Metadata-Version: 1.2
Name: incense
Version: 0.0.11
Summary: Interactively retrieve data from sacred experiments.
Home-page: https://github.com/JarnoRFB/incense
Author: Rüdiger Busche
Author-email: rbusche@uos.de
License: MIT
Description: .. image:: https://mybinder.org/badge_logo.svg
            :target: https://mybinder.org/v2/gh/JarnoRFB/incense/master?urlpath=lab/tree/demo.ipynb
        
        .. image:: https://travis-ci.org/JarnoRFB/incense.svg?branch=master
            :target: https://travis-ci.org/JarnoRFB/incense
        
        .. image:: https://codecov.io/gh/JarnoRFB/incense/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/JarnoRFB/incense
        
        .. image:: https://img.shields.io/lgtm/grade/python/g/JarnoRFB/incense.svg?logo=lgtm&logoWidth=18
            :target: https://lgtm.com/projects/g/JarnoRFB/incense/context:python
        
        .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
            :target: https://github.com/ambv/black
        
        Incense
        =======
        
        Though automated logging of machine learning experiments results is
        crucial, it does not replace manual interpretation. Incense is a toolbox
        to facilitate manual interpretation of experiments that are logged using
        `sacred <https://github.com/IDSIA/sacred>`__. It lets you find and
        evaluate experiments directly in Jupyter notebooks. Incense lets you
        query the database for experiments by id, name or any hyperparmeter
        value. For each found experiment, configuration, artifacts and metrics
        can be displayed. The artifacts are rendered according to their type,
        e.g. a PNG image is displayed as an image, while a CSV file gets
        transformed to a ``pandas DataFrame``. Metrics are by default
        transformed into ``pandas Series``, which allows for flexible plotting.
        Together with sacred and incense, Jupyter notebooks offer the perfect
        solution for interpreting experiments as they allow for a combination of
        code that reproducibly displays the experiment’s results, as well as
        text that contains the interpretation.
        
        Installation
        ------------
        
        Install the latest release
        
        ::
        
           pip install incense
           
        Or install the latest development version
        
        ::
        
           pip install git+https://github.com/JarnoRFB/incense.git
        
        Documentation
        -------------
        
        `demo.ipynb <demo.ipynb>`_ demonstrates the basic functionality of
        incense. You can also try it out interactively on
        `binder <https://mybinder.org/v2/gh/JarnoRFB/incense/master?urlpath=lab/tree/demo.ipynb>`_.
        
        Contributing
        ------------
        We recommend using the `VSCode devcontainer <https://code.visualstudio.com/docs/remote/containers>`_ for development.
        It will automatically install all dependencies and start
        necessary services, such as mongoDB and JupyterLab.
        See `<.devcontainer/docker-compose.yml>`_ for details.
        If the output of ``id -u`` is something different than ``1000`` on your system, please add
        
        ::
        
           export UID
        
        to your ``.bashrc`` or ``.zshrc``.
        
        Building the container for the first time may take some time.
        Once in the container run
        
        ::
        
          $ pre-commit install
          $ python tests/example_experiment/conduct.py
        
        
        to set up the pre-commit hooks and populate the example database.
        
        Alternatively, you can use conda to set up your local development environment.
        
        ::
        
          $ conda create -n incense-dev python=3.6
          $ conda activate incense-dev
          # virtualenv is required for the precommit environments.
          $ conda install virtualenv
          # tox-conda is required for using tox with conda.
          $ pip install tox-conda
          $ pip install -r requirements-dev.txt
          $ pre-commit install
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
