Machine Learning :: Text feature extraction (tf-idf) – Part II

Read the first part of this tutorial:Text feature extraction (tf-idf) – Part I

This post is a延续在哪里,我们开始学习有关文本特征提取和向量空间模型表示的理论和实践的第一部分。我真的建议你阅读第一部分of the post series in order to follow this second post.

Since a lot of people liked the first part of this tutorial, this second part is a little longer than the first.

介绍

In the first post, we learned how to use theterm-frequency以表示在矢量空间的文本信息。然而,与术语频率方法的主要问题是,它大大加快了频繁的条款和规模下降,这比高频方面经验更丰富罕见的条款。基本的直觉是,在许多文件中经常出现的一个术语不太好鉴别,真正有意义的(至少在许多实验测试);这里最重要的问题是:你为什么会在例如分类问题,强调术语,是在你的文档的整个语料库几乎礼物?

The tf-idf weight comes to solve this problem. What tf-idf gives is how important is a word to a document in a collection, and that’s why tf-idf incorporates local and global parameters, because it takes in consideration not only the isolated term but also the term within the document collection. What tf-idf then does to solve that problem, is to scale down the frequent terms while scaling up the rare terms; a term that occurs 10 times more than another isn’t 10 times more important than it, that’s why tf-idf uses the logarithmic scale to do that.

但是,让我们回到我们的定义\mathrm{tf}(t,d)which is actually the term count of the termt在文档中d。The use of this simple term frequency could lead us to problems likekeyword spamming, which is when we have a repeated term in a document with the purpose of improving its ranking on an IR (Information Retrieval)系统,甚至对创建长文档偏见,使他们看起来比他们只是因为手册中出现的高频更重要。

为了克服这个问题,词频\mathrm{tf}(t,d)of a document on a vector space is usually also normalized. Let’s see how we normalize this vector.

矢量归

Suppose we are going to normalize the term-frequency vector\ {VEC V_ {D_4}}我们在本教程的第一部分已经计算。该文件d4从本教程的第一部分中有这样的文字表示:

D4:我们可以看到闪亮的阳光,明亮的阳光下。

和使用非向量空间表示normalized term-frequency of that document was:

\ {VEC V_ {D_4}} =(0,2,1,0)

To normalize the vector, is the same as calculating the单位向量of the vector, and they are denoted using the “hat” notation:\帽子{V}。The definition of the unit vector\帽子{V}一个向量的\vec{v}is:

\的DisplayStyle \帽子{V} = \压裂{\ vec的{V}} {\ | \ vec的{V} \ | _p}

Where the\帽子{V}是单位矢量,或者归一化矢量,所述\vec{v}在矢量将被归一化和\ | \ VEC {V} \ | _p是矢量的范数(大小,长度)\vec{v}in theL^p空间(别担心,我将所有的解释)。

The unit vector is actually nothing more than a normalized version of the vector, is a vector which the length is 1.

The normalization process (Source: http://processing.org/learning/pvector/)
The normalization process (Source: http://processing.org/learning/pvector/)

但这里的重要问题是如何向量的长度来计算,并明白这一点,你必须了解的动机L^p空间,也被称为Lebesgue spaces

Lebesgue spaces

多久这个载体?(来源:来源:http://processing.org/learning/pvector/)
多久这个载体?(来源:来源:http://processing.org/learning/pvector/)

Usually, the length of a vector\ {VEC U】=(U_1,U_2,U_3,\ ldots,u_n)is calculated using the欧几里得范一个准则是在矢量空间中分配一个严格正长度或大小于所有矢量的函数-, which is defined by:

(Source: http://processing.org/learning/pvector/)
(Source: http://processing.org/learning/pvector/)

\|\vec{u}\| = \sqrt{u^2_1 + u^2_2 + u^2_3 + \ldots + u^2_n}

但是,这不是定义长度的唯一途径,这就是为什么你看到(有时)的数p符合规范的符号,就像在了一起\|\vec{u}\|_p。That’s because it could be generalized as:

\displaystyle \|\vec{u}\|_p = ( \left|u_1\right|^p + \left|u_2\right|^p + \left|u_3\right|^p + \ldots + \left|u_n\right|^p )^\frac{1}{p}

和simplified as:

\的DisplayStyle \ | \ VEC【U} \ | _p =(\总和\ limits_ {I = 1} ^ {N} \左| \ VEC {U】_i \右| ^ P)^ \压裂{1} {P}

所以,当你阅读有关L2-norm, you’re reading about the欧几里得范,具有规范p = 2, the most common norm used to measure the length of a vector, typically called “magnitude”; actually, when you have an unqualified length measure (without thep号),你有L2-norm(Euclidean norm).

当你阅读一L1范你正在阅读与规范p=1, defined as:

\的DisplayStyle \ | \ VEC【U} \ | _1 =(\左| U_1 \右| + \左| U_2 \右| + \左| U_3 \右| + \ ldots + \左| u_n \右|)

这无非是向量的组件的简单相加,也被称为出租汽车距离, also called Manhattan distance.

Taxicab geometry versus Euclidean distance: In taxicab geometry all three pictured lines have the same length (12) for the same route. In Euclidean geometry, the green line has length6 \倍\ SQRT {2} \约8.48,并且是唯一的最短路径。
资源:维基百科::出租车通用电气ometry

Note that you can also use any norm to normalize the vector, but we’re going to use the most common norm, the L2-Norm, which is also the default in the 0.9 release of thescikits.learn。You can also find papers comparing the performance of the two approaches among other methods to normalize the document vector, actually you can use any other method, but you have to be concise, once you’ve used a norm, you have to use it for the whole process directly involving the norm (a unit vector that used a L1-norm isn’t going to have the length 1 if you’re going to take its L2-norm later).

返回矢量归

现在你知道了矢量正常化进程是什么,我们可以尝试一个具体的例子,使用L2范数的过程(我们现在使用正确的术语),以规范我们的矢量\ {VEC V_ {D_4}} =(0,2,1,0)in order to get its unit vector\ {帽子V_ {D_4}}。To do that, we’ll simple plug it into the definition of the unit vector to evaluate it:

\帽子{V}= \frac{\vec{v}}{\|\vec{v}\|_p} \\ \\  \hat{v_{d_4}} = \frac{\vec{v_{d_4}}}{||\vec{v_{d_4}}||_2} \\ \\ \\  \hat{v_{d_4}} = \frac{(0,2,1,0)}{\sqrt{0^2 + 2^2 + 1^2 + 0^2}} \\ \\  \hat{v_{d_4}} = \frac{(0,2,1,0)}{\sqrt{5}} \\ \\  \small \hat{v_{d_4}} = (0.0, 0.89442719, 0.4472136, 0.0)

这就是它!我们的法矢\ {帽子V_ {D_4}}现在有一个L2范\ | \帽子{V_ {D_4}} \ | _2 = 1.0

请注意,这里我们归我们词频文档向量,但后来我们要做的是,TF-IDF的计算后。

术语频率 - 逆文档频率(TF-IDF)重量

Now you have understood how the vector normalization works in theory and practice, let’s continue our tutorial. Suppose you have the following documents in your collection (taken from the first part of tutorial):

火车文档集:D1:天空是蓝色的。D2:阳光灿烂。测试文档集:D3:在天空,阳光灿烂。D4:我们可以看到闪亮的阳光,明亮的阳光下。

您的文档空间可以那么作为被定义d = \ {D_1,D_2,\ ldots,D_N \}wheren是个number of documents in your corpus, and in our case asD_ {火车} = \ {D_1,D_2 \}D_ {测试} = \ {D_3,D_4 \}。The cardinality of our document space is defined by\left|{D_{train}}\right| = 2\左| {{D_测试}} \右|= 2,因为我们只有2两个用于训练和测试文档,但他们显然并不需要有相同的基数。

现在让我们看看,然后是如何IDF(逆文档频率)定义:

\的DisplayStyle \ mathrm {IDF}(T)= \日志{\压裂{\左| d \右|} {1+ \左| \ {d:吨\在d \} \右|}}

where\left|\{d : t \in d\}\right|是个number of documentswhere the termtappears, when the term-frequency function satisfies\mathrm{tf}(t,d) \neq 0, we’re only adding 1 into the formula to avoid zero-division.

为TF-IDF式则是:

\ mathrm {TF \ MBOX { - } IDF}(T)= \ mathrm {TF}(T,d)\倍\ mathrm {IDF}(t)的

和this formula has an important consequence: a high weight of the tf-idf calculation is reached when you have a high term frequency (tf) in the given document (本地参数)和整个集合中的术语的低文档频率(global parameter).

Now let’s calculate the idf for each feature present in the feature matrix with the term frequency we have calculated in the first tutorial:

M_ {}列车=  \begin{bmatrix}  0 & 1 & 1 & 1\\  0 & 2 & 1 & 0  \end{bmatrix}

Since we have 4 features, we have to calculate\mathrm{idf}(t_1),\ mathrm {IDF}(T_2),\ mathrm {IDF}(t_3处),\mathrm{idf}(t_4):

\mathrm{idf}(t_1) = \log{\frac{\left|D\right|}{1+\left|\{d : t_1 \in d\}\right|}} = \log{\frac{2}{1}} = 0.69314718

\ mathrm {IDF}(T_2)= \log{\frac{\left|D\right|}{1+\left|\{d : t_2 \in d\}\right|}} = \log{\frac{2}{3}} = -0.40546511

\ mathrm {IDF}(t_3处)= \日志{\压裂{\左| d \右|} {1+ \左| \ {d:t_3处\在d \} \右|}} = \日志{\压裂{2} {3}} = -0.40546511

\ mathrm {IDF}(T_4)= \日志{\压裂{\左| d \右|} {1+ \左| \ {d:T_4 \在d \} \右|}} = \日志{\压裂{2} {2}} = 0.0

这些IDF权重可以由矢量作为表示:

\vec{idf_{train}} = (0.69314718, -0.40546511, -0.40546511, 0.0)

现在,我们有我们的词频矩阵(M_ {}列车)和表示我们的矩阵的每个特征的IDF(矢量\vec{idf_{train}}),我们可以计算出我们的TF-IDF权重。我们要做的是矩阵中的每一列的简单乘法M_ {}列车with the respective\vec{idf_{train}}vector dimension. To do that, we can create a squarediagonal matrixcalledM_ {} IDFwith both the vertical and horizontal dimensions equal to the vector\vec{idf_{train}}尺寸:

M_ {} IDF=   \begin{bmatrix}   0.69314718 & 0 & 0 & 0\\   0 & -0.40546511 & 0 & 0\\   0 & 0 & -0.40546511 & 0\\   0 & 0 & 0 & 0   \end{bmatrix}

然后将它乘到术语频率矩阵,因此最终结果然后可以定义为:

M_{tf\mbox{-}idf} = M_{train} \times M_{idf}

Please note that the matrix multiplication isn’t commutative, the result ofA \乘以Bwill be different than the result of theB \times A,这就是为什么M_ {} IDFis on the right side of the multiplication, to accomplish the desired effect of multiplying each idf value to its corresponding feature:

\begin{bmatrix}   \mathrm{tf}(t_1, d_1) & \mathrm{tf}(t_2, d_1) & \mathrm{tf}(t_3, d_1) & \mathrm{tf}(t_4, d_1)\\   \mathrm{tf}(t_1, d_2) & \mathrm{tf}(t_2, d_2) & \mathrm{tf}(t_3, d_2) & \mathrm{tf}(t_4, d_2)   \end{bmatrix}   \times   \begin{bmatrix}   \mathrm{idf}(t_1) & 0 & 0 & 0\\   0 & \mathrm{idf}(t_2) & 0 & 0\\   0 & 0 & \mathrm{idf}(t_3) & 0\\   0 & 0 & 0 & \mathrm{idf}(t_4)   \end{bmatrix}   \\ =   \begin{bmatrix}   \mathrm{tf}(t_1, d_1) \times \mathrm{idf}(t_1) & \mathrm{tf}(t_2, d_1) \times \mathrm{idf}(t_2) & \mathrm{tf}(t_3, d_1) \times \mathrm{idf}(t_3) & \mathrm{tf}(t_4, d_1) \times \mathrm{idf}(t_4)\\   \mathrm{tf}(t_1, d_2) \times \mathrm{idf}(t_1) & \mathrm{tf}(t_2, d_2) \times \mathrm{idf}(t_2) & \mathrm{tf}(t_3, d_2) \times \mathrm{idf}(t_3) & \mathrm{tf}(t_4, d_2) \times \mathrm{idf}(t_4)   \end{bmatrix}

Let’s see now a concrete example of this multiplication:

M_{tf\mbox{-}idf} = M_{train} \times M_{idf} = \\   \begin{bmatrix}   0 & 1 & 1 & 1\\   0 & 2 & 1 & 0   \end{bmatrix}   \times   \begin{bmatrix}   0.69314718 & 0 & 0 & 0\\   0 & -0.40546511 & 0 & 0\\   0 & 0 & -0.40546511 & 0\\   0 & 0 & 0 & 0   \end{bmatrix} \\   =   \begin{bmatrix}   0 & -0.40546511 & -0.40546511 & 0\\   0 & -0.81093022 & -0.40546511 & 0   \end{bmatrix}

最后,我们可以将我们的L2归一化处理的M_{tf\mbox{-}idf}矩阵。Please note that this normalization is“row-wise”因为我们要处理矩阵的每一行作为一个分离向量进行归一化,而不是矩阵作为一个整体:

M_{tf\mbox{-}idf} = \frac{M_{tf\mbox{-}idf}}{\|M_{tf\mbox{-}idf}\|_2} = \begin{bmatrix}   0 & -0.70710678 & -0.70710678 & 0\\   0 & -0.89442719 & -0.4472136 & 0   \end{bmatrix}

And that is our pretty normalized tf-idf weight of our testing document set, which is actually a collection of unit vectors. If you take the L2-norm of each row of the matrix, you’ll see that they all have a L2-norm of 1.

Python的实践

环境中使用:Python的v.2.7.2,NumPy的1.6.1,SciPy的v.0.9.0,Sklearn(Scikits.learn)v.0.9

现在,你在等待的部分!在本节中,我将使用Python的使用,以显示TF-IDF计算的每一步Scikit.learn特征提取模块。

第一步是创建我们的训练和测试文档集和计算词频矩阵:

从sklearn.feature_extraction.text进口CountVectorizer train_set =(“天空是蓝色的。”,“阳光灿烂”。)TEST_SET =(“在天空中的太阳是光明的。”,“我们可以看到闪耀的太阳,。明亮的太阳“)count_vectorizer = CountVectorizer()count_vectorizer.fit_transform(train_set)打印 ”词汇“,count_vectorizer.vocabulary#词汇:{ '蓝':0, '太阳':1, '鲜艳':2 '天空':3} freq_term_matrix = count_vectorizer.transform(TEST_SET)打印freq_term_matrix.todense()#[[0 1 1 1]#[0 2 1 0]]

现在,我们有频率项矩阵(称为freq_term_matrix), we can instantiate theTfidfTransformer, which is going to be responsible to calculate the tf-idf weights for our term frequency matrix:

from sklearn.feature_extraction.text import TfidfTransformer tfidf = TfidfTransformer(norm="l2") tfidf.fit(freq_term_matrix) print "IDF:", tfidf.idf_ # IDF: [ 0.69314718 -0.40546511 -0.40546511 0. ]

请注意,我所指定的标准为L2,这是可选的(实际上默认为L2范数),但我已经添加了参数,使其明确向你表示,它会使用L2范数。还要注意的是,你可以通过访问称为内部属性看IDF计算权重idf_。现在fit()我thod has calculated the idf for the matrix, let’s transform thefreq_term_matrixto the tf-idf weight matrix:

tf_idf_matrix = tfidf.transform(freq_term_matrix) print tf_idf_matrix.todense() # [[ 0. -0.70710678 -0.70710678 0. ] # [ 0. -0.89442719 -0.4472136 0. ]]

And that is it, thetf_idf_matrix其实我们以前M_{tf\mbox{-}idf}矩阵。您可以通过使用达到相同的效果VectorizerScikit的类。学习是一个vectorizer that automatically combines theCountVectorizer和theTfidfTransformer给你。看到this example要知道如何使用它的文本分类过程。

I really hope you liked the post, I tried to make it simple as possible even for people without the required mathematical background of linear algebra, etc. In the next Machine Learning post I’m expecting to show how you can use the tf-idf to calculate the cosine similarity.

如果你喜欢,随时提出意见和建议,修改等。

Cite this article as: Christian S. Perone, "Machine Learning :: Text feature extraction (tf-idf) – Part II," in亚洲金博宝未知领域,03/10/2011,//www.cpetem.com/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/

参考

理解逆文档频率:对IDF理论论证

维基百科:: TF-IDF

经典的向量空间模型

Sklearn text feature extraction code

更新

13 Mar 2015Formating, fixed images issues.
03 Oct 2011Added the info about the environment used for Python examples

103 thoughts to “Machine Learning :: Text feature extraction (tf-idf) – Part II”

  1. Wow!
    完美的前奏在TF-IDF,非常感谢你!亚洲金博宝亚洲金博宝很有意思,我想学这个领域很长一段时间,你的职位是一个真正的礼物。这将是非常有趣的阅读更多亚洲金博宝关于该技术的使用情况。而且可能是你有兴趣,请,摆脱对文本语料库表示的其他方法的一些光,如果他们存在?
    (对不起,糟糕的英语,我正在努力对其进行改进,但仍然有很多工作要做的)

  2. Excellent work Christian! I am looking forward to reading your next posts on document classification, clustering and topics extraction with Naive Bayes, Stochastic Gradient Descent, Minibatch-k-Means and Non Negative Matrix factorization

    Also, the documentation of scikit-learn is really poor on the text feature extraction part (I am the main culprit…). Don’t hesitate to join the mailing list if you want to give a hand and improve upon the current situation.

    1. Great thanks Olivier. I really want to help sklearn, I just have to get some more time to do that, you guys have done a great work, I’m really impressed by the amount of algorithms already implemented in the lib, keep the good work !

  3. I like this tutorial better for the level of new concepts i am learning here.
    这就是说,学习scikits您正在使用哪个版本?
    最新通过的easy_install安装似乎有不同的模块层次结构(即没有找到sklearn feature_extraction)。如果你能提到你使用的版本,我只是尝试用这些例子。

    1. 您好阿南德,我很高兴你喜欢它。我已经增加了大约只用一节“的Python惯例”之前,我使用的是scikits.learn 0.9(发布在几个星期前)环境的信息。

  4. 哪里是第3部分?我必须提交在4天内向量空间模型的分配。把它在周末的希望吗?

  5. 再次感谢这个完整和明确的教程和我在等待即将到来的部分。

  6. 由于基督徒!与s亚洲金博宝klearn向量空间很不错的工作。我只有一个问题,假设我已经计算了“tf_idf_matrix”,我想计算成对余弦相似性(每行之间)。我是有问题的稀疏矩阵格式,你可以请给出这样的例子?也是我的基质是相当大的,由60K说25K。非常感谢!

  7. Great post… I understand what tf-idf and how to implement it with a concrete example. But I caught 2 things that I’m not sure about:
    1- You called the 2 dimensional matrix M_train, but it has the tf values of the D3 and D4 documents, so you should’ve called that matrix M_test instead of M_train. Because D3 and D4 are our test documents.
    2 - 当你计算IDF值的T2(这是“太阳”),它应该是日志(2/4)。因为文件的数目是2 D3有词“太阳” 1次,D4有它的2倍。这使得3,但是我们也加1到值摆脱0分的问题。这使得4 ...我说得对不对还是我失去了一些东西?
    Thank you.

    1. 你是正确的:这些都是优秀的博客文章,但作者真的有责任/责任回去和纠正错误,这样的(和其他人,例如,第1部分; ...):缺席训练下划线;设置STOP_WORDS参数;还我的电脑上,词汇索引是不同的。

      正如我们赞赏的努力(荣誉的作者!),它也是一个显著伤害那些谁斗争过去在原有材料的(未修正)的错误。

      1. re: my ‘you are correct comment’ (above), I should have added:

        “… noting also Frédérique Passot’s comment (below) regarding the denominator:

        “......我们用的是什么确实是在发生的一个术语,无论任何给定的文档中出现的术语次数的文件数量。在这种情况下,然后,在用于T2(“太阳”)的IDF值分母确实2 + 1(2个文件具有“太阳”术语,1以避免潜在的零分割误差)。“

    2. 哈立德,
      This is a response to a very old question. However, I still want to respond to communicate what I understand from the article.
      Your question 2: “When you calculate the idf value for the t2 (which is ‘sun’) it should be log(2/4)”
      我的理解:在数项的分母应该是(一些文件,其中术语出现+ 1),而不是长期的频率。术语“太阳”出现的文件的数目是2(1次在D3和D4中的2倍 - 完全出现3次在两个文件3是频率和2是文件号)。因此,分母为2 + 1 = 3。

  8. 优秀的帖子!
    I have some question. From the last tf-idf weight matrix, how can we get the importance of term respectively(e.g. which is the most important term?). How can we use this matrix to classify documents

  9. Thank You So Much. You explained it in such a simple way. It was really useful. Once again thanks a lot.

  10. I have same doubt as Jack(last comment). From the last tf-idf weight matrix, how can we get the importance of term respectively(e.g. which is the most important term?). How can we use this matrix to classify documents.

  11. 我有个问题..
    After the tf-idf operation, we get a numpy array with values. Suppose we need to get the highest 50 values from the array. How can we do that?

    1. high value of f(idf) denotes that the particular vector(or Document) has high local strength and low global strength, in which case you can assume that the terms in it has high significance locally and cant be ignored. Comparing against funtion(tf) where only the term repeats high number of times are the ones given more importance,which most of the times is not a proper modelling technique.

  12. Hey ,
    Thanx fr d code..was very helpful indeed !

    1.适用于文档聚类,计算反相的术语频率之后,shud我使用任何关联性系数等Jaccards系数,然后应用聚类算法中像k均值或shud我计算反转术语频率后直接适用d k均值到文档向量?

    2.您是如何评价倒词频为calcuating文档向量文本聚类?

    谢谢a ton fr the forth coming reply!

  13. @Khalid:你在1-指出什么让我困惑过了一分钟(M_train VS M_test)。我想你误会了你的第二点,不过,因为我们用的是什么是真正发生的一个术语,无论任何给定的文档中出现的术语次数的文件数量。在这种情况下,那么,在为T2(“太阳”)的IDF值分母确实2 + 1(2个文件具有的术语“太阳”,1以避免潜在的零分割误差)。

    I’d love to read the third installment of this series too! I’d be particularly interested in learning more about feature selection. Is there an idiomatic way to get a sorted list of the terms with the highest tf.idf scores? How would you identify those terms overall? How would you get the terms which are the most responsible for a high or low cosine similarity (row by row)?

    谢谢你的帖子_美好的_!

  14. Excellent article and a great introduction to td-idf normalization.

    你必须解释这些复杂的概亚洲金博宝念非常清晰,结构化的方法。

    谢谢!

      1. very good & infomative tutorial…. please upload more tutorials related to documents clustering process.

  15. 您可以为使用TFIDF所以我们有TFIDF的矩阵,我们怎么可以用它来计算余弦做余弦相似度任何引用。感谢神奇的物品。

  16. 谢谢so much for this and for explaining the whole tf-idf thing thoroughly.

  17. Please correct me if i’m worng
    与启动后的公式“我们在第一个教程中计算出的频率:”应该不MTEST Mtrain。也开始“这些IDF权重可以由矢量作为表示后:”应该是不idf_test idf_train。

    Btw great series, can you give an simple approach for how to implement classification?

  18. Very good post. Congrats!!

    Showing your results, I have a question:

    我读了维基百科:
    The tf-idf value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus, which helps to control for the fact that some words are generally more common than others.

    当我看到它,我明白,如果一个字中的所有文档apperars就是一个字只出现在一个文档中不太重要的:

    然而,在结果中,“太阳”或“明亮”是比“天空”最重要的。

    I’m not sure of understand it completly.

  19. Terrific! I was familiar with tf-idf before but I found your scikits examples helpful as I’m trying to learn that package.

  20. 优秀的帖子!一次偶然的机会找上CountVectorizer更多信息,无意中发现了这一点,但我很高兴我通过两个您的文章(第1部分和第2部分)的读取。

    Bookmarking your blog now

  21. 似乎没有fit_transform()为你描述..
    Any idea why ?
    >>> ts
    (“天空是蓝色的”,“阳光灿烂”)
    >>> V7 = CountVectorizer()
    >>> v7.fit_transform(ts)
    <2×2 sparse matrix of type '’
    用4个存储元件在坐标格式>
    >>>打印v7.vocabulary_
    {u’is’: 0, u’the’: 1}

    1. 其实,还有第一个Python样本中的两个小错误。
      1. CountVectorizer应该被实例化,如下所示:
      count_vectorizer = CountVectorizer(stop_words='english')
      这将确保“是”,“的”等被删除。

      2.要打印的词汇,你必须在末尾添加下划线。
      打印“词汇:” count_vectorizer.vocabulary_

      Excellent tutorial, just small things. hoep it helps others.

      1. 谢谢ash. although the article was rather self explanatory, your comment made the entire difference.

  22. 感谢您抽出时间来写这篇文章。发现它非常有用。亚洲金博宝

  23. 感谢伟大的解释。

    I have a question about calculation of the idf(t#).
    在第一种情况下,你写的IDF(T1)=日志(2/1),因为我们没有我们收集此类条款,因此,我们添加1分母。现在,在T2的情况下,你写的日志(2/3),所以分母等于3,而不是4(= 1 + 2 + 1)?万一t3时,你写:日志(2/3),从而分母等于3(= 1 + 1 + 1)。我在这里看到的那种不一致性。你能不能,请解释一下,你是怎么计算的分母值。

    谢谢。

    1. 你理解错了,分母你不把这个词的总和每个文档中,你只是总结所有具有词的至少一个aparition的文件。

  24. it is good if you can provide way to know how use ft-idf in classification of document. I see that example (python code) but if there is algorithm that is best because no all people can understand this language.

    谢谢

  25. 尼斯。一种解释有助于正确看待这个事情。是TF-IDF的好办法做聚类(例如,从已知的语料用杰卡德分析或方差相对于平均值设定)?

    Keep writing:)

  26. Hi Christian,

    这让我非常兴奋和幸运,读亚洲金博宝这篇文章。你理解的清晰反映了文件的清晰度。这让我重拾我的信心在机器学习领域。

    由于一吨为美丽的解释。

    Would like to read more from you.

    谢谢,

  27. Thank you for the good wrap up. You mention a number of papers which compare L1 and L2 norm, I plan to study that a bit more in depth. You still know their names?

  28. how can i calculate tf idf for my own text file which is located some where in my pc?

  29. Brilliant article.

    By far the easiest and most sound explanation of tf-tdf I’ve read. I really liked how you explained the mathematics behind it.

  30. 嗨,伟大的职位!我使用的是TfidVectorizer模块scikit学习产生与规范= L2的TF-IDF矩阵。我把它叫做tfidf_matrix语料的fit_transform后,我一直在检查TfidfVectorizer的输出。我总结了行,但他们并不总和为1的代码是VECT = TfidfVectorizer(use_idf =真,sublunar_tf =真,规范=” L2)。tfidf_matrix = vect.fit_transform(数据)。当我运行tfidf_matrix.sum(轴= 1)的载体是大于1也许我看错矩阵或我误解如何正常化的作品。我希望有人能澄清这一点!谢谢

  31. Can I ask when you calculated the IDF, for example, log(2/1), did you use log to base 10 (e) or some other value? I’m getting different calculations!

  32. 伟大的教程,刚开始一份新工作在毫升和this explains things very clearly as it should be.

  33. Execellent帖子...。!非常感谢这篇文章。

    But I need more information, As you show the practical with python, Can you provide it with JAVA language..

  34. 我有点困惑,为什么TF-IDF在这种情况下,给出了负数?我们如何解读?纠正我,如果我错了,但是当载体为正值,这意味着该组件的大小确定字是该文件中有多么重要。如果是负数,我不知道如何解释它。如果我是采取向量的点积与所有积极的部件和一个负组件,这将意味着,一些部件可能负点积贡献,即使在载体有一个特定的词非常高的重视。亚洲金博宝

  35. 嗨,
    非常感谢您对这个主题这个详细的解释,真是太好了。无论如何,你可以给我一个提示,这可能是我的错误,我不断看到的来源:

    freq_term_matrix = count_vectorizer.transform(TEST_SET)
    AttributeError: ‘matrix’ object has no attribute ‘transform’

    Am I using a wrong version of sklearn?

  36. Awesome simple and effective explaination.Please post more topics with such awesome explainations.Looking forward for upcoming articles.
    谢谢

  37. 谢谢克里斯,你是唯一一个谁是明确了对角矩阵在网络上。

  38. Great tutorial for Tf-Idf. Excellent work . Please add for cosine similarity also:)

  39. I understood the tf-idf calculation process. But what does that matrix mean and how can we use the tfidf matrix to calculate the similarity confuse me. can you explain that how can we use the tfidf matrix .thanks

  40. best explanation.. Very helpful. Can you please tell me how to plot vectors in text classification in svm.. I am working on tweets classification. I am confused please help me.

  41. 嗨,I’m sorry if i have mistaken but i could not understand how is ||Vd4||2 = 1.
    D4 =的值(0.0,0.89,0.44,0.0),因此归一化将是= SQRT(正方形(0.89)+平方(0.44))= SQRT(0.193)= 0.44
    so what did i missed ? please help me to understand .

  42. 嗨,这是一个伟大的博客!
    If I need to do bi-gram cases, how can I use sklearn to finish it?

  43. 这是非常大的亚洲金博宝。我喜欢你教。亚洲金博宝非常非常棒

  44. I am not getting same result, when i am executing the same script.
    print (“IDF:”, tfidf.idf_) : IDF: [ 2.09861229 1. 1.40546511 1. ]

    My python version is: 3.5
    Scikit了解的版本是:o.18.1

    什么我需要改变?可能是什么可能的错误?

    谢谢,

    1. It can be many things, since you’re using a different Python interpreter version and also a different Scikit-Learn version, you should expect differences in the results since they may have changed default parameters, algorithms, rounding, etc.

  45. 完美的介绍!
    No hocus pocus. Clear and simple, as technology should be.
    亚洲金博宝非常有帮助
    Thank you very much.
    请发帖!
    Obrigado

  46. 为什么| d |= 2,在IDF方程。它不应该是4,因为| d |代表的审议的文件数量,我们有2从测试,2个来自火车。

  47. hey , hii Christian
    您的文章是真正帮助我了解从基础TFD-IDF。我在分类的一个项目,其中我使用向量空间模型,这导致在确定类别在我的测试文档应该存在。机器学习的一部分。如果你认为我有关的东西这将是巨大的。我被困在这一点上。
    谢谢

  48. 看到这个例子就知道如何使用它的文本分类过程。“这个”链接不起作用了。能否请您提供相关链接,例如。

    谢谢

  49. There is certainly a great deal to learn about this subject. I really like all the points you made.

  50. 1vbXlh You have brought up a very wonderful details , appreciate it for the post.

  51. I know this site provides quality based articles or
    reviews and additional data, is there any other web page which presents these kinds of
    在质量信息?

  52. 在第一个例子。IDF(T1),日志(2/1)由计算器= 0.3010。为什么他们获得0.69 ..请有什么不对?

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