Question

我想计算亲和力传播的调整兰特指数。我有一个包含这样句子的数据集：

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My Affinity Propagation代码如下：

import nltk, string 
from sklearn.feature_extraction.text
import TfidfVectorizer from sklearn.cluster
import AffinityPropagation
import pandas as pd

punctuation_map = dict((ord(char), None) for char in string.punctuation) stemmer = nltk.stem.snowball.SpanishStemmer()

def stem_tokens(tokens):
    return [stemmer.stem(item) for item in tokens]

def normalize(text):
    return stem_tokens(nltk.word_tokenize(text.lower().translate(punctuation_map)))

vectorizer = TfidfVectorizer(tokenizer=normalize)

def get_clusters(sentences):
    tf_idf_matrix = vectorizer.fit_transform(sentences)
    similarity_matrix = (tf_idf_matrix * tf_idf_matrix.T).A
    affinity_propagation = AffinityPropagation(affinity="precomputed", damping=0.5)
    affinity_propagation.fit(similarity_matrix)

    labels = affinity_propagation.labels_
    global cluster_centers
    cluster_centers = affinity_propagation.cluster_centers_indices_


    tagged_sentences = zip(sentences, labels)
    clusters = {}

    for sentence, cluster_id in tagged_sentences:
        clusters.setdefault(sentences[cluster_centers[cluster_id]], []).append(sentence)
        #print(len(sentence))

    return clusters

#csv file filename = "/home/ubuntu/data/local_queries.csv" df = pd.read_csv(filename, header = None)

sentences = df.iloc[:, 0].values.tolist()

clusters = get_clusters(sentences) print() for cluster in clusters:
    print(cluster, ':')
    for element in clusters[cluster]:
        print('  - ', element)

对于ARI，我们需要实际标签和预测标签。我没有实际标签，因为我的数据集中只有句子。任何人都可以解释在这种情况下我应该如何计算ARI？

Answer 1

ARI是外部评估指标。

只能用于比较两个结果。通常，您将聚类与已知类标签进行比较，以测试实现是否正常。

如果您只有一个结果且没有＆＃34; true＆＃34; 标签

在这种情况下，您只能使用内部评估措施，并有其所有缺点。

亲和力传播聚类的调整兰特指数

1 个答案: