data_load.py

import numpy as np
import scipy.sparse as sp
from scipy.sparse import csr_matrix

def load_rating_file_as_list(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(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

def load_rating_file_as_matrix(filename):
    '''
    Read .rating file and Return dok matrix.
    The first line of .rating file is: num_users\t num_items
    '''
    # Get number 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")
            u, i = int(arr[0]), int(arr[1])
            num_users = max(num_users, u)
            num_items = max(num_items, i)
            line = f.readline()
    # Construct 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 get_train_instances(train, num_negatives):
    user_input, item_input, labels = [],[],[]
    num_users, num_items = train.shape
    for (u, i) in train.keys():
        # positive instance
        user_input.append(u)
        item_input.append(i)
        labels.append(1)
        # negative instances
        for t in range(num_negatives):
            j = np.random.randint(num_items)
            while (u, j) in train:
                j = np.random.randint(num_items)
            user_input.append(u)
            item_input.append(j)
            labels.append(0)
    return user_input, item_input, labels

def get_train_instances(train, num_negatives=1):
    user_input, item_input, negative_input = [],[],[]
    num_users, num_items = train.shape
    for (u, i) in train.keys():
        # positive instance
        user_input.append(u)
        item_input.append(i)
        # negative instances
        for t in range(num_negatives):
            j = np.random.randint(num_items)
            while (u, j) in train:
                j = np.random.randint(num_items)
            negative_input.append(j)

    return user_input, item_input, negative_input