tf-idf | 易学教程

how do I preserve the key or index of input to Spark HashingTF() function?

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问题 Based on the Spark documentation for 1.4 (https://spark.apache.org/docs/1.4.0/mllib-feature-extraction.html) I'm writing a TF-IDF example for converting text documents to vectors of values. The example given shows how this can be done but the input is a RDD of tokens with no keys . This means that my output RDD no longer contains an index or key to refer back to the original document. The example is this: documents = sc.textFile("...").map(lambda line: line.split(" ")) hashingTF = HashingTF()

From TF-IDF to LDA clustering in spark, pyspark

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问题 I am trying to cluster tweets stored in the format key,listofwords My first step has been to extract TF-IDF values for the list of words using dataframe with dbURL = "hdfs://pathtodir" file = sc.textFile(dbURL) #Define data frame schema fields = [StructField('key',StringType(),False),StructField('content',StringType(),False)] schema = StructType(fields) #Data in format <key>,<listofwords> file_temp = file.map(lambda l : l.split(",")) file_df = sqlContext.createDataFrame(file_temp, schema)

data frame of tfidf with python

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问题 I have to classify some sentiments my data frame is like this Phrase Sentiment is it good movie positive wooow is it very goode positive bad movie negative i did some preprocessing as tokenisation stop words stemming etc ... and i get Phrase Sentiment [ good , movie ] positive [wooow ,is , it ,very, good ] positive [bad , movie ] negative I need finaly to get a dataframe wich the line are the text which the value is the tf_idf and the columns are the words like that good movie wooow very bad

Why the following tfidf vectorization is failing?

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问题 Hello I am making the following experiment, first I created a vectorizer called: tfidf: tfidf_vectorizer = TfidfVectorizer(min_df=10,ngram_range=(1,3),analyzer='word',max_features=500) Then I vectorized the following list: tfidf = tfidf_vectorizer.fit_transform(listComments) My list of comments looks as follows: listComments = ["hello this is a test","the car is red",...] I tried to save the model as follows: #Saving tfidf with open('vectorizerTFIDF.pickle','wb') as idxf: pickle.dump(tfidf,

compute tf-idf with corpus

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问题 So, I have copied a source code about how to create a system that can run tf-idf, and here is the code : #module import from __future__ import division, unicode_literals import math import string import re import os from text.blob import TextBlob as tb #create a new array words = {} def tf(word, blob): return blob.words.count(word) / len(blob.words) def n_containing(word, bloblist): return sum(1 for blob in bloblist if word in blob) def idf(word, bloblist): return math.log(len(bloblist) / (1

How to Calculate cosine similarity with tf-idf using Lucene and Java

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问题 I have a query and a set of documents. I need to rank these documents based on the cosine similarity with tf-idf. Can someone please tell me what support I can get from Lucene to compute this ? What parameters I can directly calculate from Lucene (can I get tf, idf directly through some method in lucene?) and how to compute cosine similarity with Lucene (is there any function which directly returns cosine similarity if I pass two vectors of the query and the document ?) Thanx in advance 回答1:

NLTK: How to create a corpus from csv file

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I have a csv file as col1 col2 col3 some text someID some value some text someID some value in each row, col1 corresponds to the text of an entire document. I would like to create a corpus from this csv. my aim is to use sklearn's TfidfVectorizer to compute document similarity and keyword extraction. So consider tfidf = TfidfVectorizer(tokenizer=tokenize, stop_words='english') tfs = tfidf.fit_transform(<my corpus here>) so then i can use str = 'here is some text from a new document' response = tfidf.transform([str]) feature_names = tfidf.get_feature_names() for col in response.nonzero()[1]:

How is TF-IDF implemented in gensim tool in python?

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问题 From the documents which i found out from the net i figured out the expression used to determine the Term Frequency and Inverse Document frequency weights of terms in a corpus to be tf-idf(wt)= tf * log(|N|/d); I was going through the implementation of tf-idf mentioned in gensim. The example given in the documentation is >>> doc_bow = [(0, 1), (1, 1)] >>> print tfidf[doc_bow] # step 2 -- use the model to transform vectors [(0, 0.70710678), (1, 0.70710678)] Which apparently does not follow the

Calculate TF-IDF of documents using HBase as the datasource

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问题 I want to calculate the TF (Term Frequency) and the IDF (Inverse Document Frequency) of documents that are stored in HBase. I also want to save the calculated TF in a HBase table, also save the calculated IDF in another HBase table. Can you guide me through? I have looked at BayesTfIdfDriver from Mahout 0.4 but I am not getting a head start. 回答1: The outline of a solution is pretty straight forward: do a word count over your hbase tables, storing both term frequency and document frequency for

Most efficient histogram code in python

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问题 I've seen a number of questions on making histograms in clean one-liners, but I haven't yet found anyone trying to make them as efficiently as possible. I'm currently creating a lot of tfidf vectors for a search algorithm, and this involves creating a number of histograms and my current code, while being very short and readable is not as fast as I would like. Sadly, I've tried a number of other methods that turned out far slower. Can you do it faster? cleanStringVector is a list of strings