Scikit-learn balanced subsampling

I found the best solutions here

And this is the one I think it's the simplest.

dataset = pd.read_csv("data.csv")
X = dataset.iloc[:, 1:12].values
y = dataset.iloc[:, 12].values

from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler(return_indices=True)
X_rus, y_rus, id_rus = rus.fit_sample(X, y)

then you can use X_rus, y_rus data

For versions 0.4<=:

from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler()
X_rus, y_rus= rus.fit_sample(X, y)

Then, indices of the samples randomly selected can be reached by sample_indices_ attribute.

There now exists a full-blown python package to address imbalanced data. It is available as a sklearn-contrib package at https://github.com/scikit-learn-contrib/imbalanced-learn

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')