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tf-30.py
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tf-30.py
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#!/usr/bin/env python
import sys, re, operator, string
#
# Functions for map reduce
#
def partition(data_str, nlines):
"""
Partitions the input data_str (a big string)
into chunks of nlines.
"""
lines = data_str.split('\n')
for i in xrange(0, len(lines), nlines):
yield '\n'.join(lines[i:i+nlines])
def split_words(data_str):
"""
Takes a string, returns a list of pairs (word, 1),
one for each word in the input, so
[(w1, 1), (w2, 1), ..., (wn, 1)]
"""
def _scan(str_data):
pattern = re.compile('[\W_]+')
return pattern.sub(' ', str_data).lower().split()
def _remove_stop_words(word_list):
with open('../stop_words.txt') as f:
stop_words = f.read().split(',')
stop_words.extend(list(string.ascii_lowercase))
return [w for w in word_list if not w in stop_words]
# The actual work of splitting the input into words
result = []
words = _remove_stop_words(_scan(data_str))
for w in words:
result.append((w, 1))
return result
def count_words(pairs_list_1, pairs_list_2):
"""
Takes two lists of pairs of the form
[(w1, 1), ...]
and returns a list of pairs [(w1, frequency), ...],
where frequency is the sum of all the reported occurrences
"""
mapping = dict((k, v) for k, v in pairs_list_1)
for p in pairs_list_2:
if p[0] in mapping:
mapping[p[0]] += p[1]
else:
mapping[p[0]] = 1
return mapping.items()
#
# Auxiliary functions
#
def read_file(path_to_file):
with open(path_to_file) as f:
data = f.read()
return data
def sort(word_freq):
return sorted(word_freq, key=operator.itemgetter(1), reverse=True)
#
# The main function
#
splits = map(split_words, partition(read_file(sys.argv[1]), 200))
splits.insert(0, []) # Normalize input to reduce
word_freqs = sort(reduce(count_words, splits))
for (w, c) in word_freqs[0:25]:
print w, ' - ', c