Sklearn Pipeline：如何构建kmeans，聚类文本？

Question

我有如图所示的文字：

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

以上将是使用 kmeans 对文本进行聚类的训练集。

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

以上是我的测试集。

我已经建立了一个矢量化器和模型如下所示：

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)

如果我尝试为我的“list2”测试集预测集群：

km.predict(list2)

我收到以下错误：

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

有人告诉我使用Pipeline 来解决这个问题。所以我写了下面的代码：

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

但我得到了错误：

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vectorized 的输出可能没有实现拟合和转换，但在这种特殊情况下我该怎么做呢？我是机器学习的新手。 另外，如何从 kmeans 模型中获取标签？当我运行 kmeans 时，我可以使用 km.labels_ 访问集群标签。如何在 Pipeline 中做类似的事情？

Answer 1

您需要做的是使用list1 训练vectorizer，然后使用相同的vectorizer、transformlist1 和list2。这将解决问题。演示：

>>> 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)

Answer 2

你很亲密！跳过中间显式的vectorizer.fit() 步骤，并在管道中完成所有操作：

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", ngram_range = (1,3))
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)
pipe = Pipeline([('vect', vectorizer), ('kmeans', km)])
pipe.fit(list1)
pipe.transform(list2)

结果：

数组([[0.70710678, 1.41421356], [0.70710678, 1.41421356]])

• 注意：调整了几个参数，使其可以在我当前版本的 sklearn (0.22.1) 上运行...已经有几年了 :-)