Ramp - Rapid Machine Learning prototyping in Python(github.com)
github.com
Ramp - Rapid Machine Learning prototyping in Python
https://github.com/kvh/ramp
2 comments
I'm doing a machine-learning project for my company (Sauce Labs) and decided to give Ramp a try. Despite some effort put into understanding the API and digging into code to ensure I was passing in all the right kinds of parameters when I hit random errors, I think it was worth it. I was able to test and compare 20 different machine learning algorithms and even more feature sets really easily. I'd definitely recommend checking it out!
I don't know if it's this package or just Python in general, but I'm having a really hard time getting things up and running with the Kaggle insults example. To get going I ran
pip install numpy
pip install pandas
pip install scikit-learn
sudo apt-get install libyaml-dev
pip install nltk
pip install gensim
pip install numexpr
pip install Cython
sudo apt-get install libhdf5-serial-dev
pip install h5py
pip install tables
pip install ramp
Most of those packages, except gensim are available as part of the free community edition of Anaconda:
http://docs.continuum.io/anaconda/1.2/pkgs.html
https://store.continuum.io/cshop/anaconda
Getting a large chunk of the python scientific stack in one fell swoop is why I often use Anaconda or EPD.
http://docs.continuum.io/anaconda/1.2/pkgs.html
https://store.continuum.io/cshop/anaconda
Getting a large chunk of the python scientific stack in one fell swoop is why I often use Anaconda or EPD.
If you're on Mac, the Scipy Superpack is really the way to go for scientific Python:
http://fonnesbeck.github.com/ScipySuperpack/
http://fonnesbeck.github.com/ScipySuperpack/