# -*- coding: utf-8 -*-
import scipy.sparse as sp
import numpy as np
class Dataset(object):
def __init__(self, path):
self.trainMatrix = self.load_rating_file_as_matrix(path+'.train.rating')
self.testRatings = self.load_rating_file_as_list(path+'.test.rating')
self.testNegatives = self.load_negative_file(path+'.test.negative')
assert len(self.testRatings) == len(self.testNegatives)
self.num_users, self.num_items = self.trainMatrix.shape
def load_rating_file_as_matrix(self, filename):
# get numbers of users and items
num_users, num_items = 0, 0
with open(filename, 'r') as f:
line = f.readline()
while(line != None and line != ''):
arr = line.split('\t')
user, item = int(arr[0]), int(arr[1])
num_users = max(num_users, user)
num_items = max(num_items, item)
line = f.readline()
# contruct matrix
mat = sp.dok_matrix((num_users+1, num_items+1), dtype=np.float32)
with open(filename, 'r') as f:
line = f.readline()
while(line != None and line != ''):
arr = line.split('\t')
user, item, rating = int(arr[0]), int(arr[1]), float(arr[2])
if(rating > 0):
mat[user, item] = 1.0
line = f.readline()
return mat
def load_rating_file_as_list(self, filename):
ratingList = []
with open(filename, 'r') as f:
line = f.readline()
while(line != None and line != ''):
arr = line.split('\t')
user, item = int(arr[0]), int(arr[1])
ratingList.append([user, item])
line = f.readline()
return ratingList
def load_negative_file(self, filename):
negativeList = []
with open(filename, 'r') as f:
line = f.readline()
while(line != None and line != ''):
arr = line.split('\t')
negatives = []
for x in arr[1:]:
negatives.append(int(x))
negativeList.append(negatives)
line = f.readline()
return negativeList
