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cs4417 assignment is
创建日期：2024-06-26 14:28:23
标签： cs4417
assignment is

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Part 1: Calculate the frequency of a term in each document (20 points)

Given a set of documents, calculate the frequency of a term in each document.  The output should be the term, document and number of occurrences of the term in the document.  This is different from the example presented in the lectures in that the example focused on one document. In this example, the final output should consist of pairs in the following form:

 

((term, document identifier), count)

 

Submission:  You should submit a zip file with the name Part1.zip. When unzipped there should be two files: mapper.py and reducer.py.

 

Part 2: Count Bigrams (15 points)

Take the word count example and extend it to count bigrams which refers to sequences of two consecutive words.

 

You should make use of Hadoop for this part.

 

Submission: You should submit a zip file with the name Part2.zip. When unzipped there should be two files: mapper.py and reducer.py.

 

Part 3: Count Unique Bigrams (15 points)

This is an extension of part 2 where you count the number of unique bigrams. One approach is to use two MapReduce passes. The first is what you did for Part 2 and the second is something you need to develop.

 

Submission: You should submit a zip file with the name Part3.zip. When unzipped there should be two files for each MapReduce pass, i: mapperi.py and  reduceri.py. For example, if i is 1 then you should have mapper1.py and reducer1.py and if i is 2 then you should have mapper2.py and reducer2.py.

 

Part 4: Term-Frequency-Inverse Document Frequency in MapReduce (40 points)

The tf-idf metric is used to determine the importance of a word within a document.  You are to write a program that uses the MapReduce paradigm. However, you do not have to use Hadoop for doing so. It is sufficient to use pipes to test the program.

 

The formula of tf-idf for document d and term t is the following:

 

tf-idt,d = tft,d /N *log10(D/ dft)

 

where   tft,d.   is the number of occurrences of the term t in document d, N is the total number of words in document d, D is the total number of documents and dft  is the number of documents that the term t occurs in.   There are variations of the formula, but you should use the above formula since our test cases assume the above.

 

This requires multiple MapReduce jobs (more than 2).  The first MapReduce (MR) job should calculate the term count for each term and document (tft,d).

 

The second MR job should calculate dft for each term.

 

You should figure out the rest of the MR jobs needed.

 

For this assignment the number of documents is needed. You should have a file called inputParameters.  This file should have one number which represents the number of documents. This will make it easier for the TAs to test.

 

Submission: You should submit a zip file with the name Part3.zip. When unzipped there should be two files for each MapReduce pass, i: mapperi.py and reduceri.py. For example, if i is 1 then you should have mapper1.py and reducer1.py and if i is 2 then you should have mapper2.py and reducer2.py.

 

Part 4: Writeup (10)

 Please complete the following:

 

  • For each part describe the input and output for each MR job.

 

  • Please answer the following questions
  • What would you like to have done given more time?
  • How difficult was it to implement? How difficult would it be to implement another task, given this experience? What would be straightforward? What would take more time?

 

 IMPORTANT:  Keep a copy of the assignment outside of the VM.

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