I created the following data, data.file
1 1
1 2
1 3
2 4
3 5
4 6
5 7
6 1
7 2
8 8
The following code:
def parse_line(line):
tokens = line.split(' ')
return (int(tokens[0]), int(tokens[1])), 1.0
lines = sc.textFile('./data.file')
linesTest = sc.textFile('./data.file')
trainingRDD = lines.map(parse_line)\
.map(lambda l: Rating(l[0][0], l[0][1], l[1]))
testRDD = linesTest.map(parse_line)\
.map(lambda x: (x[0][0], x[0][1]))
rank = 5
numIterations = 5
model = ALS.trainImplicit(ratings=trainingRDD,
rank=5,
iterations=5)
res = model.predictAll(testRDD).collect()
for item in res: print item
produces the following output:
Rating(user=4, product=6, rating=0.6767983278562415)
Rating(user=6, product=1, rating=0.620394043421327)
Rating(user=8, product=8, rating=0.43915435032205224)
Rating(user=2, product=4, rating=0.6712931344760976)
Rating(user=1, product=2, rating=1.058575470286403)
Rating(user=1, product=1, rating=1.0710334376535875)
Rating(user=1, product=3, rating=0.7958297361341067)
Rating(user=7, product=2, rating=0.6183187594872994)
Rating(user=3, product=5, rating=0.862203908436539)
Rating(user=5, product=7, rating=0.8487787055836538)
By changing this line
res = model.predictAll(testRDD).collect()
to that
res = model.recommendProducts(1, 10)
The output is
Rating(user=1, product=2, rating=1.0664127057236918)
Rating(user=1, product=1, rating=1.054581213757793)
Rating(user=1, product=3, rating=0.7844128375421406)
Rating(user=1, product=6, rating=0.021054889001335786)
Rating(user=1, product=7, rating=0.0190815148087915)
Rating(user=1, product=8, rating=0.016932852980070745)
Rating(user=1, product=5, rating=0.005659639719215903)
Rating(user=1, product=4, rating=-0.007570583694108901)
why is that most of these ratings do not show up when using predictAll?
