ValueError:标签数为1。使用Silhouette_score时,有效值为2到n_samples-1(包括1)
silhouette score
当我找到要创建的最佳群集数时,我正在尝试进行计算,但是出现错误消息:
ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)
我无法理解其原因。这是我用来聚类和计算的代码silhouette score
。
我阅读了包含要聚类的文本的csv,并K-Means
在n
聚类值上运行。我收到此错误的原因可能是什么?
#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)
-
*产生 *该错误 是因为您有一个循环,用于不同数量的群集
n
。在第一次迭代中,n_clusters
is1
,
这导致all(km.labels_ == 0)
beTrue
。换句话说, 您只有一个标签为0的群集 (因此,
np.unique(km.labels_)
printsarray([0], dtype=int32)
)。
silhouette_score
需要超过1个群集标签 。这会导致错误。错误消息是明确的。
例:
from sklearn import datasets from sklearn.cluster import KMeans import numpy as np iris = datasets.load_iris() X = iris.data y = iris.target km = KMeans(n_clusters=3) km.fit(X,y) # check how many unique labels do you have np.unique(km.labels_) #array([0, 1, 2], dtype=int32)
我们有3个不同的集群/集群标签。
silhouette_score(X, km.labels_, metric='euclidean') 0.38788915189699597
该功能工作正常。
现在,让我们引起错误:
km2 = KMeans(n_clusters=1) km2.fit(X,y) silhouette_score(X, km2.labels_, metric='euclidean')
ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1
(inclusive)