Updated test tolerances due to MacOS. Also updated README.

README.md

@@ -19,7 +19,7 @@
 The `ikpls` software package provides fast and efficient tools for PLS (Partial Least Squares) modeling. This package is designed to help researchers and practitioners handle PLS modeling faster than previously possible - particularly on large datasets.
 
 ## Citation
-If you use the `ikpls` software package for your work, please cite [this Journal of Open Source Software paper](https://joss.theoj.org/papers/10.21105/joss.06533). If you use the fast cross-validation algorithm implemented in `ikpls.fast_cross_validation.numpy_ikpls`, please also cite [this arXiv preprint](https://arxiv.org/abs/2401.13185).
+If you use the `ikpls` software package for your work, please cite [this Journal of Open Source Software article](https://joss.theoj.org/papers/10.21105/joss.06533). If you use the fast cross-validation algorithm implemented in `ikpls.fast_cross_validation.numpy_ikpls`, please also cite [this arXiv preprint](https://arxiv.org/abs/2401.13185).
 
 ## Unlock the Power of Fast and Stable Partial Least Squares Modeling with IKPLS
 
@@ -52,9 +52,10 @@ and scaling can be enabled or disabled independently from eachother and for X an
 by setting the parameters `center_X`, `center_Y`, `scale_X`, and `scale_Y`, respectively.
 In addition to correctly handling (column-wise) centering and scaling,
 the fast cross-validation algorithm **correctly handles row-wise preprocessing**
-such as (row-wise) centering and scaling of the X and Y input matrices,
-convolution, or other preprocessing. Row-wise preprocessing can safely be
-applied before passing the data to the fast cross-validation algorithm.
+that operates independently on each sample such as (row-wise) centering and scaling
+of the X and Y input matrices, convolution, or other preprocessing. Row-wise
+preprocessing can safely be applied before passing the data to the fast
+cross-validation algorithm.
 
 ## Prerequisites
 

tests/test_ikpls.py

@@ -895,8 +895,8 @@ def test_pls_1(self) -> None:
             jax_pls_alg_2=jax_pls_alg_2,
             diff_jax_pls_alg_1=diff_jax_pls_alg_1,
             diff_jax_pls_alg_2=diff_jax_pls_alg_2,
-            atol=1e-8,
-            rtol=6e-5,
+            atol=3e-8,
+            rtol=2e-4,
         )
 
         self.check_predictions(
@@ -3178,26 +3178,26 @@ def test_fast_cross_val_pls_1(self):
         splits = self.load_Y(["split"])
         assert Y.shape[1] == 1
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=False, scale=False, atol=0, rtol=1e-8
+            X, Y, splits, center=False, scale=False, atol=0, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=False, atol=0, rtol=1e-8
+            X, Y, splits, center=True, scale=False, atol=0, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=True, atol=0, rtol=1e-8
+            X, Y, splits, center=True, scale=True, atol=0, rtol=2e-8
         )
 
         # Remove the singleton dimension and check that the predictions are consistent.
         Y = Y.squeeze()
         assert Y.ndim == 1
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=False, scale=False, atol=0, rtol=1e-8
+            X, Y, splits, center=False, scale=False, atol=0, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=False, atol=0, rtol=1e-8
+            X, Y, splits, center=True, scale=False, atol=0, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=True, atol=0, rtol=1e-8
+            X, Y, splits, center=True, scale=True, atol=0, rtol=2e-8
         )
 
     # JAX will issue a warning if os.fork() is called as JAX is incompatible with
@@ -3243,13 +3243,13 @@ def test_fast_cross_val_pls_2_m_less_k(self):
         assert Y.shape[1] > 1
         assert Y.shape[1] < X.shape[1]
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=False, scale=False, atol=0, rtol=1e-7
+            X, Y, splits, center=False, scale=False, atol=0, rtol=2e-7
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=False, atol=0, rtol=1e-7
+            X, Y, splits, center=True, scale=False, atol=0, rtol=2e-7
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=True, atol=0, rtol=1e-7
+            X, Y, splits, center=True, scale=True, atol=0, rtol=2e-7
         )
 
     # JAX will issue a warning if os.fork() is called as JAX is incompatible with
@@ -3296,13 +3296,13 @@ def test_fast_cross_val_pls_2_m_eq_k(self):
         assert Y.shape[1] > 1
         assert Y.shape[1] == X.shape[1]
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=False, scale=False, atol=0, rtol=1e-8
+            X, Y, splits, center=False, scale=False, atol=0, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=False, atol=0, rtol=1e-8
+            X, Y, splits, center=True, scale=False, atol=0, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=True, atol=0, rtol=1e-8
+            X, Y, splits, center=True, scale=True, atol=0, rtol=2e-8
         )
 
     # JAX will issue a warning if os.fork() is called as JAX is incompatible with
@@ -3448,13 +3448,13 @@ def test_fast_cross_val_pls_2_m_less_k_loocv(self):
         assert Y.shape[1] > 1
         assert Y.shape[1] < X.shape[1]
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=False, scale=False, atol=2e-6, rtol=1e-8
+            X, Y, splits, center=False, scale=False, atol=6e-6, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=False, atol=5e-6, rtol=1e-8
+            X, Y, splits, center=True, scale=False, atol=6e-6, rtol=2e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=True, atol=3e-6, rtol=1e-8
+            X, Y, splits, center=True, scale=True, atol=6e-6, rtol=2e-8
         )
 
     def test_fast_cross_val_pls_2_m_eq_k_loocv(self):
@@ -3494,10 +3494,10 @@ def test_fast_cross_val_pls_2_m_eq_k_loocv(self):
         assert Y.shape[1] > 1
         assert Y.shape[1] == X.shape[1]
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=False, scale=False, atol=1e-7, rtol=1e-8
+            X, Y, splits, center=False, scale=False, atol=2e-7, rtol=1e-8
         )
         self.check_fast_cross_val_pls(
-            X, Y, splits, center=True, scale=False, atol=1e-7, rtol=1e-8
+            X, Y, splits, center=True, scale=False, atol=2e-7, rtol=1e-8
         )
         self.check_fast_cross_val_pls(
             X, Y, splits, center=True, scale=True, atol=1e-7, rtol=1e-8
@@ -4111,7 +4111,7 @@ def test_center_scale_combinations_pls_2_m_eq_k(self):
         splits = self.load_Y(["split"])  # Contains 3 splits of different sizes
         assert Y.shape[1] > 1
         assert Y.shape[1] == X.shape[1]
-        self.check_center_scale_combinations(X, Y, splits, atol=0, rtol=1e-8)
+        self.check_center_scale_combinations(X, Y, splits, atol=0, rtol=3e-8)
 
     # JAX will issue a warning if os.fork() is called as JAX is incompatible with
     # multi-threaded code. os.fork() is called by the  other cross-validation