def test_build_recommender(self):
fi = open('simple_filter_test').readlines()
item_sim_mat,user_dict,item_pos,user_pos,item_dict = rec.build_recommender(rec.parse_array(fi),1)
test_arr = []
item_sim_mat = item_sim_mat.toarray()
print 'item sim mat'
print item_sim_mat
for item1 in self.jac_matrix:
temp = []
for item2 in self.jac_matrix:
temp.append(rec.jaccard_distance(item1,item2))
test_arr.append(temp)
for ent in self.used_coords:
self.assertEqual(test_arr[ent[0]][ent[1]],item_sim_mat[ent[0]][ent[1]])
for i in range(len(item_sim_mat)):
for j in range(len(item_sim_mat)):
if (i,j) in self.used_coords:
self.assertEqual(test_arr[i][j],item_sim_mat[i][j])
else:
# print '(%d,%d)' %(i,j)
self.assertEqual(item_sim_mat[i][j],0)
print item_pos
print user_dict
print user_pos
print item_dict
def test_speed_build_recommender(self):
NUM_CUSTOMERS = 100
NUM_ITEMS = 100
NUM_ITEMS_PER_CUST = 10
fi = {}
for i in xrange(0,NUM_CUSTOMERS):
i = str(i)
fi[i] = randint(0,NUM_ITEMS,50)
fi[i] = [str(x) for x in fi[i]]
t3 = time.time()
rec.build_naive_recommender(fi)
t4 = time.time()
print (t4-t3)/60.0
t1 = time.time()
rec.build_recommender(fi)
t2 = time.time()
print (t2-t1)/60.0
