Installation

cblearn requires Python 3.9 or newer. We recommend using Anaconda to install Python and dependencies in separated environments. The package is mainly tested on Linux, but Windows and Mac OS should work, too.

conda create -n cblearn python==3.9
conda activate cblearn

User Installation

cblearn and can be installed using pip:

pip install cblearn

This will install the minimal set of required packages, sufficient for most uses and saving disk space. However, some features require more packages that can be installed by adding an option to the install command.

Extra Requirements

Extra requirements can be installed by adding an option to the install command and enable more advanced features. Some of those extra dependencies need non-Python packages to be installed first.

$ pip install cblearn[torch,wrapper] h5py

torch: Most estimators provide an (optional) implementation using pytorch to run large datasets on CPU and GPU. This requires the pytorch package to be installed manually or by adding the torch` option to the install command. Note that pytorch might need about 1GB of disk space.
wrapper: The estimators in Wrapper provide an Python interface to the original implementation in R-lang. This requires the rpy2 package to be installed by adding the wrapper option to the install command. Additionally, this requires an installed R interpreter whit must available be in the PATH environment variable. The R packages are installed automatically upon the first use of the estimators.
h5py: The function cblearn.datasets.fetch_imagenet_similarity() requires the h5py package to load the dataset. This can package can be installed with pip. Note that some platforms require additionally the hdf5 libraries to be installed

manually.

Contributor Installation

If you want to make changes to the code or documentation, you should first download the repository and install the project in developer mode with developer dependencies. This way, changes in the code are directly considered without the need for re-installation. Additionally, packages required to run the tests and build the documentation are installed.

$ git clone git@github.com:cblearn/cblearn.git
$ cd cblearn
$ pip install -e.[tests,docs,torch,wrapper]

The -e option installs the package in developer mode such that changes in the code are considered directly without re-installation.

tests: To run the unit tests, the pytest package is required, which can be installed by adding the tests option to the install command.
docs: Building these docs requires the sphinx package, which can be installed by adding the docs option to the install command.

Now you can run the tests and build the documentation:

$ python -m pytest --remote-data  # should run all tests; this can take a while.

$ cd docs
$ make html  # should generate docs/_build/html/index.html