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Ramp - Rapid Machine Learning prototyping in Python

• benmanns 14y ago
  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 But I'm still running into some numpy issue with the cross-validation loop (`for config in factory:`).
  • synparb 14y ago
    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 http://docs.continuum.io/anaconda/1.2/pkgs.html https://store.continuum.io/cshop/anaconda 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.
  • achompas 14y ago
    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/
• jlipps 14y ago
  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!