I am basically trying to implement a recommender system by scaling it up on Hadoop.
In the first step, I am trying to calculate the similarity between every pair of items in the input file.If I store it simply as
{Item A,Item B,Similarity}
the output file size becomes very very large ( for 60kb input i am getting an output file size of 6mb).
Therefore I thought whether it will be better to store the result in python dict and print the dict ONLY ONCE after the end of the entire map reduce program.I am unsuccessful in doing this Please help me.
My python code is:
#!/usr/bin/env python
from mrjob.job import MRJob
from math import sqrt
from itertools import combinations
PRIOR_COUNT = 10
PRIOR_CORRELATION = 0
prefs={}
def correlation(size, dot_product, rating_sum, \
rating2sum, rating_norm_squared, rating2_norm_squared):
'''
The correlation between two vectors A, B is
[n * dotProduct(A, B) - sum(A) * sum(B)] /
sqrt{ [n * norm(A)^2 - sum(A)^2] [n * norm(B)^2 - sum(B)^2] }
'''
numerator = size * dot_product - rating_sum * rating2sum
denominator = sqrt(size * rating_norm_squared - rating_sum * rating_sum) * \
sqrt(size * rating2_norm_squared - rating2sum * rating2sum)
return (numerator / (float(denominator))) if denominator else 0.0
def regularized_correlation(size, dot_product, rating_sum, \
rating2sum, rating_norm_squared, rating2_norm_squared,
virtual_cont, prior_correlation):
'''
The Regularized Correlation between two vectors A, B
RegularizedCorrelation = w * ActualCorrelation + (1 - w) * PriorCorrelation
where w = # actualPairs / (# actualPairs + # virtualPairs).
'''
unregularizedCorrelation = correlation(size, dot_product, rating_sum, \
rating2sum, rating_norm_squared, rating2_norm_squared)
w = size / float(size + virtual_cont)
return w * unregularizedCorrelation + (1.0 - w) * prior_correlation
class SemicolonValueProtocol(object):
# don't need to implement read() since we aren't using it
def write(self, key, values):
return ';'.join(str(v) for v in values)
class BooksSimilarities(MRJob):
#OUTPUT_PROTOCOL = SemicolonValueProtocol
def steps(self):
return [
self.mr(mapper=self.group_by_user_rating,
reducer=self.count_ratings_users_freq),
self.mr(mapper=self.pairwise_items,
reducer=self.calculate_similarity),
self.mr(mapper=self.calculate_ranking,
reducer=self.top_similar_items)]
def group_by_user_rating(self, key, line):
'''
Emit the user_id and group by their ratings (item and rating)
17 70,3
35 21,1
49 19,2
49 21,1
49 70,4
87 19,1
87 21,2
98 19,2
'''
line=line.replace("\"","");
user_id, item_id, rating = line.split(',')
yield user_id, (item_id, float(rating))
def count_ratings_users_freq(self, user_id, values):
'''
For each user, emit a row containing their "postings"
(item,rating pairs)
Also emit user rating sum and count for use later steps.
17 1,3,(70,3)
35 1,1,(21,1)
49 3,7,(19,2 21,1 70,4)
87 2,3,(19,1 21,2)
98 1,2,(19,2)
'''
item_count = 0
item_sum = 0
final = []
for item_id, rating in values:
item_count += 1
item_sum += rating
final.append((item_id, rating))
yield user_id, (item_count, item_sum, final)
def pairwise_items(self, user_id, values):
'''
The output drops the user from the key entirely, instead it emits
the pair of items as the key:
19,21 2,1
19,70 2,4
21,70 1,4
19,21 1,2
'''
item_count, item_sum, ratings = values
for item1, item2 in combinations(ratings, 2):
yield (item1[0], item2[0]), (item1[1], item2[1])
def calculate_similarity(self, pair_key, lines):
'''
Sum components of each corating pair across all users who rated both
item x and item y, then calculate pairwise pearson similarity and
corating counts. The similarities are normalized to the [0,1] scale
because we do a numerical sort.
19,21 0.4,2
21,19 0.4,2
19,70 0.6,1
70,19 0.6,1
21,70 0.1,1
70,21 0.1,1
'''
sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
item_pair, co_ratings = pair_key, lines
item_xname, item_yname = item_pair
for item_x, item_y in lines:
sum_xy += item_x * item_y
sum_y += item_y
sum_x += item_x
sum_xx += item_x * item_x
sum_yy += item_y * item_y
n += 1
reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)
yield (item_xname, item_yname), (reg_corr_sim, n)
def calculate_ranking(self, item_keys, values):
'''
Emit items with similarity in key for ranking:
19,0.4 70,1
19,0.6 21,2
21,0.6 19,2
21,0.9 70,1
70,0.4 19,1
70,0.9 21,1
'''
reg_corr_sim, n = values
item_x, item_y = item_keys
if int(n) > 0:
yield (item_x, reg_corr_sim),(item_y, n)
def top_similar_items(self, key_sim, similar_ns):
'''
For each item emit K closest items in comma separated file:
De La Soul;A Tribe Called Quest;0.6;1
De La Soul;2Pac;0.4;2
'''
item_x, reg_corr_sim = key_sim
for item_y, n in similar_ns:
#yield None, (item_x, item_y, reg_corr_sim, n)
prefs.setdefault(item_x,{})
prefs[item_x][item_y] = float(reg_corr_sim)
prefs.setdefault(item_y,{})
prefs[item_y][item_x] = float(reg_corr_sim)
print "exiting"
if __name__ == '__main__':
BooksSimilarities.run()
So what I want after executing
python thisfile.py < input.csv -r hadoop > output.txt
is a relatively small output file with no repetitions and one dict.
In short,
Currently this program prints exiting n times but i want it to print only ONCE.
Apart from all this is there any better way to implement the collaborative filtering by scaling up on hadoop in a better manner.
Thanks a ton in advance.
