fixed tests; updated some repository files

.github/workflows/ci.yml

@@ -12,12 +12,11 @@ jobs:
       fail-fast: false
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
-        DISABLE_JIT: [0, 1]
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
@@ -29,12 +28,9 @@ jobs:
       - name: Test with unittest
         run: |
           coverage run -m unittest discover ./tests 'test_*.py'
-        env:
-          NUMBA_DISABLE_JIT: ${{ matrix.DISABLE_JIT }}
       # - name: Coverage with Code Climate
       #   uses: paambaati/codeclimate-action@v4.0.0
       #   # send report only when jit is disabled, and for python 3.11
-      #   if: ${{ matrix.DISABLE_JIT == 1 }}
       #   env:
       #     CC_TEST_REPORTER_ID: ${{ secrets.CODECLIMATE_REPORTER_ID }}
       #   with:

benchmarking/requirements-benchmarking.txt

@@ -0,0 +1,10 @@
+torch==2.1.2
+botorch==0.9.5
+matplotlib>=3.7.1
+numpy>=1.24.4
+pandas>=2.1.0
+scipy>=1.11.0
+joblib==1.3.2
+globopt==1.0.0
+gpytorch==1.11
+prettytable>=3.8.0

tests/test_regression.py

@@ -15,13 +15,13 @@ def test_fit_and_partial_fit(self) -> None:
         X = torch.as_tensor([-2.61, -1.92, -0.63, 0.38, 2], device="cpu").unsqueeze(-1)
         Y = problem(X)
         n = 3
-        mdls = [Idw(X[:n], Y[:n]), Rbf(X[:n], Y[:n], eps=0.5, eig_tol=0.0)]
+        mdls = [Idw(X[:n], Y[:n]), Rbf(X[:n], Y[:n], eps=0.5, svd_tol=0.0)]
         for i in range(len(mdls)):
             mdl = mdls[i]
             if isinstance(mdl, Idw):
                 mdls[i] = Idw(X, Y)
             else:
-                mdls[i] = Rbf(X, Y, mdl.eps, mdl.eig_tol, mdl.state)
+                mdls[i] = Rbf(X, Y, mdl.eps, mdl.svd_tol, mdl.state)
         x_hat = torch.linspace(-3, 3, 100, dtype=X.dtype).view(1, -1, 1)
         y_hat = torch.stack([mdl.posterior(x_hat).mean.squeeze() for mdl in mdls])
         y_hat_expected = torch.as_tensor(RESULTS["y_hat"][:2], dtype=y_hat.dtype)