Hi,
I've written a quick implementation of the SMOTE over-sampling algorithm (
http://arxiv.org/abs/1106.1813).
I haven't found any in Python. Here it is:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Created on 24.11.2012
@author: karsten
This is an implementation of the SMOTE Algorithm.
See: "SMOTE: synthetic minority over-sampling technique" by
Chawla, N.V et al.
'''
import numpy as np
from random import randrange, choice
from sklearn.neighbors import NearestNeighbors
def SMOTE(T, N, k):
"""
Returns (N/100) * n_minority_samples synthetic minority samples.
Parameters
----------
T : array-like, shape = [n_minority_samples, n_features]
Holds the minority samples
N : percetange of new synthetic samples:
n_synthetic_samples = N/100 * n_minority_samples. Can be < 100.
k : int. Number of nearest neighbours.
Returns
-------
S : array, shape = [(N/100) * n_minority_samples, n_features]
"""
n_minority_samples, n_features = T.shape
if N < 100:
#create synthetic samples only for a subset of T.
#TODO: select random minortiy samples
N = 100
pass
if (N % 100) != 0:
raise ValueError("N must be < 100 or multiple of 100")
N = N/100
n_synthetic_samples = N * n_minority_samples
S = np.zeros(shape=(n_synthetic_samples, n_features))
#Learn nearest neighbours
neigh = NearestNeighbors(n_neighbors = k)
neigh.fit(T)
#Calculate synthetic samples
for i in xrange(n_minority_samples):
nn = neigh.kneighbors(T[i], return_distance=False)
for n in xrange(N):
nn_index = choice(nn[0])
#NOTE: nn includes T[i], we don't want to select it
while nn_index == i:
nn_index = choice(nn[0])
dif = T[nn_index] - T[i]
gap = np.random.random()
S[n + i * N, :] = T[i,:] + gap * dif[:]
return S
I am going to test it on some bigger data.
There are a few things that might need to change: choice is the Python
implemententation. I did not find the numpy equivalent. Also, what
does neigh.kneighbors really return? I thought it was the indices of the
nearest neighbours but somehow it is an array of an array.
Any other suggestions?
Oh, if it's of any use for anybody feel free to use it or even add it to
scikit. Just tell me if you find any errors or what I should change to make
it more suitable for any API.
best regards,
Karsten
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