, tolerance = 0.001,max_iter = 5)
+                method = "global", pop_size = 10, tolerance = 0.001,max_iter = 2)
         my_gp1.train(np.array([[0.01,1],[0.01,10],[0.01,10],[0.01,10],[0.01,10],[0.01,10]]),
-                method = "hgdl", pop_size = 10, tolerance = 0.001,max_iter = 5)
+                method = "hgdl", pop_size = 10, tolerance = 0.001,max_iter = 2)
         my_gp1.train(np.array([[0.01,1],[0.01,10],[0.01,10],[0.01,10],[0.01,10],[0.01,10]]),
-                method = "mcmc", pop_size = 10, tolerance = 0.001,max_iter = 5)
+                method = "mcmc", pop_size = 10, tolerance = 0.001,max_iter = 2)
 
         res = my_gp1.posterior_mean(np.random.rand(len(x_data),len(x_data[0])))
         res = my_gp1.posterior_mean_grad(np.random.rand(10,len(x_data[0])))
@@ -128,7 +128,7 @@ def test_train_hgdl(self):
 
 
         my_gp2.train(np.array([[0.01,10],[0.01,10],[0.01,10],[0.01,10],[0.01,10],[0.01,10]]),
-                method = "hgdl", tolerance = 0.001, max_iter = 3)
+                method = "hgdl", tolerance = 0.001, max_iter = 2)
 
 
     def test_train_hgdl_async(self):
@@ -152,12 +152,12 @@ def mkernel(x1,x2,hps,obj):
 
         my_fvgp = fvGP(input_dim,1,2, x_data, y_data, np.array([1, 1]), gp_kernel_function=mkernel)
         my_fvgp.train(np.array([[0.01,1],[0.01,10]]),
-                method = "global", pop_size = 10, tolerance = 0.001, max_iter = 5)
+                method = "global", pop_size = 10, tolerance = 0.001, max_iter = 2)
 
     def test_gp2Scale(self):
         client = Client()
         input_dim = 1
-        N = 10000
+        N = 2000
         x_data = np.random.rand(N,input_dim)
         y_data = np.sin(np.linalg.norm(x_data,axis = 1) * 5.0)
         hps_n = 2
@@ -169,7 +169,7 @@ def test_gp2Scale(self):
         init_hps = np.random.uniform(size = len(hps_bounds), low = hps_bounds[:,0], high = hps_bounds[:,1])
         my_gp2S = GP(1, x_data,y_data,init_hps, gp2Scale = True, gp2Scale_batch_size= 1000)
 
-        my_gp2S.train(hps_bounds, max_iter = 5, init_hyperparameters = init_hps)
+        my_gp2S.train(hps_bounds, max_iter = 2, init_hyperparameters = init_hps)
 
         x_pred = np.linspace(0,1,1000)
         mean1 = my_gp2S.posterior_mean(x_pred.reshape(-1,1))["f(x)"]

tox.ini

@@ -3,10 +3,8 @@ envlist = py35, py36, py37, py38, flake8
 
 [travis]
 python =
-    3.8: py38
-    3.7: py37
-    3.6: py36
-    3.5: py35
+    3.9: py39
+    3.10: py310
 
 [testenv:flake8]
 basepython = python