Compare std deviation from 0 with float epsilon instead of exact equality between std and 0

.github/workflows/pull_request_test_workflow.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:  
       fail-fast: false  
       matrix:  
-        os: [ubuntu-latest, windows-latest, macos-12]
+        os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v4

.github/workflows/test_workflow.yml

@@ -15,7 +15,7 @@ jobs:
         strategy:  
             fail-fast: false  
             matrix:  
-                os: [ubuntu-latest, windows-latest, macos-12]
+                os: [ubuntu-latest, windows-latest, macos-latest]
                 python-version: ["3.9", "3.10", "3.11", "3.12"]
         steps:
             - uses: actions/checkout@v4

ikpls/__init__.py

@@ -1 +1 @@
-__version__ = "1.2.3"
+__version__ = "1.2.4"

ikpls/fast_cross_validation/numpy_ikpls.py

@@ -92,6 +92,7 @@ def __init__(
         self.scale_Y = scale_Y
         self.algorithm = algorithm
         self.dtype = dtype
+        self.eps = np.finfo(dtype).eps
         self.name = f"Improved Kernel PLS Algorithm #{algorithm}"
         if self.algorithm not in [1, 2]:
             raise ValueError(
@@ -269,7 +270,7 @@ def _stateless_fit(
                     + train_sum_sq_X
                 )
             )
-            training_X_std[training_X_std == 0] = 1
+            training_X_std[np.abs(training_X_std) <= self.eps] = 1
 
         # Compute the training set standard deviations for Y
         if self.scale_Y:
@@ -289,7 +290,7 @@ def _stateless_fit(
                     + train_sum_sq_Y
                 )
             )
-            training_Y_std[training_Y_std == 0] = 1
+            training_Y_std[np.abs(training_Y_std) <= self.eps] = 1
 
         # Subtract the validation set's contribution from the total XTY
         training_XTY = self.XTY - validation_X.T @ validation_Y
@@ -341,7 +342,7 @@ def _stateless_fit(
             # Step 2
             if self.M == 1:
                 norm = la.norm(training_XTY, ord=2)
-                if np.isclose(norm, 0, atol=np.finfo(np.float64).eps, rtol=0):
+                if np.isclose(norm, 0, atol=self.eps, rtol=0):
                     self._weight_warning(i)
                     break
                 w = training_XTY / norm
@@ -352,15 +353,15 @@ def _stateless_fit(
                     q = eig_vecs[:, -1:]
                     w = training_XTY @ q
                     norm = la.norm(w)
-                    if np.isclose(norm, 0, atol=np.finfo(np.float64).eps, rtol=0):
+                    if np.isclose(norm, 0, atol=self.eps, rtol=0):
                         self._weight_warning(i)
                         break
                     w = w / norm
                 else:
                     training_XTYYTX = training_XTY @ training_XTY.T
                     eig_vals, eig_vecs = la.eigh(training_XTYYTX)
                     norm = eig_vals[-1]
-                    if np.isclose(norm, 0, atol=np.finfo(np.float64).eps, rtol=0):
+                    if np.isclose(norm, 0, atol=self.eps, rtol=0):
                         self._weight_warning(i)
                         break
                     w = eig_vecs[:, -1:]

ikpls/jax_ikpls_base.py

@@ -101,6 +101,7 @@ def __init__(
         self.scale_Y = scale_Y
         self.copy = copy
         self.dtype = dtype
+        self.eps = jnp.finfo(self.dtype).eps
         self.reverse_differentiable = reverse_differentiable
         self.verbose = verbose
         self.name = "Improved Kernel PLS Algorithm"
@@ -236,7 +237,7 @@ def get_std(self, A: ArrayLike):
             print(f"get_stds for {self.name} will be JIT compiled...")
 
         A_std = jnp.std(A, axis=0, dtype=self.dtype, keepdims=True, ddof=1)
-        A_std = jnp.where(A_std == 0, 1, A_std)
+        A_std = jnp.where(jnp.abs(A_std) <= self.eps, 1, A_std)
         return A_std
 
     @partial(jax.jit, static_argnums=(0, 3, 4, 5, 6, 7))

ikpls/numpy_ikpls.py

@@ -88,6 +88,7 @@ def __init__(
         self.scale_Y = scale_Y
         self.copy = copy
         self.dtype = dtype
+        self.eps = np.finfo(dtype).eps
         self.name = f"Improved Kernel PLS Algorithm #{algorithm}"
         if self.algorithm not in [1, 2]:
             raise ValueError(
@@ -207,12 +208,12 @@ def fit(self, X: npt.ArrayLike, Y: npt.ArrayLike, A: int) -> None:
 
         if self.scale_X:
             self.X_std = X.std(axis=0, ddof=1, dtype=self.dtype, keepdims=True)
-            self.X_std[self.X_std == 0] = 1
+            self.X_std[np.abs(self.X_std) <= self.eps] = 1
             X /= self.X_std
 
         if self.scale_Y:
             self.Y_std = Y.std(axis=0, ddof=1, dtype=self.dtype, keepdims=True)
-            self.Y_std[self.Y_std == 0] = 1
+            self.Y_std[np.abs(self.Y_std) <= self.eps] = 1
             Y /= self.Y_std
 
         N, K = X.shape
@@ -246,7 +247,7 @@ def fit(self, X: npt.ArrayLike, Y: npt.ArrayLike, A: int) -> None:
             # Step 2
             if M == 1:
                 norm = la.norm(XTY, ord=2)
-                if np.isclose(norm, 0, atol=np.finfo(np.float64).eps, rtol=0):
+                if np.isclose(norm, 0, atol=self.eps, rtol=0):
                     self._weight_warning(i)
                     break
                 w = XTY / norm
@@ -257,15 +258,15 @@ def fit(self, X: npt.ArrayLike, Y: npt.ArrayLike, A: int) -> None:
                     q = eig_vecs[:, -1:]
                     w = XTY @ q
                     norm = la.norm(w)
-                    if np.isclose(norm, 0, atol=np.finfo(np.float64).eps, rtol=0):
+                    if np.isclose(norm, 0, atol=self.eps, rtol=0):
                         self._weight_warning(i)
                         break
                     w = w / norm
                 else:
                     XTYYTX = XTY @ XTY.T
                     eig_vals, eig_vecs = la.eigh(XTYYTX)
                     norm = eig_vals[-1]
-                    if np.isclose(norm, 0, atol=np.finfo(np.float64).eps, rtol=0):
+                    if np.isclose(norm, 0, atol=self.eps, rtol=0):
                         self._weight_warning(i)
                         break
                     w = eig_vecs[:, -1:]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "ikpls"
-version = "1.2.3"
+version = "1.2.4"
 description = "Improved Kernel PLS and Fast Cross-Validation."
 authors = ["Sm00thix <oleemail@icloud.com>"]
 maintainers = ["Sm00thix <oleemail@icloud.com>"]

tests/test_ikpls.py

@@ -677,7 +677,10 @@ def check_cpu_gpu_equality(
         if n_good_components == -1:
             n_good_components = np_pls_alg_1.A
 
-        atol = 0
+        try:
+            atol = np.finfo(np_pls_alg_1.dtype).eps
+        except AttributeError:
+            atol = 0
         rtol = 1e-4
         # Regression matrices
         assert_allclose(