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| import warnings | |
| import random | |
| import numpy as np | |
| from sklearn.neighbors import NearestNeighbors | |
| def smote(T, N, K): | |
| """ | |
| T ~ an array-like object representing the minority matrix | |
| N ~ the percent oversampling you want. e.g. 500 will give you 5 samples | |
| from the SMOTE algorithm (thus, has to be multiple of 100). | |
| K ~ K Nearest Neighbors | |
| """ | |
| ## make sure T is an array with the proper dimensions | |
| T = np.asarray(T, dtype = np.float64) | |
| nsamples = T.shape[0] | |
| nfeatures = T.shape[1] | |
| if nsamples < nfeatures: | |
| warnings.warn("Make sure the features are in the columns.") | |
| ## we want to oversample | |
| if N < 100: | |
| raise Exception("N should be at least 100") | |
| N = int(N) / 100 | |
| nn = NearestNeighbors(K) | |
| nn.fit(T) | |
| synthetic = np.zeros([N * nsamples, nfeatures]) | |
| for sample in xrange(nsamples): | |
| nn_minority = nn.kneighbors(T[sample], return_distance = False)[0] | |
| N_next = N | |
| newindex = 0 | |
| while N_next != 0: | |
| k_chosen = random.randint(0, K - 1) | |
| while nn_minority[k_chosen] == sample: # don't pick itself | |
| k_chosen = random.randint(0, K - 1) | |
| for feature in xrange(nfeatures): | |
| diff = T[nn_minority[k_chosen], feature] - T[sample, feature] | |
| gap = random.uniform(0, 1) | |
| synthetic[N*sample + newindex, feature] = T[sample, feature] + gap * diff | |
| newindex += 1 | |
| N_next -= 1 | |
| return synthetic | |
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