Scikit-learn balanced subsampling

I'm trying to create N balanced random subsamples of my large unbalanced dataset. Is there a way to do this simply with scikit-learn / pandas or do I have to implement it myself? Any pointers to code that does this?

These subsamples should be random and can be overlapping as I feed each to separate classifier in a very large ensemble of classifiers.

In Weka there is tool called spreadsubsample, is there equivalent in sklearn? http://wiki.pentaho.com/display/DATAMINING/SpreadSubsample

(I know about weighting but that's not what I'm looking for.)

Here is my first version that seems to be working fine, feel free to copy or make suggestions on how it could be more efficient (I have quite a long experience with programming in general but not that long with python or numpy)

This function creates single random balanced subsample.

edit: The subsample size now samples down minority classes, this should probably be changed.

def balanced_subsample(x,y,subsample_size=1.0):

    class_xs = []
    min_elems = None

    for yi in np.unique(y):
        elems = x[(y == yi)]
        class_xs.append((yi, elems))
        if min_elems == None or elems.shape[0] < min_elems:
            min_elems = elems.shape[0]

    use_elems = min_elems
    if subsample_size < 1:
        use_elems = int(min_elems*subsample_size)

    xs = []
    ys = []

    for ci,this_xs in class_xs:
        if len(this_xs) > use_elems:
            np.random.shuffle(this_xs)

        x_ = this_xs[:use_elems]
        y_ = np.empty(use_elems)
        y_.fill(ci)

        xs.append(x_)
        ys.append(y_)

    xs = np.concatenate(xs)
    ys = np.concatenate(ys)

    return xs,ys

For anyone trying to make the above work with a Pandas DataFrame, you need to make a couple of changes:

1. Replace the np.random.shuffle line with

   this_xs = this_xs.reindex(np.random.permutation(this_xs.index))

2. Replace the np.concatenate lines with

   xs = pd.concat(xs) ys = pd.Series(data=np.concatenate(ys),name='target')