NhuanTDBK

map_col = lambda dat,col: col+"-"+dat.map(str)
gen_hash_item = lambda field, feat: '{0}:{1}:1'.format(field,hashstr(feat))
def gen_hash_row(feats,label):
    result = []
    for idx, item in enumerate(feats):
        val = item.split('-')[-1]
        if val != 'nan':
                result.append(gen_hash_item(idx,item))
    lbl = 1
    if label == 0:

vlandham

Here's a little walkthrough of how Yannick and I are using feature branches and pull requests to develop new features and adding them to the project. Below are the steps I take when working on a new feature. Hopefully this, along with watching the process on Github, will serve as a starting point to having everyone use a similar workflow.

Questions, comments, and suggestions for improvements welcome!

Start with the latest on master

When starting a new feature, I make sure to start with the latest and greatest codebase:

git checkout master

agramfort

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""