MetaTL/sampler.py

import sys
import copy
import torch
import random
import numpy as np
from collections import defaultdict, Counter
from multiprocessing import Process, Queue


def random_neq(l, r, s):
    t = np.random.randint(l, r)
    while t in s:
        t = np.random.randint(l, r)
    return t

def sample_function_mixed(user_train, usernum, itemnum, batch_size, maxlen, result_queue, SEED):
    def sample():

        if random.random()<0.5:
            user = np.random.randint(1, usernum + 1)
            while len(user_train[user]) <= 1: user = np.random.randint(1, usernum + 1)

            
            seq = np.zeros([maxlen], dtype=np.int32)
            pos = np.zeros([maxlen], dtype=np.int32)
            neg = np.zeros([maxlen], dtype=np.int32)

            if len(user_train[user]) < maxlen:
                nxt_idx = len(user_train[user]) - 1
            else:
                nxt_idx = np.random.randint(maxlen,len(user_train[user]))

            nxt = user_train[user][nxt_idx]
            idx = maxlen - 1

            ts = set(user_train[user])
            for i in reversed(user_train[user][min(0, nxt_idx - 1 - maxlen) : nxt_idx - 1]):
                seq[idx] = i
                pos[idx] = nxt
                if nxt != 0: neg[idx] = random_neq(1, itemnum + 1, ts)
                nxt = i
                idx -= 1
                if idx == -1: break

            curr_rel = user
            support_triples, support_negative_triples, query_triples, negative_triples = [], [], [], []
            for idx in range(maxlen-1):
                support_triples.append([seq[idx],curr_rel,pos[idx]])
                support_negative_triples.append([seq[idx],curr_rel,neg[idx]])
            query_triples.append([seq[-1],curr_rel,pos[-1]])
            negative_triples.append([seq[-1],curr_rel,neg[-1]])

            return support_triples, support_negative_triples, query_triples, negative_triples, curr_rel

        else:
            user = np.random.randint(1, usernum + 1)
            while len(user_train[user]) <= 1: user = np.random.randint(1, usernum + 1)

            seq = np.zeros([maxlen], dtype=np.int32)
            pos = np.zeros([maxlen], dtype=np.int32)
            neg = np.zeros([maxlen], dtype=np.int32)

            list_idx = random.sample([i for i in range(len(user_train[user]))], maxlen + 1)
            list_item = [user_train[user][i] for i in sorted(list_idx)]

            nxt = list_item[-1]
            idx = maxlen - 1

            ts = set(user_train[user])
            for i in reversed(list_item[:-1]):
                seq[idx] = i
                pos[idx] = nxt
                if nxt != 0: neg[idx] = random_neq(1, itemnum + 1, ts)
                nxt = i
                idx -= 1
                if idx == -1: break

            curr_rel = user
            support_triples, support_negative_triples, query_triples, negative_triples = [], [], [], []
            for idx in range(maxlen-1):
                support_triples.append([seq[idx],curr_rel,pos[idx]])
                support_negative_triples.append([seq[idx],curr_rel,neg[idx]])
            query_triples.append([seq[-1],curr_rel,pos[-1]])
            negative_triples.append([seq[-1],curr_rel,neg[-1]])

            return support_triples, support_negative_triples, query_triples, negative_triples, curr_rel

    np.random.seed(SEED)
    
    while True:
        one_batch = []
        for i in range(batch_size):
            one_batch.append(sample())

        support, support_negative, query, negative, curr_rel = zip(*one_batch)

        result_queue.put(([support, support_negative, query, negative], curr_rel))

class WarpSampler(object):
    def __init__(self, User, usernum, itemnum, batch_size=64, maxlen=10, n_workers=1):
        self.result_queue = Queue(maxsize=n_workers * 10)
        self.processors = []
        for i in range(n_workers):
            self.processors.append(
                Process(target=sample_function_mixed, args=(User,
                                                      usernum,
                                                      itemnum,
                                                      batch_size,
                                                      maxlen,
                                                      self.result_queue,
                                                      np.random.randint(2e9)
                                                      )))
            self.processors[-1].daemon = True
            self.processors[-1].start()

    def next_batch(self):
        return self.result_queue.get()

    def close(self):
        for p in self.processors:
            p.terminate()
            p.join()