tf-idf | 易学教程

pyspark: sparse vectors to scipy sparse matrix

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问题 I have a spark dataframe with a column of short sentences, and a column with a categorical variable. I'd like to perform tf-idf on the sentences, one-hot-encoding on the categorical variable and then output it to a sparse matrix on my driver once it's much smaller in size (for a scikit-learn model). What is the best way to get the data out of spark in sparse form? It seems like there is only a toArray() method on sparse vectors, which outputs numpy arrays. However, the docs do say that scipy

pyspark: sparse vectors to scipy sparse matrix

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How can I create a TF-IDF for Text Classification using Spark?

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问题 I have a CSV file with the following format : product_id1,product_title1 product_id2,product_title2 product_id3,product_title3 product_id4,product_title4 product_id5,product_title5 [...] The product_idX is a integer and the product_titleX is a String, example : 453478692, Apple iPhone 4 8Go I'm trying to create the TF-IDF from my file so I can use it for a Naive Bayes Classifier in MLlib. I am using Spark for Scala so far and using the tutorials I have found on the official page and the

TF-IDF implementations in python

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问题 What are the standard tf-idf implementations/api available in python? I've come across the one in nltk. I want to know the other libraries that provide this feature. 回答1: there is a package called scikit which calculates tf-idf scores. you can refer to my answer to this question Python: tf-idf-cosine: to find document similarity and also see the question code from this. Thankz. 回答2: Try the libraries which implements TF-IDF algorithm in python. http://code.google.com/p/tfidf/ https://github

TF-IDF提取行业关键词

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1. TF-IDF简介 TF-IDF（Term Frequency/Inverse Document Frequency）是信息检索领域非常重要的搜索词重要性度量；用以衡量一个关键词 \(w\) 对于查询（Query，可看作文档）所能提供的信息。词频（Term Frequency, TF）表示关键词 \(w\) 在文档 \(D_i\) 中出现的频率： \[ TF_{w,D_i}= \frac {count(w)} {\left| D_i \right|} \] 其中， \(count(w)\) 为关键词 \(w\) 的出现次数， \(\left| D_i \right|\) 为文档 \(D_i\) 中所有词的数量。逆文档频率（Inverse Document Frequency, IDF）反映关键词的普遍程度——当一个词越普遍（即有大量文档包含这个词）时，其IDF值越低；反之，则IDF值越高。IDF定义如下： \[ IDF_w=\log \frac {N}{\sum_{i=1}^N I(w,D_i)} \] 其中， \(N\) 为所有的文档总数， \(I(w,D_i)\) 表示文档 \(D_i\) 是否包含关键词，若包含则为1，若不包含则为0。若词 \(w\) 在所有文档中均未出现，则IDF公式中的分母为0；因此需要对IDF做平滑（smooth）： \[ IDF_w=

基于Python的中文聊天机器人

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什么是聊天机器人？聊天机器人（chatbot、talkbot）本质上是基于文本和语音处理算法能够与真人进行交流的计算机程序，广泛用于客服、问答等系统中。优秀的chatbot要能够通过图灵测试。为什么需要聊天机器人？单个人的精力、时间和知识存储都是有限的。以电商平台的客服举例，按8小时工作制算如果要保持24小时人工客服在线需要招聘3个客服人员，并且客服人员必须熟悉店铺内所有商品同时熟练掌握与客户的沟通技巧并且熟知各种纠纷解决之道。且不说能不能找到同时满足上述条件的客服人员，可以确定的是雇佣这样的客服的成本会非常高。对于中小型网店，这是一笔很昂贵甚至无法负担的运行支出。试想一下，恰好此时有人向你推荐一款可降低2/3运行成本的聊天机器人，你会如何选择。聊天机器人种类根据编程的方式，聊天机器人分为：1、Rule-Based Chatbots，2、Self-Learning Chatbots 1、Rule-Based Chatbots 这一类的聊天机器人基于简单、有限的规则作出应答。 2、Self-Learning Chatbots 既可以通过经典的ML算法实现也可以通过前沿的AI算法实现。可以进一步细分为：Retrieval based、Generative。 2.1 Retrieval based Chatbots 基于原则流程图或者知识图谱从知识库中检索与问题最为匹配的答案

Cosine Similarity

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问题 I calculated tf/idf values of two documents. The following are the tf/idf values: 1.txt 0.0 0.5 2.txt 0.0 0.5 The documents are like: 1.txt = > dog cat 2.txt = > cat elephant How can I use these values to calculate cosine similarity? I know that I should calculate the dot product, then find distance and divide dot product by it. How can I calculate this using my values? One more question: Is it important that both documents should have same number of words? 回答1: a * b sim(a,b) =-------- |a|*

Spark MLLib TFIDF implementation for LogisticRegression

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问题 I try to use the new TFIDF algorithem that spark 1.1.0 offers. I'm writing my job for MLLib in Java but I can't figure out how to get the TFIDF implementation working. For some reason IDFModel only accepts a JavaRDD as input for the method transform and not simple Vector. How can I use the given classes to model a TFIDF vector for my LabledPoints? Note: The document lines are in the format [Label; Text] Here my code so far: // 1.) Load the documents JavaRDD<String> data = sc.textFile("/home

sklearn TfidfVectorizer : Generate Custom NGrams by not removing stopword in them

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问题 Following is my code: sklearn_tfidf = TfidfVectorizer(ngram_range= (3,3),stop_words=stopwordslist, norm='l2',min_df=0, use_idf=True, smooth_idf=False, sublinear_tf=True) sklearn_representation = sklearn_tfidf.fit_transform(documents) It generates tri gram by removing all the stopwords. What I want it to allow those TRIGRAM what have stopword in their middle ( not in start and end) Is there processor needs to be written for this. Need suggestions. 回答1: Yes, you need to supply your own analyzer

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

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问题 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