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# OpenReview Matcher
A tool for computing optimal paper-reviewer matches for peer review, subject to constraints and affinity scores. Comes with a simple web server designed for integration with the OpenReview server application.

Brief explanatation how the matching system works:

https://docs.google.com/presentation/d/1AljO7he87Hn9wnffDYvuk-BT-WPJvv-7-3Ems8O-VG4/edit?usp=sharing

## Installation
Clone the [GitHub repository](https://github.com/openreview/openreview-matcher.git) and install with `pip`:

```
git clone https://github.com/openreview/openreview-matcher.git
pip install ./openreview-matcher
```

## Example Usage

The matcher can be run from the command line. For example:
```
python -m matcher \
	--scores affinity_scores.txt \
	--weights 1 \
	--min_papers_default 1 \
	--max_papers_default 10 \
	--num_reviewers 3 \
	--num_alternates 3
```

Run the module with the `--help` flag to learn about the arguments:
```
python -m matcher --help
```

## Solvers

### MinMax Solver

Basic implementation using the Minimum Cost function implemented in the Google [ortools](https://developers.google.com/optimization/flow/mincostflow) library. MinMax solver tries to optimize the scores respecting the restrictions of min and max quotas for each paper and reviewer.

### FairFlow Solver

Fairflow solver tries to more fairly assign reviewers to papers in a way that each paper has at least some minimum affinity with the reviewers to which it is assigned.

For more information, see [this paper](https://arxiv.org/abs/1905.11924v1)

### FairIR Solver

FairIR solver finds a solution in the same way that FairFlow does. Both solvers address the issue of maximizing the global aggregate scores of all assignments, by requiring that the sum of the scores between a paper and its reviewers is at least some number (fairness threshold). This threshold is found through a binary search between the highest possible sum of scores for a paper and zero.

Our implementation is optimized by reducing the problem size and relaxing the fairness to speed up the matching. To reduce the space taken up by the affinity matrix, OpenReview only stores the top K scores for each reviewer and paper leading to a sparse matrix. We only initialize matcher variables between reviewer-paper pairs that have scores, which shrinks the problem. Progress in the matching could also stall if the matcher tries to maintain the fairness threshold, so the threshold moves closer to zero each time the matching stalls.

### Randomized Solver

The randomized solver (`--solver Randomized` on the command line) implements a randomized assignment algorithm. It takes as additional input limits on the marginal probability of each reviewer-paper pair being matched. The solver then finds a randomized assignment that maximizes expected total affinity, subject to the given probability limits. This randomized assignment is found with an LP, implemented in `matcher/solvers/randomized_solver.py`.

The solver returns a deterministic assignment which was sampled from this randomized assignment. The sampling algorithm is implemented in `matcher/solvers/bvn_extension`.

For more information, see [this paper](https://arxiv.org/abs/2006.16437).

### FairSequence Solver

FairSequence (`--solver FairSequence` on the command line) attempts to create an allocation of reviewers that is fair according to the weighted envy-free up to 1 item (WEF1) criterion. This criterion implies that when one paper has a higher average affinity for another papers' reviewers, it is only due to a single reviewer rather than a larger overall imbalance in affinity scores. Reviewers are assigned to papers one-by-one in priority order, with priority given to the papers with the lowest ratio of allocation size to demand. Ties in priority are resolved to favor reviewer-paper assignments with higher affinity.

For more information about the WEF1 fairness criterion, see [this paper](https://dl.acm.org/doi/abs/10.1145/3457166), and for more information about the adaptation to reviewer assignment, see [this paper](https://arxiv.org/abs/2108.02126).

### PerturbedMaximization Solver

PerturbedMaximization (`--solver PerturbedMaximization` on the command line) implements another randomized assignment algorithm. It aims to trade-off between the total affinity score and the randomness of the assignment (for the motivation and the metrics for randomness, see [this paper](https://arxiv.org/abs/2310.05995)). Like the Randomized Solver, PerturbedMaximization takes as additional input limits on the marginal probability of each reviewer-paper pair being matched. It also takes in another perturbation factor, which controls the trade-off between the total affinity score and the randomness. The solver then finds a randomized assignment that maximizes a perturbed total affinity score, subject to the given probability limits. This is done with a convex Quadratic Program, implemented in `matcher/solvers/perturbed_maximization_solver.py`.

Like the Randomized Solver, PerturbedMaximization returns a deterministic assignment that was sampled from this randomized assignment. The sampling algorithm is implemented in `matcher/solvers/bvn_extension`. For more information, see [this paper](https://arxiv.org/abs/2310.05995).

## Running the Server
The server is implemented in Flask and uses Celery to manage the matching tasks asynchronously and can be started from the command line:
```
python -m matcher.service --host localhost --port 5000
```

By default, the app will run on `http://localhost:5000`. The endpoint `/match/test` should show a simple page indicating that Flask is running.

The celery worker can be installed using:
```
 celery --app matcher.service.server.celery_app worker
```

To start multiple workers, run the same command with the name option for each worker as follows:

```
celery --app matcher.service.server.celery_app worker -n worker_name
```
For more options you may check the celery-worker documentation [here](https://docs.celeryproject.org/en/stable/reference/cli.html#celery-worker).

There's also an option to monitor the celery workers using `flower`. Make sure to install the full package:
```
pip install ./openreview-matcher[full]
```
and the flower dashboard can be started after that using
```
celery --app matcher.service.server.celery_app flower --persistent=True --state_save_interval=60000
```
For more options you may check the flower documentation [here](https://flower.readthedocs.io/en/latest/config.html).

By default, the flower dashboard will run on `http://localhost:5555`
### Configuration
Configuration files are located in `/matcher/service/config`. When started, the server will search for a `.cfg` file in `/matcher/service/config` that matches the environment variable `FLASK_ENV`, and will default to the values in `default.cfg`.

For example, with file `/matcher/service/config/development.cfg`:
```
# development.cfg
LOG_FILE='development.log'

OPENREVIEW_USERNAME='OpenReview.net'
OPENREVIEW_PASSWORD='Or$3cur3P@ssw0rd'
OPENREVIEW_BASEURL='http://localhost:3000'
```

Start the server with `development.cfg`:
```
FLASK_ENV=development python -m matcher.service
```

Note that Flask will set `FLASK_ENV` to "production" by default, so if a file `production.cfg` exists, and the `FLASK_ENV` variable is unset, then the app will overwrite default values with those in `production.cfg`.

## Unit & Integration Tests (with pytest)

The `/tests` directory contains unit tests and integration tests (i.e. tests that communicate with an instance of the OpenReview server application), written with [pytest](https://docs.pytest.org/en/latest).

### Requirements

Running the tests requires MongDB and Redis to support the OpenReview server instance used in the integration tests.

Before running integration tests, ensure that `mongod` and `redis-server` are running, and that no existing OpenReview instances are active.

Also ensure that OpenReview environment variables are unset:

```
unset OPENREVIEW_USERNAME
unset OPENREVIEW_PASSWORD
unset OPENREVIEW_BASEURL
```

Integration tests use the `test_context` [pytest fixture](https://docs.pytest.org/en/latest/fixture.html), which starts a clean, empty OpenReview instance and creates a mock conference.

### Running the Tests

The entire suite of tests can be run with the following commands from the top level project directory:

export OPENREVIEW_HOME=<path_to_openreview>
    python -m pytest tests

Individual test modules can be run by passing in the module file as the argument:

export OPENREVIEW_HOME=<path_to_openreview>
	python -m pytest tests/test_integration.py

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