Changing workflows and adding badges in relation to #26 (#28)

.github/actions/build/action.yml

@@ -22,9 +22,9 @@ runs:
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        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 . --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

.github/workflows/package_workflow.yml

@@ -0,0 +1,27 @@
+name: Python Package
+
+on:
+    workflow_run:
+        workflows: ["Tests"]
+        types:
+            - completed
+
+jobs:
+    build_package:
+        runs-on: ubuntu-latest
+        steps:
+            - uses: actions/checkout@v4
+            - uses: ./.github/actions/build
+
+    publish_package:
+        needs: build_package
+        runs-on: ubuntu-latest
+        if: ${{ github.event.workflow_run.conclusion == 'success' }}
+        steps:  
+            - uses: actions/checkout@v4
+            - uses: ./.github/actions/publish
+        permissions:
+            id-token: write
+        environment:
+            name: pypi
+            url: https://pypi.org/p/ikpls

.github/workflows/pull_request_package_workflow.yml

@@ -0,0 +1,17 @@
+name: Pull Request Python Package
+
+on:
+    pull_request:
+        branches: [ "main" ]
+        paths:
+            - 'ikpls/**'
+            - 'tests/**'
+            - '.github/**'
+            - 'pyproject.toml'
+
+jobs:
+    build_package:
+        runs-on: ubuntu-latest
+        steps:
+            - uses: actions/checkout@v4
+            - uses: ./.github/actions/build

.github/workflows/pull_request.yml → .github/workflows/pull_request_test_workflow.yml

@@ -1,14 +1,12 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
-
-name: Python package
+name: Pull Request Tests
 
 on:
   pull_request:
     branches: [ "main" ]
     paths:
       - 'ikpls/**'
       - 'tests/**'
+      - '.github/**'
       - 'pyproject.toml'
 
 jobs:
@@ -23,10 +21,4 @@ jobs:
       - uses: actions/checkout@v4
       - uses: ./.github/actions/test
         with:
-          PYTHON_VERSION: ${{ matrix.python-version }}
-
-  build_package:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: ./.github/actions/build
+          PYTHON_VERSION: ${{ matrix.python-version }}

.github/workflows/test_workflow.yml

@@ -0,0 +1,24 @@
+name: Tests
+
+on:
+    push:
+        branches: [ "main" ]
+        paths:
+            - 'ikpls/**'
+            - 'tests/**'
+            - '.github/**'
+            - 'pyproject.toml'
+
+jobs:
+    test_package:
+        runs-on: ${{ matrix.os }}  
+        strategy:  
+            fail-fast: false  
+            matrix:  
+                os: [ubuntu-latest, windows-latest, macos-12]
+                python-version: ["3.9", "3.10", "3.11", "3.12"]
+        steps:
+            - uses: actions/checkout@v4
+            - uses: ./.github/actions/test
+              with:
+                    PYTHON_VERSION: ${{ matrix.python-version }}

README.md

@@ -10,7 +10,9 @@
 
 [![Documentation Status](https://readthedocs.org/projects/ikpls/badge/?version=latest)](https://ikpls.readthedocs.io/en/latest/?badge=latest)
 
-[![Build Status](https://github.com/Sm00thix/IKPLS/actions/workflows/workflow.yml/badge.svg)](https://github.com/Sm00thix/IKPLS/actions/workflows/workflow.yml)
+[![Tests Status](https://github.com/Sm00thix/IKPLS/actions/workflows/test_workflow.yml/badge.svg)](https://github.com/Sm00thix/IKPLS/actions/workflows/test_workflow.yml)
+
+[![Package Status](https://github.com/Sm00thix/IKPLS/actions/workflows/package_workflow.yml/badge.svg)](https://github.com/Sm00thix/IKPLS/actions/workflows/package_workflow.yml)
 
 [![JOSS Status](https://joss.theoj.org/papers/ac559cbcdc6e6551f58bb3e573a73afc/status.svg)](https://joss.theoj.org/papers/ac559cbcdc6e6551f58bb3e573a73afc)
 

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/__init__.py

@@ -1 +1 @@
-__version__ = "1.2.2.post2"
+__version__ = "1.2.2.post3"

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

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "ikpls"
-version = "1.2.2.post2"
+version = "1.2.2.post3"
 description = "Improved Kernel PLS and Fast Cross-Validation."
 authors = ["Sm00thix <oleemail@icloud.com>"]
 maintainers = ["Sm00thix <oleemail@icloud.com>"]