tf-idf | 易学教程

Interpreting the sum of TF-IDF scores of words across documents

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问题 First let's extract the TF-IDF scores per term per document: from gensim import corpora, models, similarities documents = ["Human machine interface for lab abc computer applications", "A survey of user opinion of computer system response time", "The EPS user interface management system", "System and human system engineering testing of EPS", "Relation of user perceived response time to error measurement", "The generation of random binary unordered trees", "The intersection graph of paths in

TFIDF for Large Dataset

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问题 I have a corpus which has around 8 million news articles, I need to get the TFIDF representation of them as a sparse matrix. I have been able to do that using scikit-learn for relatively lower number of samples, but I believe it can't be used for such a huge dataset as it loads the input matrix into memory first and that's an expensive process. Does anyone know, what would be the best way to extract out the TFIDF vectors for large datasets? 回答1: Gensim has an efficient tf-idf model and does

tf-idf feature weights using sklearn.feature_extraction.text.TfidfVectorizer

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问题 this page: http://scikit-learn.org/stable/modules/feature_extraction.html mentions: As tf–idf is a very often used for text features, there is also another class called TfidfVectorizer that combines all the option of CountVectorizer and TfidfTransformer in a single model. then I followed the code and use fit_transform() on my corpus. How to get the weight of each feature computed by fit_transform()? I tried: In [39]: vectorizer.idf_ ------------------------------------------------------------

How do I calculate the cosine similarity of two vectors?

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问题 How do I find the cosine similarity between vectors? I need to find the similarity to measure the relatedness between two lines of text. For example, I have two sentences like: system for user interface user interface machine … and their respective vectors after tF-idf, followed by normalisation using LSI, for example [1,0.5] and [0.5,1] . How do I measure the smiliarity between these vectors? 回答1: public class CosineSimilarity extends AbstractSimilarity { @Override protected double

Simple implementation of N-Gram, tf-idf and Cosine similarity in Python

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I need to compare documents stored in a DB and come up with a similarity score between 0 and 1. The method I need to use has to be very simple. Implementing a vanilla version of n-grams (where it possible to define how many grams to use), along with a simple implementation of tf-idf and Cosine similarity. Is there any program that can do this? Or should I start writing this from scratch? Check out NLTK package: http://www.nltk.org it has everything what you need For the cosine_similarity: def cosine_distance(u, v): """ Returns the cosine of the angle between vectors v and u. This is equal to u

TfidfVectorizer in scikit-learn : ValueError: np.nan is an invalid document

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问题 I'm using TfidfVectorizer from scikit-learn to do some feature extraction from text data. I have a CSV file with a Score (can be +1 or -1) and a Review (text). I pulled this data into a DataFrame so I can run the Vectorizer. This is my code: import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer df = pd.read_csv("train_new.csv", names = ['Score', 'Review'], sep=',') # x = df['Review'] == np.nan # # print x.to_csv(path='FindNaN.csv', sep=',', na_rep

How to get word details from TF Vector RDD in Spark ML Lib?

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I have created Term Frequency using HashingTF in Spark. I have got the term frequencies using tf.transform for each word. But the results are showing in this format. [<hashIndexofHashBucketofWord1>,<hashIndexofHashBucketofWord2> ...] ,[termFrequencyofWord1, termFrequencyOfWord2 ....] eg: (1048576,[105,3116],[1.0,2.0]) I am able to get the index in hash bucket, using tf.indexOf("word") . But, how can I get the word using the index? zero323 Well, you can't. Since hashing is non-injective there is no inverse function. In other words infinite number of tokens can map to a single bucket so it is

数据挖掘经典算法概述以及详解链接

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po主最近在学习数据挖掘方面相关算法，今天就在这里总结一下数据挖掘领域的经典算法，同时提供每个算法的详解链接，就当做在这里温习吧。对于熟悉的算法我会有较多的描述，不熟悉的算法可能描述较少，以免误导，但是会贴出学习的链接。由于本人也是资历尚浅，必然有错误的地方，也希望大家能够指出来，我也会改正的，谢谢大家。数据挖掘方面的算法，主要可以用作分类，聚类，关联规则，信息检索，决策树，回归分析等。他们的界限并不是特别的明显，常常有交叉，如聚类算法在一定程度上也是一种分类算法。分类算法比较成熟，并且分支也较多。这里先介绍两个概念：监督学习与非监督学习。通俗一点说，如果我们提前设置一些标签，然后对于每个待分类项根据一定规则分类到某些标签，这就是监督学习。如果我们提前不知道标签，而是通过一定的统计手段将一定量的数据，分成一个个类别，这就是非监督学习，通常用作“聚类”（不绝对）。当然监督学习常用作分类学习，也可用作回归分析等。 1.K-Means算法 K-Means算法是一种常用的非监督学习聚类算法，也常用在图像检索领域，如K-Means+BoF算法。它的作用就是我们可以在不知道有哪些类别的情况下，将数据以K个类心，聚成K个聚类。通常我们会先确定一个相异度度量方法，常用的相异度有，欧氏距离，曼哈顿距离，马氏距离，余弦距离等。根据两个数据之间的“距离

get cosine similarity between two documents in lucene

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i have built an index in Lucene. I want without specifying a query, just to get a score (cosine similarity or another distance?) between two documents in the index. For example i am getting from previously opened IndexReader ir the documents with ids 2 and 4. Document d1 = ir.document(2); Document d2 = ir.document(4); How can i get the cosine similarity between these two documents? Thank you When indexing, there's an option to store term frequency vectors. During runtime, look up the term frequency vectors for both documents using IndexReader.getTermFreqVector(), and look up document frequency

Python: tf-idf-cosine: to find document similarity

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I was following a tutorial which was available at Part 1 & Part 2 . Unfortunately the author didn't have the time for the final section which involved using cosine similarity to actually find the distance between two documents. I followed the examples in the article with the help of the following link from stackoverflow , included is the code mentioned in the above link (just so as to make life easier) from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from nltk.corpus import stopwords import numpy as np import numpy.linalg