Updated build action flake8 linting to use max line length of 88. Als…

…o did a bunch of formatting related to linting.

.github/actions/build/action.yml

@@ -24,7 +24,7 @@ runs:
         # stop the build if there are Python syntax errors or undefined names
         flake8 ikpls/ --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 ikpls/ --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        flake8 ikpls/ --count --exit-zero --max-complexity=10 --max-line-length=88 --statistics
       shell: bash
 
     - name: Install IKPLS dependencies

examples/fast_cross_val_numpy.py

@@ -107,7 +107,8 @@ def mse_for_each_target(Y_true, Y_pred):
     best_num_components = np.asarray(
         [
             [
-                np_pls_alg_1_fast_cv_results[split][f'num_components_lowest_mse_target_{i}']
+                np_pls_alg_1_fast_cv_results[split]
+                [f'num_components_lowest_mse_target_{i}']
                 for split in unique_splits
             ]
             for i in range(M)

ikpls/fast_cross_validation/numpy_ikpls.py

@@ -34,18 +34,18 @@ class PLS:
         Whether to center `X` before fitting by subtracting its row of
         column-wise means from each row. The row of column-wise means is computed on
         the training set for each fold to avoid data leakage.
-    
+
     center_Y : bool, optional default=True
         Whether to center `Y` before fitting by subtracting its row of
         column-wise means from each row. The row of column-wise means is computed on
         the training set for each fold to avoid data leakage.
-    
+
     scale_X : bool, optional default=True
         Whether to scale `X` before fitting by dividing each row with the row of `X`'s
         column-wise standard deviations. Bessel's correction for the unbiased estimate
         of the sample standard deviation is used. The row of column-wise standard
         deviations is computed on the training set for each fold to avoid data leakage.
-    
+
     scale_Y : bool, optional default=True
         Whether to scale `Y` before fitting by dividing each row with the row of `X`'s
         column-wise standard deviations. Bessel's correction for the unbiased estimate
@@ -64,7 +64,7 @@ class PLS:
     ------
     ValueError
         If `algorithm` is not 1 or 2.
-    
+
     Notes
     -----
     Any centering and scaling is undone before returning predictions to ensure that
@@ -482,7 +482,7 @@ def _stateless_predict(
             predictions for that specific number of components is used. If
             `n_components` is None, returns a prediction for each number of components
             up to `A`.
-        
+
         Notes
         -----
         """

ikpls/jax_ikpls_alg_1.py

@@ -31,11 +31,11 @@ class PLS(PLSBase):
     center_X : bool, default=True
         Whether to center `X` before fitting by subtracting its row of
         column-wise means from each row.
-    
+
     center_Y : bool, default=True
         Whether to center `Y` before fitting by subtracting its row of
         column-wise means from each row.
-    
+
     scale_X : bool, default=True
         Whether to scale `X` before fitting by dividing each row with the row of `X`'s
         column-wise standard deviations. Bessel's correction for the unbiased estimate
@@ -413,11 +413,11 @@ def stateless_fit(
         center_X : bool, default=True
             Whether to center `X` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         center_Y : bool, default=True
             Whether to center `Y` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         scale_X : bool, default=True
             Whether to scale `X` before fitting by dividing each row with the row of
             `X`'s column-wise standard deviations. Bessel's correction for the unbiased

ikpls/jax_ikpls_alg_2.py

@@ -31,11 +31,11 @@ class PLS(PLSBase):
     center_X : bool, default=True
         Whether to center `X` before fitting by subtracting its row of
         column-wise means from each row.
-    
+
     center_Y : bool, default=True
         Whether to center `Y` before fitting by subtracting its row of
         column-wise means from each row.
-    
+
     scale_X : bool, default=True
         Whether to scale `X` before fitting by dividing each row with the row of `X`'s
         column-wise standard deviations. Bessel's correction for the unbiased estimate
@@ -389,11 +389,11 @@ def stateless_fit(
         center_X : bool, default=True
             Whether to center `X` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         center_Y : bool, default=True
             Whether to center `Y` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         scale_X : bool, default=True
             Whether to scale `X` before fitting by dividing each row with the row of
             `X`'s column-wise standard deviations. Bessel's correction for the unbiased

ikpls/jax_ikpls_base.py

@@ -5,7 +5,7 @@
 Implementations of concrete classes exist for both Improved Kernel PLS Algorithm #1
 and Improved Kernel PLS Algorithm #2.
 
-For more details, refer to the paper: 
+For more details, refer to the paper:
 "Improved Kernel Partial Least Squares Regression" by Dayal and MacGregor.
 
 Author: Ole-Christian Galbo Engstrøm
@@ -669,11 +669,11 @@ def stateless_fit(
         center_X : bool, default=True
             Whether to center `X` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         center_Y : bool, default=True
             Whether to center `Y` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         scale_X : bool, default=True
             Whether to scale `X` before fitting by dividing each row with the row of
             `X`'s column-wise standard deviations. Bessel's correction for the unbiased
@@ -949,11 +949,11 @@ def stateless_fit_predict_eval(
         center_X : bool, default=True
             Whether to center `X` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         center_Y : bool, default=True
             Whether to center `Y` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         scale_X : bool, default=True
             Whether to scale `X` before fitting by dividing each row with the row of
             `X`'s column-wise standard deviations. Bessel's correction for the unbiased
@@ -1167,11 +1167,11 @@ def _inner_cross_validate(
         center_X : bool, default=True
             Whether to center `X` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         center_Y : bool, default=True
             Whether to center `Y` before fitting by subtracting its row of
             column-wise means from each row.
-        
+
         scale_X : bool, default=True
             Whether to scale `X` before fitting by dividing each row with the row of
             `X`'s column-wise standard deviations. Bessel's correction for the unbiased

ikpls/numpy_ikpls.py

@@ -29,15 +29,15 @@ class PLS(BaseEstimator):
     ----------
     algorithm : int, default=1
         Whether to use Improved Kernel PLS Algorithm #1 or #2.
-    
+
     center_X : bool, default=True
         Whether to center `X` before fitting by subtracting its row of
         column-wise means from each row.
-    
+
     center_Y : bool, default=True
         Whether to center `Y` before fitting by subtracting its row of
         column-wise means from each row.
-    
+
     scale_X : bool, default=True
         Whether to scale `X` before fitting by dividing each row with the row of `X`'s
         column-wise standard deviations. Bessel's correction for the unbiased estimate