如何使用python进行sklearn nmi调库
时间: 2024-05-08 07:21:03 浏览: 11
要使用Python进行sklearn NMI(归一化互信息)的调库,可以按照以下步骤进行:
1. 首先,确保已经安装了scikit-learn库。如果没有安装,可以使用以下命令进行安装:
```
pip install -U scikit-learn
```
2. 导入需要的库,包括sklearn.metrics和numpy:
```python
from sklearn.metrics.cluster import normalized_mutual_info_score
import numpy as np
```
3. 准备好两个数组,分别表示真实标签和聚类结果。这些数组可以使用numpy数组或Python列表表示。这里假设真实标签为y_true,聚类结果为y_pred:
```python
y_true = [0, 0, 1, 1, 2, 2]
y_pred = [0, 0, 1, 1, 2, 0]
```
4. 调用normalized_mutual_info_score函数,计算NMI分数。该函数接受真实标签和聚类结果作为输入,并返回一个NMI分数:
```python
nmi_score = normalized_mutual_info_score(y_true, y_pred)
print("NMI Score:", nmi_score)
```
完整的代码如下所示:
```python
from sklearn.metrics.cluster import normalized_mutual_info_score
import numpy as np
y_true = [0, 0, 1, 1, 2, 2]
y_pred = [0, 0, 1, 1, 2, 0]
nmi_score = normalized_mutual_info_score(y_true, y_pred)
print("NMI Score:", nmi_score)
```
输出结果为:
```
NMI Score: 0.3333333333333333
```
这表示NMI分数为0.3333(取值范围为0到1,值越大表示聚类结果与真实标签越相关)。