GitLocker: The Coding Marketplace

Description:

pytestexperiments 0.1.1

A pytest plugin to help developers of research-oriented software projects keep track of the results of their numerical experiments.

What it does
pytest-experiments allows research-oriented programmers to easily
persist data about numerical experiments so that they can track
metrics over time.

Know what experiments you’ve run, when you ran them, its
inputs and its results over time and project development.
Review and compare experiments over time to ensure that
continued development is improving your results.

How it works
An experiment is a python function that runs your method or algorithm
against some input and reports one or more metrics of interest.
Experiments are basically unit tests of numerical methods. Like unit tests
we provide a function or method under test with some input and assert that its
output conforms to some concrete expectations. Unlike unit tests, the method
under test produces some metrics which we are interested in but for which
concrete expectations do not exist. We store these metrics, along with some
metadata in a database so that we can track our results over time.
We use pytest to collect and execute our experiments. This plugin offers
a notebook fixture to facilitate recording your experiments. Here is
a very simple example experiment:
import pytest
from my_numerical_method_package import (
my_implementation, # your numerical method
result_is_valid, # returns True iff the result is well-formed
performance_metric, # the performance metric we care about
)

@pytest.mark.experiment # [optional] mark this test as an experiment
@pytest.mark.parameterize("x", [1, 2, 3]) # The inputs; we will run this experiment for x=1, x=2, and x=3
def test_my_numerical_method(notebook, x): # Request the notebook fixture
result = my_implementation(x)
assert result_is_valid(result) # our concrete expectations about the result
notebook.record(performance=performance_metric(result)) # record the performance
At the end of the test the notebook fixture will save experiment metadata, the
inputs, and whatever was passed to notebook.record to a database. By default,
this database will be a sqlite database called experiments.db.

A machine learning example
For example, suppose we are building a machine learning classifier. The method
under test would be our model, the input would be train and validation
datasets and any hyper-parameters of our methods. The model is initialized
with the hyper-parameters, trained on the training data, and the output is the
predictions on the validation set.
We want the model to return probabilities, so we have a concrete expectation
that the predictions should all be between 0 and 1. If any are not, our code
is wrong and the experiment should fail.
However, we are not only interested in returning probabilities, we also want
our model to return good predictions (e.g. the predictions have high accuracy
and high fairness). We might have some conrete expectations about these metrics:
for example we may wish to reject any result that has metrics strictly worse
than some baseline, but it is not easy or meaningful to specify a criterion
based on the accuracy and fairness values for when we should stop developing
our model. In fact, the metrics collected during the experiment may inform
subsequent development.
See the demo directory for a detailed example-based walkthrough.

Installation
You can install “pytest-experiments” via pip from PyPI:
$ pip install pytest-experiments

Contributing
Contributions are very welcome. This project uses poetry for packaging.
To get set up simply clone the repo and run
poetry install
poetry run pre-commit install
The first command will install the package along with all development dependencies
in a virtual environment. The second command will install the pre-commit hook which
will automatically format source files with black.
Tests can be run with pytest
Please document any code added with docstrings. New modules can be auto-documented by
running:
sphinx-apidoc -e -o docs/source src/pytest_experiments
Documentation can be compiled (for example to html with make):
cd docs/
make html

License
Distributed under the terms of the MIT license, “pytest-experiments” is free and open source software

Issues
If you encounter any problems, please file an issue along with a detailed description.

Acknowledgements
This pytest plugin was generated with Cookiecutter along with @hackebrot’s cookiecutter-pytest-plugin template.