HCrystalBall was designed to have soft dependencies on the wrapped libraries giving you the opportunity to define your own subset of wrappers that are to be used.
Ideally your application should pin dependencies for wrapped libraries along with hcrystalball and other dependencies.
Install core HCrystalBall from pip or from conda-forge¶
pip install hcrystalball
conda install -c conda-forge hcrystalball
Install other libraries you want to wrap¶
conda install -c conda-forge statsmodels conda install -c conda-forge prophet conda install -c conda-forge scikit-learn pip install pmdarima pip install tbats
For parallel execution¶
conda install -c conda-forge prefect
Very often you will want to use more wrappers, than just Sklearn, run examples in jupyterlab,
or execute model selection in parallel. Getting such dependencies to play together nicely
might be cumbersome, so checking
envrionment.yml might give you faster start.
# get dependencies file, e.g. using curl curl -O https://raw.githubusercontent.com/heidelbergcement/hcrystalball/master/environment.yml # check comments in environment.yml, keep or remove as requested, than create environment using conda env create -f environment.yml # activate the environment conda activate hcrystalball # if you want to see progress bar in jupyterlab, execute also jupyter labextension install @jupyter-widgets/jupyterlab-manager # install the library from pip pip install hcrystalball # or from conda conda install -c conda-forge hcrystalball