add badges

.github/workflows/code-coverage.yml

@@ -0,0 +1,34 @@
+name: Generate Codecov
+on: [push]
+jobs:
+  run:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+    env:
+      OS: ${{ matrix.os }}
+      PYTHON: '3.8'
+    steps:
+    - uses: actions/checkout@master
+    - name: Setup Python
+      uses: actions/setup-python@master
+      with:
+        python-version: 3.8
+    - name: Generate coverage report
+      run: |
+        pip install pytest
+        pip install pytest-cov
+        pytest --cov=./ --cov-report=xml
+    - name: Upload coverage to Codecov
+      uses: codecov/codecov-action@v2
+      with:
+        # token: ${{ secrets.CODECOV_TOKEN }}
+        directory: ./coverage/reports/
+        env_vars: OS,PYTHON
+        fail_ci_if_error: true
+        files: ./coverage1.xml,./coverage2.xml
+        flags: unittests
+        name: codecov-umbrella
+        path_to_write_report: ./coverage/codecov_report.txt
+        verbose: true

.github/workflows/publish-to-test-pypi.yml

@@ -6,8 +6,7 @@ jobs:
   build-n-publish:
     name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
     runs-on: ubuntu-18.04
-
-  steps:
+    steps:
     - uses: actions/checkout@master
     - name: Set up Python 3.8
       uses: actions/setup-python@v1

README.md

@@ -1,5 +1,8 @@
 # Cyclic Boosting Machines
 
+[![PyPI version](https://badge.fury.io/py/pysarplus.svg)](https://badge.fury.io/py/cyclicbm)
+[![Academic Paper](https://img.shields.io/badge/academic-paper-7fdcf7)](https://arxiv.org/abs/2002.03425)
+
 This is an efficient and Scikit-learn compatible implementation of the machine learning algorithm [Cyclic Boosting -- an explainable supervised machine learning algorithm](https://arxiv.org/abs/2002.03425), specifically for predicting count-data, such as sales and demand.
 
 ## Usage