Hetu/examples/rec/run_tf.py

import numpy as np
import tensorflow as tf
import time
import argparse
from tqdm import tqdm
from tf_ncf import neural_mf
import heapq  # for retrieval topK
import math


def getHitRatio(ranklist, gtItem):
    for item in ranklist:
        if item == gtItem:
            return 1
    return 0


def getNDCG(ranklist, gtItem):
    for i in range(len(ranklist)):
        item = ranklist[i]
        if item == gtItem:
            return math.log(2) / math.log(i+2)
    return 0


class Logging(object):
    def __init__(self, path='logs/tflog.txt'):
        with open(path, 'w') as fw:
            fw.write('')
        self.path = path

    def write(self, s):
        print(s)
        with open(self.path, 'a') as fw:
            fw.write(s + '\n')
            fw.flush()


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--val", action="store_true",
                        help="whether to perform validation")
    parser.add_argument("--all", action="store_true",
                        help="whether to use all data")
    args = parser.parse_args()

    def validate():
        # validate phase
        hits, ndcgs = [], []
        for idx in range(num_users):
            start_index = idx * 100
            my_feed_dict = {
                user_input: testUserInput[start_index:start_index+100],
                item_input: testItemInput[start_index:start_index+100],
            }
            predictions = sess.run([y], feed_dict=my_feed_dict)
            map_item_score = {
                testItemInput[start_index+i]: predictions[0][i] for i in range(100)}

            # Evaluate top rank list
            ranklist = heapq.nlargest(
                topK, map_item_score, key=map_item_score.get)
            hr = getHitRatio(ranklist, testItemInput[start_index])
            ndcg = getNDCG(ranklist, testItemInput[start_index])
            hits.append(hr)
            ndcgs.append(ndcg)
        hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
        return hr, ndcg

    from movielens import getdata
    if args.all:
        trainData, testData = getdata('ml-25m', 'datasets')
        trainUsers = trainData['user_input']
        trainItems = trainData['item_input']
        trainLabels = trainData['labels']
        testData = testData
        testUserInput = np.repeat(
            np.arange(testData.shape[0], dtype=np.int32), 100)
        testItemInput = testData.reshape((-1,))
    else:
        trainData, testData = getdata('ml-25m', 'datasets')
        trainUsers = trainData['user_input'][:1024000]
        trainItems = trainData['item_input'][:1024000]
        trainLabels = trainData['labels'][:1024000]
        testData = testData[:1470]
        testUserInput = np.repeat(
            np.arange(testData.shape[0], dtype=np.int32), 100)
        testItemInput = testData.reshape((-1,))
    num_users, num_items = {
        'ml-1m': (6040, 3706),
        'ml-20m': (138493, 26744),
        'ml-25m': (162541, 59047),
    }['ml-25m']
    batch_size = 1024
    num_negatives = 4
    topK = 10
    user_input = tf.compat.v1.placeholder(tf.int32, [None, ])
    item_input = tf.compat.v1.placeholder(tf.int32, [None, ])
    y_ = tf.compat.v1.placeholder(tf.float32, [None, ])

    loss, y, opt = neural_mf(user_input, item_input, y_, num_users, num_items)
    train_op = opt.minimize(loss)

    init = tf.compat.v1.global_variables_initializer()
    gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
    sess = tf.compat.v1.Session(
        config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
    sess.run(init)

    log = Logging()
    epoch = 7
    iterations = trainUsers.shape[0] // batch_size
    start = time.time()
    for ep in range(epoch):
        ep_st = time.time()
        log.write('epoch %d' % ep)
        train_loss = []
        for idx in range(iterations):
            start_index = idx * batch_size
            my_feed_dict = {
                user_input: trainUsers[start_index:start_index+batch_size],
                item_input: trainItems[start_index:start_index+batch_size],
                y_: trainLabels[start_index:start_index+batch_size],
            }

            loss_val = sess.run([loss, train_op], feed_dict=my_feed_dict)
            train_loss.append(loss_val[0])

        tra_loss = np.mean(train_loss)
        ep_en = time.time()

        # validate phase
        if args.val:
            hr, ndcg = validate()
            printstr = "train_loss: %.4f, HR: %.4f, NDCF: %.4f, train_time: %.4f" % (
                tra_loss, hr, ndcg, ep_en - ep_st)
        else:
            printstr = "train_loss: %.4f, train_time: %.4f" % (
                tra_loss, ep_en - ep_st)
        log.write(printstr)
    log.write('all time:%f' % (time.time() - start))


if __name__ == '__main__':
    main()