Get branch up to date with development

.github/workflows/pytest.yml

@@ -45,7 +45,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
 
     steps:
@@ -73,6 +73,15 @@ jobs:
       run: |
         pytest ${{ env.pytest-args }} ${{ env.test-dir }}
 
+    - name: Coveralls GitHub Action
+      uses: coverallsapp/github-action@v2
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
+
+    - name: Remove Coverage file
+      uses: JesseTG/[email protected]
+      with:
+        path: coverage.lcov
+
     - name: Check for files left behind by test
       run: |
         before="${{ steps.status-before.outputs.BEFORE }}"
@@ -82,79 +91,4 @@ jobs:
             echo "git status from after: $after"
             echo "Not all generated files have been deleted!"
             exit 1
-        fi
-
-# Testing with conda
-  conda-tests:
-    name: conda-${{ matrix.python-version }}-${{ matrix.os }}
-    runs-on: ${{ matrix.os }}
-    defaults:
-      run:
-        shell: bash -l {0}  # Default to using bash on all and load (-l) .bashrc which miniconda uses
-
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.8", "3.9", "3.10"]
-        os: ["ubuntu-latest", "macos-latest", "windows-latest"]
-
-    steps:
-    - name: Checkout
-      uses: actions/checkout@v2
-
-    - name: Conda install
-      uses: conda-incubator/setup-miniconda@v2
-      with:
-        auto-update-conda: true
-        python-version: ${{ matrix.python-version }}
-
-    - name: Install ${{ env.package-name }}
-      run: |
-        python -V
-        python -m pip install --upgrade pip
-        python -m pip install wheel
-        python -m pip install -e ".${{ env.extra-requires }}"
-
-    - name: Tests
-      run: |
-          pytest ${{ env.pytest-args }} ${{ env.test-dir }}
-
-  # Testing a dist install
-  dist-test:
-    name: dist-${{ matrix.python-version }}-${{ matrix.os }}
-
-    runs-on: ${{ matrix.os }}
-    defaults:
-      run:
-        shell: bash
-
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.8", "3.9", "3.10"]
-        os: ["ubuntu-latest", "macos-latest", "windows-latest"]
-
-    steps:
-    - name: Checkout
-      uses: actions/checkout@v2
-
-    - name: Setup Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Create sdist
-      id: sdist
-      run: |
-        python -m pip install --upgrade pip
-        python setup.py sdist
-        echo "${{env.package-name}}"
-        echo "sdist_name=$(ls -t dist/${{ env.package-name }}-*.tar.gz | head -n 1)" >> $GITHUB_ENV
-
-    - name: Install ${{ env.package-name }}
-      run: |
-        python -m pip install ${{ env.sdist_name }}${{ env.extra-requires }}
-
-    - name: Tests
-      run: |
-        pytest ${{ env.pytest-args }} ${{ env.test-dir }}
+        fi

README.md

@@ -1,5 +1,9 @@
 # DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization
-
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Tests](https://github.com/automl/DEHB/actions/workflows/pytest.yml/badge.svg)](https://github.com/automl/DEHB/actions/workflows/pytest.yml)
+[![Coverage Status](https://coveralls.io/repos/github/automl/DEHB/badge.svg)](https://coveralls.io/github/automl/DEHB)
+[![PyPI](https://img.shields.io/pypi/v/dehb)](https://pypi.org/project/dehb/)
+[![Static Badge](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20-blue)](https://pypi.org/project/dehb/)
 ### Installation
 ```bash
 # from pypi
@@ -16,6 +20,7 @@ pip install -e DEHB  # -e stands for editable, lets you modify the code and reru
 * [01 - Using DEHB to optimize 4 hyperparameters of a Scikit-learn's Random Forest on a classification dataset](examples/01_Optimizing_RandomForest_using_DEHB.ipynb)
 * [02 - Optimizing Scikit-learn's Random Forest without using ConfigSpace to represent the hyperparameter space](examples/02_using%20DEHB_without_ConfigSpace.ipynb)
 * [03 - Hyperparameter Optimization for MNIST in PyTorch](examples/03_pytorch_mnist_hpo.py)
+* [04 - A generic template to use MODEHB for multi-objectives Hyperparameter Optimization](examples/04_mo_pytorch_mnist_hpo.py)
 
 To run PyTorch example: (*note additional requirements*) 
 ```bash
@@ -81,7 +86,8 @@ python examples/03_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
   --verbose --runtime 60 --scheduler_file dask_dump/scheduler.json 
 ```
 
-
+### Running DEHB to optimize multiple objectives
+To run multi-objective optimization we require 1 extra parameter mo_strategy: we provide MO-optimization using Non-dominated sorted (NDS) with crowding distance (NSGA-II) and NDS with eps-net(EPSNET). Find 04_mo_pytorch_mnist_hpo.py example to help you to get started
 
 ### DEHB Hyperparameters
 
@@ -122,3 +128,10 @@ represents the *mutation* strategy while `bin` represents the *binomial crossove
   editor    = {Z. Zhou},
   year      = {2021}
 }
+
+@online{Awad-arXiv-2023,
+title = {MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization},
+author = {Noor Awad and Ayushi Sharma and Frank Hutter},
+year = {2023},
+keywords = {}
+}