Sklearn Pipeline:如何为kmeans构建文本文本? [英] Sklearn Pipeline: How to build for kmeans, clustering text?

问题描述

我的文字如下所示:

 list1 = ["My name is xyz", "My name is pqr", "I work in abc"]

以上内容是使用kmeans对文本进行聚类的训练集.

The above will be training set for clustering text using kmeans.

list2 = ["My name is xyz", "I work in abc"]

以上是我的测试集.

我已经建立了矢量化器和模型，如下所示:

I have built a vectorizer and the model as shown below:

vectorizer = TfidfVectorizer(min_df = 0, max_df=0.5, stop_words = "english", charset_error = "ignore", ngram_range = (1,3))
vectorized = vectorizer.fit_transform(list1)
km=KMeans(n_clusters=2, init='k-means++', n_init=10, max_iter=1000, tol=0.0001, precompute_distances=True, verbose=0, random_state=None, copy_x=True, n_jobs=1)
km.fit(vectorized)

如果我尝试为"list2"的测试集预测聚类，则:

If I try to predict the cluster for my test set of "list2":

km.predict(list2)

我收到以下错误:

ValueError: Incorrect number of features. Got 2 features, expected 5

有人告诉我要使用Pipeline解决此问题.所以我写了下面的代码:

I was told to use Pipeline to solve this issue. So I wrote the code below:

pipe = Pipeline([('vect', vectorizer), ('vectorized', vectorized), ('kmeans',km )])

但是我得到了错误:

TypeError                                 Traceback (most recent call last)
/mnt/folder/Text_Mining/<ipython-input-14-321cabc3bf35> in <module>()
----> 1 pipe = Pipeline([('vect', vectorizer), ('vectorized', vectorized), ('kmeans',km )])
/usr/local/lib/python2.7/dist-packages/scikit_learn-0.13-py2.7-linux-x86_64.egg/sklearn/pipeline.pyc in __init__(self, steps)
     87                 raise TypeError("All intermediate steps a the chain should "
     88                                 "be transforms and implement fit and transform"
---> 89                                 "'%s' (type %s) doesn't)" % (t, type(t)))
     90
     91         if not hasattr(estimator, "fit"):
TypeError: All intermediate steps a the chain should be transforms and implement fit and transform'  (0, 2)     1.0
(1, 4)        0.57735026919
(1, 3)        0.57735026919
(1, 1)        0.57735026919
(2, 0)        1.0' (type <class 'scipy.sparse.csr.csr_matrix'>) doesn't)

我认为也许vectorized的输出未实现拟合和变换，但是在这种特殊情况下我该怎么做?我是机器学习的新手. 另外，如何从kmeans模型获取标签?运行kmeans时，可以使用km.labels_访问群集标签.如何在管道中做类似的事情?

I think that maybe the output of vectorized does not implement a fit and transform, but how do I do that in this particular case? I'm new to Machine Learning. Also, how to get the labels from the kmeans model? When I run kmeans, I can access the cluster labels by using km.labels_. How to do something similar in Pipeline?

推荐答案

您需要做的是使用list1然后使用相同的 vectorizer ，transform训练vectorizer list1和list2.这样可以解决问题.演示:

What you need to do is to train a vectorizer using list1, then with the same vectorizer, transform list1 and list2. This will solve the issue. Demo:

>>> list1 = ["My name is xyz", "My name is pqr", "I work in abc"]
>>> list2 = ["My name is xyz", "I work in abc"]
>>> vectorizer = TfidfVectorizer(min_df = 0, max_df=0.5, stop_words = "english", charset_error = "ignore", ngram_range = (1,3))
>>> vec=vectorizer.fit(list1)   # train vec using list1
>>> vectorized = vec.transform(list1)   # transform list1 using vec
>>> km=KMeans(n_clusters=2, init='k-means++', n_init=10, max_iter=1000, tol=0.0001, precompute_distances=True, verbose=0, random_state=None, cpy_x=True, n_jobs=1)
>>> km.fit(vectorized)
>>> list2Vec=vec.transform(list2)  # transform list2 using vec
>>> km.predict(list2Vec)
array([0, 0], dtype=int32)

这篇关于Sklearn Pipeline:如何为kmeans构建文本文本?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！