smote算法python

SMOTE算法是一种合成少数过采样技术，用于解决不平衡数据集的问题。它通过合成新的少数类样本来平衡数据集，从而提高分类器的性能。

下面是一个使用Python实现SMOTE算法的示例代码：

import random
import numpy as np
from sklearn.neighbors import NearestNeighbors

class Smote:
    def __init__(self, samples, N, k):
        self.n_samples, self.n_attrs = samples.shape
        self.N = N
        self.k = k
        self.samples = samples
        self.newindex = 0

    def over_sampling(self):
        N = int(self.N)
        self.synthetic = np.zeros((self.n_samples * N, self.n_attrs))
        neighbors = NearestNeighbors(n_neighbors=self.k).fit(self.samples)

        # 对每个少数类样本均求其在所有少数类样本中的k近邻
        for i in range(len(self.samples)):
            nnarray = neighbors.kneighbors(self.samples[i].reshape(1,-1), return_distance=False)[0]
            self._populate(N, i, nnarray)

        return self.synthetic

    # 为每个少数类样本选择k个最近邻中的N个，并生成N个合成样本
    def _populate(self, N, i, nnarray):
        for j in range(N):
            nn = random.randint(0, self.k-1)
            dif = self.samples[nnarray[nn]] - self.samples[i]
            gap = random.random()
            self.synthetic[self.newindex] = self.samples[i] + gap * dif
            self.newindex += 1