Python 评估字词在文件集的重要程度 (文档频率和逆向文档频率 TF-IDF)

#!/usr/bin/python
# -*- coding: utf-8 -*-

'''
Created on 2015-1-17
@author: beyondzhou
@name: tf_idf_sample.py
'''

from tfIdf import tf, tf_idf, idf

# Enter in a query term from the corpus variable
QUERY_TERMS = ['mr.', 'green']

corpus = \
{'a': 'Mr. Green killed Colonel Mustard in the study with the candlestick. \
Mr. Green is not a very nice fellow.',
'b': 'Professor Plum has a green plant in his study.',
'c': "Miss Scarlett watered Professor Plum's green plant while he was away \
from his office last week."}

for (k, v) in sorted(corpus.items()):
print k, ':', v
print

# Score queries by calculating cumulative tf_idf score for each term in query
query_scores = {'a':0, 'b':0, 'c':0}
for term in [t.lower() for t in QUERY_TERMS]:
for doc in sorted(corpus):
print 'TF(%s): %s' % (doc, term), tf(term, corpus[doc])
print 'IDF: %s' % (term, ), idf(term, corpus.values())
print

for doc in sorted(corpus):
score = tf_idf(term, corpus[doc], corpus.values())
print 'TF-IDF(%s): %s' % (doc, term), score
query_scores[doc] += score
print

print "Overall TF-IDF scores for query '%s'" % (' '.join(QUERY_TERMS), )
for (doc, score) in sorted(query_scores.items()):
print doc, score

from math import log

def tf(term, doc, normalize=True):
doc = doc.lower().split()
if normalize:
return doc.count(term.lower()) / float(len(doc))
else:
return doc.count(term.lower()) / 1.0

def idf(term, corpus):
num_texts_with_term = len([True for text in corpus if term.lower() in text.lower().split()])

# tf-idf calc incolves multiplying against a tf value less than 0, so it's
# neccessary to return a value greater than 1 for consistent scoring.
# (Multiplying two values less than 1 returns a value less then each of them.)
try:
return 1.0 + log(float(len(corpus)) / num_texts_with_term)
except ZeroDivisionError:
return 1.0

def tf_idf(term, doc, corpus):
return tf(term, doc) * idf(term, corpus)

a : Mr. Green killed Colonel Mustard in the study with the candlestick. Mr. Green is not a very nice fellow.
b : Professor Plum has a green plant in his study.
c : Miss Scarlett watered Professor Plum's green plant while he was away from his office last week.

TF(a): mr. 0.105263157895
TF(b): mr. 0.0
TF(c): mr. 0.0
IDF: mr. 2.09861228867

TF-IDF(a): mr. 0.220906556702
TF-IDF(b): mr. 0.0
TF-IDF(c): mr. 0.0

TF(a): green 0.105263157895
TF(b): green 0.111111111111
TF(c): green 0.0625
IDF: green 1.0

TF-IDF(a): green 0.105263157895
TF-IDF(b): green 0.111111111111
TF-IDF(c): green 0.0625

Overall TF-IDF scores for query 'mr. green'
a 0.326169714597
b 0.111111111111
c 0.0625