New example notebook, new variation on SSLALM, tests

.github/workflows/setup.yml

@@ -22,19 +22,24 @@ jobs:
       uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
+    - name: Install package dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        pip install -r requirements.txt
         pip install -e .
+    - name: Run tests
+      run: |
+        python -m unittest
+    - name: Install benchmark dependencies
+      run: |
+        pip install -r requirements_benchmark.txt
+        pip install -e .  
     - name: Run algorithms
       run: |
         python experiments/run_folktables.py alg=sslalm n_runs=2 run_maxtime=2
         python experiments/run_folktables.py alg=ghost n_runs=2 run_maxtime=2
         python experiments/run_folktables.py alg=alm n_runs=2 run_maxtime=2
         python experiments/run_folktables.py alg=sgd n_runs=2 run_maxtime=2
-        python experiments/run_folktables.py alg=fairret n_runs=2 run_maxtime=2
 
   run-on-windows:
     name: Setup on windows
@@ -50,19 +55,24 @@ jobs:
       uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
+    - name: Install package dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        pip install -r requirements.txt
+        pip install -e .
+    - name: Run tests
+      run: |
+        python -m unittest
+    - name: Install benchmark dependencies
+      run: |
+        pip install -r requirements_benchmark.txt
         pip install -e .
     - name: Run algorithms
       run: |
         python experiments/run_folktables.py alg=sslalm n_runs=2 run_maxtime=2
         python experiments/run_folktables.py alg=ghost n_runs=2 run_maxtime=2
         python experiments/run_folktables.py alg=alm n_runs=2 run_maxtime=2
         python experiments/run_folktables.py alg=sgd n_runs=2 run_maxtime=2
-        python experiments/run_folktables.py alg=fairret n_runs=2 run_maxtime=2
 
   run-on-macos:
     name: Setup on macos
@@ -78,16 +88,22 @@ jobs:
       uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
+    - name: Install package dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
-        pip install -r requirements.txt
         pip install -e .
-    - name: Run algorithms
+    - name: Run tests
       run: |
-        python experiments/run_folktables.py alg=sslalm n_runs=2 run_maxtime=2
-        python experiments/run_folktables.py alg=ghost n_runs=2 run_maxtime=2
-        python experiments/run_folktables.py alg=alm n_runs=2 run_maxtime=2
-        python experiments/run_folktables.py alg=sgd n_runs=2 run_maxtime=2
-        python experiments/run_folktables.py alg=fairret n_runs=2 run_maxtime=2
+        python -m unittest
+    # - name: Install benchmark dependencies
+    #   run: |
+    #     pip install -U --force-reinstall certifi
+    #     pip install -r requirements_benchmark.txt
+    #     pip install -e .
+    # - name: Run algorithms
+    #   run: |
+    #     python experiments/run_folktables.py alg=sslalm n_runs=2 run_maxtime=2
+    #     python experiments/run_folktables.py alg=ghost n_runs=2 run_maxtime=2
+    #     python experiments/run_folktables.py alg=alm n_runs=2 run_maxtime=2
+    #     python experiments/run_folktables.py alg=sgd n_runs=2 run_maxtime=2

.gitignore

@@ -4,6 +4,7 @@ experiments/utils/raw_data/
 experiments/utils/exp_results
 experiments/utils/saved_models
 experiments/outputs
+experiments/conf
 data/
 examples/data
 experiments/data

README.md

@@ -6,22 +6,23 @@ The toolkit implements algorithms for constrained training of neural networks ba
 <!-- , as well as a tool to compare stochastic-constrained stochastic optimization algorithms on a _fair learning_ task in the `experiments` folder. -->
 
 ## Table of Contents
+
 1. [Basic installation instructions](#basic-installation-instructions)
 2. [Using the toolkit](#using-the-toolkit)
-3. [Extending the toolkit](#extending-the-toolkit) 
+3. [Extending the toolkit](#extending-the-toolkit)
 4. [Reproducing the Benchmark](#reproducing-the-benchmark)
 5. [License and terms of use](#license-and-terms-of-use)
 6. [References](#references)
 
 humancompatible-train is still under active development! If you find bugs or have feature
 requests, please file a
-[Github issue](https://github.com/humancompatible/train/issues). 
+[Github issue](https://github.com/humancompatible/train/issues).
 
 ## Installation
 
 Use
 
-```
+```bash
 pip install humancompatible-train
 ```
 
@@ -31,11 +32,11 @@ The only dependencies of this package are `numpy` and `torch`.
 
 The toolkit implements algorithms for constrained training of neural networks based on PyTorch.
 
-The algorithms follow the `dual_step()` - `step()` framework: taking inspiration from PyTorch, the `double_step` does updates related to the dual parameters and prepares for the primal update (by, e.g., saving constraint gradients), and `step()` updates the primal parameters.
+The algorithms follow the `dual_step()` - `step()` framework: taking inspiration from PyTorch, the `dual_step()` does updates related to the dual parameters and prepares for the primal update (by, e.g., saving constraint gradients), and `step()` updates the primal parameters.
 
 In general, your code using `humancompatible-train` would look something like this:
 
-```
+```python
 for inputs, labels in dataloader:
   # inference
   outputs = model(inputs)
@@ -54,13 +55,20 @@ for inputs, labels in dataloader:
 ```
 
 Our idea is to
+
 1. Deviate minimally from the usual PyTorch workflow
 2. Make different stochastic-constrained stochastic optimization algorithms nearly interchangable in the code.
 
 ### Code examples
 
 You are invited to check out our new API presented in notebooks in the `examples` folder.
 
+The example notebooks have additional dependencies, such as `fairret`. To install those, run
+
+```
+pip install humancompatible-train[examples]
+```
+
 *The legacy API used for the benchmark is presented in `examples/_old_/algorithm_demo.ipynb` and `examples/_old_/constraint_demo.ipynb`.*
 
 ## Extending the toolkit
@@ -74,21 +82,27 @@ You are invited to check out our new API presented in notebooks in the `examples
 The code used in [our benchmark paper](https://arxiv.org/abs/2507.04033) is not migrated to the new API yet (WIP).
 
 ### Basic installation instructions
+
 The code requires Python version ```3.11```.
 
 1. Create a virtual environment
 
 **bash** (Linux)
+
 ```
 python3.11 -m venv fairbenchenv
 source fairbenchenv/bin/activate
 ```
+
 **cmd** (Windows)
+
 ```
 python -m venv fairbenchenv
 fairbenchenv\Scripts\activate.bat
 ```
+
 2. Install from source.
+
 ```
 git clone https://github.com/humancompatible/train.git
 cd train
@@ -97,11 +111,12 @@ pip install .
 ```
 
 If you wish to edit the code of the algorithms, install as an editable package:
+
 ```
 pip install -e .
 ```
 
-__Warning__: it is recommended to use Stochastic Ghost with the mkl-accelerated version of the scipy package with Stochastic Ghost; to install it, run
+**Warning**: it is recommended to use Stochastic Ghost with the mkl-accelerated version of the scipy package with Stochastic Ghost; to install it, run
 
 ```pip install --force-reinstall -i https://software.repos.intel.com/python/pypi scipy```
 
@@ -115,12 +130,14 @@ after installing requirements.txt; otherwise, the algorithm will run slower. How
 ### Running the algorithms
 
 The benchmark comprises the following algorithms:
+
 - Stochastic Ghost [[2]](#2),
 - SSL-ALM [[3]](#3),
 - Stochastic Switching Subgradient [[4]](#4).
 
 To reproduce the experiments of the paper, run the following:
-``` python
+
+```
 cd experiments
 python run_folktables.py data=folktables alg=sslalm
 python run_folktables.py data=folktables alg=alm
@@ -129,23 +146,25 @@ python run_folktables.py data=folktables alg=ssg
 python run_folktables.py data=folktables alg=sgd     # baseline, no fairness
 python run_folktables.py data=folktables alg=fairret # baseline, fairness with regularizer
 ```
+
 Each command will start 10 runs of the `alg`, 30 seconds each.
 The results will be saved to `experiments/utils/saved_models` and `experiments/utils/exp_results`.
 <!-- In the repository, we include the configuration needed to reproduce the experiments in the paper. To do so, go to `experiments` and run `python run_folktables.py data=folktables alg=sslalm`. -->
 <!-- Repeat for the other algorithms by changing the `alg` parameter. -->
 
-This repository uses [Hydra](https://hydra.cc/) to manage parameters; see `experiments/conf` for configuration files. 
-* To change the parameters of the experiment, such as the number of runs for each algorithm, run time, the dataset used (*note: for now supports only Folktables*) - use `experiment.yaml`. 
-* To change the dataset settings - such as file location - or do dataset-specific adjustments - such as the configuration of the protected attributes - use `data/{dataset_name}.yaml`
-* To change algorithm hyperparameters, use `alg/{algorithm_name}.yaml`.
-* To change constraint hyperparameters, use `constraint/{constraint_name}.yaml`
+This repository uses [Hydra](https://hydra.cc/) to manage parameters; see `experiments/conf` for configuration files.
+
+- To change the parameters of the experiment, such as the number of runs for each algorithm, run time, the dataset used (*note: for now supports only Folktables*) - use `experiment.yaml`.
+- To change the dataset settings - such as file location - or do dataset-specific adjustments - such as the configuration of the protected attributes - use `data/{dataset_name}.yaml`
+- To change algorithm hyperparameters, use `alg/{algorithm_name}.yaml`.
+- To change constraint hyperparameters, use `constraint/{constraint_name}.yaml`
 
 <!-- ; it is installed as one of the dependencies. -->
 <!-- To learn more about using Hydra, please check out the [official tutorial](https://hydra.cc/docs/tutorials/basic/your_first_app). -->
 
 ### Producing plots
-The plots and tables like the ones in the paper can be produced using the two notebooks. `experiments/algo_plots.ipynb` houses the convergence plots, and `experiments/model_plots.ipynb` - all the others.
 
+The plots and tables like the ones in the paper can be produced using the two notebooks. `experiments/algo_plots.ipynb` houses the convergence plots, and `experiments/model_plots.ipynb` - all the others.
 
 ## License and terms of use
 
@@ -155,7 +174,7 @@ The benchmark part of the package relies on the Folktables package, provided und
 It provides code to download data from the American Community Survey
 (ACS) Public Use Microdata Sample (PUMS) files managed by the US Census Bureau.
 The data itself is governed by the terms of use provided by the Census Bureau.
-For more information, see https://www.census.gov/data/developers/about/terms-of-service.html
+For more information, see <https://www.census.gov/data/developers/about/terms-of-service.html>
 
 <!-- ## Cite this work -->
 
@@ -181,18 +200,7 @@ For more information, see https://www.census.gov/data/developers/about/terms-of-
 
 If you use this work, we encourage you to cite [our paper](https://arxiv.org/abs/2509.21254),
 
-```
-@misc{kliachkin2025train,
-      title={humancompatible.train: Implementing Optimization Algorithms for Stochastically-Constrained Stochastic Optimization Problems}, 
-      author={Andrii Kliachkin and Jana Lepšová and Gilles Bareilles and Jakub Mareček},
-      year={2025},
-      eprint={2509.21254},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2509.21254},
-      note={Workshop on Constrained Optimization for Machine Learning at NeurIPS 2025}
-}
-
+```bibtex
 @misc{kliachkin2025benchmarkingstochasticapproximationalgorithms,
       title={Benchmarking Stochastic Approximation Algorithms for Fairness-Constrained Training of Deep Neural Networks}, 
       author={Andrii Kliachkin and Jana Lepšová and Gilles Bareilles and Jakub Mareček},
@@ -204,14 +212,14 @@ If you use this work, we encourage you to cite [our paper](https://arxiv.org/abs
 }
 ```
 
-<a id="1">[1]</a> 
+<a id="1">[1]</a>
 Ding, Hardt & Miller et al. (2021) Retiring Adult: New Datasets for Fair Machine Learning, Curran Associates, Inc..
 
-<a id="2">[2]</a> 
+<a id="2">[2]</a>
 Facchinei & Kungurtsev (2023) Stochastic Approximation for Expectation Objective and Expectation Inequality-Constrained Nonconvex Optimization, arXiv.
 
-<a id="3">[3]</a> 
+<a id="3">[3]</a>
 Huang, Zhang & Alacaoglu (2025) Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints, arXiv.
 
-<a id="4">[4]</a> 
+<a id="4">[4]</a>
 Huang & Lin (2023) Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization, Curran Associates Inc..