如何使用python使用K-Means将标签集群与真实标签匹配

我对使用Kmeans算法的标签数据有问题。我的测试句子得到了真正的聚类，但我没有得到真正的标签。我已经使用numpy将集群与真标签匹配，但是这个kmeans可以移动集群，真标签与集群的数量不匹配。我需要帮助解决这个问题。这是我的密码

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cluster import KMeans
from nltk.corpus import stopwords 
from nltk.stem.wordnet import WordNetLemmatizer
import string
import re
import numpy as np
from collections import Counter

stop = set(stopwords.words('indonesian'))
exclude = set(string.punctuation) 
lemma = WordNetLemmatizer()

# Cleaning the text sentences so that punctuation marks, stop words & digits are removed  
def clean(doc):
    stop_free = " ".join([i for i in doc.lower().split() if i not in stop])
    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)
    normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split())
    processed = re.sub(r"\d+","",normalized)
    y = processed.split()
    #print (y)
    return y

path = "coba.txt"

train_clean_sentences = []
fp = open(path,'r')
for line in fp:
    line = line.strip()
    cleaned = clean(line)
    cleaned = ' '.join(cleaned)
    train_clean_sentences.append(cleaned)

#print(train_clean_sentences)
       
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(train_clean_sentences)

# Clustering the training 30 sentences with K-means technique
modelkmeans = KMeans(n_clusters=3, init='k-means++', max_iter=200, n_init=100)
modelkmeans.fit(X)

teks_satu = "Aplikasi Machine Learning untuk mengenali daun mangga dengan metode CNN"

test_clean_sentence = []

cleaned_test = clean(teks_satu)
cleaned = ' '.join(cleaned_test)
cleaned = re.sub(r"\d+","",cleaned)
test_clean_sentence.append(cleaned)
    
Test = vectorizer.transform(test_clean_sentence) 

true_test_labels = ['AI','VR','Sistem Informasi']

predicted_labels_kmeans = modelkmeans.predict(Test)
print(predicted_labels_kmeans)

print ("\n-------------------------------PREDICTIONS BY K-Means--------------------------------------")
print ("\nIndex of Virtual Reality : ",Counter(modelkmeans.labels_[5:10]).most_common(1)[0][0])
print ("Index of Machine Learning : ",Counter(modelkmeans.labels_[0:5]).most_common(1)[0][0]) 
print ("Index of Sistem Informasi : ",Counter(modelkmeans.labels_[10:15]).most_common(1)[0][0])
print ("\n",teks_satu,":",true_test_labels[np.int(predicted_labels_kmeans)],":",predicted_labels_kmeans)

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我也有同样的问题：我的集群（kmeans）确实返回了不同的类（集群编号），然后才是真正的类。实际标签和预测标签不匹配的结果。对我有效的解决方案是代码（滚动到“最大化对角线元素和的排列”）。尽管这种方法很有效，但我认为在某些情况下它可能是错误的。

下面是一个具体的示例，展示了如何将

KMeans

集群ID与训练数据标签相匹配。基本思想是假设分类正确，矩阵的对角线上应具有较大的值。以下是将群集中心ID与培训标签关联之前的混淆矩阵：

cm = 
array([[  0, 395,   0,   5,   0],
       [  0,   2,   5, 391,   2],
       [  2,   0,   0,   0, 398],
       [  0,   0, 400,   0,   0],
       [398,   0,   0,   0,   2]])

现在我们只需要重新排列混淆矩阵，使其大值重新定位在对角线上。它可以很容易地实现

cm_argmax = cm.argmax(axis=0)
cm_argmax
y_pred_ = np.array([cm_argmax[i] for i in y_pred])

这里我们得到了新的混乱矩阵，现在看起来很熟悉，对吗

cm_ = 
array([[395,   5,   0,   0,   0],
       [  2, 391,   2,   5,   0],
       [  0,   0, 398,   0,   2],
       [  0,   0,   0, 400,   0],
       [  0,   0,   2,   0, 398]])

您可以使用

准确度\u分数

y_pred_ = np.array([cm_argmax[i] for i in y_pred])
accuracy_score(y,y_pred_)
# 0.991

完整的独立代码如下所示：

import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.metrics import confusion_matrix,accuracy_score
blob_centers = np.array(
    [[ 0.2,  2.3],
     [-1.5 ,  2.3],
     [-2.8,  1.8],
     [-2.8,  2.8],
     [-2.8,  1.3]])
blob_std = np.array([0.4, 0.3, 0.1, 0.1, 0.1])
X, y = make_blobs(n_samples=2000, centers=blob_centers,
                  cluster_std=blob_std, random_state=7)

def plot_clusters(X, y=None):
    plt.scatter(X[:, 0], X[:, 1], c=y, s=1)
    plt.xlabel("$x_1$", fontsize=14)
    plt.ylabel("$x_2$", fontsize=14, rotation=0)

plt.figure(figsize=(8, 4))
plot_clusters(X)
plt.show()

k = 5
kmeans = KMeans(n_clusters=k, random_state=42)
y_pred = kmeans.fit_predict(X)
cm = confusion_matrix(y, y_pred)
cm
cm_argmax = cm.argmax(axis=0)
cm_argmax
y_pred_ = np.array([cm_argmax[i] for i in y_pred])
cm_ = confusion_matrix(y, y_pred)
cm_
accuracy_score(y,y_pred_)

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请在您的答案中添加更多细节。请查看以开始。