www.allitebooks.com

The process starts by creating dictionaries to store how many times we see the
premise leading to the conclusion (a correct example of the rule) and how many
times it doesn't (an incorrect example). Let's look at the code:
correct_counts = defaultdict(int)
incorrect_counts = defaultdict(int)
We iterate over all of the users, their favorable reviews, and over each candidate
association rule:
for user, reviews in favorable_reviews_by_users.items():
for candidate_rule in candidate_rules:
premise, conclusion = candidate_rule
Chapter 4
We then test to see if the premise is applicable to this user. In other words, did the
user favorably review all of the movies in the premise? Let's look at the code:
if premise.issubset(reviews):
If the premise applies, we see if the conclusion movie was also rated favorably.
If so, the rule is correct in this instance. If not, it is incorrect. Let's look at the code:
if premise.issubset(reviews):
if conclusion in reviews:
correct_counts[candidate_rule] += 1
else:
incorrect_counts[candidate_rule] += 1
We then compute the confidence for each rule by dividing the correct count by the
total number of times the rule was seen:
rule_confidence = {candidate_rule: correct_counts[candidate_rule]
/ float(correct_counts[candidate_rule] +
incorrect_counts[candidate_rule])
for candidate_rule in candidate_rules}
Now we can print the top five rules by sorting this confidence dictionary and
printing the results:
from operator import itemgetter
sorted_confidence = sorted(rule_confidence.items(),
key=itemgetter(1), reverse=True)
for index in range(5):
print("Rule #{0}".format(index + 1))
(premise, conclusion) = sorted_confidence[index][0]
[ 73 ]