机器学习::文本特征提取(TF-IDF) - 第二部分

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

这个职位是一个延续of the first part where we started to learn the theory and practice about text feature extraction and vector space model representation. I really recommend you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?

在TF-IDF权重来解决这个问题。什么TF-IDF给出的是如何重要的是一个集合中的文档的话,这就是为什么TF-IDF结合本地和全球的参数,因为它考虑到不仅需要隔离的期限,但也文献集内的术语。什么TF-IDF然后做来解决这个问题,是缩小,同时扩大了难得的条件频繁的条款;出现比其他的10倍以上期限不为10倍比它更重要的是,为什么TF-IDF采用对数刻度的做到这一点。

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 (信息检索)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。

To overcome this problem, the term frequency\ 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d4from the first part of this tutorial had this textual representation:

d4: We can see the shining sun, the bright sun.

和使用该文件的非归一化项频向量空间表示为:

\vec{v_{d_4}} = (0,2,1,0)

规范化的向量,是一样的说话g theUnit Vector矢量,而他们使用的是“帽子”符号表示:\hat{v}。The definition of the unit vector\hat{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\hat{v}是单位矢量,或者归一化矢量,所述\ VEC {V}是个vector going to be normalized and the\ | \ 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}

并简化为:

\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-norm你正在阅读与规范p=1, defined as:

\displaystyle \|\vec{u}\|_1 = ( \left|u_1\right| + \left|u_2\right| + \left|u_3\right| + \ldots + \left|u_n\right|)

这无非是向量的组件的简单相加,也被称为Taxicab distance,也被称为曼哈顿距离。

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::出租车几何

请注意,您也可以使用任何规范正常化的载体,但我们将使用最常用的规范,L2范数,这也是在0.9版本的默认scikits.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you know what the vector normalization process is, we can try a concrete example, the process of using the L2-norm (we’ll use the right terms now) to normalize our vector\vec{v_{d_4}} = (0,2,1,0)in order to get its unit vector\hat{v_{d_4}}。To do that, we’ll simple plug it into the definition of the unit vector to evaluate it:

\hat{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\hat{v_{d_4}}现在有一个L2范\|\hat{v_{d_4}}\|_2 = 1.0

Note that here we have normalized our term frequency document vector, but later we’re going to do that after the calculation of the tf-idf.

术语频率 - 逆文档频率(TF-IDF)重量

现在您已经了解如何向量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):

Train Document Set: d1: The sky is blue. d2: The sun is bright. Test Document Set: d3: The sun in the sky is bright. d4: We can see the shining sun, the bright sun.

您的文档空间可以那么作为被定义d = \ {D_1,D_2,\ ldots,D_N \}哪里n是个number of documents in your corpus, and in our case asD_ {火车} = \ {D_1,D_2 \}D_{test} = \{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|}}

哪里\left|\{d : t \in d\}\right|是个number of documents哪里the termt出现,术语频率函数满足当\ mathrm {TF}(T,d)\neq 0, we’re only adding 1 into the formula to avoid zero-division.

The formula for the tf-idf is then:

\mathrm{tf\mbox{-}idf}(t) = \mathrm{tf}(t, d) \times \mathrm{idf}(t)

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)= \日志{\压裂{\左| d \右|} {1+ \左| \ {d:T_1 \在d \} \右|}} = \日志{\压裂{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_列车}}= (0.69314718, -0.40546511, -0.40546511, 0.0)

现在we have our matrix with the term frequency (M_ {}列车) and the vector representing the idf for each feature of our matrix (\ {VEC {idf_列车}}),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_列车}}vector dimension. To do that, we can create a squarediagonal matrixM_ {} IDFwith both the vertical and horizontal dimensions equal to the vector\ {VEC {idf_列车}}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 \times Bwill be different than the result of the乙\一个时代,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:

{bmatrix} \ \开始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}

And finally, we can apply our L2 normalization process to theM_{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} = \压裂{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 practice

环境中使用:Python 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.learnfeature extraction module.

The first step is to create our training and testing document set and computing the term frequency matrix:

from sklearn.feature_extraction.text import CountVectorizer train_set = ("The sky is blue.", "The sun is bright.") test_set = ("The sun in the sky is bright.", "We can see the shining sun, the bright sun.") count_vectorizer = CountVectorizer() count_vectorizer.fit_transform(train_set) print "Vocabulary:", count_vectorizer.vocabulary # Vocabulary: {'blue': 0, 'sun': 1, 'bright': 2, 'sky': 3} freq_term_matrix = count_vectorizer.transform(test_set) print 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),我们可以实例化TfidfTransformer,这将是负责来计算我们的词频矩阵TF-IDF权重:

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矢量器Scikit的类。学习是一个vectorizer that automatically combines theCountVectorizerTfidfTransformer给你。看到这个例子to know how to use it for the text classification process.

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

Wikipedia :: tf-idf

经典的向量空间模型

Sklearn text feature extraction code

更新

13 Mar 2015格式化,固定图像的问题。
03 Oct 2011添加了有关使用Python示例环境信息

103个想法“机器学习::文本特征提取(TF-IDF) - 第二部分”

  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

    而且,scikit学习的文档上的文本特征提取部分(我是罪魁祸首?)真的很差。如果你想给一个手并改善目前的状况,不要犹豫,加入邮件列表。

    1. 十分感谢奥利弗。我真的想帮助sklearn,我只是得到一些更多的时间来做到这一点,你们都做了伟大的工作,我真的在lib中已经实现的算法量折服,保持良好的工作!

  3. I like this tutorial better for the level of new concepts i am learning here.
    That said, which version of scikits-learn are you using?.
    The latest as installed by easy_install seems to have a different module hierarchy (i.e doesn’t find feature_extraction in sklearn). If you could mention the version you used, i will just try out with those examples.

    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. Where’s part 3? I’ve got to submit an assignment on Vector Space Modelling in 4 days. Any hope of putting it up over the weekend?

  5. 谢谢again for this complete and explicit tutorial and I am waiting for the coming section.

  6. 由于基督教! a very nice work on vector space with sklearn. I just have one question, suppose I have computed the ‘tf_idf_matrix’, and I would like to compute the pair-wise cosine similarity (between each rows). I was having problem with the sparse matrix format, can you please give an example on that? Also my matrix is pretty big, say 25k by 60k. Thanks a lot!

  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. 回复:我“你是正确的注释”(上),我应该补充:

        “......还注意到康斯登Passot的评论(下同)关于分母:

        “......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. Khalid,
      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. 我有同样的疑问,杰克(最后的评论)。从上个TF-IDF权重矩阵,我们怎么能拿到各自任期的重要性(例如,这是最重要的用语?)。我们如何利用这个矩阵来区分文档。

  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. F(IDF)的高值,表示特定载体(或文件)具有较高的局部强度和低全球实力,在这种情况下,你可以假设,在它的条款具有很高的重要性本地和不能忽视的。针对funtion(TF),其中只有长期重复大量的时间给予更多重视的那些,其中大部分时间是不正确的建模技术比较。

  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)。

    我喜欢阅读本系列的第三批呢!我特别想了解更多有关特征选择。是否有一个惯用的方式来获得最高的分数TF.IDF条款的排序列表?你将如何确定这些方面的整体?你将如何得到这是最负责高或低的余弦相似度(逐行)的条款?

    Thank you for the _great_ posts!

  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. 请纠正我,如果我拨错
    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?

  17. 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.

  18. Hello,

    The explanation is awesome. I haven’t seen a better one yet. I have trouble reproducing the results. It might be because of some update of sklearn.
    Would it be possible for you to update the code?

    It seem that the formula for computing the tf-idf vector has changed a little bit. Is a typo or another formula. Below is the link to the source code.

    https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L954

    Many thanks

  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
    (‘The sky is blue’, ‘The sun is bright’)
    >>> V7 = CountVectorizer()
    >>> v7.fit_transform(ts)
    <2×2 sparse matrix of type '’
    with 4 stored elements in COOrdinate format>
    >>>打印v7.vocabulary_
    {u’is’: 0, u’the’: 1}

    1. Actually, there are two small errors in the first Python sample.
      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. 谢谢for the great explanation.

    我有一个关于IDF(T#)的计算问题。
    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。

  23. 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

  24. 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)?

    继续写:)

  25. 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,

  26. 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?

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

  28. 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.

  29. 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

  30. 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!

  31. 伟大的教程,刚开始在ML一份新工作,这很清楚,因为它应该是解释的事情。亚洲金博宝

  32. Execellent post….!!! Thanks alot for this article.

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

  33. I’m a little bit confused why tf-idf gives negative numbers in this case? How do we interpret them? Correct me if I am wrong, but when the vector has a positive value, it means that the magnitude of that component determines how important that word is in that document. If the it is negative, I don’t know how to interpret it. If I were to take the dot product of a vector with all positive components and one with negative components, it would mean that some components may contribute negatively to the dot product even though on of the vectors has very high importance for a particular word.

  34. 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?

  35. 真棒简单而有效的explaination.Please发布更多的话题与这样真棒explainations.Looking着为即将到来的文章。
    谢谢

  36. Thank you Chris, you are the only one on the web who was clear about the diagonal matrix.

  37. 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

  38. 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.

  39. I learned so many things. Thanks Christian. Looking forward for your next tutorial.

  40. 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
    所以我有没有遗漏了什么?请帮我明白了。

  41. 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?

  42. 我没有得到相同的结果,当我执行相同的脚本。
    print (“IDF:”, tfidf.idf_) : IDF: [ 2.09861229 1. 1.40546511 1. ]

    我的Python版本: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. 它可以是很多东西,因为你使用的是不同的Python解释器的版本也不同Scikit-学习版,你应该会在结果的差异,因为他们可能已经改变了默认参数,算法,圆等

  43. 完美的介绍!
    No hocus pocus. Clear and simple, as technology should be.
    亚洲金博宝很有帮助
    Thank you very much.
    Keep posting!
    Obrigado

  44. 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。

  45. 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

  46. 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

  47. 当然有很大的了解这个问题。我真的很喜欢所有的点,你做。

  48. 1vbXlh你提出了一个非常美妙的细节,欣赏它的职位。亚洲金博宝

  49. I know this site provides quality based articles or
    评论和其他数据,还有没有其他的网页呈现这类
    在质量信息?

  50. 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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