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Description:

curators 0.0.0b4

Curators

A package for action selection and optimization.
Action types currently supported are context-naive, contextual, and sequential.

Context-naive actions, which we'll call simply "naive" assumes the expected
return is simply a function of the action itself. This is commonly referred
to as a multi-armed bandit.
Contextual actions enable use of observable variables to further improve the
accuracy of the expected return of actions. For now, we enabled
integration of clustering algorithms with a traditional bandit. Longer
term, this will grow into more broad support for contextual bandits.
Sequential algorithms extend the previous optimizations of single actions
into the optimization of multiple ordered actions.

To facilitate maintaining the mathematical purity of classic models while
maintaining practical interfaces for real world actions, objects are
categorized into three object types: models, agents, and applications.

Model - distinct and mathematically pure units of learning.
Agent - integration of identically shaped models into a single interface.
Application - tooling around connecting an agent to some real-world medium.

Installation
Pip
pip install curators
Package Build and Install
We use Python's setuptools to handle installation.
You can build the wheel and install it elsewhere manually using:
# from within this repo's root:
python setup.py bdist_wheel

# from within your other project that you're installing this wheel into:
pip install curators-0.0.1-py3-none-any.whl

Or you can install it directly from Github usisng the library name
git+ssh://git@github.com/BellwethrInc/curators.git as you would
install any other library through either a pip install [x] or including it
directly in your requirements.txt file.
Or if you prefer using https rather than ssh to connect to github,
git+https://github.com/BellwethrInc/curators.git works as well.
After we get proper releases set up, you'll be able to do
git+ssh://git@github.com/BellwethrInc/curators.git@v0.0.0#egg=curators
to specify a release as well.
Coming soon:

proper versioning

As a Developer
Within this repo's root, create a virtualenv (or your conda equivalent) and
install the dependencies.
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Testing
Within a developer's environment, run all tests simply with
python -m unittest

Coverage
To see testing coverage, run
coverage run -m unittest discover # to generate coverage data
coverage report # to render the coverage data to console
coverage-badge -o docs/images/coverage.svg # update the coverage badge

Docs
To build docs

within your venv
pip install -r requirements.txt
cd docs
make html

Or if you're within pycharm:

be sure your current environment also has the docs requirements installed

pip install -r docs/requirements.txt

Create and configure a new "Spinx Task" configuration:

Be sure html is the command selected
select docs/source as the source directory
select docs/build directory.

Make this directory yourself or let sphinx make it for you.
this path is within .gitignore so don't worry about stepping on toes within your build dir.

rerun docs build as needed
docs/build/index.html is the file you want to open in your browswer

Notebooks
When running the notebooks, you need to either be working within a virtualenv with
curators installed or have the root of this repo within your environment's PATH
or PYTHONPATH. For example,
PYTHONPATH="${PYTHONPATH}:path/to/this/repos/root" jupyter notebook

will leave the rest of your environments unaffected while giving python access to this repo directly.