代码出错了,line 17, in <module> RI = adjusted_rand_score(true_labels, labels) NameError: name 'true_labels' is not defined
时间: 2024-03-20 07:41:32 浏览: 22
很抱歉,是我疏忽了。`true_labels`应该是你手动标注的数据集中的每个样本的真实类别标签,所以需要你先将数据集的真实标签读入并进行转换。可以这样修改代码:
```python
true_labels = pd.read_csv('iris.csv')['species'] # 读入真实标签
true_labels = true_labels.replace({'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}).values # 转换为0、1、2这样的数字标签
# 计算外部指标
RI = adjusted_rand_score(true_labels, labels)
FMI = fowlkes_mallows_score(true_labels, labels)
```
这样就可以成功计算得到外部指标了。非常抱歉给你带来不便,希望这次回答能够解决你的问题。
相关问题
AttributeError: module 'keras.metrics' has no attribute 'adjusted_rand_score'
在Keras中,`adjusted_rand_score`不是`keras.metrics`模块的属性。`adjusted_rand_score`是一个用于计算聚类算法性能的指标,通常在`sklearn.metrics`模块中使用。
如果你想在Keras中使用`adjusted_rand_score`,你需要导入`sklearn.metrics`模块,并使用它来计算聚类算法的性能。以下是一个示例代码:
```python
from sklearn.metrics import adjusted_rand_score
# 假设你有两个聚类结果的标签
labels_true = [0, 0, 1, 1, 2, 2]
labels_pred = [0, 0, 1, 1, 3, 3]
# 计算adjusted_rand_score
score = adjusted_rand_score(labels_true, labels_pred)
print("Adjusted Rand Score:", score)
```
import numpy as np import pandas as pd from sklearn.decomposition import PCA import matplotlib.pyplot as plt # 加载数据集 iris = pd.read_csv('iris_pca.csv') X = iris.iloc[:, :-1] y = iris.iloc[:, -1] # PCA降维 pca = PCA(n_components=2) X_pca = pca.fit_transform(X) # DBSCAN聚类 def dbscan(X, eps=0.5, min_samples=5): m, n = X.shape visited = np.zeros(m, dtype=bool) labels = np.zeros(m, dtype=int) cluster_id = 1 for i in range(m): if not visited[i]: visited[i] = True neighbors = get_neighbors(X, i, eps) if len(neighbors) < min_samples: labels[i] = -1 else: expand_cluster(X, i, neighbors, visited, labels, cluster_id, eps, min_samples) cluster_id += 1 return labels def get_neighbors(X, i, eps): dists = np.sum((X - X[i]) ** 2, axis=1) neighbors = np.where(dists < eps ** 2)[0] return neighbors def expand_cluster(X, i, neighbors, visited, labels, cluster_id, eps, min_samples): labels[i] = cluster_id for j in neighbors: if not visited[j]: visited[j] = True new_neighbors = get_neighbors(X, j, eps) if len(new_neighbors) >= min_samples: neighbors = np.union1d(neighbors, new_neighbors) if labels[j] == 0: labels[j] = cluster_id labels = dbscan(X_pca, eps=0.5, min_samples=5) # 簇的总数 n_clusters = len(set(labels)) - (1 if -1 in labels else 0) print("簇的总数:", n_clusters) # 各样本所归属簇的编号 print("各样本所归属簇的编号:", labels) # 外部指标 from sklearn.metrics import adjusted_rand_score, fowlkes_mallows_score ri = adjusted_rand_score(y, labels) fmi = fowlkes_mallows_score(y, labels) print("RI:", ri) print("FMI:", fmi) # 内部指标 from sklearn.metrics import davies_bouldin_score dbi = davies_bouldin_score(X_pca, labels) print("DBI:", dbi) # 可视化输出 plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels) plt.show(),请为我分析这段代码的运行结果
这段代码首先使用PCA对iris数据集进行降维,将数据降到了2维。然后使用自己实现的DBSCAN聚类算法对降维后的数据进行聚类,其中eps和min_samples是DBSCAN算法的超参数。聚类完成后,输出簇的总数和各样本所归属簇的编号。接着计算聚类结果的外部指标RI和FMI,以及内部指标DBI,并将聚类结果可视化。
RI和FMI都是用来评估聚类结果和真实标签之间的相似度的指标,RI的取值范围为[-1, 1],取值越接近1说明聚类结果越好;FMI的取值范围也为[0, 1],取值越接近1说明聚类结果越好。DBI是一种内部评价指标,用于评估聚类结果的紧密度和分离度,取值越小说明聚类结果越好。
最后的可视化结果将聚类结果以不同颜色的散点图展示出来,便于直观观察聚类效果。