我基本上是在尝试通过在 Hadoop 上扩展来实现推荐系统。
在第一步中，我试图计算输入文件中每对项目之间的相似性。如果我将它简单地存储为
{项目 A，项目 B，相似性}
输出文件大小变得非常非常大（对于 60kb 输入，我得到的输出文件大小为 6mb）。
因此，我认为是否最好将结果存储在python dict中并在整个map reduce程序结束后仅打印一次dict。我这样做不成功请帮助我。
我的python代码是：
#!/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()
所以执行后我想要什么
python thisfile.py < input.csv -r hadoop > output.txt
是一个相对较小的输出文件，没有重复和一个字典。
简而言之，
目前这个程序打印退出n 次，但我希望它只打印一次。
除此之外，还有任何更好的方法可以通过以更好的方式扩展 hadoop 来实现协同过滤。
提前致谢。