🎨 dpi argument and tight_layout (#16)

* 🎨 dpi argument and tight_layout

* 🎨 format

* 🐛 rename parameter

now njab and scikit-learn have different api names... keep for compatibility for now

* 🐛 installation was moved to notebook

* 🐛 move installation further to notebooks

* 🐛 install non PyPI version...

* 🐛 fix workflow

* 🐛 commit id did not work for install

* 🐛 try to use njab...

* 🎨 see if tag is picked up from local install

* :arrow-up: fix minimal scikit-learn  version

- as cross-validate needs to have `params` parameter

---------

Co-authored-by: enryh <henry.webel@cpr.ku.dk>

Author: enryH

.github/workflows/colab.yml

@@ -14,12 +14,24 @@ jobs:
       image: europe-docker.pkg.dev/colab-images/public/runtime:latest
     steps:
       - uses: actions/checkout@v4
+      - name: Dev - Install njab (from branch) - optional
+        if: github.event_name == 'push'
+        run: |
+          echo GITHUB_REF= $GITHUB_REF
+          echo GITHUB_SHA= $GITHUB_SHA
+          echo "Branch or Tag: ${{ github.ref }}"
+          pip install .
+          # pip install git+https://github.com/RasmussenLab/njab.git@$GITHUB_SHA
+          cd docs
+          echo
+          echo Installed version of njab:
+          python3 -c "import njab; print(njab.__version__)"
       - name: Test tutorials
         run: | 
           python3 -m pip install papermill ipykernel njab
           cd docs/tutorial
           papermill explorative_analysis.ipynb --help-notebook
           papermill log_reg.ipynb --help-notebook
-          papermill explorative_analysis.ipynb explorative_analysis_tested.ipynb
-          pip install njab heatmapz openpyxl "matplotlib<3.7" plotly
           papermill log_reg.ipynb log_reg_tested.ipynb
+          papermill explorative_analysis.ipynb explorative_analysis_tested.ipynb
+

pyproject.toml

@@ -17,7 +17,7 @@ dependencies = [
   "omegaconf",
   "numpy",
   "pandas",
-  "scikit-learn",
+  "scikit-learn>=1.4",
   "statsmodels",
   "matplotlib",
   "mrmr_selection",

src/njab/plotting/__init__.py

@@ -8,7 +8,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-
 from njab.plotting.lifelines import plot_lifelines
 
 __all__ = ['plot_lifelines']
@@ -26,19 +25,24 @@
 
 def savefig(fig: matplotlib.figure.Figure,
             name: str,
-            folder: pathlib.Path = '.',
-            pdf=True):
+            folder: str = '.',
+            dpi: int = 300,
+            pdf: bool = True,
+            tight_layout: bool = True) -> None:
     """Save matplotlib Figure (having method `savefig`) as pdf and png."""
     folder = pathlib.Path(folder)
     fname = folder / name
     folder = fname.parent  # in case name specifies folders
     folder.mkdir(exist_ok=True, parents=True)
-    if not fig.get_constrained_layout():
+    if tight_layout and not fig.get_constrained_layout():
         fig.tight_layout()
-    fig.savefig(fname.with_suffix('.png'), bbox_inches='tight')
+    fname = fname.with_suffix('.png')
+    fig.savefig(fname, dpi=dpi, bbox_inches='tight')
+    logger.info("Saved Figures to %s", fname)
     if pdf:
-        fig.savefig(fname.with_suffix('.pdf'), bbox_inches='tight')
-    logger.info(f"Saved Figures to {fname}")
+        fname = fname.with_suffix('.pdf')
+        fig.savefig(fname, bbox_inches='tight')
+        logger.info("Saved Figures to %s", fname)
 
 
 def select_xticks(ax: matplotlib.axes.Axes, max_ticks: int = 50) -> list:

src/njab/sklearn/__init__.py

@@ -122,7 +122,7 @@ def find_n_best_features(
             scoring=scoring,
             cv=cv,
             return_train_score=return_train_score,
-            fit_params=fit_params,
+            params=fit_params,
             error_score='raise')
         scores['n_features'] = n_features
         scores['test_case'] = name