Python Shell Jupyter Notebook Dockerfile Ruby TeX Vim script
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
docker
docs
python/eskapade
tests
vagrant
.gitignore
.readthedocs.yml
Jenkinsfile
LICENSE
MANIFEST.in
NOTICE
README.rst
setup.py
tox.ini

README.rst

Eskapade: Modular Analytics

Eskapade is a light-weight, python-based data analysis framework, meant for developing and modularizing all sorts of data analysis problems into reusable analysis components.

Eskapade uses a modular approach to analytics, meaning that you can use pre-made operations (called 'links') to build an analysis. This is implemented in a chain-link framework, where you define a 'Chain', consisting of a number of Links. These links are the fundamental building block of your analysis. For example, a data loading link and a data transformation link will frequently be found together in a pre-processing Chain.

Each chain has a specific purpose, for example: data quality checks of incoming data, pre-processing of data, booking and/or training of predictive algorithms, validation of these algorithms, and their evaluation. By using this work methodology, analysis links can be more easily reused in future data analysis projects.

Eskapade is analysis-library agnostic. It is used to set up typical data analysis problems from multiple packages, e.g.: scikit-learn, Spark MLlib, and ROOT. Likewise, Eskapade can use a manner of different data structures to handle data, such as: pandas DataFrames, numpy arrays, Spark DataFrames/RDDs, and more.

For example, Eskapade has been used as a self-learning framework for typical machine learning problems. Trained algorithms can predict real-time or batch data, these models can be evaluated over time, and Eskapade can bookkeep and retrain their algorithms.

Documentation

The entire Eskapade documentation including tutorials can be found here.

Check it out

Eskapade requires Python 3 and is pip friendly. To get started, simply do:

$ pip install Eskapade

or check out the code from out github repository:

$ git clone git@github.com:KaveIO/Eskapade.git
$ pip install -e Eskapade/

where in this example the code is installed in edit mode (option -e).

You can now use Eskapade in Python with:

import eskapade

Congratulations, you are now ready to use Eskapade!

Quick run

To see the available Eskapade example, do:

$ export TUTDIR=`pip show Eskapade | grep Location | awk '{ print $2"/eskapade/tutorials" }'`
$ ls -l $TUTDIR/

E.g. you can now run:

$ eskapade_run $TUTDIR/esk101_helloworld.py

For all available Eskapade example macros, please see our tutorials section.

Release notes

In version 0.8 of Eskapade (August 2018) the modules root-analysis and spark-analysis have been split off into separate packages called Eskapade-ROOT and Eskapade-Spark .

So the (core) Eskapade package no longer depends on ROOT and Spark, just on plain python packages. This make it much easier for people to try out the core functionality of Eskapade.

To install Eskapade-ROOT and Eskapade-Spark, do:

$ pip install Eskapade-ROOT
$ pip install Eskapade-Spark

or check out the code from out github repository:

$ git clone git@github.com:KaveIO/Eskapade-ROOT.git eskapade-root
$ pip install -e eskapade-root/
$ git clone git@github.com:KaveIO/Eskapade-Spark.git eskapade-spark
$ pip install -e eskapade-spark/

where in this example the code is installed in edit mode (option -e).

You can now use these in Python with:

import eskapadespark
import esroofit

See release notes for previous versions of Eskapade.

Contact and support

Please note that the KPMG Eskapade group provides support only on a best-effort basis.