写一段borderline-smote的代码
时间: 2023-07-10 14:36:44 浏览: 71
Borderline-SMOTE算法是一种改进的SMOTE算法,它可用于处理不平衡数据集的问题。以下是一个基于Python的简单实现:
```python
import numpy as np
from collections import Counter
from sklearn.neighbors import NearestNeighbors
def BorderlineSMOTE(X, y, minority_class, k_neighbors=5, n_samples=100):
"""
Implementation of Borderline-SMOTE algorithm for oversampling of minority class samples.
:param X: Feature matrix
:param y: Target vector
:param minority_class: The minority class label
:param k_neighbors: Number of nearest neighbors
:param n_samples: Number of synthetic samples to generate
"""
# Find indices of minority class samples
minority_indices = np.where(y == minority_class)[0]
# Calculate the number of synthetic samples to generate per sample in minority class
N = len(minority_indices)
N_synthetic = int(n_samples / N) + 1
# Fit a k-nearest neighbors model on the whole dataset
knn = NearestNeighbors(n_neighbors=k_neighbors).fit(X)
# For each minority class sample, find its k-nearest neighbors and generate synthetic samples
synthetic_samples = []
for i in minority_indices:
nn_indices = knn.kneighbors([X[i]], n_neighbors=k_neighbors, return_distance=False)[0]
nn_minority_indices = [index for index in nn_indices if y[index] == minority_class]
if len(nn_minority_indices) > 0:
for j in range(N_synthetic):
# Choose one of the k-nearest neighbors randomly
nn_index = np.random.choice(nn_minority_indices)
# Generate a synthetic sample by linearly interpolating between the minority class sample and its k-nearest neighbor
synthetic_sample = X[i] + np.random.rand(X.shape[1]) * (X[nn_index] - X[i])
synthetic_samples.append(synthetic_sample)
# Append the synthetic samples to the original dataset
synthetic_samples = np.array(synthetic_samples)[:n_samples]
X_resampled = np.vstack([X, synthetic_samples])
y_resampled = np.hstack([y, np.array([minority_class] * len(synthetic_samples))])
return X_resampled, y_resampled
```
上述代码中,我们首先找到少数类样本的索引,然后计算每个少数类样本需要生成多少个合成样本。接下来,我们使用 k-最近邻模型查找每个少数类样本的 k 个最近邻居,选择其中的一个随机样本,并通过线性插值生成一个合成样本。最后,我们将这些合成样本添加到原始样本中,并返回新的样本和标签。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)