Map Reduce In Machine Learning

In a MapReduce program, Map() and Reduce() are two functions. The Map function performs actions like filtering, grouping and sorting. While Reduce function aggregates and summarizes the result produced by map function. The result generated by the Map function is a key value pair (K, V) which acts as the input for Reduce function.

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Basic Steps To Perform Map Reduce

Input Splits:

It is a fixed-size pieces called input splits Input split is a chunk of the input that is consumed by a single map

Mapping

In this phase data in each split is passed to a mapping function to produce output values. In our example, a job of mapping phase is to count a number of occurrences of each word from input splits (more details about input-split is given below) and prepare a list in the form of <word, frequency>

Shuffling

This phase consumes the output of Mapping phase. Its task is to consolidate the relevant records from Mapping phase output. In our example, the same words are clubed together along with their respective frequency.

Reducing

In this phase, output values from the Shuffling phase are aggregated. This phase combines values from Shuffling phase and returns a single output value. In short, this phase summarizes the complete dataset.

Perform Map Reduce For Integer Number

Problem statement:

You are given a large number of files containing positive integers. Design the MapReduce process to compute the number of even integers across all files.

Before answering the question we will creating MapReduce job using positive integers with, <key, value>

Using MapReduce, determine how many odd and even numbers of jobs in positive integer jobs.

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Lets Understand It Workflow 

What Mapper Do?

Maps input key/value pairs to a set of intermediate key/value pairs.

Maps are the individual tasks which transform input records into a intermediate records. The transformed intermediate records need not be of the same type as the input records. A given input pair may map to zero or many output pairs.

What are the key/value pairs that are INPUT to the first Mapper?

We can easily say that input for mapper is the combination of key/value, where key is offset and value is particular job.

{#offset, “2 4 5”}

This is the input for mapper which has argument where key = #offset and value = “2 4 5”

What are the key/value pairs that are OUTPUT of the first Mapper?

The output of Mapper the pairs of key/value, which is generated after filtering it with given condition, in above figure we will filter pairs of job which has even numbers:

Example: (2,“2 4 5”) and (1,“9 7 6”)

Applications of Map Reduce

MapReduce is used in many applications let us have a look at some of the applications:

Entertainment 

Hadoop and MapReduce are used by Netflix to recommend the user some popular movies based on what they watched and which movie they like.

MapReduce can determine how users are watching movies, analyzing their logs and clicks.

E-commerce: 

Many E-commerce companies such as Flipkart, Amazon, and eBay use MapReduce to analyze the buying behavior of the customers based on customers’ interests or their buying behavior

MapReduce programming offers several benefits

Scalability. Businesses can process petabytes of data stored in the Hadoop Distributed File System (HDFS).

Flexibility. Hadoop enables easier access to multiple sources of data and multiple types of data.

Speed. With parallel processing and minimal data movement, Hadoop offers fast processing of massive amounts of data.

Simple. Developers can write code in a choice of languages, including Java, C++ and Python.

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Sample Code

Requirement

This problem is similar to the problem we worked on in lecture with a small twist (see below). Instead of printing out how many times a word appears in the file, you want to print out how many words have the exact same type of vowels. For this problem, only the number of vowels matters and the case does not matter (i.e cat is the same as CAt). A vowel is any letter from this set {a,e,i,o,u,y}. A word is any sequence of characters that does not contain whitespace. Whitespace is defined as: space, newline or tab. All of the following are 1 single word:

cats
c@ts
ca7s
cat’s.and:d0gs!

The output will be the vowel set, followed by a colon, followed by the number of words that contained exactly the vowel set. The output will have one answer per line (see example below).


Example:
‘hEllo’ and ‘pOle’ both contain exactly 1 e and exactly 1 o. The order of the vowels and the case from the original input word does not matter.
Imagine the following example:
hEllo    moose
pOle cccttt.ggg


We would end up with the following output:
:1
eo:2
eoo:1

The format should be as seen above: the vowels on each line are in alphabetical order, followed by a colon, then followed by the number of words that contained exactly those vowels. If there are words with no vowels, nothing is printed before the colon.

 

Solution

Mapper.py

res=[]
with open('input.txt','r') as file:
    # reading each line    
    for line in file:
   
        # reading each word        
        for word in line.split():
          word_new = word.lower()
          ans=''
          for w in word_new:
            if w in ['a','e','i','o','u','y']:
              ans+=w
          # s.sort(key=str.lower)
          res.append(''.join(sorted(ans)))

print(res)
with open('inp1.txt', 'w') as the_file:
    for r in res:
        if r=='':
            r+=' '
        the_file.write(r+' '+'1\n')

Reducer.py

import sys
import re
data_dict=dict()
with open("/content/inp1.txt",'r') as file:
    # reading each line    
    for line in file:
      line = line.strip()
      l=line[:-1]
      l=l.strip()
      if l=='':
        l=' '
      if l in dd.keys():
        data_dict[l]+=1
      else:
        data_dict[l]=1

for key in data_dict:
  print(key,' : ',data_dict[key])

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