Metadata-Version: 1.2
Name: incense
Version: 0.0.9
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
        ------------
        To use incense you need the newest development version of sacred, so that
        content-types of artifacts are automatically detected. Therefore, you first
        have to install sacred from github and then install incense from PyPI.
        
        ::
        
           pip install git+https://github.com/IDSIA/sacred.git
           pip install incense
        
        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 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 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
Requires-Python: >=3.5
