# Build colisten matrix from triplet CSV and save in mtx format
# Usage: python colisten.py <infile> <outfile>
import scipy.sparse, scipy.io
import sys
import util
infile, outfile = sys.argv[1:]
colisten = scipy.sparse.lil_matrix((util.N_SONGS, util.N_SONGS))
for listens in util.songs_by_user(infile):
for s, _ in listens:
for t, _ in listens:
colisten[s-1, t-1] += 1 # Songs are 1-indexed, but scipy uses 0-indexing
scipy.io.mmwrite(file(outfile, 'wb'), colisten)
# Build colisten matrix from triplet CSV and save in mtx format
# Usage: python colisten.py <infile> <outfile>
import scipy.sparse, scipy.io
import sys
import util
infile, outfile = sys.argv[1:]
colisten = scipy.sparse.lil_matrix((util.N_SONGS, util.N_SONGS))
for listens in util.songs_by_user(infile):
for s, _ in listens:
for t, _ in listens:
colisten[s - 1, t -
1] += 1 # Songs are 1-indexed, but scipy uses 0-indexing
scipy.io.mmwrite(file(outfile, 'wb'), colisten)
print "it takes %f secs to read the colisten matrix " % timetoread
listens = colisten.diagonal()
listenranked = numpy.argsort(-listens)[:500]
predict_start = time.clock()
print " predict starts at %f :\n" % predict_start
with open(outfile,'w') as out:
i = 0
for history in util.songs_by_user(evalfile):
i=i+1
print " we are predict for %d user" % i
songs,counts = zip(*history)
sim = numpy.array(counts)[numpy.newaxis,:]*\
colisten[numpy.array(songs)-1,:]
simidxs = sim.nonzero()[1]
srt = numpy.lexsort((-listens[simidxs],-sim[0,simidxs]))
rankidxs = simidxs[srt]
# Usage: python predict_colisten.py <mtxfile> <evalfile> <outfile>
import sys
import scipy.io
import numpy
import util
mtxfile, evalfile, outfile = sys.argv[1:]
colisten = scipy.io.mmread(file(mtxfile)).tocsr()
listens = colisten.diagonal()
listenranked = numpy.argsort(-listens)[:500]
with open(outfile, 'w') as out:
for history in util.songs_by_user(evalfile):
songs, counts = zip(*history)
sim = numpy.array(counts)[numpy.newaxis, :] * colisten[
numpy.array(songs) - 1, :]
# All this nonsense is an optimization to avoid the fact that
# sorting 300,000 numbers 110,000 times is bad for your health.
# I only sort the songs where sim > 0
simidxs = sim.nonzero()[1]
srt = numpy.lexsort((-listens[simidxs], -sim[0, simidxs]))
rankidxs = simidxs[srt]
guess = []
for s in rankidxs:
if s + 1 in songs:
