Dev

.github/workflows/publish_package.yml

@@ -0,0 +1,39 @@
+name: Publish Python Package
+
+on:
+  release:
+    types: [published]
+    tags:
+      - 'v*'
+  workflow_dispatch:
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"  # Python version for building/uploading
+
+      - name: Install build tools
+        run: |
+          python -m pip install --upgrade pip
+          pip install build twine
+
+      # Optional: Generate version file from GitHub tag
+      # - name: Generate version file
+      #   run: echo "__version__ = '${GITHUB_REF##refs/tags/}'" > your_package/_version.py
+
+      - name: Build package
+        run: python -m build
+
+      - name: Publish to PyPI
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+        run: twine upload dist/*
+

.github/workflows/tests.yml

@@ -0,0 +1,36 @@
+name: Python Package CI
+
+on:
+  push:
+    branches: [main,dev]
+  pull_request:
+    branches: [main,dev]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Upgrade pip, install build tools
+        run: |
+          python -m pip install --upgrade pip setuptools wheel build
+
+      - name: Install your package with test dependencies
+        run: |
+          pip install .[test]   # assumes you’ve defined [project.optional-dependencies] in pyproject.toml
+
+      - name: Run pytest
+        run: |
+          pytest --disable-warnings --maxfail=1 --tb=short

.gitignore

@@ -174,4 +174,5 @@ cython_debug/
 .pypirc
 
 #Project specific
-output/
+output/
+version.py

README.md

@@ -29,10 +29,20 @@ A schematic detailing how the HEROS algorithm works is given below:
 ***
 <a id="item-two"></a>
 ## Installation
-HEROS can currently only be installed by cloning this repository. Make sure that you have also installed all prerequisite packages included in requirements.txt prior to running (Note: this list may need updating - so if you get errors, check first that you have necessary packages installed). 
+HEROS can be installed with pip or by cloning this repository. 
 
+### Pip
+HEROS can most easily be installed using the following pip command:
+```
+pip install skheros
+```
+In order to run the [HEROS_Demo_Notebook](https://github.com/UrbsLab/heros/blob/main/HEROS_Demo_Notebook.ipynb), download it and make sure to set the following notebook parameter to False in order to import HEROS from the above pip installation.
+```
+load_from_cloned_repo = False
+```
 
 ### Clone Respository
+To install/run HEROS from this cloned repository, run the following commands from the desired folder:
 ```
 git clone --single-branch https://github.com/UrbsLab/heros
 cd heros
@@ -62,7 +72,7 @@ Lastly, the fit() method can optionally be passed 'pop_df', a dataframe object,
 
 ### Demonstration Notebook
 A Jupyter Notebooks has been included to demonstrate how HEROS (and it's functions) can be applied to train, evaluate, and apply models with a wide variety of saved outputs, visualizations and model prediction explanations. We strongly recommend exploring this demonstration notebook to get familiar with HEROS and its capabilities. 
-* [DEMO Notebook](https://github.com/UrbsLab/heros/blob/main/HEROS_Demo_Notebook.ipynb)
+* [HEROS_Demo_Notebook](https://github.com/UrbsLab/heros/blob/main/HEROS_Demo_Notebook.ipynb)
 
 This notebook is currently set up to run by cloning this repository and running the included notebook. 
 

pyproject.toml

@@ -0,0 +1,59 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "skheros"
+version = "0.2.3"
+description = "The Heuristic Evolutionary Rule Optimization System (HEROS) is a supervised rule-based machine learning algorithm designed to agnostically model diverse 'structured' data problems and yield compact human interpretable solutions. This implementation is scikit-learn compatible."
+readme = "README.md"
+requires-python = ">=3.8"
+license = {file = "LICENSE" }
+keywords = ["machine learning","rule-based","scikit-learn", "interpretable", "classification", "supervised learning", "evolutionary computation"]
+authors = [{name = "Ryan Urbanowicz", email = "ryanurbanowicz@gmail.com"}]
+
+classifiers = [
+  "Intended Audience :: Science/Research",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Topic :: Scientific/Engineering :: Artificial Intelligence",
+  "License :: Other/Academic Software License",
+  "Framework :: Scikit-Learn",
+  "Operating System :: OS Independent", 
+]
+
+# dependencies = ["numpy>=2.3.0","pandas>=2.3.0","scikit-learn>=1.7.0","matplotlib>=3.10.3","seaborn>=0.13.2","scipy>=1.15.3","skrebate==0.7","networkx>=3.5"]
+
+dependencies = [
+"numpy",
+"pandas",
+"scikit-learn",
+"matplotlib",
+"seaborn",
+"scipy",
+"skrebate==0.7",
+"networkx"]
+
+[project.optional-dependencies]
+test = [
+  "pytest>=7.0",
+  "pytest-cov",
+  "pytest-mock",
+  "flake8",
+  "mypy",
+]
+
+[project.urls]
+"Source Code" = "https://github.com/UrbsLab/heros"
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+
+[tool.setuptools.packages.find]
+where = ["src"]
+include = ["heros*"]
+exclude = ["tests*"]

src/skheros/__init__.py

@@ -5,7 +5,3 @@
 """
 
 from .heros import HEROS
-
-__version__ = "0.0.1"
-__author__ = 'Ryan Urbanowicz'
-__credits__ = 'Cedars-Sinai Health Systems'

src/test_heros.py → tests/test_heros.py

@@ -1,6 +1,7 @@
+import pytest
 import os
 import pandas as pd
-from skheros.heros import HEROS
+from src.skheros.heros import HEROS
 from sklearn.metrics import classification_report
 from skrebate import MultiSURF # Install using: pip install skrebate==0.7
 
@@ -169,7 +170,7 @@ def test_multiclass():
     print("HEROS Top Model Training Data Performance Report:")
     print(classification_report(predictions, y, digits=8))
 
-
+"""
 def test_quantitative_outcome():
     print("------------------------------------------------------")
     print("Test: 6-bit MUX as quantiative outcome problem - binary features, No NA's")
@@ -190,14 +191,14 @@ def test_quantitative_outcome():
                 track_performance=1000,model_tracking=True,stored_rule_iterations=None,stored_model_iterations=None,random_state=42,verbose=True)
     heros = heros.fit(X, y, None, cat_feat_indexes=cat_feat_indexes, ek=ek)
     #Incomplete implementation
-
-
-if __name__ == "__main__":
-    test_6mux()
-    test_na()
-    test_mixed_feature_types()
-    test_mixed_feature_types_na()
-    test_multiclass()
-    #test_quantitative_outcome() #incomplete
+"""
+
+#if __name__ == "__main__":
+#    test_6mux()
+#    test_na()
+#    test_mixed_feature_types()
+#    test_mixed_feature_types_na()
+#    test_multiclass()
+#    test_quantitative_outcome() #incomplete