问题描述
我有一堆句子，我想使用scikit-learn光谱聚类对它们进行聚类.我已经运行了代码并毫无问题地得到了结果.但是，每次运行它，我都会得到不同的结果.我知道这是启动问题，但我不知道如何解决.这是我在句子上运行的代码的一部分:
I have a bunch of sentences and I want to cluster them using scikit-learn spectral clustering. I've run the code and get the results with no problem. But, every time I run it I get different results. I know this is the problem with initiation but I don't know how to fix it. This is my a part of my code that runs on sentences:
vectorizer = TfidfVectorizer(norm='l2',sublinear_tf=True,tokenizer=tokenize,stop_words='english',charset_error="ignore",ngram_range=(1, 5),min_df=1) X = vectorizer.fit_transform(data) # connectivity matrix for structured Ward connectivity = kneighbors_graph(X, n_neighbors=5) # make connectivity symmetric connectivity = 0.5 * (connectivity + connectivity.T) distances = euclidean_distances(X) spectral = cluster.SpectralClustering(n_clusters=number_of_k,eigen_solver='arpack',affinity="nearest_neighbors",assign_labels="discretize") spectral.fit(X)
数据是句子列表.每次运行代码，我的聚类结果都会不同.如何使用光谱聚类获得一致的结果.我对Kmean也有同样的问题.这是我给Kmean的代码:
Data is a list of sentences. Everytime the code runs, my clustering results differs. How can I get consistent results using Spectral clustering. I also have the same problem with Kmean. This is my code for Kmean:
vectorizer = TfidfVectorizer(sublinear_tf=True,stop_words='english',charset_error="ignore") X_data = vectorizer.fit_transform(data) km = KMeans(n_clusters=number_of_k, init='k-means++', max_iter=100, n_init=1,verbose=0) km.fit(X_data)
感谢您的帮助.
推荐答案
使用k-means时，您要在KMeans中设置random_state参数(请参见).将其设置为int或 RandomState 实例.
When using k-means, you want to set the random_state parameter in KMeans (see the documentation). Set this to either an int or a RandomState instance.
km = KMeans(n_clusters=number_of_k, init='k-means++',
max_iter=100, n_init=1, verbose=0, random_state=3425)
km.fit(X_data)
这很重要，因为k均值不是确定性算法.它通常以某种随机的初始化过程开始，这种随机性意味着不同的运行将在不同的点开始.为伪随机数生成器播种可确保对于相同的种子，此随机性将始终相同.
This is important because k-means is not a deterministic algorithm. It usually starts with some randomized initialization procedure, and this randomness means that different runs will start at different points. Seeding the pseudo-random number generator ensures that this randomness will always be the same for identical seeds.
我不确定光谱聚类的例子.从参数的文档中: 伪随机数生成器，用于在eigen_solver == 'amg'时并通过K-Means初始化来初始化lobpcg本征向量.在这些情况下，OP的代码似乎不包含在其中，尽管设置参数可能值得一试.
I'm not sure about the spectral clustering example though. From the documentation on the random_state parameter: "A pseudo random number generator used for the initialization of the lobpcg eigen vectors decomposition when eigen_solver == 'amg' and by the K-Means initialization." OP's code doesn't seem to be contained in those cases, though setting the parameter might be worth a shot.
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