我正试图计算silhouette score
,因为我找到了要创建的最佳集群数,但是得到一个错误,它说:
ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)
我无法理解这件事的原因。这是我用来集群和计算silhouette score
的代码。
我读取了包含要集群的文本的csv,并对集群值运行n
。为什么我会犯这个错误?
#Create cluster using K-Means
#Only creates graph
import matplotlib
#matplotlib.use('Agg')
import re
import os
import nltk, math, codecs
import csv
from nltk.corpus import stopwords
from gensim.models import Doc2Vec
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.metrics import silhouette_score
model_name = checkpoint_save_path
loaded_model = Doc2Vec.load(model_name)
#Load the test csv file
data = pd.read_csv(test_filename)
overview = data['overview'].astype('str').tolist()
overview = filter(bool, overview)
vectors = []
def split_words(text):
return ''.join([x if x.isalnum() or x.isspace() else " " for x in text ]).split()
def preprocess_document(text):
sp_words = split_words(text)
return sp_words
for i, t in enumerate(overview):
vectors.append(loaded_model.infer_vector(preprocess_document(t)))
sse = {}
silhouette = {}
for k in range(1,15):
km = KMeans(n_clusters=k, max_iter=1000, verbose = 0).fit(vectors)
sse[k] = km.inertia_
#FOLLOWING LINE CAUSES ERROR
silhouette[k] = silhouette_score(vectors, km.labels_, metric='euclidean')
best_cluster_size = 1
min_error = float("inf")
for cluster_size in sse:
if sse[cluster_size] < min_error:
min_error = sse[cluster_size]
best_cluster_size = cluster_size
print(sse)
print("====")
print(silhouette)
目前没有回答
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